Rendezvous design algorithms for wireless sensor networks with a mobile base station

Recent research shows that significant energy saving can be achieved in wireless sensor networks with a mobile base station that collects data from sensor nodes via short-range communications. However, a major performance bottleneck of such WSNs is the significantly increased latency in data collection due to the low movement speed of mobile base stations. To address this issue, we propose a rendezvous-based data collection approach in which a subset of nodes serve as the rendezvous points that buffer and aggregate data originated from sources and transfer to the base station when it arrives. This approach combines the advantages of controlled mobility and in-network data caching and can achieve a desirable balance between network energy saving and data collection delay. We propose two efficient rendezvous design algorithms with provable performance bounds for mobile base stations with variable and fixed tracks, respectively. The effectiveness of our approach is validated through both theoretical analysis and extensive simulations.

[1]  Eylem Ekici,et al.  Responsible Editor: I.F. Akyildiz , 2006 .

[2]  Wei Wang,et al.  Using mobile relays to prolong the lifetime of wireless sensor networks , 2005, MobiCom '05.

[3]  Mani B. Srivastava,et al.  Mobile Element Scheduling with Dynamic Deadlines , 2007, IEEE Transactions on Mobile Computing.

[4]  Jie Lin,et al.  Towards mobility as a network control primitive , 2004, MobiHoc '04.

[5]  Gaurav S. Sukhatme,et al.  Networked infomechanical systems: a mobile embedded networked sensor platform , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[6]  Ellen W. Zegura,et al.  Message ferry route design for sparse ad hoc networks with mobile nodes , 2006, MobiHoc '06.

[7]  B. R. Badrinath,et al.  DV Based Positioning in Ad Hoc Networks , 2003, Telecommun. Syst..

[8]  Waylon Brunette,et al.  Data MULEs: modeling a three-tier architecture for sparse sensor networks , 2003, Proceedings of the First IEEE International Workshop on Sensor Network Protocols and Applications, 2003..

[9]  Mani B. Srivastava,et al.  Multiple Controlled Mobile Elements (Data Mules) for Data Collection in Sensor Networks , 2005, DCOSS.

[10]  Chenyang Lu,et al.  A spatiotemporal communication protocol for wireless sensor networks , 2005, IEEE Transactions on Parallel and Distributed Systems.

[11]  Guoliang Xing,et al.  Real-time Power-Aware Routing in Sensor Networks , 2006, 200614th IEEE International Workshop on Quality of Service.

[12]  M. Birkner,et al.  Blow-up of semilinear PDE's at the critical dimension. A probabilistic approach , 2002 .

[13]  David M. Warme,et al.  Exact Algorithms for Plane Steiner Tree Problems: A Computational Study , 2000 .

[14]  Eylem Ekici,et al.  Mobile element based differentiated message delivery in wireless sensor networks , 2006, 2006 International Symposium on a World of Wireless, Mobile and Multimedia Networks(WoWMoM'06).

[15]  Hyung Seok Kim,et al.  Dynamic delay-constrained minimum-energy dissemination in wireless sensor networks , 2005, TECS.

[16]  Peter Desnoyers,et al.  Ultra-low power data storage for sensor networks , 2006, 2006 5th International Conference on Information Processing in Sensor Networks.

[17]  Andreas Savvides,et al.  XYZ: a motion-enabled, power aware sensor node platform for distributed sensor network applications , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[18]  AroraSanjeev Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems , 1998 .

[19]  Wei Hong,et al.  Proceedings of the 5th Symposium on Operating Systems Design and Implementation Tag: a Tiny Aggregation Service for Ad-hoc Sensor Networks , 2022 .

[20]  J. Kruskal On the shortest spanning subtree of a graph and the traveling salesman problem , 1956 .

[21]  Gaurav S. Sukhatme,et al.  Robomote: enabling mobility in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[22]  Marco Zuniga,et al.  Analyzing the transitional region in low power wireless links , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[23]  Edward K. Morlok,et al.  Vehicle Speed Profiles to Minimize Work and Fuel Consumption , 2005 .

[24]  Sanjeev Arora,et al.  Polynomial time approximation schemes for Euclidean traveling salesman and other geometric problems , 1998, JACM.

[25]  Milind Dawande,et al.  Energy efficient schemes for wireless sensor networks with multiple mobile base stations , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

[26]  Deborah Estrin,et al.  Controllably mobile infrastructure for low energy embedded networks , 2006, IEEE Transactions on Mobile Computing.

[27]  Peter Desnoyers,et al.  Ultra-low power data storage for sensor networks , 2009, TOSN.

[28]  Jun Luo,et al.  Joint mobility and routing for lifetime elongation in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[29]  Guoliang Xing,et al.  Rendezvous Planning in Mobility-Assisted Wireless Sensor Networks , 2007, 28th IEEE International Real-Time Systems Symposium (RTSS 2007).

[30]  Eylem Ekici,et al.  Mobility-based communication in wireless sensor networks , 2006, IEEE Communications Magazine.

[31]  Gaurav S. Sukhatme,et al.  Call and response: experiments in sampling the environment , 2004, SenSys '04.

[32]  Emanuel Melachrinoudis,et al.  Exploiting Sink Mobility for Maximizing Sensor Networks Lifetime , 2005, Proceedings of the 38th Annual Hawaii International Conference on System Sciences.