A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks

  • Authors:

  • Engin Masazade;Ramesh Rajagopalan;Pramod K. Varshney;Chilukuri K. Mohan;Gullu Kiziltas Sendur;Mehmet Keskinoz

  • Affiliations:

  • Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey;Department of Electrical and Computer Engineering, Florida State University, Panama City, FL;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Department of Electrical Engineering and Computer Science, Syracuse University, Syracuse, NY;Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey;Faculty of Engineering and Natural Sciences, Sabanci University, Istanbul, Turkey

  • Venue:
  • IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics
  • Year:
  • 2010

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Abstract

For distributed detection in a wireless sensor network, sensors arrive at decisions about a specific event that are then sent to a central fusion center that makes global inference about the event. For such systems, the determination of the decision thresholds for local sensors is an essential task. In this paper, we study the distributed detection problem and evaluate the sensor thresholds by formulating and solving a multiobjective optimization problem, where the objectives are to minimize the probability of error and the total energy consumption of the network. The problem is investigated and solved for two types of fusion schemes: 1) parallel decision fusion and 2) serial decision fusion. The Pareto optimal solutions are obtained using two different multiobjective optimization techniques. The normal boundary intersection (NBI) method converts the multiobjective problem into a number of single objective-constrained subproblems, where each subproblem can be solved with appropriate optimization methods and nondominating sorting genetic algorithm-II (NSGA-II), which is a multiobjective evolutionary algorithm. In our simulations, NBI yielded better and evenly distributed Pareto optimal solutions in a shorter time as compared with NSGA-II. The simulation results show that, instead of only minimizing the probability of error, multiobjective optimization provides a number of design alternatives, which achieve significant energy savings at the cost of slightly increasing the best achievable decision error probability. The simulation results also show that the parallel fusion model achieves better error probability, but the serial fusion model is more efficient in terms of energy consumption.