Wireless Data Traffic: A Decade of Change

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Wireless Data Traffic: A Decade of Change Emir Halepovic, Carey Williamson, and Majid Ghaderi, University of Calgary

Abstract This article presents an overview of the most significant changes in wireless data traffic and its main driving forces throughout the past decade (1998–2008). The main axes of change are discussed: wireless technology, user population, and applications. Evolution of wireless technology realized a 200-fold increase in data rate, and facilitated a continuous enrichment of the traffic mix traversing legacy and modern wireless networks. New applications emerged, such as peer-to-peer file sharing, online gaming, and multimedia, establishing a trend of significant increase in traffic volume. User population has also increased and even saturated some markets. However, not all benefits of wireless technologies are equally exploited, with only a few users exercising high mobility or regularly enjoying multimedia services. Future indications include a requirement for either integration or interoperability of two mainstream wireless technologies, WiFi and cellular, as well as continuous user demand for more bandwidth, broader coverage, and better mobility support.

T

he past decade may well be recorded in history books as the time when we all became dependent on “wireless.” Wireless local area networks (WLANs) allowed us to enjoy the high-speed networking and Internet access wire-free, and the Bluetooth earpiece finally allowed us to be mobile and communicate hands-free and wire-free, all at the same time. This article discusses wireless data traffic and its evolution over the past decade, from the first wireless data networks to the ubiquity of WiFi and cellular access today. The most significant axes of change are reviewed, including wireless technology, user population, and applications. These axes of change are the main factors that shape and influence wireless network usage, and they are presented using observations of their past, present, and possible future. There is a cyclic interaction between wireless technology, products, markets, user population, applications, and network traffic (Fig. 1). It starts with the enabling technology. Once the technology is deployed, products are launched through various markets where people, from early adopters through to peak population, use these products. For example, WLAN technology was offered in the form of wireless cards and base stations, and first launched in enterprise and campus markets. Students adopted the products very quickly since it enabled wireless connectivity for their laptops. With more users, more products are needed and markets are expanded. Higher product demand leads to increased production and higher affordability, which for WLANs meant expansion to homes, small offices, coffee shops, and so on. People like attractive products, and a variety of supported applications makes a device more attractive. Users run applications that in turn generate data traffic, and a larger user population means more traffic. As new applications are created, new types of traffic are produced, which usually pushes

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technology to evolve, so the cycle starts again. Bandwidthhungry applications have pushed WLAN and cellular technologies to evolve up to a 200-fold increase in data rates over the past 10 years. In the following sections we focus on wireless technology, user population, and applications, using two mainstream examples of wireless networks: WLAN or WiFi, and wireless wide area networks (WWANs) or cellular networks. This article is not an exhaustive review of WiFi or cellular networking; rather, it focuses on wireless data traffic and its driving forces, and not on issues like mobility management, security, and so forth.

Wireless Technologies In this article we focus on WiFi and cellular networks as the mainstream wireless data technologies of the past decade. As Ethernet LANs prevailed over asynchronous transfer mode (ATM) in the late 1990s, they set the stage for “wireless Ethernet” development, with WiFi being the logical choice following the principles of simplicity and lower cost. For cellular networks, the path was already set. By the mid1990s, cellular networks had transitioned from analog to digital, and claimed the market for mobile long-range voice service using connection-oriented service and predominantly circuit-switching technology, which has persisted to the present day. An early wireless multihop technology that offered longrange data networking was Ricochet [1]. The Ricochet network predates WiFi, and one of the first wireless data traffic reports came after its deployment in 1998. Ricochet was a packet radio network operating at data rates of 56–128 kb/s in the 900 MHz band. This was the first widely deployed wireless packet data network that offered an alternative to dial-up ser-

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Users

Markets

Applications

WiFi protocol

Year ratified

Freq. (GHz)

Max. rate (Mb/s)

Max. range (m)

802.11

1997

2.4

1, 2

~100

802.11a

1999

5

54

~100

802.11b

1999

2.4

11

~130

802.11g

2003

2.4

54

~130

802.11n

2009 est.

2.4, 5

248

~250

n Table 1. Summary of WiFi standards. Products

Traffic

Technology

n Figure 1. Cyclic relationship between factors that influence wireless data traffic.

vice. The network architecture was a wireless mesh consisting of repeaters and access points (APs) on street lamp posts. Ricochet technology was successful for several years, but it did not evolve to compete with wired broadband standards.

Wireless LAN For broadband wireless data networks, the past decade was marked by the beginning of the WiFi era with the completion of the legacy 802.11 standard. The standard was based on existing “draft 802.11” products, but it was soon replaced by the revolutionary standard-product combination for WLAN, 802.11b. It improved the raw data rate from 1 or 2 Mb/s to 11 Mb/s, slightly extended range to 35 m indoors, and introduced the true broadband experience, interoperability, and a lowcost solution, which spawned massive worldwide adoption. The 802.11b WLAN operates in the now crowded 2.4 GHz radio frequency band, suffers interference from other cordless devices, and uses often criticized carrier sense multiple access with collision avoidance (CSMA/CA) multiple access, but nevertheless persisted throughout the past decade as the primary choice for home, public, and especially enterprise hotspot deployments. Three more variants of the same standard emerged at intervals of several years, each adding improvements. 802.11a was concurrently ratified with 802.11b in 1999 [2]. It increased the data rate to 54 Mb/s, and moved to the newly allocated unlicensed 5 GHz band. Somewhat smaller range and higher cost hindered its wide deployment. In 2003 the introduction of 802.11g combined the best of its predecessors, a high data rate of 54 Mb/s, with range and cost comparable to 802.11b, and included backward compatibility with 802.11b. Keeping with the “tradition” that products appear before the official standard, we are currently witnessing “draft” 802.11n devices with peak data rates of 250 Mb/s (110 Mb/s in practice) and about twice the range of their cousins from the same standard family. 802.11n brings multiple-input multipleoutput (MIMO) devices to WiFi. The MIMO approach uses separate channels to vastly improve data rates. Table 1 shows the WiFi standards with data rates and ranges [2]. WiFi was originally intended to support bursty non-realtime traffic, such as file transfer, email, and the World Wide Web, which are all throughput-oriented applications, with the

Web being interactive as well. Increasing data rates made WiFi attractive for media streaming too, but its wireless channels are not friendly to real-time traffic due to random loss, longer delay, and contention-based access. WiFi technology has evolved significantly in terms of throughput, but at the moment it still lacks range, mobility, and real-time support. An alternative technology, called HiperLAN, backed by a European standard, was developed in parallel with WiFi. HiperLAN emphasized quality of service (QoS) support but lost the battle to the simpler WiFi, which was also available sooner.

Wireless WAN In contrast to WiFi technology that progressed mainly through a single standard family, WWANs or cellular networks have evolved through several standards around the world. The starting point in this discussion is the second generation (2G) of cellular technologies. The 2G standards replaced analog with digital voice technology and through the 1990s became well-established, mainly through Global System for Mobile Communications (GSM) and code-division multiple access (CDMA) networks. The two incompatible cellular technologies both carried digital voice primarily and provided low data rates, usually up to 9.6 kb/s, combined with per-byte billing. Cellular network technology progressed slowly through the past decade, starting from primary support for circuit-switched voice and gradually transforming into a data-optimized system (3.5G). Table 2 shows the evolution of the cellular network standards and their support for a varying mixture of voice and data traffic. Long term evolution (LTE) aims to transform cellular technology to an all-IP platform with voice over IP, rich multimedia support, and high data rates of over 10 Mb/s. LTE may bring a unified standard and compatibility across carriers. It is also a contender to become the main technology for future wireless networking. Integration of cellular networks and the Internet is the main feature of 4G technologies, based on the Internet multimedia subsystem (IMS) [3]. IMS is a complex service platform that will allow cellular network operators to remain true service providers rather than just “bit pipe” owners, as circuit-switched voice would no longer exist. The IMS “service platform” main functions include single sign-on, assisting and controlling sessions established between peers, and QoS management.

Data Traffic Measurement When designing any communication network, the most important question is what kind of traffic it should support. By analyzing real traffic from existing similar networks, we can make realistic predictions. However, wireless networks introduced new difficulties for capturing representative traffic samples in comparison to wired networks. These difficulties include, but are not limited to, ownership and the method of capturing traffic.

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Introduced

Generation

Traffic support

Peak data rate

Pre-1990

1G

Analog voice

N/A

1991

2G (GSM, IS-95)

Digital voice, SMS, Circuit data

9.6–14.4 kb/s

1999

2.5G (GPRS, cdmaOne)

Improved voice, circuit, and packet data

115 kb/s

2002

3G (WCDMA, EDGE, CDMA2000 1xRTT)

Improved voice, packet data, multimedia

384 kb/s–2 Mb/s

2003

3.5G (UMTS, EV-DO)

Voice over IP, packet data, MMS, multimedia

2–3 Mb/s

2010 (est.)

4G (HSDPA, HSUPA, EV-DO Rev. B, LTE)

All-IP, Internet Multimedia Subsystem (IMS)

10–100 Mb/s

n Table 2. Cellular technology evolution and standards.

User Population Over the past decade, the user population of WLANs has grown steadily. To put the number of users in proper context, user population is here expressed relative to the infrastructure, such as the number of APs. Using campus WLAN examples, tracing of the first adopters of WiFi technology reported small populations per AP, such as 74 users per 12 campus APs in 1999 [6]. In 2001 the reported user population increased to 1706 using 476 APs, with the same campus (Dartmouth) reporting 7000 users per 550 APs in 2003–2004 [8, 9]. A similar population size of 6775 campus users is reported in a recent campus trace using only 97 APs (Calgary) [7]. This indicates increasing demand on the infrastructure by larger user populations, which are steadily increasing (Fig. 2). The increase in traffic volume follows the increase in user population. Overall user traffic doubled over a two-year period on the Dartmouth network, and average daily traffic per active network card increased by a factor of 2.6 [8, 9]. The

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aggregate peak throughput of WLANs on their busiest day increased from 5.6 Mb/s in 1999 to 22.2 Mb/s in 2001 to 37 Mb/s in 2003. These WLANs were equipped with APs that supported peak data rates of up to 10 Mb/s in 1999 (802.11compatible WaveLAN II), and 11 Mb/s in 2001 and 2003 (802.11b). An increasing user population not only causes an overall increase in traffic volume, but introduces non-uniform distribution of traffic load across the coverage area, due to user diversity. Dartmouth traffic reports show that overall daily traffic is significantly skewed, with a majority concentrated in the student residential area [8]. Calgary traces report that the number of users is skewed toward social and learning areas [7]. Such traffic imbalance across the network coverage area requires careful infrastructure planning. Looking at a different aspect of user population, some may recall that the WiFi user base around 1997 was projected to come from healthcare, factory floors, banking, and education. It turned out that the enterprise environment, including university campuses, became the main market for WiFi deployment, and the associated population the main user segment. With the ubiquity of WiFi, users started to expect to be able to do on the wireless network everything that wired networks allow them to do. However, the performance of current wireless technologies does not match the performance of wired networks. This is where we define the “performance gap” between wireless and wired networks. This gap is primarily reflected in data rates and delay, and it is equivalent to at least 10 years of time lag when Ethernet and cellular networks are compared (Fig. 3). To reduce this gap, a combination of radio technology, antenna design, improved signal processing, new available spectrum, and cross-layer techniques are being employed.

100

Number of users per AP

The main ownership-related problem is that only network operators are able to trace their own network, and proper traffic characterization can occur only under the operator’s umbrella. Published results are usually based on traces with short durations and small user populations [4], or only a single (e.g., physical) layer of traffic [5]. Even when network operators are willing to provide traces for analysis, these traces usually do not include all the necessary information for full analysis, because they were collected by the operator for a different purpose. The difficulties related to the method of capturing traffic emerged as the wireless network user population, geographic coverage, and volume of data increased. Initially, WiFi network traffic was captured on the wired side of the AP or a major wired router [6]. Wireless traffic was then extracted from the total traffic for analysis. While this method provided a good starting point for high-level understanding of traffic and usage in a smaller and isolated network, it did not offer a complete picture. Capturing on the wired side does not record medium access control (MAC) layer retransmissions and the frame data rate used, and is therefore not adequate for understanding MAC-layer behavior. To fully understand what is happening with the traffic on lower layers, newer tracing methodologies include traffic capture from the wireless side, or vicinity sniffing [7]. However, it was soon realized that this approach still fails to capture all transmissions due to signal attenuation and frame collisions, especially in “WiFi jungles,” where multiple service regions of APs overlap. To fully capture traffic in this scenario, multiple wireless sniffers are needed in combination with innovative techniques for synchronizing and merging separate traces.

80

Calgary

60

UNC

40

20

Dartmouth Stanford

Dartmouth

1999

2001

0 2003 Year

2003

2006

n Figure 2. Increase in WLAN user population.

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1000

100 LTE

WiFi

10

HSPA

Mobile wireless

UMTS 1 1990

1995

2000

2005 Year

2010

2015

n Figure 3. Performance gap between wireless and wired networks.

In parallel with WiFi, cellular networks experienced a similar increase in user population. Figure 4 shows the subscriber numbers steadily increasing over the past decade worldwide and reaching over 3 billion subscribers in 2007, or 46 percent of the world population. As of the middle of 2008, penetration had surpassed the 50 percent mark. While the overall statistics show the trend very well, it is also important to note that the actual penetration levels in some European countries have surpassed 100 percent, simply by subscribers starting to use multiple cell phones [10]. In fact, the rate of increase in cellular subscriptions was much higher than the rate of increase in fixed Internet subscribers over the past decade [10]. The number of people who have a cell phone and not a computer combined with the data capabilities of modern cell phones appear to have materialized the prediction that cell phones will dominate the future Internet [11].

User Mobility and Traffic Patterns Another important requirement users put on the wireless network is mobility, which really is a key issue in wireless networks. The two mainstream technologies are still very different in this respect. Cellular networks are designed around a single application with high mobility support. WiFi has achieved high data rates that enable many applications, but lacks good mobility support. WiFi does not always allow seamless transition between APs, especially if they do not belong to the same subnet, in which case all data connections are severed and must be reestablished. An additional effect of mobility on traffic is that it also introduces overhead in terms of processing and data. We distinguish between mobility, indicated by a change of location during a connection and involving a handoff between base stations, and roaming, indicated by a change of location between connections. Location is usually defined as an association with the AP or base station. Surprisingly, when looking at handoff statistics, we realize that few wireless users exhibit high mobility or frequent roaming in both WiFi and cellular networks. The 1999 study shows that 50 percent of WiFi users are stationary throughout the measurement period, with only 1.3 users on average moving between APs per hour [6]. Access point handoffs occur rarely too; about five handoffs per 5-min period. Dartmouth WLAN users in 2001 were low roamers, with 1/3 roamers and very few high roamers, measured by the number of wireless cards [9]. In 2003–2004, roamers increased to half of the user population [8]. However, half of the users stayed close to one location 98 percent of the time [8]. Only about 15 percent are mobile users. The most recent Calgary trace from 2006 still agrees with these findings [7].

The majority of cellular data users are also stationary (55 percent) and use only their “home cell” or a couple of cell sites, whereas very few roam excessively [12]. It is surprising to see so few highly mobile or roaming users. User characteristics are reflected in traffic, and we can therefore understand user behavior from the traffic. Since the early Ricochet network, the daily usage pattern emerged, corresponding to users’ daily work activity [1]. Users’ work cycle produces more traffic during midday and little traffic in early morning, with weekday loads usually higher than on weekends and holidays [1, 9]. However, some reports indicated that afternoons had the highest load in early campus deployments [6]. This trend persists on campus WLANs throughout the past decade. On the other hand, early adopters of cellular data networks exhibited different behavior, by initiating peak load in late afternoon and evening hours [4, 5]. Therefore, the traffic pattern corresponds more to the leisure cycle than the work cycle. The difference in cellular data traffic pattern could be related to pricing or demographics. The pricing structure often includes “free evening” option for voice calls that users apply to both voice and data usage. The demographic effect can be explained by more young people using data services during their after-school hours. Recent surveys indicate that data services are more popular among young early adopters (Fig. 5). There is a strong indication that a younger population of “digital natives” will lead the way for mobile data communication, by growing up surrounded by wireless technology and the ability to get “connected” anywhere, anytime [10].

Applications Applications transform user commands into the actual wireless network traffic, and each application affects in its own way the volume, data rate, and other traffic characteristics. The changing mix of applications that produced wireless data traffic over the past decade strongly reflects the wired data traffic, as well as new “wireless-inspired” applications. In 1999 Web traffic accounted for most data on wireless networks [6]. It was characterized by simple document structure and smaller document size [13]. Secondary applications were file transfer, remote login, and email. Web traffic complexity and object size increased over the years [13], and it is still evolving. One example of Web traffic evolution is the emergence of photo and video sharing as well as social portals, such as flickr.com, youtube.com, and facebook.com, which contribute lots of data and objects to the Web document structure. This is a good example of how a digital photography and video revolution affected a broad user Source: The GSM Association, http://www.gsmworld.org/news/statistics/

Year

Available data rate (Mb/s)

4G, LTE-A ?

Ethernet

2008 2007 2006 2005 2004 2003 2002 2001 2000 1999 1998 1997 0

2000 3000 1000 Number of worldwide subscribers (millions)

4000

n Figure 4. Increase in cellular subscriber population.

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Voice calls

Voice mail Camera (photo) Play games Image messaging Music/MP3 Email Handsfree

Young early adopters Total

Camera (video) 0%

20%

40%

60%

80%

100%

n Figure 5. Mobile services used in 2006 [10]. population, which in turn influenced the network traffic through adaptation of the existing application. At the beginning of the past decade, peer-to-peer (P2P) file sharing as we know it today did not exist. The turn of the century marked the beginning of the P2P file sharing popularity that immensely contributed to the changing traffic mix and volume (Fig. 6). Over the two-year period on the Dartmouth campus WLAN, the P2P contribution to total bytes transferred jumped more than threefold, from 5.3 to 19.3 percent [8]. In 2006 P2P contributed up to 33 percent [7]. The two most recent reports note that traffic shaping, which reduces the throughput of P2P applications, was applied to the traffic. Without traffic shaping, contribution of P2P applications to the overall traffic would be even higher. If we look at each network as a whole, we see that campus WLANs are still net consumers of data; inbound volume of data is higher [7–9]. Another important traffic component is media streaming applications. Figure 6 shows that around the beginning of the past decade, streaming was an insignificant component of the traffic mix. It is surprising that streaming is still a fairly low contributor to overall volume of data (~5 percent in [7]). However, video streaming is on the upswing. Total streaming traffic increased by 405 percent over two years at Dartmouth [8], and Calgary campus traces show that 40 percent of users use streaming applications. In Fig. 6 we can see the steady increase in the proportion of P2P and multimedia traffic, but not a consistent trend for Web traffic. This is actually not surprising for the following reasons. From 1999 to 2001, increase in Web traffic share can be attributed to its own success and prevalence among network applications. The drop in 2003 marks the point of high popularity of P2P applications and their increase in contribution to the traffic mix in the Internet, and taking over the Web as the most bandwidth-hungry application. The further increase in proportion of Web traffic in 2006 can be explained by two phenomena: the stricter traffic shaping policies on campuses to curb bandwidth usage by P2P applications, leaving more bandwidth for the Web, and the increasing complexity and overall size of Web pages that include more images and video clips [13]. The structure of traffic in cellular networks started to change at the turn of the century, when applications using Wireless Application Protocol (WAP) started to appear and data services were introduced. WAP allowed simple data services to be used on a cell phone, with the ultimate vision to bring Web content to the display of a resource-constrained device. WAP is also used for multimedia messaging service (MMS). By 2003, the composition of data traffic in General Packet Radio Service (GPRS) cellular networks was 76 percent WAP, 15 percent Web, 1 percent email, and 8 percent

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other, as shown in Fig. 7 [14]. WAP was the dominant traffic category through 2006, but other types of traffic appeared in cellular networks, as the data rates increased [15]. Non-WAP UDP traffic increased from 2 percent to 14 percent between 2004 and 2006 due to contribution of video streaming as a new service (Fig. 7) [16]. Another study of GPRS network traffic shows that the data volume of non-WAP applications grows more than WAP. Between December 2004 and November 2005, Web traffic in GPRS grew by a factor of 4.39, whereas WAP grew by a factor of 1.8 [15]. The data volume follows the user population increase and the change in traffic mix. Measurements of the Australian GPRS network show that the mean bit rate increased by a factor of 4.6 during a 17-month study from 2002 to 2004 [17]. In advanced Universal Mobile Telecommunications System (UMTS) networks that offer higher data rates and laptop cards, the dominant application in November 2005 was the Web with 61 percent, followed by a combination of HTTPS and TCP flows at 18 percent, and non-WAP UDP at 11 percent of traffic volume [15]. The WAP traffic in the same UMTS network almost disappeared, presumably due to widespread use of laptop UMTS cards. Over the years, both WiFi and cellular networks have seen enrichment of the traffic mix, with multimedia applications and services becoming more popular. The present day is characterized by increasing complexity and requirements of existing applications, but also emerging ones, such as VoIP and IPTV, with IPTV encompassing any kind of video distribution over IP, including video clips, TV channels and live video. The demand is strongly increasing for bandwidth-intensive applications. As the television industry is switching to highdefinition (HD) video, user demand will shift towards HD streaming over wireless networks. The traffic mix in both mainstream wireless network arenas seems to converge to the same point, supported by the products on the market and driven by the Internet.

Future Outlook There is no doubt that mainstream wireless technologies will have to further improve in order to satisfy all of our current needs. In the cellular network arena, as user penetration approaches saturation and data rates of cellular technologies approach those of fixed cable and digital subscriber line (DSL) access, providers are looking for new applications to increase revenue and retain market share. One of the most important goals is to enable cellular networks to support all user applications

70%

1999 2001 2003 2006

60% Percentage of total data

SMS Calendar

50% 40% 30% 20% 10% 0% Web

P2P

Streaming Application type

Unknown

Other

n Figure 6. Evolution of campus WLAN traffic mix.

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(a)

(b)

Other, Email, 8% 1% Web, 15%

(c) Web + email + other, 31%

WAP 1.x, 21%

Other, 10% Email, 5% Web, 14% Other UDP 2% WAP 2.x, 4%

WAP, 76%

WAP 1.x, 65%

UDP including streaming, 14%

WAP 2.x, 34%

n Figure 7. Evolution of application mix in GPRS networks (2004 and 2006 are from the same network): a) 2003; b) 2004; c) 2006.

running over TCP/IP. Integration of TCP/IP support into cellular networks created a situation where the primary cellular application, voice, is the only one that is circuit-switched and treated separately. Introduction of VoIP on wired TCP/IP networks shows that voice can run over the TCP/IP stack, so circuit-switched voice could be converted too. Moreover, cell phones are IP addressable and have become the largest device segment of the modern Internet [11]. The trend is clear for wired networks, with BT Group (United Kingdom) being one of the first to convert a national telephone system to IP. At this time, it is estimated that data traffic has caught up to voice in cellular networks, and it is projected to keep increasing at a much higher rate than voice in years to come (Fig. 8). Given that users continue to expect to do everything on wireless as on wired networks, the next big challenge for wireless technology is to support IPTV, which is becoming the most dominant application on the wired Internet, according to some projections (Fig. 8). The spectrum of Internet applications, including IPTV, has been brought to mobile users by IMS. The WiFi front is pushing too, with improvements including wider-area coverage, contention-based access improvements and support for meshing [2]. The first wireless mesh networks (WMNs) are being deployed as the alternative architecture to the traditional base station with backhaul link. WMNs improve coverage and make deployment much easier by using wireless medium as a backbone link. Using WMN architecture, it became possible to blanket entire cities with hotspots. WMNs could be the next breakthrough by enabling extensive wireless coverage, even larger than cellular networks provide today.

It is interesting that WMNs are having a rebirth since their first use by Ricochet networks for backhaul links. It is intriguing that an idea from the past may be building the future. WiFi and cellular networks started from distant points in the multidimensional space of data rates, coverage, and realtime traffic support. WiFi started with high data rates and local coverage. The goal of higher data rates was met by technology improvements, and the goal of wide coverage is being achieved by new hotspots on a daily basis. Some challenges still remain, especially with respect to support for real-time traffic and issues with performance degradation stemming from a contention-based MAC layer. Cellular networks started with very low data rates, wide coverage, and support for voice only. They evolved to broadband data rates and support for TCP/IP applications and multimedia traffic. In fact, multimedia is a major type of service planned for 3G and beyond. Throughout the past decade, two fundamentally different mainstream technologies took separate paths to arrive at a similar point. The prognosis for the future might include comparable coverage between WiFi and cellular networks, once urban centers are covered by hotspots. Cellular networks are consistently “chasing” the data rates of WiFi, but do not seem to be catching up, and real-time traffic support and seamless handover are still not fully realized by WiFi. The question remains whether the two technologies will continue to coexist separately or somehow integrate into one system. The future of wireless networking may belong to one of the currently emerging technologies. A promising one at the time of this writing is WiMAX, based on IEEE 802.16 standards [2]. WiMAX seems to include the best of WiFi and cellular worlds:

(b)

(a) 400

5

4 Traffic (Exabytes)

Traffice (Exabytes)

300

200 IPTV 100

3

2 Data 1

Voice Internet 0 2004

2005

2006

2007

2008

2009

2010

2011

Voice 2012

Year

0 2004

2005

2006

2007

2008

2009

2010

2011

2012

Year

n Figure 8. Projections of a) fixed and b) mobile traffic growth [10].

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high data rates, wide area coverage, mobility, and QoS support. WiMAX is commonly classified as a wireless metropolitan area network, and is being deployed as an alternative to cable and DSL for residential and small business access. However, initial deployments indicate that WiMAX has several performance problems. TCP throughput is quite low, and delay is high and variable, which raises questions about support for both throughput-oriented and delay-sensitive applications [18].

Conclusion This article reviews the evolution of wireless data traffic over the past decade (1998–2008). The main factors influencing wireless traffic are discussed, including wireless technology, user population, and applications. A clear trend toward a rich traffic mix is presented for two mainstream wireless technologies: WiFi and cellular. It is expected that traffic volume will also keep increasing, following the consistent pattern over the past decade. With increasing coverage and traffic volume, challenges arise to capture and better understand wireless traffic and network usage, hence novel methods and techniques are needed. It remains to be seen how user needs for broadband wireless performance will be satisfied, by seamless interoperability and handoff between cellular and WiFi networks, or perhaps widespread deployment of a new technology such as WiMAX.

[9] D. Kotz and K. Essien, “Analysis of a Campus-wide Wireless Network,” 8th ACM Int’l. Conf. Mobile Comp. Net., Atlanta, GA, 2002, pp. 107–18. [10] M. S. Thomas. “Trends and Evolution in the Wireless Industry,” Ericsson Canada; http://www.wirelessconnections.ca/wc2007/documents/ WC07_Thomas.pdf [11] S. Keshav, “Why Cell Phones Will Dominate the Future Internet,” SIGCOMM Comp. Commun. Rev., vol. 35, 2005, pp. 83–86. [12] E. Halepovic and C. Williamson, “Characterizing and Modeling User Mobility in a Cellular Data Network,” ACM Int’l. Wksp. Performance Evaluation Wireless Ad Hoc, Sensor, and Ubiquitous Net., Montreal, Canada, 2005, pp. 71–78. [13] F. Hernandez-Campos, K. Jeffay, and F. D. Smith, “Tracking the Evolution of Web Traffic: 1995–2003,” IEEE/ACM MASCOTS, Orlando, FL, 2003, pp. 16–25. [14] R. Kalden and H. Ekstrom, “Searching for Mobile Mice and Elephants in GPRS Networks,” SIGMOBILE Mobile Comp. Commun. Rev., vol. 8, 2004, pp. 37–46. [15] P. Svoboda et al., “Composition of GPRS/UMTS Traffic: Snapshots From a Live Network,” 4th Int’l. Wksp. Internet Performance, Simulation, Monitoring, Measurement, Salzburg, Austria, 2006, pp. 40–51. [16] H. Dahmouni, Reconnaissance des Services, Caracterisation du Trafic et Evaluation de Performances Dans les Reseaux Mobiles Multiservices, Ph.D. thesis, ENST-Bretagne, 2007. [17] M. Ivanovich et al., “Modelling GPRS Data Traffic,” Global Telecommun. Conf., Dallas, TX, 2004, pp. 3300–04. [18] E. Halepovic et al., “TCP over WiMAX: A Measurement Study,” IEEE/ACM MASCOTS, Baltimore, MD, 2008, pp. 327–36.

Additional Reading [1] V. Sridhara, J. Kim, and S. Bohacek, “Performance of Urban Mesh Networks,” 8th ACM/IEEE Int’l. Symp. Modeling, Analysis, Simulation Wireless and Mobile Sys., Montreal, Canada, 2005, pp. 269–77.

References

Biographies

[1] D. Tang and M. Baker, “Analysis of a Metropolitan Area Wireless Network,” ACM Int’l. Conf. Mobile Comp. Net., Seattle, WA, 1999, pp. 13–23. [2] IEEE Wireless Standards Zone; http://standards.ieee.org/wireless/ [3] A. Cuevas et al., “The IMS Service Platform: A Solution for Next-Generation Network Operators to Be More than Bit Pipes,” IEEE Commun. Mag., vol. 44, 2006, pp. 75–81. [4] T. Varga, B. Haverkamp, and B. Sanders, “Analysis and Modeling of WAP Traffic in GPRS Networks,” ITC Specialist Seminar, Antwerp, Belgium, 2004. [5] C. Williamson et al., “Characterization of CDMA2000 Cellular Data Network Traffic,” Int’l. IEEE Wksp. Wireless Local Net., Sydney, Australia, 2005, pp. 712–19. [6] D. Tang and M. Baker, “Analysis of a Local-Area Wireless Network,” 6th Int’l. Conf. Mobile Comp. Net., Boston, MA, 2000, pp. 1–10. [7] A. Mahanti, C. Williamson, and M. Arlitt, “Remote Analysis of a Distributed WLAN Using Passive Wireless-side Measurement,” Performance Evaluation, vol. 64, 2007, pp. 909–32. [8] T. Henderson, D. Kotz, and I. Abyzov, “The Changing Usage of a Mature Campus-wide Wireless Network,” 10th ACM Int’l. Conf. Mobile Comp. Net., Philadelphia, PA, 2004, pp. 187–201.

EMIR HALEPOVIC ([email protected]) received his B.Sc and M.Sc. degrees in computer science from the University of Saskatchewan, Saskatoon, Canada. He worked as a research assistant at the University of Saskatchewan and University of Calgary, where he is currently a Ph.D. candidate in the Department of Computer Science. He is a recipient of the NSERC Canada Graduate Scholarship and Alberta Ingenuity Graduate Student Scholarship.

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CAREY WILLIAMSON ([email protected]) is a professor in the Department of Computer Science at the University of Calgary, where he holds an iCORE Chair in Broadband Wireless Networks, Protocols, Applications, and Performance. His research interests include Internet protocols, wireless networks, network traffic measurement, network simulation, and Web performance. M AJID G HADERI ([email protected]) is an assistant professor in the Computer Science Department at the University of Calgary, Canada. He received B.Sc. and M.Sc. degrees from Sharif University of Technology, and a Ph.D. degree from the University of Waterloo, all in computer science. His research interests include wireless networking and mobile computing, with an emphasis on modeling and performance analysis of network protocols.

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