Efficient Mobile Data Offloading Using WiFi Access Points

Eyuphan BulutCisco Systems

2200 President George Bush HighwayRichardson, TX 75082

ebulut@cisco.com

Boleslaw K. SzymanskiRensselaer Polytechnic Institute

110 8th StTroy, NY 12180

szymansk@cs.rpi.edu

The wide-spread proliferation of smartphone-like de-vices has caused a tremendous increase in mobile datausage unmatched by the slower increasing availablebandwidth. Consequently, this has caused severe traf-fic overloading in cellular networks. An immediateavailable remedy for this problem is offloading thetraffic through WiFi access points. In this paper, westudy the deployment and recruitment of WiFi accesspoints (AP) in a metropolitan area for efficient of-floading of mobile data traffic. We analyze a largescale real user mobility traces and propose a deploy-ment algorithm based on the density of user data ac-cess requests. With simulation results, we demon-strate that the proposed algorithm can achieve closeto optimal offloading ratio that is higher than offload-ing ratios achievable by the existing algorithms usingthe same number of APs. Moreover, we also showthat remarkably high offloading ratios can be achievedthrough recruitment of APs available in the area with-out new deployments.

I. Introduction

Recently, there has been a rapid growth in mobile datausage fueled by increasing ubiquity of various mo-bile devices (i.e., smartphones, laptops) among usersas well as the increasing demand for mobile data us-age by each user. These devices are used for differentactivities such as web browsing, video/audio down-loading and photo sharing. Cisco recently announcedin [1] that global mobile data traffic would grow 18-fold from 2011 to 2016, reaching 10.8 exabytes permonth. About 48.3% of this traffic will be generatedby smartphones as shown in Figure 1.

As a result of mobile data explosion unmatched bythe growth of available bandwidth, it is inevitable thatcellular networks will be overloaded and congested inthe near future. Even today, subscribers of some op-erators have been experiencing problems in urban ar-eas. Especially during the peak data usage times (rushhours etc.), some calls are being interrupted due to the

Figure 1: Mobile data traffic growth

insufficient network bandwidth. Unless cellular op-erators provide solutions to this problem quickly, sub-scribers unsatisfied with the access quality may cancelthe service, yielding loss in the revenue of operators.

This problem has also attracted the attention of net-work research community recently, and some initialefforts have been made to solve this challenging prob-lem. Basically, these solutions can be classified intofour different categories: (i) increasing the numberof radio base stations or selectively improving someof them [2], (ii) increasing network coverage throughsome small scale base stations (i.e. femtocells [3])that are usually deployed at homes, (iii) upgrading cel-lular radio access technology to advanced next gen-eration technologies (such as Long Term Evaluation(LTE)) to increase the bandwidth, and (iv) utilizingWiFi networks for offloading the burden of cellularnetwork [4, 5, 6].

Each of the above solutions can help in mitigatingthis problem and has unique advantages and disad-vantages compared to the others. However, the firstand the third solutions may require high financial in-vestment and their deployment process may take long.Furthermore, due to the commonly used flat price pol-icy, they may bring low return gains. There have beensome tiered pricing mechanisms adopted by operatorsin an attempt to slow the increase in demand but as

the users are willing to pay even the high prices, tieredpricing solution will eventually become ineffective. Itis also interesting that some providers even engaged ina futile attempt to educate their users about what eachmegabyte represents in terms of data and asked themfor responsible and unwasteful access.

The second solution, which proposes to use fem-tocells, is a quite feasible and reasonable solution.However, femtocells are somewhat new compared toWiFi APs which are already deployed in many places.Femtocells are usually deployed for coverage exten-sion but they can also provide indoor offloading. Theyoperate on the same licensed spectrum as macrocells(i.e., base stations) of the cellular network, so theydo not require special hardware support on mobilephones. Their biggest disadvantage is the one-timecost of buying a new device with the price rangingfrom $100 to $200. Surveys on femtocells and theirdeployment are presented in [3, 8].

In view of concerns for other solutions, the remain-ing fourth solution, utilization of WiFi networks, cur-rently is the most promising1 that can offer an im-mediate remedy to cellular traffic offloading prob-lem. There are many WiFi access points available atmany user locations such as homes, shops and uni-versities. In addition to them, operators also deploytheir own APs at the locations where the access de-mand is high. Since these devices operate in differentspectrum than the base stations, they do not cause in-terference. Moreover, the bandwidth offered by WiFiis much higher than the bandwidth of cellular access.

In this paper, we study the problem of mobile dataoffloading via WiFi networks. We first focus on thedeployment of WiFi access points (AP) for efficientoffloading. To this end, we analyze a city wide realuser mobility traces and propose an efficient AP de-ployment. We measure how much offloading canbe achieved with different numbers of APs. More-over, we look at the offloading efficiency of proposeddeployment for future data usage by network users.We also find the optimal deployment by modelingthe problem as an Integer Linear Programming (ILP)(see [16]) problem and solving it using the IBM ILOGCPLEX software [17]. The results indicate that pro-posed greedy heuristic based algorithm produces re-sults that are close to the optimal solution.

As an alternative to deployment of new WiFi APs,

1This is also in agreement with the results of a recent sur-vey [7] done by WBA/ITM among the world wide operator lead-ers who think WiFi offloading will be the most significant strategyin managing mobile network volume and rank this solution higherthan femtocell deployment and upgrade to advanced technologieslike LTE.

we also propose a simple framework for the recruit-ment of available APs owned by private parties. Thesimulation results demonstrate that the alternative so-lution is able to provide good offloading ratios withoutrequiring the deployment of new APs.

The rest of the paper is organized as follows. InSection II, we talk about related work. In Section III,we present the details of the proposed greedy heuristicand ILP based deployment algorithms. In Section IV,we present the details of the framework for recruitingavailable APs. In Section V, we give the details of oursimulation setting and present the simulation results.In Section VI, we provide discussion on the proposedmechanisms and outline the future work. Finally, weconclude in Section VII.

II. Related Work

Recently, several studies have been published propos-ing different strategies for mobile data offloading.Some of them focus on an interesting type of offload-ing strategy called delay tolerant offloading. The ap-plicability of such strategy is justified by the fact thatthere is a remarkable amount of mobile data content(such as photo uploads to Flickr [5]) which are up-loaded by users much later than the time at whichthey were created at the mobile devices. Thus, userdevices can delay uploading of this kind of data up tosome threshold time and offload them automatically toWiFi access points [12] once they get into the range ofthem. If the opportunity to connect to a WiFi accesspoint does not arise before the end of tolerable delayduration, regular cellular connection is used for up-loading. A similar approach is proposed in [2] but theoffloading is done via some top base stations throughwhich most of the content of users are uploaded.

In some studies, this type of delayed offloadingstrategy is further extended by incorporating DTNtype [9] communication among users (via Bluetoothor WiFi connections). Users with data route it op-portunistically towards other users which travel withinoffloading regions more frequently than others so thatthe chance of offloading in the given time constraintis increased. In [10], Han et. al propose to select aset of most active users (using social relations [11])through which the offloading of all user data traffic isconducted. Similarly, in MixZones algorithm in [12],opportunistic exchanges between users together withcaching the popular content at users is proposed toachieve more efficient offloading.

This type of delayed offloading strategies can re-lieve the burden of cellular network to some extent,

WiFi AP

Internet

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Macrocell

UE

UE

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UEUE

Offloaded traffic

Figure 2: Mobile data offloading via Wifi APs. Thetraffic of User Equipments (UE) in the range of WiFiAPs can be offloaded from operators’ core network.

however, they cannot be applied to users requestingreal time data downloading, which forms the biggestportion of today’s mobile data traffic. Thus, their ef-fective overall impact on offloading is small. More-over, they also raise several user related concerns,such as passing of other users’ data via some pop-ular user devices and causing extra power consump-tion at such devices without benefits to their users.These concerns can be addressed through some in-centive mechanisms [13], but a complete solution to-gether with methods to address its risks still has to beprovided.

In this paper, we focus on an offloading solutionusing WiFi access points (Figure 2). An importantbenefit of WiFi networks is that they operate on unli-censed frequency bands, thus they do not cause inter-ference with cellular networks. They also offer morebandwidth than what current cellular networks pro-vide. Moreover, user mobile devices are already ca-pable of communicating using WiFi. Capabilities ofWiFi networks for traffic offloading have already beendemonstrated in some studies (e.g., [5]). Moreover,operators have already been deploying their own WiFiAPs to some points of interests (such as malls, mar-kets etc.).

Despite the advances described above, the deploy-ment of such WiFi APs has not been studied yet inlarge scale using real mobility traces. The worksthat are closest to our study in this paper are [6] and

[12]. They both propose algorithms for the deploy-ment of WiFi APs for cellular traffic offloading. How-ever, in [6], the AP locations are decided in a sequen-tial manner without considering the efficiency of de-ployment. Similarly, the HotZones algorithm in [12]proposes to deploy APs to cover the areas of mostused cell towers. Yet, this solution does not considerthe internal differences of subregions in the area ofa cell tower in terms of user content generation den-sity. A subregion inside an area covered by a densecell tower may not generate as much data as a subre-gion of another cell tower which is overall used lessfrequently. Moreover, the algorithm does not mentionexactly how the APs are deployed in the areas of se-lected cell towers.

In this paper, we use fine grid cells in a city wideuser area and propose to deploy the APs to the mostdense grid cells in terms of user data request fre-quency using a greedy-based fast heuristic. We alsoshow how close the results of this greedy approach areto the optimal solution. Moreover, in simulation sec-tion, we compare our algorithm with the algorithmspresented in [6] and [12]. Partial initial results of thiswork are also presented in [31].

Apart from the AP deployment for offloading pur-poses, there are also some studies that propose al-gorithms for deploying APs for different reasons.For example, in [14], Liao et al. propose an algo-rithm to deploy minimum number of APs that providefull communication coverage while at the same timeachieving the ability to locate a mobile device within acertain area no larger than a given accuracy parameter.Also, in another work [15], authors try to minimize thenumber of access points required while ensuring thatthe received Signal to Noise Ratio (SNR) at each lo-cation is high enough to meet the offered load at thatlocation. Our work differs from such studies since weaim to achieve efficient offloading through a better de-ployment of APs.

III. The Problem Statement

It is obvious that to increase the offloading efficiency,AP locations need to coincide with the popular mo-bile data access request locations. That’s why someoperators have already started to deploy WiFi APsin places like malls, markets and coffee shops wherethere might be a high population of their users. How-ever, this type of deployment of APs to some limitednumber of user populated areas has to be extended tooutdoor locations for a large scale offloading strategy.

Consider a city-wide area where many users from

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Figure 3: An example problem with 25 data access re-quests. The numbers inside the circles are the weightsof the requests over the range centered there.

different city neighborhoods use their mobile devicesto access Internet. The users may move around, thus,their locations and therefore the points from which theuser data access requests come change. In general ac-cess is not continuous, as users may finish a session,turn their devices off or use the Internet only duringcertain times or under certain conditions. For exam-ple, it is expected to be more likely for people to usetheir phones while they are traveling in a taxi or usingpublic transportation rather than when they are drivingthemselves.

Let < = {(r1, w1), (r2, w2), . . . (rm, wm)} be thelist of pairs with each pair representing a location fromwhich users make mobile data access requests and thecorresponding weights of these accesses. Let APD

= {a1, a2, . . . aK} be the candidate deployment ofK AP’s (each with communication range of R), thatis the list of locations in which WiFi APs are to bedeployed. Let ID = {i ∈ [1.m] | ∃j ∈ [1,K] : |ri −aj | ≤ R} be the set of indexes of data access requestlocations covered by the candidate deployment APD.The goal is to maximize the following sum over allpossible candidate deployments (APD’s):∑

i∈ID

wiri

In Figure 3, we show an example problem with 25data access request points together with their weights.Our goal is to deploy APs in such a way that the of-floading is maximized.

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Figure 4: Greedy Solution with 4 APs (total offloadedweight = 64)

III.A. The complexity of the problem

The presented problem can be mapped to awell-known Maximal Covering Location Problem(MCLP) [20] that deals with locating K facilities to anarea with demand locations (with different weights)such that the total demand under the coverage area(which is decided by time or travel distance) of all fa-cilities is maximized. The locations of facilities can bethe demand locations or non-demand locations. Themain focus of MCLP problem is to guarantee a worstcase performance (by satisfying all demands withindistance x or travel time t.).

Considering the data access request demands com-ing from users as the demands in MCLP problem andthe facilities as WiFi APs that will be deployed, ourproblem of deploying the APs with the range of R(i.e., maximum distance of a demand from a facility atwhich that facility is able to satisfy this demand) mapsto MCLP problem. The MCLP problem is known tobe NP-hard as proved by Megiddo et al. in [21].

There are many variants of MCLP problem with ap-plication specific additional constraints. In our prob-lem of WiFi AP deployment with maximum demandsatisfied using a given number of APs, some addi-tional constraints can be considered. For example,there is usually a capacity (i.e., bandwidth) limit ofAPs. This simply maps to MCLP instance with ca-pacitated facilities [22].

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Figure 5: Extended Greedy Solution with 4 APs (totaloffloaded weight = 78)

III.B. Heuristic Solutions

Since the problem is NP-hard, we first use a greedyheuristic approach to solve it. We start by dividing theentire region into equal size grid cells2 such that eachcell represents the coverage of a single AP deployedat its center. Since an AP has an effective range ofR, the side of each cell is equal to

√2R, i.e. the side

of the biggest square that will fit inside a circular APrange with radius R. Then, one by one, we find themost dense grid cells in terms of user mobile data re-quest frequency denoting coordinates of their centersas (mi,mj). The top K highest density grid cells willhave APs placed at their centers. Figure 4 shows thesolution of the example problem with the greedy ap-proach.

Note that by placing APs only at the centers ofgrid cells, some most dense areas may be covered byneighbor cells only partially. To mitigate this draw-back, we propose an extended approach shown in Al-gorithm 1 in which AP centers are allowed to be in anycorner of a cell in nxn sub-grid located over each gridcell representing an AP range. This extended versionfirst divides the area into SxS grid and then furtherdivides each of the resulting grid cells into nxn sub-grid. The algorithm then finds the possible AP loca-tion that gives the highest offloading ratio (i.e., whichhas the maximum data request frequency) under itscoverage. Then, the other APs (with total AP countof K) are found similarly one-by-one, with each step

2We worked with squares for simplicity. However, hexagonsfitting inside the AP ranges can be used to achieve better accuracy.

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Figure 6: ILP Solution with 4 APs (total offloadedweight = 83)

removing the areas already covered by deployed APs.Although the disadvantage of introducing nxn sub-grid is that the complexity of the algorithm increasesas n increases, as we will show in simulation section,the advantage is that the results of heuristic get closerto the optimum. In Figure 5, we give the solution ofthe example problem with extended greedy approach.

Algorithm 1 Greedy Solver (S, n, K)1: Find densities for (nxS) x (nxS) grid2: for each possible AP location (i,j) do3: AP[i][j]=total covered density under nxn sub-

grid centered at (i,j)4: end for5: c=06: while c < K do7: (mi, mj)= argmax{AP[i][j]} ∀ i j8: Place an AP at (mi,mj)9: Set covered[x][y]=1 ∀ x y under nxn grid cen-

tered at (mi,mj)10: Update AP[i][j] ∀ i j by taking out the densities

of all cells with covered[x][y]=111: c=c+112: end while

III.C. ILP Solution

To see how close the results of the greedy approachare to the optimum, we also formulate and solve ex-actly the problem of deploying APs using Integer Lin-ear Programming (ILP) approach [16]. We first divide

the entire area into N x N small cells. Then, rep-resenting an AP as an nxn small frame, we find theoptimum deployment of K APs that maximizes theoffloading ratio.

In the ILP model, the goal is to find the optimalplacement of K small frames to grid locations, whereplacement of all small frames is defined by a pair ofinteger vectors I = [i1, . . . , iK ] and J = [j1, . . . , jK ],each of size K such that for any 1 ≤ k ≤ K thepair 1 ≤ ik ≤ N − n + 1, 1 ≤ jk ≤ N − n + 1defines the location of the South West corner of thesmall frame k. Let IJ denote the set of all possiblevectors I , J with their values being integers from theinterval [1, N−n+1]. We used the following notation(in x-y coordinate system):

Sk,i,j =

{1 if i− n ≤ ik ≤ i and j − n ≤ jk ≤ j0 otherwise

yi,j =

{1 if

∑Kk=1 Sk,i,j > 0

0 otherwise

wi,j = weight (data request frequency) of cell (i,j)

The binary variable yi,j indicates if cell (i, j) is cov-ered by any small frame. Given the above variables,the objective function is:

maxI,J∈IJ

N∑i=1

N∑j=1

wi,jyi,j

To see how good the extended greedy approachis, its results are compared to ILP solution. We setN=(nxS) in ILP solution3 and solve it using IBMILOG CPLEX [17] software. Figure 6 shows the ILPsolution of the example problem.

III.D. Complexity of the Solutions

The pure greedy heuristic algorithm, which consid-ers only centers of grid cells as the AP locations, hasthe cost of O(S2 log(S)) and it is dominated by theinitial step of sorting densities. In the case of ex-tended greedy approach where the APs are allowedto move over inner nxn subgrid, the complexity be-comes O((Sn)2 log(Sn)), yielding an increase byfactor of O(n2 log(n)). Since each AP can be locatedin one of the N2 possible points, the cost of bruteforce solution is O(N2K), or O((nS)2K) when N isset to nxS.

3Much smaller cells yielding larger N can be used to increasethe accuracy of ILP results. However, this also increases the runtime of ILP problem.

Figure 7: WiFi and cellular distribution of smartphonetraffic on a weekday (UK, Jan 2012) [24].

IV. Recruitment of Available WifiAPs

In previous section, we focused on the problem offinding the locations of APs that will be deployed toachieve a maximized mobile data offloading. An al-ternative to operators buying and deploying the APsat optimal locations (which may take time to fully im-plement) is to recruit the already deployed APs ownedby private-parties (people, companies etc.) providingthe benefits of offloading immediately.

A recent study [23] that examined a database of fivemillion APs collected through wardriving of SkyhookWireless in 2005 showed that the density of APs perkm2 in major metropolitan areas are well above the 33APs/km2, which is noted as the required4 number ofAPs to cover a 1 km2 region, under the assumption ofeven distribution of APs. For example, within the 213km2 in the city of San Francisco, there were 69502access points, yielding the density of 326 APs/km2.

Most of the smartphones today can switch to wire-less networks when they are in the range of APs thatthey are eligible to access. These APs can be ownedby the smartphone user or a friend or they can be opento everyone since they do not use any security. It hasbeen noted in [25] that, in 2010, almost 40% of allWiFi APs recorded in the US are unlocked and do notrequire a security password, compared with only 25%of total access points in Europe. Our focus here is torecruite the remaining 60% of APs with secured ac-cess for use of offloading purposes. To this end, weneed to encourage the owners of those secured WiFiAPs to authorize the cellular operators to permit theirsubscribers to access the secured AP’s in their vicin-ity in time periods during which these APs are not ac-tively used by their authorized owners. To obtain suchauthorizations, we propose that the secure AP hold-

4This calculation assumes that the nominal range of standard802.11b/g is 100 m.

WiFi AP

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Data access requestData access request

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Figure 8: System architecture for utilizing availableWiFi APs for offloading.

ers should be given incentives such as credits for extradata usage or discounts on their current billing. Utiliz-ing APs made available by their owner authorizationshave the following advantages: (i) the operators willsave the deployment and maintenance cost of the APsneeded to replace the authorized ones, (ii) the currentusers will have the opportunity to monetize their de-vices during the times they are unused, and (iii) theoperators will be able to start offloading their usersdata immediately after reaching agreements with theirowners; it may also prevent equipment wasting in casethey would like to stop using WiFi APs and continuewith other solutions (that might appear in future withnew enhancements in technology).

Here, an important parameter is the relation be-tween the usage patterns of WiFi and cellular net-works. Figure 7 shows the WiFi, cellular or total(WiFi + cellular) hourly usage distribution of smart-phone traffic in a weekday in UK in 2012. As the plotshows, the patterns show great divergence and there isa potential opportunity to offload cellular traffic dur-ing day time (between 8 a.m. and 7:00 p.m.).

The architecture of such a system is depicted in Fig-ure 8. The WiFi AP owners subscribe to the operator’ssystem providing the time ranges during which theyallow their APs to be used by the users selected by theoperator. The users requesting data access are thengiven access to an available WiFi AP if there is one inthe range of the user’s current location.

V. Simulations

To evaluate the performance of the proposed deploy-ment and recruitment strategies, we have built anevent driven simulator that uses the real mobilitytraces.

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Figure 9: Distribution of occupancy durations.

V.A. DataSet

We used a publicly available [18] taxi data set anddetermined the mobility of the potential mobile datausers accordingly. The dataset consists of the tracesof 536 taxis that operate in the city of San Francisco.For each taxi, the mobility trace records include theGPS location and the occupancy over a 30 day pe-riod. There are some discrepancies in the recordingintervals. Thus, to make the evaluation with a dataset that is less likely to include measurement errors,we pruned the dataset following the method presentedin [6]. The method removes the data about all taxiswith the average sampling interval larger than 100 secand standard deviation larger than 1000 sec. After thisinitial data cleaning process, we obtained a set of dataabout 343 taxis. Moreover, using available data, wealso extrapolated the locations of each of these taxisbetween the two consecutive locations recorded in thetaxi data.

V.B. Data traffic generation

We generated data traffic using an approach similar tothe one applied in [6] and assumed that users gener-ate mobile data traffic (i.e., download requests) dur-ing the times taxis were occupied. This is justifiedby the fact that it is more reasonable to find a personusing mobile devices (iPhone, iPad etc.) to access In-ternet when traveling by taxi (as a passenger) ratherthan in the case when driving a car or a taxi. We referto a taxi occupancy duration as a journey. We assumethat there is one person (passenger of the taxi) thatuses the mobile device to access Internet. The amountof the data that will be downloaded during this timethrough the cellular network depends on several fac-tors including the type of the application (e.g. email,video, social network) used by the person, the speedof the cellular technology in that area, and the status

of the network congestion. For simplicity, we assumethat there will be a continuous download request bythe user over the entire extent of a journey.

We assume that 3G connection is used for all cellu-lar communications. Since 3G is used for a family ofdifferent technologies, there might be different speedranges for cellular communication. In [27], the In-ternational Telecommunications Union (ITU) defines3G services as providing a minimum speed of 2 Mbpswhen subscribers are stationary or walking slowly.This number goes down to 384 kbps when moving athigher speeds, such as in a car (corresponding to ourcase). Even though in the real world deployments to-day we can see higher speeds provided through someenhancements, to see the impact in the worst case sce-nario, we use the minimum speed in our simulationsetting.

In Figure 9, we show the distribution and the cumu-lative distribution function (cdf) of journey durationsin all dataset. As the figure shows, the majority (about86%) of journey durations (and the corresponding se-quence of download requests) last between 100 and1000 sec, with an average of 612 sec. These statisticalcharacteristics of journeys seem reasonable since theyindeed represent the taxi occupancy durations in SanFrancisco.

Having the average journey duration of 612 secwith 384 kbps cellular access speed, the average sizeof downloadable data becomes 29.3 MB. Here, to runour ILP problem we make the download requests dis-crete assuming that at every 5 sec there will be adownload request of 240 KB. In case there is no WiFiaccess point located in the range of the download re-quest location initiated by the user, the download ofthe requested file is achieved through cellular net-work. Otherwise, the download of the file is achievedvia the closest WiFi AP, offloading the correspondingtraffic from the cellular network.

V.C. WiFi Setting and AP deployment

We assume that at the beginning of the simulationthere are no WiFi APs deployed in the area. Then, us-ing each algorithm, we find the offloading ratios fromcellular network with different number of APs. Wecompare our greedy algorithm and the optimal results(from ILP solution) with the two previously intro-duced algorithms. In the first one [6], the AP locationsare decided sequentially, one by one (we refer to it as“Sequential” in the discussion of results). That is, if adownload request cannot be offloaded through a WiFiAP, the algorithm puts an AP there, and considers thatthe other download requests that fall in the range of

Figure 10: WeFi Wifi AP locations in San Fran-cisco [30].

this AP will also be offloaded through it. Then, thenumber of APs that will be deployed increases in thismanner until allowed AP count is reached. The sec-ond algorithm is called HotZones [12] where APs aredeployed to cover the areas of most used cell towers.Since, we simulate a user area which can be coveredby several towers, we used four cell towers to get theresults for this algorithm.

According to the empirical measurements pre-sented in [28], the WiFi range in outdoor environ-ment can vary significantly, from 5m to 75m. Thus,the amount of data that can be downloaded throughWiFi APs depends on the distance of user devicesfrom the APs. We used the trend from [28] and as-signed a bandwidth (with the maximum of 18 Mbps)to each user device according to its distance from theAP. Moreover, we allocated bandwidth, Bi, to eachuser i such that the total bandwidth given to p con-nected users of an AP has to satisfy

∑i=pi=1Bi ≤ 18

Mbps.To see the impact of different AP ranges on the

results, we also used three different values for R,namely 70m, 35m and 14m. These values set the sideof a grid cell (Gs) to 100m, 50m and 20m, respec-tively, for the greedy allocation algorithms. The areaover which we deploy APs covers a rectangle overSan Francisco with width of 12 km and height of 10km. Thus, when Gs = 100m, we create Sw = 120 bySh = 100 grid.

V.D. WiFi AP Recruitment Settings

To see the potential offloading ratio that can beachieved by available WiFi APs, we used5 the AP

5Note that even though there are thousands of APs in SanFrancisco area, only some portion of them may subscribe to orbe eligible for the recruitment. We will study the encouragement

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OptimalGreedySequentialHot−Zone

Figure 11: Number of APs vs. Offloading ratio whenGs=100 m.

locations of a wireless AP provider company calledWeFi. In Figure 10, we show the distribution of WeFiAPs in San Francisco. Since the Wefi website [30]does not provide the AP locations, we first generatedthe big map of San Francisco (by combining zoomedimages) and did some image processing to find outthe center of AP symbols. By this method, we coulddetect the locations of 558 devices.

The deployment locations of the APs cover a widerange of location types including cafes, restaurants,libraries and universities. There are also some res-idential deployments. Clearly, the traffic load overthese WiFi APs (generated by their regular users) mayvary due to the different profiles of their users. APsin restaurants can be highly utilized during lunch anddinner times, and the evening time load on APs in res-idences may be higher than the load during the work-ing hours. Moreover, for a given AP, depending on itslocation, the traffic load changes significantly depend-ing on the time of the day. For simulation purposes,we created traffic loads on each AP using the trend inFigure 7.

V.E. Simulation Results

Figure 11, Figure 12 and Figure 13 show the num-ber of APs that needs to be deployed to achieve agiven offloading ratio for all four algorithms with dif-ferent grid sizes (so with different AP ranges). Firstof all, it is easy to observe that our greedy approach(without extension) provides results closer to the op-timal solution than other algorithms. It can provideup to 13% and 24% higher offloading ratio with thesame AP count than the ratios achieved by the Sequen-

of all available APs to the system using an auction based mecha-nism in our future work. In this paper, we currently use a portionof available APs to see offloading ratios that could be achievedthrough them.

0 2000 4000 6000 8000 100000

20

40

60

80

100

Number of APs

Offlo

ad

ing

ra

tio (

%)

OptimalGreedySequentialHot−Zone

Figure 12: Number of APs vs. Offloading ratio whenGs=50 m.

0 1 2 3 4 5

x 104

0

20

40

60

80

100

Number of APs

Offlo

ad

ing

ra

tio (

%)

OptimalGreedySequentialHot−Zone

Figure 13: Number of APs vs. Offloading ratio whenGs=20 m.

tial [6] and HotZones [12] algorithms, respectively.Moreover, looking at AP counts required to achievea given offloading ratio shows that greedy algorithmsometimes needs only 65% and 45% of what Sequen-tial and HotZones algorithms need, respectively. Thisclearly shows that greedy approach can provide theoperators with remarkable savings. Moreover, as Fig-ure 14 shows, if the number of APs (K) is small andwe let the AP centers to be placed more arbitrarilywithin each grid cell, then extended greedy approachcan give results much closer to optimal solution thanthe solution with placement of APs at the center ofgrid cells.

The above results consider the user data requestsfrom all dataset and computes the offloading ratio ifthere were APs deployed in advance. To see the im-pact of variance in the user behavior on the offloadingstrategy, as well as to see if this type of deploymentworks for future download requests, we also lookedat the predictability of future node behavior and usedonly x% of the data6 to decide where to deploy the

6Since the processed data covers 576 hours of data after prun-ing, x% of the data refers to the first 5.76x hours of user data.

50 100 150 200 250 3000

2

4

6

8

10

12

14

16

Number of APs

Diff

ere

nce

fro

m o

ptim

al (

%)

GreedyGreedy(n=2)Greedy(n=4)

Figure 14: Results with greedy extensions.

250 500 1000 20000

10

20

30

40

50

60

70

80

90

100

Offl

oadi

ng ra

tio (%

)

Number of APs

25% trained50% trained100% trained

Figure 15: Effect of training data on offloading ratioof Greedy Algorithm (Gs=100 m).

APs. Then, to measure the offloading ratio, we usedthe user requests that come from the rest of the data7.In Figure 15, we show the effect of the size of thetraining data used on the offloading ratio achieved bythe heuristic. We used 25%, 50% and 100% of thedata to find locations with the highest density of userdemands for mobile bandwidth and deployed the APsaccordingly. As the graph demonstrates, the offload-ing ratios achieved with deployment after using 25%,50% and 100% of data are very close to each other.This clearly shows that user behavior is more or lessthe same and deploying the APs to locations with thehighest density of user requests for mobile data ingreedy manner is a promising solution even for futuremobile data offloading.

We also look at the potential offloading ratiosthrough the available WiFi APs that are already de-ployed in the area (with R = 70m). In Figure 16,we show the percentage of each hours data traffic thatcan be offloaded through available WiFi APs (i.e.,558 WeFi brand APs). On average, 35% of all datatraffic can be offloaded using these available WeFi

7At 100% we used all data both for training and evaluation.

Hour of the day

% o

f dat

a re

ques

ts w

ithin

the

hour

of d

ay

0 5 10 15 20 250

0.01

0.02

0.03

0.04

0.05

0.06

0.07WeFi AP coverableWeFi AP not−coverable

Figure 16: Potential hourly offloading ratios throughavailable WiFi APs by the hour of the day.

APs. When we look at the offloading ratios in Fig-ure 11 that can be achieved with the deployment of thesame (558) number of APs using different deploymentmethods, we see that greedy and ILP solutions canachieve 47% and 55% of offloading. This shows thatrecruitment of available WiFi APs (of WeFi) in theircurrent locations has the efficiency of (35/55)=63%compared to ILP solution, while greedy algorithm hasthe efficiency of (47/55)=85%.

VI. Discussion and Future Work

Even though it was not the case in the real taxi traceswe used, simultaneous data access requests from usersin the vicinity of an AP can be higher than the capacityof the AP. In such a case, the greedy algorithm can beupdated such that once an AP is deployed in the mostdense region, the maximum possible sum of data ac-cess request weights (without exceeding the capacityof the AP) that will be served by that AP is determinedand those data access requests are excluded from thefuture considerations. The algorithm can then con-tinue in the same way with the remaining data accessrequests.

For the optimal solution, the constraint equationsto handle the capacity limit (Ck) of APs can also bedefined as follows:

yi,j =

{1 if

∑Kk=1 Sk,i,jAk,i,j > 0

0 otherwise

where Ak,i,j = 1 if wi,j is served by AP k or 0, other-wise. Also, the following constraints must be satisfiedto make sure each wi,j is served by a single AP andthe total capacity of each AP is not exceeded:

K∑k=1

Ak,i,j ≤ 1 ∀ i, j

N∑i=1

N∑j=1

wi,jSk,i,jAk,i,j ≤ Ck ∀ k

However, the above equations make the problem morecomplex and need a non-linear equation system solveror some approximations. We will study this problemin our future work in detail where we will also studymarket based incentive mechanisms [19] to motivateowners of WiFi APs to participate in offloading, soboth users and operators can benefit together.

VII. Conclusion

In this paper, we studied offloading of cellular net-work load through WiFi networks. We first studied ac-cess point (AP) deployment problem for efficient mo-bile data offloading. Analyzing user mobility traces,we proposed to deploy the APs to the locations withthe highest density of user data access requests. Wealso found the optimal deployment by formulatingthe problem as an Integer Linear Programming prob-lem and solving it using IBM ILOG CPLEX package.In simulation results, we showed that our algorithmachieves higher efficiency than the efficiency yieldedby the previous algorithms and gives results closerto the optimal solution. We also demonstrated thatour approach is also beneficial in offloading the fu-ture data requests of users. In addition to the deploy-ment of APs, we also evaluated offloading that canbe achieved using available WiFi APs and with sim-ulations we showed that operators can achieve goodamount of offloading through available APs whilesaving the cost of new deployment.

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