CSI: A paradigm for behavior-oriented profile-cast services in mobile networks

We propose a new behavior-oriented communication paradigm in mobile networks, profile-cast, motivated by tight user-network coupling in mobile societies. In this novel paradigm, messages are sent to sender-specified target profiles, instead of machine IDs. We present a systematic framework for such services. First, we analyze the spatio-temporal stability of user mobility profiles constructed from empirical data sets, and they turn out to be surprisingly stable. The similarity of the current mobility profile of a user to its future mobility profile remains above 0.6 for five weeks, while the correlation coefficient of the similarity metrics between a user pair at different time instants is above 0.5 for two weeks. Second, we present a protocol for the profile-cast service, named CSI, and provide a fully distributed solution utilizing behavioral profile space gradients and small world structures to selectively diffuse information across the network towards the intended recipients. Leveraging stability in user behaviors, the two modes of CSI achieve good performance compared to the theoretical optimal protocols. Both CSI:Target mode and CSI:Dissemination mode achieve more than 94% delivery ratio. Comparing with the delay-optimal protocol, they show no more than 47% and 32% more delay, respectively, with at most 10% more transmission overhead. Comparing with the overhead-optimal protocol, they use no more than 7% more overhead while achieving dramatic improvement in delay (up to 150% less). Both CSI:T and CSI:D significantly outperform the epidemic routing, using less than 7% overhead, and variants of random walk, where CSI:T doubles the delivery ratio using less overhead, and CSI:D shows at least 50% less delay under similar overhead. We believe the profile-cast paradigm would enable many behavior-oriented services efficiently, such as targeted announcements and profile-based alert notifications, in various mobile networks.

[1]  Alex Pentland,et al.  Reality mining: sensing complex social systems , 2006, Personal and Ubiquitous Computing.

[2]  Ahmed Helmy,et al.  Geographic rendezvous-based architectures for emergency data dissemination , 2010, Wirel. Commun. Mob. Comput..

[3]  Ahmed Helmy,et al.  Extended Abstract : Mining Behavioral Groups in Large Wireless LANs , 2007 .

[4]  Ahmed Helmy,et al.  Small worlds in wireless networks , 2003, IEEE Communications Letters.

[5]  Timur Friedman,et al.  Evaluating Mobility Pattern Space Routing for DTNs , 2005, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.

[6]  Hector Garcia-Molina,et al.  Publish/Subscribe in a Mobile Environment , 2004, Wirel. Networks.

[7]  Anders Lindgren,et al.  Probabilistic routing in intermittently connected networks , 2003, MOCO.

[8]  R. Stephenson A and V , 1962, The British journal of ophthalmology.

[9]  Tristan Henderson,et al.  CRAWDAD: a community resource for archiving wireless data at Dartmouth , 2005, CCRV.

[10]  Ellen W. Zegura,et al.  A message ferrying approach for data delivery in sparse mobile ad hoc networks , 2004, MobiHoc '04.

[11]  Ahmed Helmy,et al.  On Nodal Encounter Patterns in Wireless LAN Traces , 2010, IEEE Transactions on Mobile Computing.

[12]  Cecilia Mascolo,et al.  Socially-aware routing for publish-subscribe in delay-tolerant mobile ad hoc networks , 2008, IEEE Journal on Selected Areas in Communications.

[13]  Eric Paulos,et al.  The familiar stranger: anxiety, comfort, and play in public places , 2004, CHI.

[14]  Ahmed Helmy,et al.  Profile-Cast: Behavior-Aware Mobile Networking , 2008, 2008 IEEE Wireless Communications and Networking Conference.

[15]  Mads Haahr,et al.  Social network analysis for routing in disconnected delay-tolerant MANETs , 2007, MobiHoc '07.

[16]  Bill N. Schilit,et al.  Place Lab: Device Positioning Using Radio Beacons in the Wild , 2005, Pervasive.

[17]  Duncan J. Watts,et al.  Collective dynamics of ‘small-world’ networks , 1998, Nature.

[18]  Arobinda Gupta,et al.  Group Based Routing in Disconnected Ad Hoc Networks , 2006, HiPC.

[19]  Paal E. Engelstad,et al.  Evaluation of Service Discovery Architectures for Mobile Ad Hoc Networks , 2005, Second Annual Conference on Wireless On-demand Network Systems and Services.

[20]  Ahmed Helmy,et al.  Mining behavioral groups in large wireless LANs , 2006, MobiCom '07.

[21]  Ahmed Helmy,et al.  RUGGED: RoUting on finGerprint Gradients in sEnsor Networks , 2004, The IEEE/ACS International Conference on Pervasive Services.

[22]  Krishna P. Gummadi,et al.  Exploiting Social Interactions in Mobile Systems , 2007, UbiComp.

[23]  Amin Vahdat,et al.  Epidemic Routing for Partially-Connected Ad Hoc Networks , 2009 .

[24]  Charles R. Johnson,et al.  Matrix analysis , 1985, Statistical Inference for Engineers and Data Scientists.

[25]  Nitin H. Vaidya,et al.  Flooding-Based Geocasting Protocols for Mobile Ad Hoc Networks , 2002, Mob. Networks Appl..

[26]  Mike Y. Chen,et al.  Practical Metropolitan-Scale Positioning for GSM Phones , 2006, UbiComp.

[27]  Mostafa Ammar,et al.  Multicasting in delay tolerant networks: semantic models and routing algorithms , 2005, WDTN '05.

[28]  Pan Hui,et al.  BUBBLE Rap: Social-Based Forwarding in Delay-Tolerant Networks , 2008, IEEE Transactions on Mobile Computing.

[29]  Ahmed Helmy,et al.  Geographic rendezvous-based architectures for emergency data dissemination , 2010, CMC 2010.

[30]  Jian Li,et al.  Group communications in mobile ad hoc networks , 2004, Computer.

[31]  David Kotz,et al.  Periodic properties of user mobility and access-point popularity , 2007, Personal and Ubiquitous Computing.

[32]  Vikram Srinivasan,et al.  PeopleNet: engineering a wireless virtual social network , 2005, MobiCom '05.

[33]  Tristan Henderson,et al.  CRAWDAD: A Community Resource for Archiving Wireless Data at Dartmouth , 2005, IEEE Pervasive Comput..

[34]  Chunming Qiao,et al.  On profiling mobility and predicting locations of wireless users , 2006, REALMAN '06.