Research Repository

Renewal theory sleep time optimisation for scheduling events in Wireless Sensor Networks

Udenze, Adrian and McDonald-Maier, Klaus (2007) Renewal theory sleep time optimisation for scheduling events in Wireless Sensor Networks. In: 2007 2nd NASA/ESA Conference on Adaptive Hardware and Systems, 2007-08-05 - 2007-08-08, Edinburgh.

Renewal Theory Sleep Time Accepted.pdf - Accepted Version

Download (485kB) | Preview


This paper addresses the problem of optimised decision making in scheduling non deterministic events for WSN nodes. Scheduling events for highly constrained WSN nodes with finite resources can significantly increase the lifetime of the network. Optimising the scheduling of events ensures that under any given constraint the network lifetime is maximised. The presented technique uses Renewal theory to formulate a stochastic decision making process. By observing network events, optimised decisions are made regarding node sleep times. This technique links the time a node spends in the sleep state to the rate of traffic throughput in the network making the process able to adapt to changes. The proposed technique also has the added advantage of using data available locally to a node thus minimising control overheads. It can be employed in both static and ad hoc networks, as well as for autonomous decision making in nodes that have to self configure. Finally, this policy driven technique exploits the heterogeneous nature of a typical WSN architecture by using less constrained nodes for formulating policies which can then be implemented in more constrained nodes. Theoretical and empirical results are presented.

Item Type: Conference or Workshop Item (Paper)
Additional Information: Published proceedings: Second NASA/ESA Conference on Adaptive Hardware and Systems (AHS 2007)
Divisions: Faculty of Science and Health
Faculty of Science and Health > Computer Science and Electronic Engineering, School of
SWORD Depositor: Elements
Depositing User: Elements
Date Deposited: 24 Jul 2020 09:07
Last Modified: 18 Aug 2022 10:41

Actions (login required)

View Item View Item