Swarm intelligence techniques are population-based stochastic methods used in combinatorial optimization problems in which the collective behaviour of relatively simple individuals arises from their local interactions with their environment to produce functional global patterns. Swarm technology makes it possible to solve complex problems that might be impossible to solve using traditional technologies and approaches. In this report, we focus on applying particle swarm optimization techniques to the problem of sensor selection. First, an overview of developments in swarm intelligence and its application in various fields is presented. Then the results of a recent paper on the application of swarm intelligence to sensor selection problems is reviewed. The motivation for selecting few sensors rather than using all includes minimizing the parameter estimation error and maximizing computational efficiency and energy consumption efficiency of the sensors.

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