Distributed computing paradigms for collaborative signal and information processing in sensor networks

In this paper, we report the development of an energy-efficient, high-performance distributed computing paradigm to carry out Collaborative Signal and Information Processing (CSIP) in sensor networks using mobile agents. In this paradigm, the processing code is moved to the sensor nodes through mobile agents, in contrast to the client/server-based computing, where local data are transferred to a processing center. Although the client/server paradigm has been widely used in distributed computing, the many advantages of the mobile agent paradigm make it more suitable for sensor networks. The paper first presents simulation models for both the client/server paradigm and the mobile agent paradigm. We use the execution time, energy and energy*delay as metrics to measure the performance. Several experiments are designed to show the effect of different parameters on the performance of the paradigms. Experimental results show that the mobile agent paradigm performs much better when the number of nodes is large while the client/server paradigm is advantageous when the number of nodes is small. Based on this observation, we then propose a cluster-based hybrid computing paradigm to combine the advantages of these two paradigms. There are two schemes in this paradigm and simulation results show that there is always one scheme which performs better than either the client/server or the mobile agent paradigms. Thus, the cluster-based hybrid computing provides an energy-efficient and high-performance solution to CSIP.

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