Sleep scheduling and lifetime maximization in sensor networks: fundamental limits and optimal solutions

Energy efficiency is a very critical consideration in the design of low cost sensor networks, which typically have fairly low node battery life. This raises the need for providing periodic sleep cycles for the radios in the sensor nodes. Keeping sensors in sleep state also implies that node to sink communication incurs certain delays and there exists a threshold on the duty cycling for the communication delay to be bounded, giving rise to an upperbound on the lifetime of the network i.e., the time until at least one node in the network is able to communicate its sensed data to the sink. This paper aims at establishing tight analytical bounds on the sleeping probabilities of nodes and on the achievable lifetime of wireless sensor networks in a very generic setting. Bounds on the sleeping probability need to be satisfied for proper network functionality. Further, an energy efficient deployment scheme is suggested wherein the battery power depletion is fairly uniformly deployed throughout the network. This scheme makes use of the availability of low power auxiliary channel listening radio. With this scheme, we shown that an improvement in lifetime by a factor of O(radicn/log n) over uniform distribution of nodes is achievable, where n is the number of nodes in the network. We also show that the throughput capacity of the network is also improved by the same factor. We show also that the maximum lifetime of the network is bounded above by O(n3/2/radiclog n). Further, the accuracy of our analysis is verified by the simulation results presented

[1]  Ian F. Akyildiz,et al.  Sensor Networks , 2002, Encyclopedia of GIS.

[2]  Deborah Estrin,et al.  An energy-efficient MAC protocol for wireless sensor networks , 2002, Proceedings.Twenty-First Annual Joint Conference of the IEEE Computer and Communications Societies.

[3]  J. Dall,et al.  Random geometric graphs. , 2002, Physical review. E, Statistical, nonlinear, and soft matter physics.

[4]  Panganamala Ramana Kumar,et al.  Maximizing the functional lifetime of sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[5]  Panganamala Ramana Kumar,et al.  RHEINISCH-WESTFÄLISCHE TECHNISCHE HOCHSCHULE AACHEN , 2001 .

[6]  Deborah Estrin,et al.  Medium access control with coordinated adaptive sleeping for wireless sensor networks , 2004, IEEE/ACM Transactions on Networking.

[7]  Koen Langendoen,et al.  An adaptive energy-efficient MAC protocol for wireless sensor networks , 2003, SenSys '03.

[8]  Brad Karp,et al.  GPSR: greedy perimeter stateless routing for wireless networks , 2000, MobiCom '00.

[9]  Nitin H. Vaidya,et al.  Minimizing energy consumption in sensor networks using a wakeup radio , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[10]  Bhaskar Krishnamachari,et al.  Delay efficient sleep scheduling in wireless sensor networks , 2005, Proceedings IEEE 24th Annual Joint Conference of the IEEE Computer and Communications Societies..

[11]  Rong Zheng,et al.  On-demand power management for ad hoc networks , 2005, Ad Hoc Networks.

[12]  Jan M. Rabaey,et al.  Low power distributed MAC for ad hoc sensor radio networks , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[13]  Yunghsiang Sam Han,et al.  Balanced-energy sleep scheduling scheme for high density cluster-based sensor networks , 2004, 2004 4th Workshop on Applications and Services in Wireless Networks, 2004. ASWN 2004..

[14]  Tarek F. Abdelzaher,et al.  Towards optimal sleep scheduling in sensor networks for rare-event detection , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..