Mobile Ad Hoc Networking (Cutting Edge Directions) || Mobility Models in Opportunistic Networks

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<ul><li><p>10MOBILITY MODELS INOPPORTUNISTIC NETWORKS</p><p>Kyunghan Lee, Pan Hui, and Song Chong</p><p>ABSTRACT</p><p>Human mobility is critically important in many academic disciplines ranging fromcommunication networking research to epidemiology and urban planning. To specif-ically benefit communication networks by discovering key features of human move-ments, many researchers in communication networks have been increasingly focusingon developing measurement-based mobility models. Although large-scale measure-ment data collections are generally expensive and time-consuming, unique under-standings and inspirations of futuristic networks given by realism of human move-ments captured from the measurement data are considered to be significantly valu-able. In this chapter, we introduce recent measurement studies on human mobilityand representative realistic mobility models inspired by the measurements in twomajor categories: contact-based and trajectory-based. Based on the key observationsof human mobility, we then discuss new paradigms in designing network protocols,especially forwarding protocols in delay-tolerant networks, and network architecturesproviding fundamental tradeoffs between delay and diverse network resources.</p><p>10.1 INTRODUCTION</p><p>Human mobility is critically important in many academic disciplines, ranging fromcommunication networking research to epidemiology [1] and urban planning [2].</p><p>Mobile Ad Hoc Networking: Cutting Edge Directions, Second Edition. Edited by Stefano Basagni,Marco Conti, Silvia Giordano, and Ivan Stojmenovic. 2013 by The Institute of Electrical and Electronics Engineers, Inc. Published 2013 by John Wiley &amp; Sons, Inc.</p><p>360</p></li><li><p>CONTACT-BASED MEASUREMENT, ANALYSIS, AND MODELING 361</p><p>Real mobility traces will not only benefit the wireless and mobile networking researchcommunities, but will also impact fundamental research in other areas allowing morefeatures about human behavior to be uncovered.</p><p>After its first use in the evaluation of Dynamic Source Routing [3], the randomwaypoint model became the de facto standard mobility model in the mobile net-working community. For example, of 10 papers in ACM MobiHoc 2002 consideringnode mobility, nine used the random waypoint model [4]. This trend has dramaticallychanged over recent years after the introduction of real mobility traces for evaluation:of the 10 papers considered node mobility in MobiHoc 2008, seven used real mobilitytraces for evaluation. The community has realized that unrealistic models are harm-ful for scientific research. Although real traces may suffer from limited numbers ofparticipants, coarse granularity, and short experimental duration, they at least reflectsome aspects of real life.</p><p>Data collection is usually expensive and time-consuming. Instead, realistic mo-bility models by extracting characteristics from the available data can serve as a toolfor simulating different environment setting. Much work has been done in modelinghuman mobility for mobile ad hoc network simulation [5]. Researchers have pro-posed more realistic models by incorporating obstacles [6], social information [7],and clustering features observed in mobility datasets [8]. Analysis of real traces hasdemonstrated power law intercontact time distributions with cutoff [9,10], Levy walkpatterns consisting of lots of small moves followed by long jumps [11], fractal dis-persion of locations where people make pause [12], heterogeneous centralities [13](i.e., popularity), and clustering structure [14]. Some researchers have extrapolatedfrom these by assuming, for instance, that the way people move in a city is corre-lated to the centrality distribution of the city graph [2], but this has yet to be verifiedempirically. Gonzalez et al. [15] extracted coarse-grained Levy walk properties fromlarge-scale cell tower association logs of mobile phones.</p><p>There are a lot more research papers focused on modeling mobility, which unfor-tunately cannot be included in this chapter due to limitation of space. Our primarygoal is to give a general picture about the recent progress in the area and encouragethe readers to refer to the original papers for more details.</p><p>10.2 CONTACT-BASED MEASUREMENT, ANALYSIS, AND MODELING</p><p>In an opportunistic network, mobility determines the communication opportunitieswhen access infrastructure is not available. Studying human mobility can help usunderstand the constraints of opportunistic communication and to design practicaland effective forwarding strategies. Killer applications and security measures canalso be inferred from the human mobility and interaction pattern.1</p><p>1For example, citywide alternative reality gaming applications can plan where to put their infrastruc-tures and distribute hints if they know how the people move; community-based data sharing and cachingapplications can plan their caching strategies if they know the community structures.</p></li><li><p>362 MOBILITY MODELS IN OPPORTUNISTIC NETWORKS</p><p>Figure 10.1 iMote for the experiment.</p><p>This section concentrates on (a) several peer-contact-based human mobilityexperiments conducted by the Haggle project2 using iMotes and (b) the analysisof the human mobility patterns from these traces. To ensure generality of the analy-sis results, we also include mobility traces from diverse experimentsthat is, WiFiaccess point logs from Dartmouth College [16].</p><p>10.2.1 Measurements</p><p>In this section we mainly use an experiment conducted within a group of conferenceattendees to represent the general features of the iMote experiment by Hui et al. [9,17].Other experiments have a similar setup and deployment approach, which involveddifferent participants and environments.</p><p>The device used to collect connection opportunity data and mobility statistics inthis experiment is the Intel iMote. This is a small platform designed for embeddedoperation, comprising an ARM processor, Bluetooth radio, and flash RAM, and isshown with a CR2 battery in Figure 10.1a. these devices were packaged in a dentalfloss box, as shown in Figure 10.1b, due to their ideal size, low weight, and hardplastic shell.</p><p>Fifty-four of these boxes were distributed to attendees at the IEEE Infocom con-ference in Miami in March 2005 (which had 800 attendees in total). The volunteerswere asked to keep the iMote with them for as much of their day as possible. Vol-unteers were chosen to belong to a wide range of organizationsmore than 30 wererepresented. To assure the participants of their anonymity, the MAC address of theiMotes that they were given was not recorded; instead only an uncorrelated numberprinted on the outside of the box was recorded, so that the logistics of distributionand collection can be performed. Of the 54 iMotes distributed, 41 yielded useful data,11 did not contain useful data because of various failures with the battery and pack-aging, and two were not returned.</p><p>2Most of the material in this section has been previously published by the Haggle Project [9,14,17].</p></li><li><p>CONTACT-BASED MEASUREMENT, ANALYSIS, AND MODELING 363</p><p>The iMotes were configured to perform a Bluetooth baseband layer inquirydiscovering the MAC addresses of other Bluetooth nodes in range, with the inquirymode enabled for 5 s. Despite the Bluetooth specification recommending that inquirylast for 10 seconds, preliminary experiments showed that 5 seconds is sufficient toconsistently discover all nearby devices, while halving the battery-hungry inquiryphase. Between inquiry periods, the iMotes were placed in a sleep mode in whichthey respond to inquiries but are not otherwise active, for a duration of 120 s plusor minus 12 seconds in a uniform random distribution. The randomness was addedto the sleep interval in order to avoid a situation where iMotes timers were in sync,since two iMotes performing inquiry simultaneously cannot see each other. However,iMotes are expected to fail to see each other during inquiry around four percent ofthe time (when they are doing inquiry at the same time).</p><p>The results of inquiry were written to flash RAM. Since flash capacity is limited(64K for data), it is impossible to store the full results of each inquiry without runningthe risk of exhausting the memory. Hence, contact periods are only recorded. This isachieved by maintaining an in-contact list comprising the Bluetooth MAC addressesof the nodes that are currently visible. When a device on this list stops responding toinquiries, a record of the form {MAC, start time, end time} is recorded. Preliminarytests revealed the following problem: Bluetooth devices on a specific brand of mobilephone did not show up consistently during inquiries (and increasing the inquiry periodto 10 seconds did not help). Therefore, a small number of nodes were causing thememory to fill too quickly. To avoid this problem, a device is kept in the in-contactlist even if it is not seen for one inquiry interval. If it comes back in-contact on thenext interval, nothing is stored. If it does not, a record is stored as normal. This solvesthe problem, at the expense of not being able to detect actual cases where a nodemoved out of range during one two-minute period and back into range for the nexttwo-minute period.</p><p>10.2.2 Contact-Based Datasets</p><p>In this section we introduce the contact-based datasets available for mobility mod-eling, which include five iMote datasets and five other datasets involve Bluetoothor WiFi. The characteristics of the iMote datasets, explained below, are shown inTable 10.1.</p><p> In Cambridge04, the data were obtained from 12 doctoral students and facultycomprising a research group at the University of Cambridge Computer Lab. Thisis an early experiment and hence has small participant population.</p><p> In Infocom05, the devices were distributed to approximately 50 students attend-ing the Infocom student workshop. Participants belong to different social com-munities (depending on their country of origin, research topic, etc.). However,they all attended the same event for 4 consecutive days, and most of them stayedin the same hotel and attended the same sessions (note, though, that Infocom isa multitrack conference).</p></li><li><p>364 MOBILITY MODELS IN OPPORTUNISTIC NETWORKS</p><p>Table 10.1 Characteristics of the Five iMote DatasetsExperimental Dataset Cambridge04 Infocom05 Hong Kong Cambridge05 Infocom06</p><p>Device iMote iMote iMote iMote iMoteNetwork type Bluetooth Bluetooth Bluetooth Bluetooth BluetoothDuration (days) 3 3 5 11 3Granularity (seconds) 120 120 120 600 120Number of experimental</p><p>devices12 41 37 54 98</p><p>Number of internalcontacts</p><p>4,229 22,459 560 10,873 191,336</p><p>Average # contacts/pair/day</p><p>10 4.6 0.084 0.345 6.7</p><p>Number of externaldevices</p><p>148 264 868 11,357 14,036</p><p>Number of externalcontacts</p><p>2,441 1,173 2,507 30,714 63,244</p><p> In Hong Kong, the people carrying the wireless devices were chosen indepen-dently in a Hong Kong bar, to avoid any particular social relationship betweenthem. These people were invited to come back to the same bar after a week.They are unlikely to see each other during the experiment.</p><p> In Cambridge05, the iMotes were distributed mainly to two groups of studentsfrom University of Cambridge Computer Laboratory, specifically undergraduatestudents, and also some PhD and Masters students. In addition to this, a numberof stationary nodes were deployed in various locations that were expected manypeople to visit, such as grocery stores, pubs, market places, and shopping centersin and around the city of Cambridge, UK. However, the data from these stationaryiMotes will not be used in this chapter. This dataset covers 11 days.</p><p> In Infocom06, the scenario was similar to Infocom05 except that the scale islarger, with 80 participants. Participants were selected so that 34 out of 80 formfour subgroups by academic affiliations. In addition, 20 long-range iMotes weredeployed at several places in the conference site to act as access points.</p><p>Table 10.2 summarizes the characteristics of the four other experiments (but fivedatasets): UCSD [18], Dartmouth College [16], University of Toronto [19], and MITReality Mining project [20]. They are named UCSD, Dartmouth, Toronto, and MIT,respectively.</p><p>UCSD and Dartmouth make use of WiFi networking, with the former includingclient-based logs of the visibility of access points (APs), while the latter includesSNMP logs from the access points. The durations of the different logs are threeand four months, respectively. Since the data about device-to-device transmissionopportunities are required, the raw datasets were unsuitable for the experiment andrequired preprocessing. For both datasets, it is assumed that mobile devices seeingthe same AP would also be able to communicate directly (in ad hoc mode); and they</p></li><li><p>CONTACT-BASED MEASUREMENT, ANALYSIS, AND MODELING 365</p><p>Table 10.2 Comparison of Data Collected in the External Experiments</p><p>User Population Toronto UCSD Dartmouth MIT BT MIT GSM</p><p>Device PDA PDA Laptop/PDA Cell Phone Cell PhoneNetwork type Bluetooth WiFi WiFi BT GSMDuration (days) 16 77 114 246 246Granularity (seconds) 120 120 300 300 10Devices participating 23 273 6648 100 100Number of internal contacts 2,802 195,364 4,058,284 54,667 572,190Average # contacts/pair/day 0.35 0.034 0.00080 0.022 0.23Recorded external devices N/A N/A N/A N/A N/ANumber of external contacts N/A N/A N/A N/A N/A</p><p>created a list of transmission opportunities by determining, for each pair of devices,the set of time regions for which they shared at least one AP.</p><p>The traces from the Reality Mining project at MIT Media Lab include recordsof visible Bluetooth devices and GSM cell towers, collected by 100 cellphones dis-tributed to student and faculty on the campus during 9 months. These sets of contactsare treated as two different datasets. For the GSM part, it is assumed, as done above,that two devices are in contact whenever they are connected with the same cell tower.</p><p>Unfortunately, this assumption introduces inaccuracies. On one hand, it is overlyoptimistic since two devices attached to the same (WiFi or GSM) base station maystill be out of range of each other. On the other hand, the data might omit connectionopportunitiesfor example, when two devices pass each other at a place where thereis no instrumented access point. Another potential issue with these datasets is that thedevices are not necessarily co-located with their owners at all times (i.e., they do notalways characterize human mobility). Despite these inaccuracies, these traces are avaluable source of data spanning many months and including thousands of devices. Inaddition, considering two devices connected to the same base station being potentiallyin contact is not altogether unreasonable. These devices would indeed be able tocommunicate locally through the base station without using end-to-end connectivity,or even by using the Internet.</p><p>The traces from the University of Toronto have been collected by 20 Bluetooth-enabled PDAs distributed to a group of students. These devices performed a Bluetoothinquiry each 100 s, and this data was logged. This methodology does not requiredevices to be in range...</p></li></ul>