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The Cost of a DDoS Attack on the Darknet

17 Mar

Distributed Denial of Service attacks, commonly called DDoS, have been around since the 1990s. Over the last few years they became increasingly commonplace and intense. Much of this change can be attributed to three factors:

1. The evolution and commercialization of the dark web

2. The explosion of connected (IoT) devices

3. The spread of cryptocurrency

This blog discusses how each of these three factors affects the availability and economics of spawning a DDoS attack and why they mean that things are going to get worse before they get better.

Evolution and Commercialization of the Dark Web

Though dark web/deep web services are not served up in Google for the casual Internet surfer, they exist and are thriving. The dark web is no longer a place created by Internet Relay Chat or other text-only forums. It is a full-fledged part of the Internet where anyone can purchase any sort of illicit substance and services. There are vendor ratings such as those for “normal” vendors, like YELP. There are support forums and staff, customer satisfaction guarantees and surveys, and service catalogues. It is a vibrant marketplace where competition abounds, vendors offer training, and reputation counts.

Those looking to attack someone with a DDoS can choose a vendor, indicate how many bots they want to purchase for an attack, specify how long they want access to them, and what country or countries they want them to reside in. The more options and the larger the pool, the more the service costs. Overall, the costs are now reasonable. If the attacker wants to own the bots used in the DDoS onslaught, according to SecureWorks, a centrally-controlled network could be purchased in 2014 for $4-12/thousand unique hosts in Asia, $100-$120 in the UK, or $140 to $190 in the USA.

Also according to SecureWorks, in late 2014 anyone could purchase a DDoS training manual for $30 USD. Users could utilize single tutorials for as low as $1 each. After training, users can rent attacks for between $3 to $5 by the hour, $60 to $90 per day, or $350 to $600 per week.

Since 2014, the prices declined by about 5% per year due to bot availability and competing firms’ pricing pressures.

The Explosion of Connected (IoT) Devices

Botnets were traditionally composed of endpoint systems (PCs, laptops, and servers) but the rush for connected homes, security systems, and other non-commercial devices created a new landing platform for attackers wishing to increase their bot volumes. These connected devices generally have low security in the first place and are habitually misconfigured by users, leaving the default access credentials open through firewalls for remote communications by smart device apps. To make it worse, once created and deployed, manufactures rarely produce any patches for the embedded OS and applications, making them ripe for compromise. A recent report distributed by Forescout Technologies identified how easy it was to compromise home IoT devices, especially security cameras. These devices contributed to the creation and proliferation of the Mirai botnet. It was wholly comprised of IoT devices across the globe. Attackers can now rent access to 100,000 IoT-based Mirai nodes for about $7,500.

With over 6.4 billion IoT devices currently connected and an expected 20 billion devices to be online by 2020, this IoT botnet business is booming.

The Spread of Cryptocurrency

To buy a service, there must be a means of payment. In the underground no one trusts credit cards. PayPal was an okay option, but it left a significant audit trail for authorities. The rise of cryptocurrency such as Bitcoin provides an accessible means of payment without a centralized documentation authority that law enforcement could use to track the sellers and buyers. This is perfect for the underground market. So long as cryptocurrency holds its value, the dark web economy has a transactional basis to thrive.


DDoS is very disruptive and relatively inexpensive. The attack on security journalist Brian Krebs’s blog site in September of 2016 severely impacted his anti-DDoS service providers’ resources . The attack lasted for about 24 hours, reaching a record bandwidth of 620Gbps. This was delivered entirely by a Mirai IoT botnet. In this particular case, it is believed that the original botnet was created and controlled by a single individual so the only cost to deliver it was time. The cost to Krebs was just a day of being offline.

Krebs is not the only one to suffer from DDoS. In attacks against Internet reliant companies like Dyn, which caused the unavailability of Twitter, the Guardian, Netflix, Reddit, CNN, Etsy, Github, Spotify, and many others, the cost is much higher. Losses can reach multi- millions of dollars. This means a site that costs several thousands of dollars to set up and maintain and generates millions of dollars in revenue can be taken offline for a few hundred dollars, making it a highly cost-effective attack. With low cost, high availability, and a resilient control infrastructure, it is sure that DDoS is not going to fade away, and some groups like Deloitte believe that attacks in excess of 1Tbps will emerge in 2017. They also believe the volume of attacks will reach as high as 10 million in the course of the year. Companies relying on their web presence for revenue need to strongly consider their DDoS strategy to understand how they are going to defend themselves to stay afloat.

33 Billion Internet Devices By 2020: Four Connected Devices For Every Person In World

22 Oct
  • Traditional connected devices like PCs, smartphones and tablets now account for less than a third of all connected devices in use.
  • Emerging categories alone will connect an additional 17.6 billion devices to the internet by 2020.
  • The Internet of Things is leading to rapid growth in new categories like M2M, smart objects, smart grid and smart cities.

“Back in 2007 PCs accounted for two thirds of internet devices – now it’s only 10 per cent,” notes David Mercer, Principal Analyst and the report’s joint author. “The impact of the internet on daily lives has increased rapidly in recent years. Huge growth potential still lies ahead, in terms of both the number of devices relying on internet connectivity and its geographic reach.”

“The Internet of Things has already connected five billion devices and we are only at the beginning of this revolution”, says Andrew Brown, Executive Director and the report’s joint author. “Smart cities and smart grid are just two of the ways in which the internet of things will touch everyone’s lives over the coming years and decades.”

EE unveils new 4G Mobile Broadband Devices, Tablet en Revised Plans

22 May

Given EE’s massive head start over the competition, its 4G network is coming on leaps and bounds. And today, EE has announced new mobile broadband tariffs, released three new 4G mobile broadband devices, plus a new own-brand tablet.

EE’s 4G coverage now covers 2,588 villages and small towns around the UK (many with populations of well under 10,000 – such as Silverburn in Scotland with just 65 people), following the second phase of its roll out. Meanwhile, the current ‘Double Speed’ network is being doubled in size to 40 towns and cities by the end of 2014.

Coverage is also a priority on areas of high-usage, such as airports, roads, railway lines and stations. EE announced that it has 47 major train stations covered, and 22 of the busiest airports. Coverage of the M25 London Orbital motorway and the M62 exceeds 80%.

EE is now connecting more people to 4G plans than 3G on its ‘legacy’ Orange and T-Mobile brands. Data usage is now up 66% as people watch more television and video, and other data intensive tasks, on the move.

New Mobile Broadband devices

EE already offers both dongles and portable hotspot (‘MiFi’) devices, but today it has announced three new products:

In-car Wi-Fi
Osprey: In-car Wi-Fi

Buzzard (£49.99 on PAYG and cheaper on contract) is an in-car device that connects to any 12v cigarette lighter socket, sharing a data connection with up to ten devices. It comes with a storage container designed to sit in a cup holder.


Kite (£69.99 on PAYG and cheaper on contract) is a more traditional portable hotspot, while Osprey is a more rugged and colourful device selling for £49.99 on PAYG (and cheaper on contract).

Eagle - 4G enabled tablet (made by Huawei)
Eagle: 4G enabled tablet (made by Huawei)

There’s a new tablet too, a rebadged Huawei unit called Eagle, available for £199 on PAYG or £49.99 on a two-year contract with a monthly plan of £15 or above.

New Mobile Broadband tariffs


EE Extra 
More data and double-speed 4G

Data allowance**






24 Month






1 Month





** Depending on 4G WiFi device chosen


  • 100MB – £1
  • 1GB – £5
  • 10GB – £15

Video (via

Original Press Release

LONDON 21 May, 2014 – EE, the UK’s most advanced digital communications company, today unveiled the next phase of its world class 4G service, including rapid network expansion andthe launch of new 4G plans and devices. The company will dramatically improve accessibility of superfast mobile internet across the UK, and set a new benchmark for the 4G user experience.

The move comes as EE 4G customer numbers hit 3.6 million and sales of EE 4G plans outstrip 3G for the first time, representing a tipping point for the business. 4G is rapidly changing mobile behaviour, with customers increasing their data usage by 66% in a year – watching more television and video, working, banking and shopping on the go.

Olaf Swantee, CEO of EE, said: “With more experience of 4G than any other UK operator, we have unique insight into how the technology is changing behaviour. Our mobile devices have effectively become mini TVs with video traffic already making up over half the data on our 4G network.

“We’re committed to remaining one step ahead – adapting our network to make 4G available where it matters most, with a focus on more rural areas, transport links and offering our customers the best network experience and great value, innovative devices and plans.”


EE is accelerating network rollout in rural areas and ontransport routes, as well doubling the footprint of its uniquedouble speed network, meeting the growing demand for consistently fast speeds across the busiest areas of the UK.

1.     Rural revolution: EE completes second phase of rural roll out

EE is bringing the UK’s fastest network to 2,588 villages and small towns with populations under 10,000, including many rural areas. From Silverburn in Scotland with less than 65 people, to Aberdulais in South Wales with 500, more than three million people in rural areas now have access to a mobile network to rival the UK’s largest cities.

Businesses and residents in many of those rural areas of the UK will now have access to high speed internet in the home or office for the first time, with 4G speeds often faster than the fixed line connection they previously depended on.

2.      4G in the fast lane

In addition to residential zones, EE is focusing on areas of high usage, including airports, roads, and train lines and stations. 47 major train stations and 22 of the busiest airports, from Bournemouth to Aberdeen, are now 4G-enabled, along with upwards of 50% 4G coverage on 50 of the busiest motorways and A-roads. Coverage of major roads in more built up areas, such as the M25 and M62 is greater than 80%.

The ongoing investment forms part of EE’s plan to offer a superfast digital network in the places customers want it the most, recognising the increasing need for a reliable, high-speed data connection on the move to support in-car navigation and passenger data usage.

By enhancing the 4G network on the busiest roads in the UK, EE will allow business customers to remain productive on the move. A programme is in place to cover the busiest motorway service stations, completing the connected journey.

3.       Extending EE’s unique double speed 4G network

EE will double the footprint of its unique, high capacity network to 40 towns and cities by the end of the year, allowing millions more customers to enjoy the UK’s fastest speeds, and targeting the parts of the country where data usage is showing the quickest growth.

EE’s double speed network ensures that its customers are guaranteed the best mobile experience, whether that’s downloading large files, uploading images or streaming video.


From 28 May, EE is introducing a range of EE-branded 4G broadband devices and plans, designed to take advantage of EE’s double speed network. Available on pay as you go (PAYG) and consumer and business pay monthly plans, the devices include:

·         The UK’s first 4G car WiFi – the ‘Buzzard’ is the first in a series of superfast in-car devices available direct from EE. The simple and affordable plug-and-play device (only £49.99 on PAYG) will enable any car with a 12v connection to instantly become a 4G WiFi hotspot for up to 10 connections, and avoid the need for expensive in-built solutions. It also comes in a unique storage container designed to sit conveniently in a cup holder.

·         Superfast 4G for work and play – EE is introducing two new 4G WiFi devices optimised for EE’s double speed 4G network. For busy professionals a new sleek pocket-sized premium   device called the ‘Kite’* (£69.99 on PAYG); and for a younger audience, the UK’s most affordable 4G WiFi unit – the colourful and rugged ‘Osprey’ (only £49.99 on PAYG).

·         The UK’s most affordable 4G tablet – with the ‘Eagle’, an Android tablet offering specifications and features traditionally associated with more expensive devices for just £199 on PAYG or £49.99 on 24 month pay monthly plans of £15 and above.

In support of the new devices, EE is also introducing the UK’s best value set of 4G WiFi plans. A new range of 24 and one month plans will be available, offering a variety of data allowances for unparalleled value and choice. New plans for small businesses will also be available.

The Buzzard, Kite and Osprey will be available on EE’s Shared plans, as well as the new 4G WiFi pay monthly plans, and are free on 24-month plans of £15-per-month and over.


Video usage on the EE network has risen significantly in recent months, and early findings from EE’s latest Mobile Living Index[i] reveal that the video experience is now as important as making calls and web surfing for 4G customers. Video and TV are expected to increase data traffic 1100% in the next four years, and will be more than two thirds of data on the network by 2018.

To meet growing customer expectations, EE is putting the video experience at the heart of its network plans, ensuring content owners can give customers a reliable, high-quality, user experience.

On the 20th May, Minister Ed Vaizey announced the launch of the Mobile Video Alliance (MVA), founded by EE and run by the Digital TV Group (DTG), which represents the UK’s broadcast industry. The MVA connects broadcasters and content owners with mobile operators for the first time to create the best possible experience of mobile video apps. Broadcasters have mastered content delivery in the home, to the web, and now they’re focusing on the third axis – mobile platforms.

EE has also embarked on a programme for selected video and TV content owners, who desire an optimum user experience for their customers over EE’s 4G network. For example, work with the BBC iPlayer team has already seen significant improvements for their customers on the speed of programme load times.

For more information on the new plans and devices, please visit:

For more information on EE’s network roll-out, please


The Internet of Things: Interconnectedness is the key

14 Apr

I was at an Internet of Things event a couple of weeks ago and listening to the examples it was clear there is too much focus on connecting devices, and not enough focus on interconnecting devices.

Connecting devices implies building devices that are designed specifically to work within a closed ecosystem, to report back to some central hub that manages the relationship with the purpose-built device. Interconnected devices are designed in such a way that they can learn to collaborate with devices they were never designed to work with and react to events of interest to them. So what will this look like? For one possible scenario, let’s start with the ubiquitous “smart fridge” example and expand this to look at the way we buy our food. There has been talk for years about how fridges will be telling us about the contents, how old they are, whether anything in them has been reserved for a special meal, what is on the shopping list etc. Even to the idea of placing automatic orders with the food suppliers, but what if we want to still be involved in the physical purchasing process, how will the Internet of Things, with interconnected devices work in that scenario? Here’s a chain of steps involved:

  1. Assuming our fridge is the central point for our shopping list, and we want to physically do the shopping ourselves, we can tap the fridge with our phones and the shopping list will be transferred to the phone.
  2. The fridge or our phone can tell us how busy the nearby supermarkets currently are, and based on regular shopping patterns, how many people will likely be there at certain times in the immediate future. Sensors in the checkout will let us know what the average time is for people to be cleared. Any specials that we regularly buy will be listed for us to help make the decision about which store to visit.
  3. We go to the supermarket and the first thing that happens is the supermarket re-orders our shopping list in accordance with the layout of the store.
  4. The phone notifies our family members that we are at the supermarket and lets them know we are there so they can modify our shopping list.
  5. We get a shopping trolley, which immediately introduces itself to our phone. It checks with our preferences in the phone as to whether we want its assistance, whether it is allowed to record our shopping experience for our use, or to assist the store with store planning
  6. As we walk around the store, the phone or the trolley alerts us to the fact that we are near one of the items on our shopping list.
  7. If we have allowed it, the trolley can make recommendations based on our shopping list of related products, compatible recipes, with current costs, and offer to place the additional products into the shopping list on the phone and even into our shopping list template stored in the fridge if we want.
  8. As we make our way to the checkout, the trolley checks its contents against what is on our shopping list and alerts us to anything missing. Clever incentives might also be offered at this time based on the current purchase.
  9. As soon as the trolley is told by the cash register that the goods have been paid for, it will clear its memory, first uploading any pertinent information you have allowed.
  10. Independent of the shopping experience and the identifiability of the shopper and their habits, the store will be able to store the movements of the trolley through the store, and identify how fast, any stopping points to identify interest and analyse for product placement.
  11. Once we get home, we stock the cupboard and the fridge, both of which update our shopping list.
  12. As soon as we put the empty wrapper in the trash, the trash can will read the wrapper and add the item to a provisional entry in the shopping list, unless we have explicitly pre-authorised that product for future purchase.

Another example would be linking an airline live schedule to your alarm clock and taxi booking, to give you extra sleep in the morning if the flight is delayed. Or having your car notify the home that it looks like it is heading home and to have the air conditioner check whether it should turn on. While we focus only on pre-ordaining the way devices should work during their design, we limit their ability to improve our lives. By building devices that are capable of being  interconnected with other devices in ways that can be exploited at run time we open up a world of possibilities we haven’t begun to imagine. Source:

Gartner predicts the presence of 26 billion devices in the ‘Internet of Things’ by 2020

19 Mar

Gartner, a globally recognized research firm dealing with technological innovations and businesses has come up with a prediction recently. The American Information Technology Research and Advisory firm has projected that by the year 2020; around 26 billion devices will make “Internet of Things” a definitive source. The Connecticut based firm assures that this large number of sensors and device connections will surely open up an array of business opportunities to data centers and companies that address that market.

Gartner predicts that around $300 billion revenue will be racked up by vendors and service providers which rely on internet till the specified period.

For those who are interested in knowing what “Internet of Things” is in simple terms, here is a definitive answer. This buzz worth term is nothing but to define a vast array of Internet enabled gadgets and remote sensors connected to web. It also relates the IT systems and services that enable organizations to collect, store, manage and analyze the vast amounts of data generated from billions of devices.

Gartner’s research also reveals that data center operators will feel the impact on a more propounding note. And that is due to the fact that it will not be an easy task to technically and economically transfer massive amounts of input data into their entirety which is a central processing location.

Joe Skorupa, Vice President of Garter has come up with a solution and that is to distribute the data into multiple small mini datacenters, where initial processing can occur and then relevant data can be pushed to a central site for additional processing.

However, one area which needs a fresh approach will be to look into the needs of data center network. Gartner argues that current data center bandwidths are sized in such a way that they can moderately cater to the needs of human interactions with applications. But when billions of devices need to interact a bandwidth increase of atleast 1000 times from present becomes truly essential.

For this reason, Gartner wants the data center heads to look into these issues in time and come up with an apt solution in next couple of years. The research firm also insists on revamping of present data center design and architecture by 2018, in order to reduce the complexity and boost of on-demand capacity to deliver reliability and business continuity.

But with so much predicted about Internet of Things” will consumer privacy remain intact in this commotion? Hmmmm…..hard to predict. Isn’t it?



How To Build Habits In A Multi-Device World

9 Mar
Allow me to take liberties with a philosophical question reworked for our digital age. If an app fails in the App Store and no one is around to use it, does it make a difference? Unlike the age-old thought experiment involving trees in forests, the answer to this riddle is easy. No!

Without engagement, your product might as well not exist. No matter how tastefully designed or ingeniously viral, without users coming back, your app is toast.

How, then, to design for engagement? And as if that were not challenging enough, how should products that touch users across multiple devices, like smartphones, tablets, and laptops, keep people coming back?

The answer is habits. For the past several years, I have studiedwritten, and lectured on how products form habits, and my work has uncovered some conclusions I hope will prove helpful to product designers.

 If the company’s business model requires users to come back on their own accord, unprompted by calls to action, a habit must be formed.


To be clear, not every product requires user habits; plenty of businesses drive traffic through emails, search engine optimization, advertising, and other means. However, if the company’s business model requires users to come back on their own accord, unprompted by calls to action, a habit must be formed.

The good news is that in today’s multi-screen world, the ability to interact with multiple devices has the potential to increase the odds of forming lasting user habits. By designing across devices, developers have a unique opportunity to leverage an ecosystem approachto drive higher engagement.

Through my research, I have found a recurring pattern endemic to these products, which I call “the Hook Model.” This simple four-phase model is intended to help designers build more engaging products.

Building for habits boils down to the four fundamental elements of the Hook: a trigger, an action, a reward, and an investment. This pattern can be found in any number of products we use automatically, almost without thinking.


Designing for multiple interfaces means your product is more readily accessible throughout the day. The more often the user chooses a particular solution to meet her needs, the faster a habit is formed. A trigger is the event in the user’s life that prompts her to use the product.

It is important that companies understand the users’ internal triggers so they can build the product to meet those needs.


Sometimes the trigger can be external, such as when a user receives a notification with a call to action. Other times, the trigger is internal and the information for what to do next is imprinted in the users’ memory through an association. For example, many products cue off of emotions as internal triggers. We use Facebook to socially connect with friends and family when we’re lonely and check ESPN or Pinterest when we’re bored.

It is important that companies understand the users’ internal triggers so they can build the product to meet those needs. Designers should be able to fill in the blank for the phrase, “Every time the user (______), they use my app.” The blank should be the internal trigger.

Take, for example, the experience Nike has constructed for its aspiring athlete customers. Nike’s suite of products includes wearable monitors, which track physical activity throughout the day, as well as a host of smartphone apps to be used while running, playing basketball, or golfing.

For Nike, it is critical that users attach the company’s products to a discrete moment in their lives. To succeed, Nike has to own the instant just before the user heads out the door to work out. Athletes want to know their effort matters and Nike helps them meet that need. By digitally recording the workout, Nike’s apps tap into a deeper emotional need to feel that all that sweating is not going to waste, that the amateur athlete is making progress.

By creating an association with a moment in time—in this case, every time the user exercises—Nike begins the process of creating a habit.


When it comes to multi-screen experiences, it is important to design a narrative for how the product is actually used. Knowing the sequence of behaviors leading up to using the products, as well as the deeper emotional user needs, is critical for successfully executing the next step of the Hook, the action phase.

The action is the simplest behavior the user can take in anticipation of reward. Minimizing the effort to get to the payoff is a critical aspect of habit-forming design. The sooner users can get to the reward, the faster they can form automatic behaviors.

Knowing the sequence of behaviors leading up to using the products, as well as the deeper emotional user needs, is critical for successfully executing the next step of the Hook, the action phase.


In Nike’s case, simply opening the app or wearing one of their body-mounted devices alleviates the user’s fear that the exercise will be in vain. Clicking a button marked Run in the Nike+ running app, for example, begins tracking the workout and gets the user closer to the relief he was looking for.

Finding ways to minimize the effort, be it by eliminating unnecessary logins or distracting functionality, improves the experience both on mobile and web interfaces. Nike makes the action of tracking exercise easier by building products designed to make recording even easier — for example, shoe-mounted devices that passively collect information.

Designers should consider how many steps they put in the way of users getting what they came for. The more complex the actions, the less likely users are to complete the intended behavior.


The reward phase of the Hook is where the user finally gets relief. After being triggered and taking the intended action, the user expects to have the internal trigger satisfied. If the user came to relieve boredom, she should be entertained. If the trigger was curiosity, she expects to find answers.

Thus, the reward phase gives the user what she came for, and quickly! When the athlete uses the Nike app, for example, a 3-2-1 countdown displays to signify that the workout is about to begin. The user can get on with her run, knowing it is being recorded. But the Nike suite of products layers on more rewards. The apps not only record the workout, they also motivate it.

When products have an element of variability or surprise, they become more interesting and engaging.


 To boost their habit-forming effects, many products utilize what psychologists call intermittent reinforcement. When products have an element of variability or surprise, they become more interesting and engaging. For example, scrolling on Twitter or Pinterest offers the allure of what might be found with the next flick of the thumb.

In Nike’s case, the element of variability can be found in various forms throughout its product experience. For example, when athletes connect to Facebook, the app posts to the social network and runners hear the sound of a cheering crowd every time one of their friends “likes” their update. The athlete never knows when they’ll hear the encouragement and the social rewards help them keep pushing forward.

Nike also implements a point system called NikeFuel, which is meant to be a quantification of physical activity. However, the mechanics of how rapidly points are earned is intentionally opaque, giving it an element of variability. Finally, exercising itself has an element of surprise, which Nike’s products accentuate by encouraging users to complete new and increasingly challenging activities.


Lastly, a critical aspect of products that keep users coming back is their ability to ask for an investment. This phase of the Hook involves inviting the user to do a bit of work to personalize the experience. By asking the user to add some effort into the app, the product increases in value with use, getting better the more the user commits to it.

Investments are actions that increase the likelihood of the next pass through the Hook by loading the next trigger, storing value, and creating preference for using the product. It is important that the four phases of the Hook are followed in sequential order for maximum impact.

Every time the user exercises with a Nike app or body monitor, she accrues a history of performance. The product becomes her digital logbook, which becomes more valuable as a way of tracking progress the more entries she makes. Additionally, each purchase of a Nike+ device—like a FuelBand, for example—is a further financial and psychological investment in the ecosystem.

Nike and other exercise training apps, like Strava, allow athletes to follow other athletes to compare performance. The action of selecting and following other users is a form of investment; it improves the product experience with use and increases the user’s likelihood of using the product again.

In the future, products like Nike+ could automatically collect information from multiple touchpoints to create an individualized workout plan. The product could improve and adapt the more the user invests in using it.

An Engagement Advantage

For multi-interface products that rely upon repeated user engagement, understanding the fundamentals of designing habits is critical. By following the Hook Model of a trigger, an action, a reward, and finally an investment, product designers can ensure they have the requisite components of a habit-forming technology.

By building products that follow users throughout their day, on smartphones, tablets, and more recently wearable devices, companies have an opportunity to cycle users through the four phases of the Hook more frequently and increase the odds of creating products people love.


Cyber Security is Not Prepared for the Growth of Internet Connected Devices

3 Mar

The estimated growth of devices connected to the Internet is staggering.  By 2020 Cisco estimates that 99% of devices (50 billion) will be connected to the Internet.  In contrast, currently only around 1% is connected today. The sheer numbers as well as the complexity of new types of devices will be problematic. Although traditional computing devices such as personal computers, tablets and smartphones will increase, it is the Internet of Things (IoT) which will grow significantly, to around 26 billion units. That represents nearly a 30-fold increase according to Gartner.

Device Estimates.jpg

The industry is in a vicious fight protecting current platforms, such as PC’s from malware and compromise. New malware is generated at a mind boggling rate of ~200k unique samples each day. With the rise of smartphones and tablets, we are witnessing the fastest growth of malware in this sector and expect the complexity of attacks to increase. Security companies work tirelessly to keep up with the increasing pace.
But the wildcards to this equation will be the radical growth of IoT devices which have different architectures, software, and usages. Wearables, transportation, and smart appliances which will grow at an alarming rate. These represent challenges as they will differ greatly from familiar computers and longstanding security controls will need to be reworked or rethought entirely. The processes and tools currently in use by security organizations are not easily extensible to meet the new challenge. This will give attackers a diverse area to scrutinize for vulnerabilities and new opportunities to exploit for their gain.
Security resources across the industry are already stretched thin. It will be very difficult to adapt to the new scope, requiring new tools, expertise, and ways of thinking. The security industry is not giving up and throwing in the towel just yet, but the challenge they face is undeniable.
Product vendors can play an important role by designing and testing products with security in mind.  Such hardening techniques can reinforce both hardware and software to deny attackers opportunities of compromise. Hardware features, software capabilities, and security services must be designed to work together for maximum effect. This holistic strategy is necessary to establish a common front of cooperative defenses. Security services must look ahead and begin adaptation to serve emerging form factors, supporting infrastructures, and user demands.
Perhaps most importantly, the everyday user must begin to take responsibility for their own security. Users have a tremendous amount of control over their security and can strongly influence the industry by demanding proper embedded controls. User behaviors must shift to more reasonable actions.  Not every link must be clicked. Not every survey or request for personal information must be fulfilled. Not every application, including those from untrustworthy sources, must be installed. Socially, we must act with more discretion to protect our valuables.
Our world is changing quickly with the staggering increase of interconnected devices melding into cyberspace. The security risks rise equally as fast. We will face challenges, but it is up to all of us to determine how secure we will be.

Matthew Rosenquist is an information security strategist, with a passion for his chosen profession. Benefiting from nearly 20 years of experience in Fortune 100 corporations, he has thrived on establishing strategic organizations and capabilities which deliver cost effective information security services.


Intel Touts New Ultra-High-Speed Wireless Data Technology

27 Feb

Small base stations could achieve huge data capacity increases using Intel’s modular antenna arrays.

Intel says it has prototyped a chip-based antenna array that can sit in a milk-carton-sized cellular base station. The technology could turbocharge future wireless networks by using ultrahigh frequencies.

Intel’s technology, known as a millimeter wave modular antenna array, is expected to be demonstrated today at the Mobile World Congress conference in Barcelona, Spain, says Ali Sadri, director of the millimeter wave standards and advanced technology group at Intel.

Any one such cell could send and receive data at speeds of more than a gigabit per second over up to few hundred meters—and far more at shorter distances—compared to about 75 megabits per second for the latest standard, known as 4G LTE.

For mobile cellular communications, both the Intel and Samsung technologies could eventually use frequencies of 28 or 39 gigahertz or higher. These frequencies are known as millimeter wave and carry far more data than those used in cellular networks today. But they are easily blocked by objects in the environment—and even water droplets in the air. So they’ve traditionally been seen as impractical for mobile devices.

To get around the blockage problem, processors dynamically shape how a signal is combined among 64, 128, or even more antenna elements, controlling the direction in which a beam is sent from each antenna array, making changes on the fly in response to changing conditions.

Several groups are working on such antenna arrays, but Intel says its version is more efficient. “We can scale up the number of modular arrays as high as practical to increase transmission and reception sensitivity. The barrier is only regulatory issues, not technological ones,” Sadri says.

A major problem is finding a way to get so many antennas into a mobile device. The NYU technology used a benchtop gadget hauled around the sidewalks of Manhattan for testing. It steers beams mechanically toward intended users. The Intel chip does the same thing by shaping the direction of the signal electronically, and is now packaged in a gadget smaller than a shoebox.

A number of companies are betting next-generation wireless technologies will need to use millimeter wave links to deliver all the data people want. The European Commission, for example, last year launched a $1.8 billion 5G research effort to help develop this and other technologies.



Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2013–2018

6 Feb



The Cisco® Visual Networking Index (VNI) Global Mobile Data Traffic Forecast Update is part of the comprehensive Cisco VNI Forecast, an ongoing initiative to track and forecast the impact of visual networking applications on global networks. This paper presents some of Cisco’s major global mobile data traffic projections and growth trends.

Executive Summary

The Mobile Network in 2013

Global mobile data traffic grew 81 percent in 2013. Global mobile data traffic reached 1.5 exabytes per month at the end of 2013, up from 820 petabytes per month at the end of 2012.

Last year’s mobile data traffic was nearly 18 times the size of the entire global Internet in 2000. One exabyte of traffic traversed the global Internet in 2000, and in 2013 mobile networks carried nearly 18 exabytes of traffic.

Mobile video traffic exceeded 50 percent for the first time in 2012. Mobile video traffic was 53 percent of traffic by the end of 2013.

Over half a billion (526 million) mobile devices and connections were added in 2013. Global mobile devices and connections in 2013 grew to 7 billion, up from 6.5 billion in 2012. Smartphones accounted for 77 percent of that growth, with 406 million net additions in 2013.

Globally, smart devices represented 21 percent of the total mobile devices and connections in 2013, they accounted for 88 percent of the mobile data traffic. In 2013, on an average, a smart device generated 29 times more traffic than a non-smart device.

Mobile network connection speeds more than doubled in 2013. Globally, the average mobile network downstream speed in 2013 was 1,387 kilobits per second (Kbps), up from 526 Kbps in 2012.

In 2013, a fourth-generation (4G) connection generated 14.5 times more traffic on average than a non-4G connection. Although 4G connections represent only 2.9 percent of mobile connections today, they already account for 30 percent of mobile data traffic.

The top 1 percent of mobile data subscribers generated 10 percent of mobile data traffic, down from 52 percent at the beginning of 2010. According to a mobile data usage study conducted by Cisco, mobile data traffic has evened out over the last year and is now lower than the 1:20 ratio that has been true of fixed networks for several years.

Average smartphone usage grew 50 percent in 2013. The average amount of traffic per smartphone in 2013 was 529 MB per month, up from 353 MB per month in 2012.

Smartphones represented only 27 percent of total global handsets in use in 2013, but represented 95 percent of total global handset traffic. In 2013, the typical smartphone generated 48 times more mobile data traffic (529 MB per month) than the typical basic-feature cell phone (which generated only 11 MB per month of mobile data traffic).

Globally, there were nearly 22 million wearable devices (a sub-segment of M2M category) in 2013 generating 1.7 petabytes of monthly traffic.

Globally, 45 percent of total mobile data traffic was offloaded onto the fixed network through Wi-Fi or femtocell in 2013. In 2013, 1.2 exabytes of mobile data traffic were offloaded onto the fixed network each month. Without offload, mobile data traffic would have grown 98 percent rather than 81 percent in 2013.

Per-user iOS mobile devices (smartphones and tablets) data usage marginally surpassed that of Android mobile devices data usage. By the end of 2013, average iOS consumption exceeded average Android consumption in North America and Western Europe.

In 2013, 18 percent of mobile devices were potentially IPv6-capable. This estimate is based on network connection speed and OS capability.

In 2013, the number of mobile-connected tablets increased 2.2-fold to 92 million, and each tablet generated 2.6 times more traffic than the average smartphone. In 2013, mobile data traffic per tablet was 1,374 MB per month, compared to 529 MB per month per smartphone.

There were 149 million laptops on the mobile network in 2013, and each laptop generated 4.6 times more traffic than the average smartphone. Mobile data traffic per laptop was 2.45 GB per month in 2013, up 17 percent from 2.1 GB per month in 2012.

Average nonsmartphone usage increased 39 percent to 10.8 MB per month in 2013, compared to 7.8 MB per month in 2012. Basic handsets still make up the vast majority of handsets on the network (73 percent).

The Mobile Network Through 2018

Mobile data traffic will reach the following milestones within the next five years.

• Monthly global mobile data traffic will surpass 15 exabytes by 2018.

• The number of mobile-connected devices will exceed the world’s population by 2014.

• The average mobile connection speed will surpass 2 Mbps by 2016.

• Due to increased usage on smartphones, smartphones will reach 66 percent of mobile data traffic by 2018.

• Monthly mobile tablet traffic will surpass 2.5 exabyte per month by 2018.

• Tablets will exceed 15 percent of global mobile data traffic by 2016.

• 4G traffic will be more than half of the total mobile traffic by 2018.

• There will be more traffic offloaded from cellular networks (on to Wi-Fi) than remain on cellular networks by 2018.

Global mobile data traffic will increase nearly 11-fold between 2013 and 2018. Mobile data traffic will grow at a compound annual growth rate (CAGR) of 61 percent from 2013 to 2018, reaching 15.9 exabytes per month by 2018.

By the end of 2014, the number of mobile-connected devices will exceed the number of people on earth, and by 2018 there will be nearly 1.4 mobile devices per capita. There will be over 10 billion mobile-connected devices by 2018, including machine-to-machine (M2M) modules-exceeding the world’s population at that time (7.6 billion).

Mobile network connection speeds will increase two-fold by 2018. The average mobile network connection speed (1,387 Kbps in 2013) will exceed 2.5 megabits per second (Mbps) by 2018.

By 2018, 4G will be 15 percent of connections, but 51 percent of total traffic. By 2018, a 4G connection will generate 6 times more traffic on average than a non-4G connection.

By 2018, over half of all devices connected to the mobile network will be “smart” devices. Globally, 54 percent of mobile devices will be smart devices by 2018, up from 21 percent in 2013. The vast majority of mobile data traffic (96 percent) will originate from these smart devices by 2018, up from 88 percent in 2013.

By 2018, 48 percent of all global mobile devices could potentially be capable of connecting to an IPv6 mobile network. Over 4.9 billion devices will be IPv6-capable by 2018.

Over two-thirds of the world’s mobile data traffic will be video by 2018. Mobile video will increase 14-fold between 2013 and 2018, accounting for 69 percent of total mobile data traffic by the end of the forecast period.

By 2018, mobile-connected tablets will generate nearly double the traffic generated by the entire global mobile network in 2013. The amount of mobile data traffic generated by tablets by 2018 (2.9 exabytes per month) will be 1.9 times higher than the total amount of global mobile data traffic in 2013 (1.5 exabytes per month).

The average smartphone will generate 2.7 GB of traffic per month by 2018, a 5-fold increase over the 2013 average of 529 MB per month. By 2018, aggregate smartphone traffic will be 11 times greater than it is today, with a CAGR of 63 percent.

By 2018, more than half of all traffic from mobile-connected devices (almost 17 exabytes) will be offloaded to the fixed network by means of Wi-Fi devices and femtocells each month. Without Wi-Fi and femtocell offload, total mobile data traffic would grow at a CAGR of 65 percent between 2013 and 2018 (12-fold growth), instead of the projected CAGR of 61 percent (11-fold growth).

The Middle East and Africa will have the strongest mobile data traffic growth of any region at 70 percent CAGR. This region will be followed by Central & Eastern Europe at 68 percent and Asia Pacific at 67 percent.

Appendix A summarizes the details and methodology of the VNI forecast.

2013 Year in Review

Global mobile data traffic grew 81 percent in 2013, a rebound over the 2012 slowdown in mobile traffic. Growth rates varied widely by region. All of the emerging market regions experienced a doubling of mobile data traffic in 2013. (Middle East and Africa grew 107 percent, Latin America grew 105 percent, and Central and Eastern Europe grew 99 percent.) Mobile data traffic grew 86 percent in Asia Pacific, 77 percent in North America, and 57 percent in Western Europe.

Table 1. Examples of Mobile Data Traffic Growth in 2013

Region Mobile Traffic Growth Examples
Korea As reported by Korean regulator KCC, mobile data traffic on 2G, 3G, and 4G networks increased approximately 70% between 3Q 2012 and 3Q 2013.
China Mobile data traffic of China’s top 3 mobile operators grew 90% in 2012 and 72% from mid-2012 to mid-2013.
Japan Mobile data traffic grew 92% in 2012 and 66% from 3Q 2012 to 3Q 2013, according to Japan’s Ministry of Internal Affairs and Communications.
India Bharti Airtel reported mobile data traffic growth of 112% between 3Q 2012 and 3Q 2013.

Reliance Communications reported mobile data traffic growth of 116% between 3Q 2012 and 3Q 2013.

Australia As reported by Australian regulator ACMA, mobile data traffic grew 47% from mid-2012 to mid-2013.
Italy As reported by Italian regulator AGCOM, mobile traffic in Italy in 3Q13 was up 34% year-over-year.
France As reported by French regulator ARCEP, mobile traffic in France was up 60% from 2Q 2013 to 2Q 2012.
Germany As reported by German regulator BNA, mobile traffic in Germany grew 40% in 2012.
Sweden As reported by Swedish regulator PTS, mobile traffic in Sweden grew 69 percent from mid-2012 to mid-2013.
Russia Vimpelcom reported mobile data traffic growth of 106% from 3Q 2012 to 3Q 2013.
Other Vodafone’s year-over-year global mobile traffic growth was 60% from 1Q FY12 to 1Q FY13.

Vodafone’s European traffic grew 35% during fiscal year 2012-2013, up from 18% the previous fiscal year.


Global Mobile Data Traffic, 2013 to 2018

Overall mobile data traffic is expected to grow to 15.9 exabytes per month by 2018, nearly an 11-fold increase over 2013. Mobile data traffic will grow at a CAGR of 61 percent from 2013 to 2018 (Figure 1).

Figure 1. Cisco Forecasts 15.9 Exabytes per Month of Mobile Data Traffic by 2018

The Asia Pacific and North America regions will account for almost two-thirds of global mobile traffic by 2018, as shown in Figure 2. Middle East and Africa will experience the highest CAGR of 70 percent, increasing 14-fold over the forecast period. Central and Eastern Europe will have the second highest CAGR of 68 percent, increasing 13-fold over the forecast period. The emerging market regions of Asia Pacific and Latin America will have CAGRs of 67 percent and 66 percent respectively.

Figure 2. Global Mobile Data Traffic Forecast by Region

Top Global Mobile Networking Trends

The sections that follow identify nine major trends contributing to the growth of mobile data traffic.

1. Transitioning to Smarter Mobile Devices

2. Measuring Internet of Everything Adoption-Emerging Wearable Devices

3. Analyzing Mobile Applications-Video Dominance

4. Profiling Bandwidth Consumption by Device

5. Assessing Mobile Traffic/Offload by Access Type (2G, 3G, and 4G)

6. Comparing Mobile Network Speeds

7. Reviewing Tiered Pricing-Managing Top Mobile Users

8. Adopting IPv6-Beyond an Emerging Protocol

9. Defining Mobile “Prime Time”-Peak vs. Average Usage

Trend 1: Transitioning to Smarter Mobile Devices

The increasing number of wireless devices that are accessing mobile networks worldwide is one of the primary contributors to global mobile traffic growth. Each year several new devices in different form factors and increased capabilities and intelligence are being introduced in the market. Over half a billion (526 million) mobile devices and connections were added in 2013. Global mobile devices and connections grew, in 2013, to 7 billion, up from 6.5 billion in 2012. Globally, mobile devices and connections will grow to 10.2 billion by 2018 at a CAGR of 8 percent (Figure 3). By 2018, there will be 8.2 billion handheld or personal mobile-ready devices and 2 billion machine-to-machine connections (e.g., GPS systems in cars, asset tracking systems in shipping and manufacturing sectors, or medical applications making patient records and health status more readily available, et al). Regionally, North America and Western Europe are going to have the fastest growth in mobile devices and connections with 12 percent and 10 percent CAGR from 2013 to 2018 respectively.

Figure 3. Global Mobile Devices and Connections Growth

We see a rapid decline in the share of nonsmartphones from more than 66 percent in 2013 (4.7 billion) to less than 34 percent by 2018 (3.5 billion). The most noticeable growth is going to occur in tablets, followed by machine-to-machine connections (M2M), both growing nearly six-fold over the forecast period. Tablets are going to grow at 41 percent CAGR from 2013 to 2018, and the M2M category is going to grow at 43 percent CAGR during the same period.

While there is an overall growth in the number of mobile devices and connections, there is also a visible shift in the device mix. Throughout the forecast period, we see that the device mix is getting smarter with an increasing number of devices with higher computing resources, network connection capabilities that create a growing demand for more capable and intelligent networks. We define smart devices and connections as those having advanced computing and multimedia capabilities with a minimum of 3G connectivity. As mentioned previously, 526 million mobile devices and connections were added in 2013, and smartphones accounted for 77 percent of that growth at 406 million net adds. The share of smart devices and connections as a percentage of the total will increase from 21 percent in 2013 to more than half, at 54 percent, by 2018, growing 3.8 fold during the forecast period (Figure 4).

Figure 4. Global Growth of Smart Mobile Devices and Connections

Although it is a global phenomenon, some regions are ahead in this device mix conversion. North America will have over 90 percent of its installed base converted to smart devices and connections, followed by Western Europe with 83 percent smart devices and connections (Table 2).

Table 2. Regional Share of Smart Devices and Connections (Percent of the Regional Total)

Region 2013 2018
North America 65% 93%
Western Europe 45% 83%
Central and Eastern Europe 15% 61%
Latin America 14% 55%
Asia Pacific 17% 47%
Middle East and Africa 10% 36%


Source: Cisco VNI Mobile, 2014

Figure 5 shows the impact of mobile smart devices and connections growth on global traffic. Globally, smart traffic is going to grow from 88 percent of the total global mobile traffic to 96 percent by 2018. This is significantly higher than the ratio of smart devices and connections (54% by 2018), because on average a smart device generates much higher traffic than a nonsmart device.

Figure 5. Effect of Smart Mobile Devices and Connections Growth on Traffic

Mobile devices and connections are not only getting smarter in their computing capabilities but are also evolving from lower-generation network connectivity (2G) to higher-generation network connectivity (3G, 3.5G, and 4G or LTE). When device capabilities are combined with faster, higher bandwidth and more intelligent networks, it leads to wide adoption of advanced multimedia applications that contribute to increased mobile and Wi-Fi traffic.

The explosion of mobile applications and phenomenal adoption of mobile connectivity by end users, on the one hand, and the need for optimized bandwidth management and network monetization, on the other hand, is fueling the growth of global 4G deployments and adoption. Service providers around the world are busy rolling out 4G networks to help them meet the growing end-user demand for more bandwidth, higher security, and faster connectivity on the move (Appendix B).

Globally, the relative share of 3G and 3.5G-capable devices and connections will surpass 2G-capable devices and connections by 2016 (48 percent and 44 percent relative share). By 2018, 15 percent of all global devices and connections will be 4G capable (Figure 6). The global mobile 4G connections will grow from 203 million in 2013 to 1.5 billion by 2018 at a CAGR of 50 percent.

Figure 6. Global Mobile Devices and Connections by 2G, 3G, and 4G

In addition to transition from 2G to 3G, 4G deployment is also a global phenomenon. In fact, by 2018, North America will have the majority (51 percent) of its mobile devices and connections with 4G capability, surpassing 3G-capable devices and connections. Western Europe (24 percent) will have the second highest ratio of 4G connections by 2018 (Appendix B). Among countries, Japan will have over 56 percent of the country’s total connections on 4G by 2018, with Korea having 54 percent of all its connections on 4G by 2018. The United States is going to lead the world in terms of its share of the total global 4G connections with 23 percent of global 4G connections.

The growth in 4G, with its higher bandwidth, lower latency, and increased security, will help regions bridge the gap between their mobile and fixed network performance. This will lead to even higher adoption of mobile technologies by end users, making access to any content on any device from anywhere more of a reality.

Trend 2: Measuring Internet of Everything Adoption-Emerging Wearable Devices

The phenomenal growth in smarter end-user devices and M2M connections is a clear indicator of the growth of the Internet of everything (IoE), which is bringing together people, processes, data, and things to make networked connections more relevant and valuable. In this section, we will focus on the continued growth of M2M connections and the emerging trend of wearable devices. Both M2M and wearable devices are making computing and connectivity very pervasive in our day-to-day lives.

M2M connections-such as home and office security and automation, smart metering and utilities, maintenance, building automation, automotive, healthcare and consumer electronics, and more-are being used across a broad spectrum of industries, as well as in the consumer segment. As real-time information monitoring helps companies deploy new video-based security systems, while also helping hospitals and healthcare professionals remotely monitor the progress of their patients, bandwidth-intensive M2M connections are becoming more prevalent. Globally, M2M connections will grow from 341 million in 2013 to over 2 billion by 2018, a 43 percent CAGR. M2M capabilities similar to end-user mobile devices are migrating from 2G to 3G and 4G technologies. In 2013, 71 percent of global mobile M2M connections were connected using 2G connectivity, while 28 percent used 3G, and less than 0.5 percent used 4G. By 2018, only 35 percent of M2M modules will have 2G connectivity; 51 percent will have 3G connectivity; and 14 percent will have 4G connectivity (Figure 7).

Figure 7. Global Machine-to-Machine Growth and Migration from 2G to 3G and 4G

An important factor contributing to the growing adoption of IoE is the emergence of wearable devices, a category with high growth potential. Wearable devices, as the name suggests, are devices that can be worn on a person, which have the capability to connect and communicate to the network either directly through embedded cellular connectivity or through another device (primarily a smartphone) using Wi-Fi, Bluetooth or another technology. These devices come in various shapes and forms, ranging from smart watches, smart glasses, heads-up displays (HUD), health and fitness trackers, health monitors, wearable scanners and navigation devices, smart clothing, and so forth. The growth in these devices has been fuelled by enhancements in technology that have supported compression of computing and other electronics (making the devices light enough to be worn). These advances are being combined with fashion to match personal styles, especially in the consumer electronics segment, along with network improvements and the growth of applications, such as location-based services and augmented reality. While there have been vast technological improvements to make wearables possible as a significant device category, the embedded cellular connectivity still has some barriers, such as technology, regulatory, and health concerns, to overcome before it becomes widely available and adopted.

By 2018, we estimate that, there will be 177 million wearable devices globally, growing eight-fold from 22 million in 2013 at a CAGR of 52 percent (Figure 8). As mentioned earlier, there will be limited embedded cellular connectivity in wearables through the forecast period. Only 13 percent will have embedded cellular connectivity by 2018, up from 1 percent in 2013. Currently, we do not include wearables as a separate device and connections category because it is at a nascent stage, so there is a noted overlap with the M2M category. We will continue to monitor this segment, and as the category grows and becomes more significant, we may break it out in future forecast iterations.

Figure 8. Global Connected Wearable Devices

Regionally, North America will lead through the forecast period in its relative share of wearables, with a 42 percent share in 2013 going to 34 percent by 2018 (Appendix B). Other regions with significant share include Western Europe with 25 percent share in 2013, growing to 26 percent by 2018, and Asia Pacific with 21 percent share, growing to 25 percent by 2018.

The wearables category will have a tangible impact on mobile traffic, because even without embedded cellular connectivity, they can connect to mobile networks through smartphones. Globally, traffic from wearables will account for 0.5 percent of smartphone traffic by 2018 (Figure 9).Globally, traffic from wearable devices will grow 36-fold from 2013 to 61 petabytes per month by 2018 (CAGR 105 percent). Globally, traffic from wearable devices will account for 0.4 percent of total mobile data traffic by 2018, compared to 0.1 percent at the end of 2013.

Figure 9. Global Wearable Devices Traffic Impact

Trend 3: Analyzing Mobile Applications-Video Dominance

Because mobile video content has much higher bit rates than other mobile content types, mobile video will generate much of the mobile traffic growth through 2018. Mobile video will grow at a CAGR of 69 percent between 2013 and 2018, the highest growth rate of any mobile application category that we forecast, other than machine-to-machine traffic. Of the 15.9 exabytes per month crossing the mobile network by 2018, 11 exabytes will be due to video (Figure 10). Mobile video represented more than half of global mobile data traffic beginning in 2012, indicating that it is having an immediate impact on traffic today, not just in the future.

Figure 10. Mobile Video Will Generate Over 69 Percent of Mobile Data Traffic by 2018

Because many Internet video applications can be categorized as cloud applications, mobile cloud traffic follows a curve similar to video. Mobile devices have memory and speed limitations that might prevent them from acting as media consumption devices, were it not for cloud applications and services. Cloud applications and services such as Netflix, YouTube, Pandora, and Spotify allow mobile users to overcome the memory capacity and processing power limitations of mobile devices. Globally, cloud applications will account for 90 percent of total mobile data traffic by 2018, compared to 82 percent at the end of 2013 (Figure 11). Mobile cloud traffic will grow 12-fold from 2013 to 2018, a compound annual growth rate of 64 percent.

Figure 11. Cloud Applications Will Account for 90 Percent of Mobile Data Traffic by 2018

Trend 4: Profiling Bandwidth Consumption by Device

The proliferation of high-end handsets, tablets, and laptops on mobile networks is a major traffic generator, because these devices offer the consumer content and applications not supported by previous generations of mobile devices. As shown in Figure 12, a single smartphone can generate as much traffic as 49 basic-feature phones; a tablet as much traffic as 127 basic-feature phones; and a single laptop can generate as much traffic as 227 basic-feature phones.

Figure 12. High-End Devices Significantly Multiply Traffic

Average traffic per device is expected to increase rapidly during the forecast period, as shown in Table 3.

Table 3. Summary of Per-Device Usage Growth, MB per Month

Device Type 2013 2018
Nonsmartphone 10.8 45
M2M Module 61 451
Wearable Device 78 345
Smartphone 529 2,672
4G Smartphone 1,984 5,371
Tablet 1,374 5,609
4G Tablet 2,410 9,183
Laptop 2,455 5,095


Source: Cisco VNI Mobile, 2014

The growth in usage per device outpaces the growth in the number of devices. As shown in Table 4, the growth rate of mobile data traffic from new devices is two to five times greater than the growth rate of users.

Table 4. Comparison of Global Device Unit Growth and Global Mobile Data Traffic Growth

Device Type Growth in Devices,
2013-2018 CAGR
Growth in Mobile Data Traffic,
2013-2018 CAGR
Smartphone 18% 63%
Tablet 41% 87%
Laptop 13% 30%
M2M Module 43% 113%


Source: Cisco VNI Mobile, 2014

A few of the main promoters of growth in average usage are described in the following list:

• As mobile network connection speeds increase, the average bit rate of content accessed through the mobile network will increase. High-definition video will be more prevalent, and the proportion of streamed content, as compared to side-loaded content, is also expected to increase with average mobile network connection speed.

• The shift toward on-demand video will affect mobile networks as much as it will affect fixed networks. Traffic can increase dramatically, even while the total amount of time spent watching video remains relatively constant.

• As mobile network capacity improves, and the number of multiple-device users grows, operators are more likely to offer mobile broadband packages comparable in price and speed to those of fixed broadband. This is encouraging mobile broadband substitution for fixed broadband, where the usage profile is substantially higher than average.

• Mobile devices increase an individual’s contact time with the network, and it is likely that this increased contact time will lead to an increase in overall minutes of use per user. However, not all of the increase in mobile data traffic can be attributed to traffic migration to the mobile network from the fixed network. Many uniquely mobile applications continue to emerge, such as location-based services, mobile-only games, and mobile commerce applications.

Trend 5: Assessing Mobile Traffic/Offload by Access Type (2G, 3G, and 4G)

Impact of 4G

While 3G and 3.5G account for the majority (60 percent) of mobile data traffic today, 4G will grow to represent over half of all mobile data traffic by 2018, despite a connection share of only 15 percent (Figure 13).

Currently, a 4G connection generates nearly 15 times more traffic than a non-4G connection. There are two reasons for this. The first is that many 4G connections today are for high-end devices, which have a higher average usage. The second is that higher speeds encourage the adoption and usage of high-bandwidth applications, such that a smartphone on a 4G network is likely to generate 50 percent more traffic than the same model smartphone on a 3G or 3.5G network. As smartphones come to represent a larger share of 4G connections, the gap between the average traffic of 4G devices and non-4G devices will narrow, but by 2018 a 4G connection will still generate 6 times more traffic than a non-4G connection.

Figure 13. 51 Percent of Total Mobile Data Traffic Will Be 4G by 2018


Much mobile data activity takes place within users’ homes. For users with fixed broadband and Wi-Fi access points at home, or for users served by operator-owned femtocells and picocells, a sizable proportion of traffic generated by mobile and portable devices is offloaded from the mobile network onto the fixed network. For the purposes of this study, offload pertains to traffic from dual mode devices (i.e., supports cellular and Wi-Fi connectivity; excluding laptops) over Wi-Fi and small cell networks. Offloading occurs at the user/device level when one switches from a cellular connection to Wi-Fi/small cell access. Our mobile offload projections include traffic from both public hotspots as well as residential Wi-Fi networks.

As a percentage of total mobile data traffic from all mobile-connected devices, mobile offload increases from 45 percent (1.2 exabytes/month) in 2013 to 52 percent (17.3 exabytes/month) by 2018 (Figure 14). Without offload, Global mobile data traffic would grow at a CAGR of 65 percent instead of 61 percent. Offload volume is determined by smartphone penetration, dual-mode share of handsets, percentage of home-based mobile Internet use, and percentage of dual-mode smartphone owners with Wi-Fi fixed Internet access at home.

Figure 14. 52 Percent of Total Mobile Data Traffic Will Be Offloaded by 2018

The amount of traffic offloaded from smartphones will be 51 percent by 2018, and the amount of traffic offloaded from tablets will be 69 percent by 2018.

A supporting trend is the growth of cellular connectivity for devices such as tablets which in their earlier generation were limited to Wi-Fi connectivity only. With increased desire for mobility and mobile carriers offer of data plans catering to multi-device owners, we find that the cellular connectivity is on a rise albeit cautiously as the end users are testing the waters. As a point in case, we estimate that by 2018, 42 percent of all tablets will have a cellular connection up from 34 percent in 2013 (Figure 15).

Figure 15. 42 Percent of Global Tablets Will Be Cellular Connected by 2018

Some have speculated that Wi-Fi offload will be less relevant once 4G networks are in place because of the faster speeds and more abundant bandwidth. However, 4G networks will attract high-usage devices such as advanced smartphones and tablets, and it appears that 4G plans will be subject to data caps similar to 3G plans. For these reasons, Wi-Fi offload is higher on 4G networks than on lower speed networks, now and in the future according to our projections. The amount of traffic offloaded from 4G was 54 percent at the end of 2013 and will be 56 percent by 2018 (Figure 16). The amount of traffic offloaded from 3G will be 49 percent by 2018, and the amount of traffic offloaded from 2G will be 40 percent by 2018.

Figure 16. Mobile Data Traffic and Offload Traffic, 2018

Trend 6: Comparing Mobile Network Speeds

Globally, the average mobile network connection speed in 2013 was 1,387 Kbps. The average speed will grow at a compound annual growth rate of 13 percent, and will exceed 2.5 Mbps by 2018. Smartphone speeds, generally third-generation (3G) and higher, are currently almost three times higher than the overall average. Smartphone speeds will nearly double by 2018, reaching 7 Mbps.

There is anecdotal evidence to support the idea that usage increases when speed increases, although there is often a delay between the increase in speed and the increased usage, which can range from a few months to several years. The Cisco VNI Forecast relates application bit rates to the average speeds in each country. Many of the trends in the resulting traffic forecast can be seen in the speed forecast, such as the high growth rates for developing countries and regions relative to more developed areas (Table 5).

Table 5. Projected Average Mobile Network Connection Speeds (in Kbps) by Region and Country

2013 2013 2014 2015 2016 2018 CAGR
Global speed: All handsets 1,387 1,676 1,908 2,147 2,396 2,509 13%
Global speed: Smartphones 3,983 4,864 5,504 6,132 6,756 7,044 12%
Global speed: Tablets 4,591 5,584 6,298 6,483 8,018 8,998 14%
By Region
Middle East & Africa 529 605 675 753 832 900 11%
Central & Eastern Europe 1,351 1,446 1,711 1,945 2,128 2,269 11%
Latin America 684 734 793 856 924 999 8%
Western Europe 1,585 1,735 1,946 2,183 2,452 3,003 14%
Asia-Pacific 1,327 1,492 1,617 1,728 1,863 1,992 8%
North America 1,728 2,010 2,304 2,620 2,988 4,549 21%


Source: Cisco VNI Mobile, 2014

Current and historical speeds are based on data from Cisco’s GIST (Global Internet Speed Test) application and Ookla’s Speedtest. Forward projections for mobile data speeds are based on third-party forecasts for the relative proportions of 2G, 3G, 3.5G, and 4G among mobile connections through 2018. For more information about Cisco GIST, please visit

The speed at which data can travel to and from a mobile device happen in two places: the infrastructure speed capability outside the device, and the connectivity speed from the network capability inside the device. These speeds are actual and modeled end user speeds and not theoretical speeds that the devices, connection or technology is capable of providing. There are several variables that affect the performance of a mobile connection. Roll out of 2G/3G/4G in various countries and regions, technology used by the cell towers, spectrum availability, terrain, signal strength, and number of devices sharing a cell tower. The type of application being used by the end user is also an important factor. Download speed, upload speed and latency characteristics vary widely depending on the type of application, be it video, radio or instant messaging.

Figure 17. Mobile Speeds by Technology 2G vs. 3G vs. 4G

4G Speeds will be 6 times higher than that of an average mobile connection by 2018. In comparison, 3G speeds will be twice as fast as the average mobile connection by 2018.

Figure 18. Mobile Speeds by Device

Trend 7: Reviewing Tiered Pricing-Managing Top Mobile Users

An increasing number of service providers worldwide are moving from unlimited data plans to tiered mobile data packages. To make an initial estimate of the impact of tiered pricing on traffic growth, we repeated a case study based on the data of two Tier 1 global service providers from mature mobile markets. The study tracks data usage from the timeframe of the introduction of tiered pricing three years ago. The findings in this study are based on Cisco’s analysis of data provided by a third-party data analysis firm. This firm maintains a panel of volunteer participants who have given the company access to their mobile service bills, including KB of data usage. The data in this study reflects usage associated with over 38,889 devices and spans 12 months (October 2012 through September 2013) and also refers to the study from the previous update for longer term trends. The overall study spans three years. Cisco’s analysis of the data consists of categorizing the pricing plans, operating systems, devices, and users; incorporating additional third-party information on device characteristics; and performing exploratory and statistical data analysis. While the results of the study represent actual data from Tier 1 mobile data operators, global forecasts that include emerging markets, and Tier 2 providers may lead to lower estimates.

Over the period of the nearly 3-year study, the percentage of tiered plans compared to all data plans increased from 4 percent to 55 percent, while unlimited plans dropped from 81 percent to 45 percent. This has not, however, constrained usage patterns. From 2012 to 2013, average usage per device on a tiered plan grew from 922 MB per month to 1,081 MB per month, while usage per device of unlimited plans grew at from a higher base of 1,261 MB per month to 1,890 MB per month.

However, tiered plans are effective. There is a narrowing of the bandwidth consumption gap between tiered and unlimited data plan connections, showing the general increase in consumption of mobile data traffic due to the increased consumption of services such as Pandora, YouTube, Facebook, and Netflix. Unlimited plans have promoted the adoption of mobile applications and increased web usage through mobile broadband.

Tiered pricing plans are often designed to constrain the heaviest mobile data users, especially the top 1 percent of mobile data consumers. An examination of heavy mobile data users reveals that the top 1 percent of mobile users is actually the top 3.5 percent, because the top 1 percent of users varies each month. For example, for a mobile data subscriber base of 1000 users; the top 1 percent is 10 users. However, the same set of 10 users does not appear in the top 1 percent category in each month; rather, a larger set of 35 subscribers rotates though the top 1 percent. This top 3.5 percent are the users who have the potential of being in the top 1 percent bracket in any given month and substitute for each other in subsequent months. The trend is due to the nature of consumption of mobile data applications.

The usage per month of the average top 1 percent of mobile data users has been steadily decreasing compared to overall usage. At the beginning of the 3-year study, 52 percent of the traffic was generated by the top 1 percent. At the end of the three year time frame, the top 1 percent generated 10 percent of the overall traffic per month compared to 16 percent in September 2012 (Figure 19). The top 10 percent of mobile users generate as much traffic as the remaining 90 percent of mobile data traffic (Figure 20).

Figure 19. Top 1 Percent Generates 52 Percent of Monthly Data Traffic in Jan 2010 Compared to 10 Percent in Sept 2013

Figure 20. Top 10 Percent Consumes Nearly As Much As the Remaining 90 Percent

Figure 21. Remaining 99 Percent Growing Faster Than Top 1 Percent

Additional evidence that tiered pricing plans are effectively constraining the top 1 percent of mobile users, and that the growth is being made up by those outside the top 1 percent, is that the usage of the remaining 99 percent is growing much more rapidly than the top 1 percent (Figure 21). With the introduction of new larger screen smartphones and tablets, there is continued increase in usage in terms of megabytes per month per user in all the top tiers (Figure 22).

Figure 22. All Top Tiers Increase in Absolute Usage (MB per Month) from 2011 to 2013

The proportion of mobile users generating more than 2 gigabytes per month has increased significantly over the past year, reaching 24 percent of users towards the end of 2013 (Figure 23).

Figure 23. 3 Percent of Users Consume 5 GB per Month and 24 Percent Consume over 2 GB per Month

More detail on the tiered pricing case study is available in Appendix C.

iOS Marginally Surpasses Android in Data Usage

At the beginning of the three year tiered pricing case study, Android data consumption was equal to if not higher than other smartphone platforms. However, Apple-based devices have now caught up and their data consumption is marginally higher than that of Android devices in terms of megabytes per month per connection usage (Figure 24).

Figure 24. Megabytes per Month by Operating System

Tiered plans outnumber unlimited plans; unlimited plans continue to lead in data consumption.

Figure 25. Tiered vs. Unlimited Plans

The number of shared plans is increasing; there is no clear effect on usage during the short time frame of the study.

Figure 26. Shared vs. Regular Data Plans

More detail on consumption by operating system is available in Appendix C.

Trend 8: Adopting IPv6-Beyond an Emerging Protocol

The transition to IPv6 is well underway, which helps connect and manage the proliferation of newer-generation devices that are contributing to mobile network usage and data traffic growth. Continuing the Cisco VNI focus on IPv6, the Cisco VNI 2013-2018 Mobile Data Traffic Forecast provides an update on IPv6-capable mobile devices and connections and the potential for IPv6 mobile data traffic.

Focusing on the high-growth mobile-device segments of smartphones and tablets, the forecast projects that globally 79 percent of smartphones and tablets (3.5 billion) will be IPv6 capable by 2018 (up from 46 percent or 837 million smartphones and tablets in 2013). This estimation is based on OS support of IPv6 (primarily Android and iOS) and the accelerated move to higher-speed mobile networks (3.5G or higher) capable of enabling IPv6. (This forecast is intended as a projection of the number of IPv6-capable mobile devices, not mobile devices with an IPv6 connection actively configured by the ISP.)

Figure 27. Global IPv6-Capable Smartphones and Tablets Reach 3.5 Billion by 2018

For all mobile devices and connections, the forecasts project that, globally, nearly half (48 percent) will be IPv6-capable by 2018, up from 18 percent (1.3 billion) in 2013. M2M emerges as a key segment of growth for IPv6-capable devices, reaching nearly 600 million by 2018, a 46-fold increase during the forecast period. With its capability to vastly scale IP addresses and manage complex networks, IPv6 is critical in supporting the IoE of today and in the future. (Refer to Table 15 in Appendix D for more device detail.)

Regionally, Asia Pacific will lead throughout the forecast period with the highest number of IPv6-capable devices and connections, reaching 2.2 billion by 2018. Middle East and Africa and Asia-Pacific will have the highest growth rates during the forecast period, at 35 percent CAGR and 34 percent CAGR respectively. (Refer to Table 16 in Appendix D for more regional detail.)

Figure 28. Global IPv6-Capable Mobile Devices Reach 4.9 Billion by 2018

Considering the significant potential for mobile device IPv6 connectivity, the Cisco VNI Mobile Forecast provides an estimation for IPv6 network traffic based on a graduated percentage of IPv6-capable devices becoming actively connected to an IPv6 network. Looking to 2018, if 50 percent of IPv6-capable devices are connected to an IPv6 network, the forecast estimates that, globally, IPv6 traffic will amount to 6.6 exabytes per month or 40 percent of total mobile data traffic, a 73-fold growth from 2013 to 2018.

Figure 29. Projected IPv6 Mobile Data Traffic Forecast 2013-2018

For additional views on the latest IPv6 deployment trends, visit the Cisco 6Lab site. The Cisco 6Lab analysis includes current statistics by country on IPv6 prefix deployment, IPv6 web content availability, and estimations of IPv6 users. With the convergence of IPv6 device capability, content availability, and network deployment, the discussion of IPv6 moves from “what if” to “how soon will” service providers and end users realize the potential IPv6 has to offer.

Trend 9: Defining Mobile “Prime Time”-Peak vs. Average Usage

Mobile video applications have a “prime time” in that they are predominantly used during certain times of day. Web and general data usage tends to occur throughout the day, but video consumption is highest in the evening. Video therefore has a higher peak-to-average ratio than web and data. Live video and video communications have higher peak-to-average ratios than video-on-demand. As the mobile network application mix shifts towards video, and as the video mix increasingly includes live video and video communication, the overall mobile data peak-to-average ratio increases. Busy hour mobile traffic is growing at a slightly higher pace than average hour traffic, and by 2018 mobile busy hour traffic will be 83 percent higher than average hour traffic by 2018, compared to 66 percent in 2013 (Figure 30).

Figure 30. Mobile Busy Hour Is 66% Higher Than Average Hour in 2013, 83% by 2018

The faster growth of busy hour traffic is not as pronounced on mobile networks as on fixed networks because mobile networks never had a large amount of peer-to-peer file sharing traffic, which brought down the peak-to-average ratio on fixed networks until video overtook peer-to-peer as the dominant application. Even though the trend is less pronounced, mobile operators will need to plan for a mobile busy hour compound annual growth rate of 64 percent between 2013 and 2018.


Mobile data services are well on their way to becoming necessities for many network users. Mobile voice service is already considered a necessity by most, and mobile data, video, and TV services are fast becoming an essential part of consumers’ lives. Used extensively by consumer as well as enterprise segments, with impressive uptakes in both developed and emerging markets, mobility has proven to be transformational. Mobile subscribers are growing rapidly and bandwidth demand due to data and video is increasing. Mobile M2M connections continue to increase. The next 5 years are projected to provide unabated mobile video adoption despite uncertain macroeconomic conditions in many parts of the world. Backhaul capacity must increase so mobile broadband, data access, and video services can effectively support consumer usage trends and keep mobile infrastructure costs in check.

Deploying next-generation mobile networks requires greater service portability and interoperability. With the proliferation of mobile and portable devices, there is an imminent need for networks to allow all these devices to be connected transparently, with the network providing high-performance computing and delivering enhanced real-time video and multimedia. This openness will broaden the range of applications and services that can be shared, creating a highly enhanced mobile broadband experience. The expansion of wireless presence will increase the number of consumers who access and rely on mobile networks, creating a need for greater economies of scale and lower cost per bit.

As many business models emerge with new forms of advertising, media and content partnerships, mobile services including M2M, live gaming, and (in the future) augmented reality, a mutually beneficial situation needs to be developed for service providers and over-the-top providers. New partnerships, ecosystems, and strategic consolidations are expected as mobile operators, content providers, application developers, and others seek to monetize the video traffic that traverses mobile networks. Operators must solve the challenge of effectively monetizing video traffic while increasing infrastructure capital expenditures. They must become more agile and able to quickly change course and provide innovative services to engage the Web 3.0 consumer. While the net neutrality regulatory process and business models of operators evolve, there is an unmet demand from consumers for the highest quality and speeds. As wireless technologies aim to provide experiences formerly only available through wired networks, the next few years will be critical for operators and service providers to plan future network deployments that will create an adaptable environment in which the multitude of mobile-enabled devices and applications of the future can be deployed.

For More Information

Inquiries can be directed to

Appendix A: The Cisco VNI Global Mobile Data Traffic Forecast

Table 6 shows detailed data from the Cisco VNI Global Mobile Data Traffic Forecast. The portable device category includes laptops with mobile data cards, USB modems, and other portable devices with embedded cellular connectivity.

Table 6. Global Mobile Data Traffic, 2013-2018

2013 2014 2015 2016 2017 2018 CAGR
By Application Category (TB per Month)
Data 606,405 957,382 1,437,249 2,073,797 2,832,137 3,531,107 42%
File Sharing 66,671 127,235 221,808 308,643 391,641 466,347 48%
Video 793,944 1,458,730 2,579,242 4,370,458 7,094,943 10,956,123 69%
M2M 20,736 49,286 113,415 246,198 490,226 907,472 113%
By Device Type (TB per Month)
Nonsmartphones 50,425 68,087 91,030 118,901 143,427 154,258 25%
Smartphones 923,361 1,684,096 2,883,253 4,679,786 7,217,671 10,534,617 63%
Laptops 365,011 500,827 678,627 882,051 1,117,171 1,365,892 30%
Tablets 127,027 287,996 581,401 1,065,826 1,829,859 2,881,415 87%
M2M 20,736 49,286 113,415 246,198 490,226 907,472 113%
Other Portable Devices 1,196 2,341 3,987 6,333 10,593 17,394 71%
By Region (TB per Month)
North America 388,583 624,586 969,032 1,453,312 2,100,830 2,953,875 50%
Western Europe 253,679 389,397 592,818 888,378 1,310,517 1,900,486 50%
Asia Pacific 523,918 953,085 1,670,216 2,777,483 4,441,514 6,717,828 67%
Latin America 91,863 177,273 307,822 505,265 789,313 1,158,090 66%
Central and Eastern Europe 124,059 241,016 434,096 723,186 1,135,470 1,641,205 68%
Middle East and Africa 105,655 207,277 377,731 651,472 1,031,304 1,489,565 70%
Total (TB per Month)
Total Mobile Data Traffic 1,487,756 2,592,634 4,351,714 6,999,096 10,808,947 15,861,049 61%


Source: Cisco, 2014

The Cisco VNI Global Mobile Data Traffic Forecast relies in part upon data published by Informa Telecoms and Media, Strategy Analytics, Infonetics, Ovum, Gartner, IDC, Dell’Oro, Synergy, ACG Research, Nielsen, comScore, Arbitron Mobile, Maravedis and the International Telecommunications Union (ITU).

The Cisco VNI methodology begins with the number and growth of connections and devices, applies adoption rates for applications, and then multiplies the application’s user base by Cisco’s estimated minutes of use and KB per minute for that application. The methodology has evolved to link assumptions more closely with fundamental factors, to use data sources unique to Cisco, and to provide a high degree of application, segment, geographic, and device specificity.

• Inclusion of fundamental factors. As with the fixed IP traffic forecast, each Cisco VNI Global Mobile Data Traffic Forecast update increases the linkages between the main assumptions and fundamental factors such as available connection speed, pricing of connections and devices, computational processing power, screen size and resolution, and even device battery life. This update focuses on the relationship of mobile connection speeds and the KB-per-minute assumptions in the forecast model. Proprietary data from the Cisco Global Internet Speed Test (GIST) application was used as a baseline for current-year smartphone connection speeds for each country.

• Device-centric approach. As the number and variety of devices on the mobile network continue to increase, it becomes essential to model traffic at the device level rather than the connection level. This Cisco VNI Global Mobile Data Traffic Forecast update details traffic to smartphones; nonsmartphones; laptops, tablets, and netbooks; e-readers; digital still cameras; digital video cameras; digital photo frames; in-car entertainment systems; and handheld gaming consoles.

• Estimation of the impact of traffic offload. The Cisco VNI Global Mobile Data Traffic Forecast model now quantifies the effect of dual-mode devices and femtocells on handset traffic. Proprietary data from Cisco’s IBSG Connected Life Market Watch was used to model offload effects.

• Increased application-level specificity. The forecast now offers a deeper and wider range of application specificity.

Appendix B: Global 4G Networks and Connections

Table 7. Regional 4G Connections Growth

  2013 2018
  Number of 4G Connections % of Total
Number of 4G Connections % of Total
Asia Pacific 80,920,533 2.3% 667,956,749 13.1%
Central and Eastern Europe 1,846,331 0.3% 88,665,716 10.1%
Latin America 936,408 0.1% 86,222,002 9.1%
Middle East and Africa 3,648,081 0.3% 86,576,973 5.3%
North America 104,290,345 24.5% 372,559,550 50.6%
Western Europe 11,458,739 1.9% 228,065,764 24.3%
Global 203,100,439 2.9% 1,530,046,754 15.0%


Source: Cisco, 2014

Table 8. Regional Wearable Devices Growth

  2013 2018
  Number of Wearable Devices % of Global Number of Wearable Devices % of Global
Asia Pacific 4,502,201 20.8% 43,810,250 24.8%
Central and Eastern Europe 1,078,646 5.0% 9,864,884 5.6%
Latin America 984,497 4.5% 9,709,040 5.5%
Middle East and Africa 712,403 3.3% 7,955,103 4.5%
North America 9,063,366 41.8% 59,829,286 33.8%
Western Europe 5,347,081 24.7% 45,775,527 25.9%
Global 21,688,195 100.0% 176,944,090 100.0%


Source: Cisco, 2014

Appendix C: A Case Study on the Initial Impact of Tiered Pricing on Mobile Data Usage

The Changing Role of the Top 1 Percent of Mobile Data Subscribers

Three years ago, the top 1 percent of mobile data subscribers was responsible for a disproportionate amount of mobile data traffic. However, according to the data from this study, this disproportion is becoming less pronounced with time. The amount of traffic due to the top 1 percent of subscribers declined from 52 percent in January 2010 to 10 percent in September 2013.In the recent iteration of the study from October 2012 to September 2013, the amount of traffic due to the top 1 percent of the subscribers declined from 15 percent to 10 percent (Table 9).

Table 9. Percentage of Traffic by User Tier, Months October 2012-September 2013

Data Users Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sept 13
% traffic due to Top 1% 15% 14% 15% 15% 19% 17% 15% 13% 14% 13% 12% 10%
% traffic due to Top 10% 49% 48% 48% 47% 59% 59% 48% 46% 46% 42% 42% 40%


Source: Cisco VNI, 2014

Table 10. Average Traffic by User Tier in MB per Month

Average MB per Month Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Top 1% 12,445 12,635 12,278 13,230 12,180 10,699 15,697 12,738 15,807 16,281 16,424 12,785
Top 5% 5,399 5,632 5,450 5,724 5,225 4,750 6,525 5,958 6,748 7,213 7,501 6,799
Top 10% 3,827 4,008 3,857 4,059 3,687 3,438 4,626 4,341 4,824 5,179 5,392 5,048
Top 20% 2,727 2,862 2,701 2,953 2,549 2,422 3,293 3,166 3,451 3,690 3,840 3,689


Source: Cisco VNI, 2014

Tiered pricing plans have lower megabyte-per-month consumption compared to unlimited plans. However, the overall measures displayed healthy growth with few signs of growth slowing, and the move to tiered pricing does not appear to have an immediate effect on overall mobile data traffic.

The number of mobile data users generating more than 2 GB per month has doubled over the course of the study, and the percentage of users generating over 200 MB per month reached 75 percent (Table 11).

Table 11. One Percent of Mobile Data Users Consume 5 GB per Month

% Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Greater than 5 GB 1% 2% 1% 2% 2% 2% 2% 2% 2% 3% 3% 3%
Greater than 2 GB 12% 13% 11% 15% 16% 17% 16% 17% 18% 20% 23% 24%
Greater than 200 MB 54% 56% 56% 56% 57% 58% 59% 60% 62% 74% 75% 75%
Greater than 20 MB 70% 72% 72% 73% 74% 74% 74% 76% 77% 93% 93% 93%


Source: Cisco VNI, 2013

The rapid increase in data usage presents a challenge to service providers who have implemented tiers defined solely in terms of usage limits. Mobile data caps that fall too far behind usage volumes may create opportunities for competitors in the market. Therefore, many service providers are creating more nuanced tiers, shared data plans and data add-ons, such as a separate charge for tethering and hotspot functionality. Such offerings tend to require less vigilance on the part of subscribers than data caps, yet still monetize scenarios that tend to have high data usage. Shared data family plans are being introduced and their effects on overall mobile data traffic are yet to be determined.

Mobile Data Traffic Volume by Operating System

While the effect of the tiered plan is clear, the average consumption per connection continues to increase for both tiered and unlimited plans Both Android- and Apple-based devices are prominent bandwidth promoters in tiered as well as unlimited plans. Android-based devices led in average megabyte-per-month usage with unlimited plans and Apple-based iOS led in usage with tiered plans (Tables 12 and 13).

Table 12. MB per Month Usage per Mobile Operating System in Unlimited Plans

Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Android 1,497 1,585 1,601 1,733 1,938 1,857 2,288 1,964 2,435 2,447 2,583 2,226
iOS 1,131 1,246 1,191 1,211 1,311 1,214 1,380 1,449 1,559 1,635 1,759 1,767
Palm OS 819 414 497 543 876 1,288 882 1,144 1,658 228 491 886
Windows 501 630 484 1,259 2,083 2,332 1,685 1,480 1,655 1,678 1,079 804
Blackberry 168 192 167 138 152 128 243 308 302 309 437 411


Source: Cisco VNI, 2014

Table 13. MB per Month Usage per Mobile Operating System in Tiered Pricing Plans

Operating System Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
iOS 748 835 782 893 929 943 958 983 1,049 1,068 1,132 1,122
Android 451 468 462 515 565 582 585 583 632 944 1,000 1,051
Windows 607 531 611 632 731 829 748 760 876 926 882 976
Blackberry 229 250 203 261 292 277 263 314 345 403 445 415
Palm OS 75 130 157 199 176 213 171 224 154 261 253 244


Source: Cisco VNI, 2014

Shared data plans have been introduced in mature markets and the initial findings show lower traffic usage in shared plans; but both shared as well as regular plans continue to grow in terms of usage per month.

Table 14. Table 14: Shared vs. Regular Plans

Shared vs. Regular Plan (MB/month) Oct-12 Nov-12 Dec-12 Jan-13 Feb-13 Mar-13 Apr-13 May-13 Jun-13 Jul-13 Aug-13 Sep-13
Regular Plan 792 840 808 877 953 942 1,006 995 1,107 1,345 1,432 1,416
Shared Plan 648 733 703 725 752 742 808 798 841 908 952 946


Source: Cisco VNI, 2014

Appendix D: IPv6-Capable Devices, 2013-2018

Table 15 provides regional IPv6-capable forecast detail. Table 16 provides the segmentation of IPv6-capable devices by device type.

Table 15. IPv6-Capable Devices by Device Type, 2013-2018

Devices (K) 2013 2014 2015 2016 2017 2018 CAGR
Global 1,294,935 1,841,342 2,471,825 3,173,249 3,961,932 4,934,672 31%
Laptops 121,760 150,661 178,331 204,668 232,977 258,095 16%
M2M 12,720 37,258 86,692 176,847 329,307 586,186 115%
Nonsmartphones 309,801 435,880 544,815 577,148 548,431 546,823 12%
Other Portables 14,005 13,293 13,040 15,069 18,573 22,698 10%
Smartphones 766,567 1,089,696 1,473,281 1,944,511 2,476,418 3,049,246 32%
Tablets 70,082 114,554 175,667 255,006 356,226 471,625 46%


Source: Cisco, 2014

Table 16. IPv6-Capable Devices by Region, 2013-2018

Devices (K) 2013 2014 2015 2016 2017 2018 CAGR
Global 1,294,935 1,841,342 2,471,825 3,173,249 3,961,932 4,934,672 31%
Asia Pacific 526,332 770,169 1,063,814 1,378,683 1,759,816 2,261,164 34%
Latin America 110,158 164,800 228,623 301,506 377,236 465,365 33%
North America 185,044 237,832 291,560 357,057 424,930 503,103 22%
Western Europe 235,026 313,392 390,085 473,251 553,732 639,970 22%
Central and Eastern Europe 105,196 156,350 219,743 298,522 385,027 467,562 35%
Middle East and Africa 133,179 198,800 278,000 364,231 461,190 597,508 35%


Source: Cisco, 2014



More on MIMO

3 Feb


One of the technologies that is going to be needed to make the Internet of Things work better is MIMO. MIMO stands for multiple-input, multiple output and refers to using an array of antennas to communicate instead of a single antenna. MIMO technology can apply to different kinds of wireless including WiFi and cellular.

MIMO has been around for a few years and the latest high performance WiFi routers include the first generation MIMO technology. These wireless routers include multiple antennas that work together and the purpose for the antennas is to establish separate wireless routes to different devices.

When done smartly, MIMI dynamically sets up a different wireless path to a given device, so there would be a separate wireless path to your cell phone, your TV and your speaker system. The current MIMO routers can only establish a few separate paths at a time. So if you have more than a few wireless devices running at the same time (which many of us now do), then there is also a general broadcast signal that can be picked up by any device within range.

As you can imagine, establishing separate paths and doing it well can be a challenge. Some devices like cell phones and tablets are mobile within the environment and the router has to keep track of where each device is at. Done well the router will determine the right amount of power and bandwidth to give to each device.

But fast forward a few years when you also have a host of IoT devices in your home. Today in my house we often are running seven WiFi devices, but add to this an array of smart appliances, smoke detectors, security cameras, medical monitors and various toys and it’s easy to see that the normal home router could get overwhelmed in a hurry.

Scientists are already working on more sophisticated MIMO devices so that they can understand the challenges of handling large numbers of multiple devices simultaneously. Scientists at Rice University have constructed an array of 96 MIMO antennas that is letting them a look into our future. They have named their array Argos and it is giving them a tool for exploring the ways to process and integrate inputs and outputs rom many sources. They are calling their application mammoth MIMO.

Mammoth MIMO antenna arrays are more efficient than a bunch of single antennas. The large array that Rice is studying can do a whole lot more than connect to 96 devices and they are claiming that  the multiplicative efficiency appears to make the large array as much as ten times more efficient than using a host of individual routers.

That kind of efficiency is going to be necessary in the future in two circumstances. First, this technology could be used immediately in crowded environments. We are all aware of how hard it is to get a cell phone signal when there are a lot of people together in a convention center or stadium. Mammoth MIMO could enable many more connections.

But the more widespread use will be in a world where the normal home or business is filled with scores of IoT devices all wanting to make connections to the network. Without improved MIMO this is not going to be possible.

Massive MIMO is going to require massive processing power to make sense of the huge inflow of simultaneous signals. That will require more computational and data storage locally just to process and make sense of IoT data. I have several friends who work in the field of artificial intelligence and they think their technology is going to be needed to help make sense of the massive data flood that will flow out of IoT.



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