Abstract
Energy is one of the most crucial aspects in real deployments of mobile sensor networks. As a result of scarce resources, the duration of most real deployments can be limited to just several days, or demands considerable maintenance efforts (e.g., in terms of battery substitution). A large portion of the energy of sensor applications is spent in node discovery as nodes need to periodically advertise their presence and be awake to discover other nodes for data exchange. The optimization of energy consumption, which is generally a hard task in fixed sensor networks, is even harder in mobile sensor networks, where the neighbouring nodes change over time.
In this paper we propose an algorithm for energy efficient node discovery in sparsely connected mobile wireless sensor networks. The work takes advantage of the fact that nodes have temporal patterns of encounters and exploits these patterns to drive the duty cycling. Duty cycling is seen as a sampling process and is formulated as an optimization problem. We have used reinforcement learning techniques to detect and dynamically change the times at which a node should be awake as it is likely to encounter other nodes. We have evaluated our work using real human mobility traces, and the paper presents the performance of the protocol in this context.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Ye, W., Silva, F., Heidemann, J.: Ultra-low duty cycle MAC with scheduled channel polling. In: SenSys 2006: Proceedings of the 4th international conference on Embedded networked sensor systems, pp. 321–334. ACM Press, New York (2006)
Polastre, J., Hill, J., Culler, D.: Versatile low power media access for wireless sensor networks. In: SenSys 2004: Proceedings of the 2nd international conference on Embedded networked sensor systems, pp. 95–107. ACM Press, New York (2004)
Kaelbling, L.P., Littman, M.L., Moore, A.P.: Reinforcement Learning: A Survey. Journal of Artificial Intelligence Research 4, 237–285 (1996)
Eagle, N., Pentland, A.S.: Reality mining: sensing complex social systems. Personal Ubiquitous Comput 10(4), 255–268 (2006)
Dyo, V., Mascolo, C.: A Node Discovery Service for Partially Mobile Sensor Networks. In: MIDSENS 2007: Proceedings of IEEE International Workshop on Sensor Network Middleware, IEEE Computer Society, Los Alamitos (2007)
Su, J., Chan, K.K.W., Miklas, A.G., Po, K., Akhavan, A., Saroiu, S., de Lara, E., Goel, A.: A preliminary investigation of worm infections in a Bluetooth environment. In: WORM 2006: Proceedings of the 4th ACM workshop on Recurring malcode, pp. 9–16. ACM, New York (2006)
Author information
Authors and Affiliations
Editor information
Rights and permissions
Copyright information
© 2008 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Dyo, V., Mascolo, C. (2008). Efficient Node Discovery in Mobile Wireless Sensor Networks. In: Nikoletseas, S.E., Chlebus, B.S., Johnson, D.B., Krishnamachari, B. (eds) Distributed Computing in Sensor Systems. DCOSS 2008. Lecture Notes in Computer Science, vol 5067. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-69170-9_33
Download citation
DOI: https://doi.org/10.1007/978-3-540-69170-9_33
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-69169-3
Online ISBN: 978-3-540-69170-9
eBook Packages: Computer ScienceComputer Science (R0)