Citywide Mobile Internet Access Using Dense Urban Wifi Coverage
Total Page：16
File Type：pdf, Size：1020Kb
Citywide Mobile Internet Access Using Dense Urban WiFi Coverage Maria Eugenia Berezin, Franck Rousseau, Andrzej Duda Grenoble Institute of Technology, CNRS Grenoble Informatics Laboratory UMR 5217, France {berezin, rousseau, duda}@imag.fr ABSTRACT are expensive and new technologies such as 4G still have is- We investigate if it is feasible to use the WiFi coverage sues with their service performance. in urban areas for mobile Internet access and which type WiFi networks are a viable solution to reduce the use of of applications can benefit from the Internet access pro- 3G networks, offering high bandwidth and a cheap infras- vided by the already deployed WiFi Access Points (APs). tructure [13]. In fact, mobile operators have already started Nowadays, most smartphones and other mobile handsets are to use WiFi for data offloading. Thus, dense deployment of WiFi-enabled. Moreover, mobile Internet data traffic is ex- WiFi Access Points (APs) could ensure Internet connectiv- pected to grow significantly in the next few years. WiFi ity, falling back to 3G where service cannot be offered. access is an interesting alternative to cellular networks, as it WiFi APs can be found almost everywhere nowadays: mu- is a widespread wireless technology, provides high data rates nicipal wireless networks, caf´es, hotels, airports, and private and has a low deployment cost. In this paper, we analyze environments at home or work. However, only a fraction the characteristics of WiFi coverage and connectivity of mo- of all these APs are open for association to any user. In bile users using different mobility speeds and varying some some other cases, such as residential APs, their owners are AP settings. These results allow us to analyze different ap- members of a "community network", and they can share plications that can be supported and the challenges to face their Internet bandwidth with other members (e.g. FON to create a citywide WiFi network given the current infras- [9], FreeWifi [10], CableWifi [7]). tructure composed of residential WiFi APs and hotspots. WiFi APs are generally unmanaged with a default con- figuration. Moreover, they are deployed indoors and in an unplanned manner, which can cause poor reception and in- Categories and Subject Descriptors terference between neighbor APs, and result in low through- C.2.1 [Network Architecture and Design]: Wireless Com- put. They connect to the Internet over a broadband access, munication such as DSL, which provides high data rates. In this paper, we consider the use of already deployed WiFi APs to provide urban mobile Internet access. If their General Terms coverage is sufficiently dense, users moving with different Design, Performance speeds may benefit from WiFi connectivity. The question is whether such an architecture is feasible, on what parame- Keywords ters depend its performance, and what type of applications can benefit from its services. We investigate these issues by Wireless Networks, Mobile Internet Access, WiFi, Mobile simulating connectivity of mobile users in a city based on de- Clients tailed traces provided by the Nokia Mobile Data Challenge [15]. 1. INTRODUCTION With the proliferation of mobile Internet-enabled devices, 2. MOBILE INTERNET ACCESS THROUGH Internet connection is expected by users anywhere and any- WIFI time. Indeed, many forecasts predict an exponential growth for mobile data traffic [8]. Cellular broadband networks are WiFi APs are largely deployed in urban areas so we can therefore facing the problems of traffic congestion and net- consider providing a seamless mobile Internet access based work capacity. But improvements to these wireless networks on dense WiFi coverage. Unplanned placement of APs may lead to the existence of some areas that lack coverage, how- ever short disconnections may be tolerated if the duration of connectivity is sufficient. The applications that could be Permission to make digital or hard copies of all or part of this work for supported in a citywide WiFi network, without any modifi- personal or classroom use is granted without fee provided that copies are cation in the infrastructure or the devices, are delay-tolerant not made or distributed for profit or commercial advantage and that copies applications, that can either wait between connections or al- bear this notice and the full citation on the first page. To copy otherwise, to ternate between a 3G and a WiFi link [4, 13]. Short, but republish, to post on servers or to redistribute to lists, requires prior specific frequent connectivity can be used for opportunistic sensing. permission and/or a fee. UrbaNE’12, December 10, 2012, Nice, France. For instance, traffic and road conditions can be shared by Copyright 2012 ACM 978-1-4503-1781-8/12/12 ...$15.00. mobile phones and disseminated to vehicles [16]. Due to 31 the large coverage, location-based services can use the great address. During that time, the client cannot send or receive number of APs for positioning, even in indoor locations, traffic so that the handoff duration has an important impact without using a GPS. on the delay and packet loss that applications may tolerate. When moving among APs, users need to benefit from We simplified the handoff process by modeling it as a fixed short handoffs. Depending on the expected handoff de- delay. We have assumed the following values for the handoff lay, different strategies can be implemented to decrease this duration: 0 seconds, 150 ms, 1 s, 2 s, and 5 s. The first value value: we can use different handoff and association algo- corresponds to an instantaneous handoff, for instance the rithms (e.g. multiple simultaneous associations [18]) or man- case of a network supported mobility management. The next age mobility in the network [5], which results in instanta- value, 150 ms, is the maximum acceptable latency in end- neous handoff. to-end communications for VoIP applications. The latter Currently, there is no a homogeneous way of authentica- values are commonly measured handoff durations. tion, the selection of APs has to be done manually, and mo- Handoff strategy: When the device is not associated bility (session transfer) is inexistent. Nevertheless, there are with any AP, it chooses the AP with the strongest signal some efforts in this direction: the Hotspot 2.0 and the Next strength. When the current AP's signal strength (SS1) is Generation Hotspot initiatives, based on the IEEE 802.11u weaker than a fixed Threshold, the client attempts to asso- standard. These specifications allow mobile phones to log ciate with another AP whose signal strength (SS2) is greater into WiFi networks in a seamless way. than SS1. We used a -90 dBm threshold, which is a common limit for the lowest data rates: 1 Mb/s. 3. TRACE BASED EVALUATION User speed: We wanted to represent different speeds of a To evaluate the feasibility of such an architecture, we run mobile user. We used the following values: walking (1 m/s), by bicycle (5 m/s =∼ 18 km/h), by bus (11 m/s =∼ 40 km/h), simulations of mobile user connectivity in a city based on ∼ detailed traces provided by the Nokia Mobile Data Challenge and by car (20 m/s = 70 km/h). (we provide the details of the dataset in the next section). Paths: We generated 10,000 different paths using the We have built a model of the city of Lausanne, as it was Markov chain mobility model. Each path has segments of the city in the data set with the biggest number of APs. We 50 m and the transition probabilities are applied after each ran several simulations to analyze and understand the char- segment to change direction. The total distance of each acteristics of the WiFi coverage and connectivity provided path is 3,600 m. As we wanted to recreate this path in by the deployed APs. the downtown part of the city of Lausanne, the path was centered in the densest area. 3.1 Simulation Scenario 3.2 Results We have focused on the central area of the city of Lau- sanne, where the APs distribution is the most dense. We We define the following performance indices: have developed a simple simulator in which users move along Temporal Coverage: period of time during which a user random paths modeled as a Markov chain [19] to describe stays in a WiFi coverage area [13]. the probabilities of transition from one direction to another. The model reflects the behavior of users|the dataset con- Connection: period of time during which the user is as- tained the recorded GPS coordinates of users. sociated with a same AP. The simulator computes the position of a mobile user with Disconnection: period of time during which the user is the granularity of 1 m and verifies if a handoff (or an asso- out of the range of any WiFi signal. ciation, if the device has lost the WiFi connectivity) has to be performed. It computes the distance of the user to the Internet access session: period of time during which current AP and uses the formula of the Log-distance Path the user is associated with APs and is able to send Loss Model [17] to obtain the current AP signal strength. If and receive Internet traffic (i.e. is not doing a handoff needed, the simulator performs a handoff and changes the at that time), until a disruption of communications, AP. due to the loss of WiFi coverage. The simulator takes into account the following parame- ters: We have measured the mean temporal coverage for dif- ferent APs ranges in Figure 1 using \All APs" and \Open Range: We used three different coverage ranges for an APs". When analyzing the results of \All APs", a common AP: 20 m, 50 m, and 100 m.