Design and Implementation of a Distributed Scheduling Algorithm using Period Inflation for Sensor Networks.
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Date
2007-03-22
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Abstract
Wireless Sensor Networks (WSNs) are fast emerging as a new and ubiquitous networking arena which will enables many new applications and pervades many old ones. One of the motivations for the development of WSNs is their ability to be deployed in any environment in a comparatively ad-hoc manner. The most important challenge faced by WSNs is battery-limited lifetime of the network. Physically replacing batteries is infeasible in most real-life deployments of WSNs. It has been demonstrated both theoretically and practically that intelligent operation of WSN nodes can improve network lifetime. For example, turning off wireless transceivers at WSN nodes, minimizing idle listening, can increase battery lifetimes by large factors, especially in many passive data sensing applications where the sense-receive-transmit cycle of the sensors is periodic.
In particular, we focus on some previous work in which an adaptive scheduling algorithm was proposed for this purpose, under unpredictable but small clock drift (so called quasi-periodic traffic). While this approach can adapt effectively to unknown transmission periods and unknown changes in transmission periods, the fundamental problem remains: a few nodes close to the base station deplete their batteries sooner than the rest resulting in early network death. Further, this phenomenon reduces the effectiveness of the method even more when (a) the periods of the various nodes are very disparate, and (b) when nodes artificially reduce their periods to maintain end-to-end delay bounds. In this thesis, we advance a new technique called "period inflation", by which the nodes of a WSN can cooperatively create a schedule in which nodes close to the base station have higher periods. We investigate the performance of the inflated and non-inflated cases for scenarios where all nodes have similar periods as well as when some nodes have very disparate periods, and also under bounded delay conditions. Numerical results show that the new technique of period inflation performs better, as expected.
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period inflation, adaptive algorithm
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MS
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Computer Networking
