Routing in delay tolerant mobile sensor networks (DTMSNs) is challenging due to the networks’ intermittent connectivity. Most existing routing protocols for DTMSNs use simplistic random mobility models for algorithm design and performance evaluation. In the real world, however, due to the unique characteristics of human mobility, currently existing random mobility models may not work well in environments where mobile sensor units are carried (such as DTMSNs). Taking a person’s social activities into consideration, in this paper, we seek to improve DTMSN routing in terms of social structure and propose an agenda based routing protocol (ARP). In ARP, humans are classified based on their agendas and data transmission is made according to sensor nodes’ transmission rankings. The effectiveness of ARP is demonstrated through comprehensive simulation studies.
References
[1]
Wang, Y; Wu, HY. DFT-MSN: The Delay/Fault-Tolerant Mobile Sensor Network for Pervasive Information Gathering. Proceedings of IEEE INFOCOM, Barcelona, Spain, April 2006.
[2]
Zhu, JQ; Cao, JN; Liu, M; Zheng, Y; Gong, HG; Chen, GH. A Mobility Prediction-based Adaptive Data Gathering Protocol for Delay Tolerant Mobile Sensor Network. Proceedings of IEEE GLOBECOM, New Orleans, LA, USA, December 2008.
[3]
Akyildiz, I; Su, W; Sankarasubramania, Y; Cayirci, E. A survey on sensor networks. IEEE Commun. Mag？2002, 40, 102–114.
[4]
Christian, B; Hannes, H; Xavier, PC. Stochastic properties of the random waypoint mobility model. Wirel. Netw？2004, 10, 555–567, doi:10.1023/B:WINE.0000036458.88990.e5.
[5]
Bettstetter, C. Modeling in wireless networks: Categorization, smooth movement, and border effects. ACM SIGMOBILE Mobile Compu. Commun. Rev？2001, 5, 55–66.
[6]
Hui, P; Crowcroft, J. How Small Labels create Big Improvements. Proceedings of IEEE PerCom, White Plains, NY, USA, March 2007.
[7]
Chaintreau, A; Hui, P; Crowcroft, J; Diot, C; Gass, R; Scott, J. Impact of human mobility on the design of opportunistic forwarding algorithms. Proceedings of IEEE INFOCOM, Barcelona, Spain, April 2006.
[8]
Wang, Y; Wu, H. Delay/fault-tolerant mobile sensor network (DFT-MSN): A new paradigm for pervasive information gathering. IEEE Trans. Mobile Comput？2006, 6, 1021–1034.
[9]
Small, T; Haas, ZJ. The shared wireless infostation model––A new ad hoc networking paradigm (or where there is a whale, there is a Way). Proceedings of ACM MOBIHOC, Annapolis, MD, USA, June 2003.
[10]
Philo, J; Hidekazu, O; Yong, W. Energy-efficient computing for wildlife tracking: Design tradeoffs and early experiences with ZebraNet. ACM Operating Syst. Rev？2002, 36, 96–107, doi:10.1145/635508.605408.
[11]
Shah, RC; Roy, S; Jain, S; Brunette, W. Data mules: Modeling a three-tier architecture for sparse sensor networks. Proceedings of IEEE International Workshop on SNPA, Anchorage, AK, USA, May 2003.
[12]
Spyropoulos, T; Psounis, K; Raghavendra, CS. Single-copy routing in intermittently connected mobile networks. Proceedings of IEEE SECON, Santa Clara, CA, USA, October 2004.
[13]
Leguay, J; Friendman, T; Conan, V. Evaluating mobility pattern space routing for DTNs. Proceedings of IEEE INFOCOM, Barcelona, Spain, April 2006.
[14]
Wang, Y; Wu, HY. Replication-Based efficient data delivery scheme (RED) for Delay/Fault-Tolerant mobile sensor network (DFT-MSN). Proceedings of PerCom Workshops, Pisa, Italy, March 2006.
[15]
Wang, Y; Wu, HY; Dang, H; Lin, F. Analytic, simulation, and empirical evaluation of delay/fault-tolerant mobile sensor Networks. IEEE Trans. Wirel. Commun？2007, 1, 3287–3296.
[16]
Lindgren, A; Doria, A; Schelen, O. Probabilistic routing in intermittently connected networks. Proceedings of the First International Workshop of SAPIR, Fortaleza, Brazil, August 2004.
[17]
Daly, E; Haahr, M. Social network analysis for routing in disconnected delay tolerant MANETs. Proceedings of ACM MOBIHOC, Montreal, Canada, September 2007.
[18]
Vahdat, A; Becker, D. Epidemic Routing for Partially Connected Ad Hoc Networks. Technical Report VolCS-200006;; Duke University: Durham, NC, USA, 2000.
[19]
Eagle, N; Pentl, A. Reality mining: sensing complex social systems. Personal Ubiquitous Comput？2006, 10, 255–268, doi:10.1007/s00779-005-0046-3.
[20]
Zheng, QW; Hong, XY; Liu, J. An agent based mobility model. Proceedings of IEEE ANSS, San Jose, CA, USA, December 2006.
