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

Abstract — In sensor networks, collaborative processing between multiple sensor nodes is essential in order to complement for each other’s sensing capability, tolerate faults, and provide reliable information. The client/server-based paradigm is typical for distributed processing. However, it is not the most efficient in the context of sensor networks. In this paper, we present a mobileagent-based paradigm to carry out collaborative processing, where instead of each sensor node sending local information to a processing center, as is typical in the client/server-based computing, the processing code is moved to the sensor nodes through mobile agents. This approach has great potential in providing energy-efficient and scalable collaborative processing with low latency. We design two metrics (execution time and energy consumption) and use simulation tools to quantitatively measure the performance of different computing models in collaborative processing. 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 result, we develop a cluster-based hybrid computing paradigm to combine the advantages of both paradigms. We analyze two different scenarios in hybrid computing and simulation results show that there is always one scenario that performs better than either the client/server- or mobile-agent-based paradigm