Optimal Scheduling in Sensor Networks Using Swarm Intelligence

This paper presents a swarm intelligence based approach for optimal scheduling in sensor networks. Sensors are characterized by their transaction times and interdependencies. In the presence of interdependencies the problem of optimal scheduling to minimize the overall transaction/response time is modeled as a graph partitioning problem. Graph partitioning problem is a well known NPcomplete problem. A methodology and cost function is developed to solve the problem. A swarm intelligence based algorithm, particle swarm optimization (PSO) is used to solve the problem. The PSO algorithm solves the problem and emerges with a optimal schedule.

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