Dynamic Content and Route Management inWireless Networks

Sanjay Kumar Madria∗, Anirban Mondal† and Tridib Mukherjee‡

∗Missouri University of Science and Technology, Rolla, USA, Email: madrias@mst.edu†Xerox Research Center, INDIA, Email: anirban.mondal@xerox.com‡Xerox Research Centre, INDIA, Email: tridib.mukherjee@xerox.com

I. INTRODUCTION

Significant advances in wireless and mobile technolo-gies have been witnessed over the past decade. The ever-increasing prevalence and proliferation of mobile devices cou-pled with the popularity of social networking and increasinglytechnology-savvy residents have fuelled the growth of mobilecrowdsourcing and participatory sensing. This has catalyzedthe growth of new-age applications, which necessitate dynamicmanagement of information as well as content in wirelessnetworks of different types such as Mobile Peer-to-Peer (M-P2P), Vehicle-to-Vehicle (V2V) and Delay-Tolerant Networks(DTNs).

Mobile crowdsourcing can occur in M-P2P and V2V net-works in various ways. For example, it can help users inobtaining location-based services such as identifying cab-sharing partners, finding the top-k restaurants with “happyhours” within a given radius, identifying the top-k availableparking slots [1] and locating the top-k stores selling Levisjeans in a shopping mall with criteria such as (low) priceduring a specific time duration. Additionally, spatio-temporalinformation sharing in transportation application scenarios[2], [3] can be facilitated by M-P2P interactions in V2Vnetworks. Given that free-riding is a serious concern for mo-bile crowdsourcing applications and mobile devices typicallysuffer from resource constraints (e.g., energy, bandwidth),incentives need to be provided to users for combating free-riding and to encourage them to collaborate towards sharingdata. Furthermore, privacy issues also need to be addressedfor facilitating effective mobile crowdsourcing.

Participatory sensing can occur in various ways by means ofdevices (e.g., mobile phones, PDAs, laptops and various typesof sensors) or by including humans in the loop or both. Inter-estingly, participatory sensing can also potentially act as a keyenabling technology for various applications involving smartercities initiatives. Observe that existing automated sensor-baseddata collection mechanisms cannot always take human judg-ment and the context of a given city-related event (e.g., trafficcongestion, dysfunctional traffic lights, traffic accidents) intoconsideration. Moreover, the costs of deploying automatedsensors (e.g., camera-sensors) would be prohibitively expen-sive, especially for emerging economies. Since mobile devicesoften come equipped with various kinds of sensors, collection

of event data using the mobile devices of users is also well-aligned with current technological trends. However, note thatincentives need to be provided for effectively encouragingusers to contribute such event data.

Given the resource constraints of mobile devices, theystore data and content locally, thereby precluding them fromperforming high-level integrated analytics over a wide gamutof data stored across a large number of possibly heterogeneousmobile devices. Given the popularity and proliferation of cloudcomputing, mobile cloud is an attractive option for realizingsuch integrated data analytics.

This tutorial will cover the following topics:• Social-context based content and Route management in

Delay-Tolerant Networks• Incentive-based dynamic content and route management

in Mobile-P2P and V2V networks• Sensed data management in mobile sensor networks• Dynamic data management services in mobile cloud

networksThe objective of this tutorial is to provide a broad overviewof the research challenges and issues associated with dynamiccontent and route management in wireless networks for facil-itating academicians and researchers working in the area ofwireless data processing. It is to help computer and databaseprofessionals/business analysts, database and system adminis-trators, designers, project and technical managers, and peopleinvolved in planning, designing, developing, implementingand administrating wireless applications. Furthermore, we willdiscuss some of the open research issues in this area andprovide our perspectives on those issues.

II. SOCIAL-CONTEXT BASED CONTENT AND ROUTE

MANAGEMENT IN DELAY-TOLERANT NETWORKS

Delay Tolerant Networks (DTNs) comprise a collectionof devices that route data opportunistically. These networkscan be used for distributing content such as news articles,advertisements and media, to interested clients. For effectivelyfacilitating targeted content delivery, social context and requestpatterns need to be taken into account [4]. In DTNs, socialcontext can be used to improve the efficiency of certain taskssuch as routing or caching.

Social Content Distribution is based on the assumption thathumans in a social group have a tendency to share interests.

2014 IEEE 15th International Conference on Mobile Data Management

978-1-4799-5705-7/14 $31.00 © 2014 IEEE

DOI 10.1109/MDM.2014.58

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Allocating nodes into distince groups can be performed basedon the concept of k-clique, which is defined as a set of k

fully connected nodes. In this regard, a method for k-cliquecommunity grouping has been proposed in [5]. However, themethod relies on comprehensive knowledge of the nodes’contacts, hence it cannot be directly used in an ongoing DTN.

The R-P2P system [6] allocates content through a DTNwith the goal of ensuring consistent availability of contentto clients. It designates certain nodes in the environment asthrowboxes, which are responsible for serving data queriesand maintaining updated content. Throwboxes use a distributedhash table indexing mechanism among themselves to facilitateefficient search for content that is request by the clients.The OnMove protocol [7], an earlier attempt to positionthrowboxes in DTNs, uses social context by incorporatingfactors such as social similarity, meeting frequency, connectionquality and content similarity. In effect, it aims at determiningwhich nodes are ideal for distributing content based on theabove parameters. Furthermore, the Profile-Cast paradigm [8]determines the similarity of nodes by identifying the frequencyof their visits to specified locations at similar times.

III. INCENTIVE-BASED DYNAMIC CONTENT AND ROUTE

MANAGEMENT IN MOBILE-P2P AND V2V NETWORKS

Proliferation of mobile devices (e.g., laptops, PDAs, mo-bile phones) coupled with the ever-increasing popularity ofthe P2P paradigm (e.g., Kazaa, Gnutella) strongly motivateM-P2P and V2V network applications. For realizing suchapplications, key challenges to effective peer collaborationstowards data discovery and management include free-riding[9], peer mobility, privacy and the resource constraints (e.g.,energy) of mobile devices. To combat free-riding, variousincentive schemes have been proposed. Improvement of dataand service availability by associating a price with data itemsand services has been investigated in [10], [11], [12], [13].The proposals in [2] and [3] provide incentives to mobilepeers for opportunistically disseminating resource informationin M-P2P transportation application scenarios. The work in[10] proposes an economic scheme for adaptive revenue-load-based dynamic replication of data in M-P2P networks withthe goal of improving data availability. Incentive schemes forincentivizing nodes to forward messages in Mobile ad hocnetworks have been proposed in [14], [15], [16], [17].

The proliferation of mobile devices with embedded GPSsensors has created new avenues for improving vehicular traf-fic management in road networks [18], [19]. The proposal in[20] uses an incentive-compatible pricing and routing scheme,which also compensates users for sharing their vehicularmovement information. Moreover, the work in [21] proposesa dynamic congestion pricing model, which captures users’personal choices by means of a discrete choice framework.Furthermore, the work in [22] proposed a dynamic pricingmodel, which assumes that the travel-time is a function ofvehicles types.

A predictive model for dynamic pricing in traffic man-agement has been proposed in [23]. Based on the types of

measurements of volume, speed and occupancy of vehicles,three types of toll collections are proposed; pass-based, peruse-based and distance-based associated with different types ofrate patterns. The feasibility of applying dynamic congestionpricing to traffic management has been studied in [24] withfocus on spatio-temporal variations in road-usage patterns foradjusting tolls based on road conditions and congestion.

IV. SENSED DATA MANAGEMENT IN MOBILE SENSOR

NETWORKS

Crowdsourcing platforms, such as Amazon MechanicalTurk [25], have been used for solving problems that cannot behandled by automated approaches since they require humanjudgment. The prevalence and proliferation of mobile tech-nologies (e.g., mobile sensors embedded in smartphones) haveopened up several interesting avenues for collecting crowd-sensed data. Such crowd-sensed data has significant applica-tions in several important and diverse applications involvinghealthcare, transportation, smarter cities, environmental sus-tainability, retail and so on.

Crowdsensing efforts in the context of creating smartercities include the FixMyStreet platform [26] and the Ushahidiplatform [27]. Using such platforms, users can report varioustypes of city-related problems (e.g., potholes, dysfunctionaltraffic lights), thereby facilitating urban planning and manage-ment. In a similar vein, Nericell system [28] and the works in[29] and [30] use mobile sensors to detect road conditions suchas speed breakers and potholes. In such systems, the humaninvolvement brings in the contextual angle, thereby facilitatingmore effective city-related problem detection and resolution.

Given the multi-modal data collected from possibly diversesources, prediction techniques can be used to improve theaccuracy of crowdsourced data collection [31] as well asobtaining better understanding of the overall context of thedata [32], [33]. The use of mobile crowdsourcing/sensing forimproving city management has also been investigated in [34],[35]. Furthermore, societal aspects of mobile crowdsourcinghave been studied in [36], [37] with emphasis on emergingmarkets, where low-end mobile devices are prevalent.

V. DYNAMIC DATA MANAGEMENT SERVICES IN MOBILE

CLOUD

Mobile Cloud Computing (MCC) typically refers to a cloudinfrastructure dedicated for storage and processing of mobiledata i.e., data originated from mobile devices. This allowslight-weight load on the mobile devices, thereby bringingapplications of mobile computing to not just smartphone users,but also a much broader range of mobile subscribers. MCC iswidely considered as a new paradigm for mobile applications,where the data management are moved from the mobile deviceto powerful and centralized cloud infrastructure. Examplesof various cloud-based services [38], [39] that can be usedto enable MCC include: Amazon Simple Storage Service(Amazon S3) [40], which supports file storage service; anymobile photo sharing services [41], [42], which utilizes thelarge storage space in clouds for mobile users to share photos.

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MCC also helps in reducing the execution overhead forcompute-intensive applications, that can take long time andlarge amount of energy when performed on the resource-constrained mobile devices. Cloud computing can efficientlysupport various tasks for data warehousing, managing andsynchronizing multiple documents online, e.g., transcoding,playing chess, or broadcasting multimedia services to mobiledevices. In these cases, all the complex calculations fortranscoding or offering an optimal chess move that take along time when performed on mobile devices can be processedquickly on the cloud.

Many sensing applications (e.g., participatory sensing) canuse MCC to use the resources in the cloud for crunching dataoriginated from multi-modal sources e.g., photos, videos, andtext from mobile devices.

VI. OPEN RESEARCH ISSUES AND PERSPECTIVES

Several open research issues exist in this increasingly impor-tant research area. Many of these issues are associated with thehuman angle associated with dynamic content management inwireless networks. As a single instance, when data is collectedby means of mobile crowdsourcing/sensing for designingsmarter cities, the quality of data provided by crowdworkersis generally an important concern. Given that such data willbe used for performing analytics, it is important to ensurethat the analytics results are not compromised by low-quality,inaccurate or noisy data. Thus, the human factor in collectingsuch data should be taken into consideration. In some cases,training may need to be imparted to crowdworkers with theintent of ensuring that the data collected by them is indeedof practical use. For example, if a crowdworker reports that atraffic light is not working without mentioning the location, thedata would most likely not be useful to city traffic managementagencies.

Another interesting issue concerns the ranking of the datasent by the crowdworkers. If incentive mechanisms are used,one can expect city-related event reports from a significantlyhigh number of crowdworkers. This would be especiallytrue for emerging economies, where relatively low-earningcrowdworkers would see city-related event reporting as a wayto supplement their meagre incomes. In such cases, the eventreports sent by the crowdworkers would need to be rankedbased on factors such as report quality and timeliness of eventreporting. Furthermore, existing sensors (e.g., camera-sensorsused for traffic monitoring) can also be used to enhance thedata returned by the crowdworkers. In essence, the goal is touse human judgment of the crowdworkers in conjunction withexisting sensors for effectively performing city management.

In conclusion, we believe that the research area of dynamiccontent and route management in wireless networks presentsan exciting set of challenges and opportunities. We hope thatresearchers from both academia and industry will work to-wards making the next-generation services, which are relevantto this research area, a reality in the near future.

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SPEAKER BIOS

Sanjay Kumar Madria is a Professor in the Departmentof Computer Science at the Missouri University of Scienceand Technology, USA, and site director of the NSF I/UCRCCentre on Net-Centric Software Systems. He has publishedover 200 journal and conference papers in the areas of mo-bile data management, sensor computing, cyber security andtrust management. He has served in international conferences

as a general co-chair (e.g., IEEE MDM, IEEE SRDS andothers), and presented tutorials/talks in the areas of mobiledata management and sensor computing at various venues. Hisresearch is supported by several grants from federal sourcessuch as NSF, DOE, AFRL, ARL, ARO, NIST and industrieslike Boeing. He served as an IEEE Distinguished Speaker,and currently, he is an ACM Distinguished Speaker and IEEESenior and Golden Core Member.

Anirban Mondal is a Senior Research Scientist at XeroxResearch Centre India. His expertise is in the area of mobileand P2P data management, economic incentive models, spatialdatabase indexing and load-balancing. Prior to joining Xerox,he had been an Associate Professor at IIIT Delhi for threeyears. He also had a long tenure of seven years at the Uni-versity of Tokyo, Japan. He has numerous publications in keyconferences/journals, where he also maintains an active levelof involvement as PC Chair/Co-chair, PC member, journalreviewer as well as keynote/tutorial speaker. He has also filedseveral US patents. His awards include the prestigious JSPS(Japanese Society for Promotion of Science) Fellowship aswell as a DST Fast Track project for Young Scientists of India.Anirban completed his PhD degree in Computer Science fromthe National University of Singapore (NUS). He also has anMBA degree from the University of Massachusetts Amherst(UMass), USA.

Tridib Mukherjee is a Research Scientist at Xerox ResearchCentre India. He works in the broad areas of DistributedComputing, Cloud Computing, Green Computing, Sensor Net-works, and Services Computing. Tridib has published numer-ous articles in reputed journals and conferences. He has furtherfiled more than 20 US patents. He has also co-presentedtutorials and has been involved in organizing workshops invarious prestigious conferences. He has also co-authored abook on body sensor networks from the Cambridge UniversityPress. His work on cloud-based service optimization andrecommendation has won the prestigious IEEE Cloud Cupin 2013. Tridib received his PhD in Computer Science fromArizona State University in 2009.

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