## Özet

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.

Orijinal dil | İngilizce |
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Makale numarası | 5200494 |

Sayfa (başlangıç-bitiş) | 444-457 |

Sayfa sayısı | 14 |

Dergi | IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics |

Hacim | 40 |

Basın numarası | 2 |

DOI'lar | |

Yayın durumu | Yayınlandı - Nis 2010 |

Harici olarak yayınlandı | Evet |

### Finansman

Manuscript received September 15, 2008; revised February 15, 2009 and April 29, 2009. First published August 11, 2009; current version published March 17, 2010. The work of E. Masazade was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) under the Research Abroad Support Scheme. The work of M. Keskinoz was supported by TUBITAK under Grant 105E161. The work of R. Rajagopalan, P. K. Varshney, and C. K. Mohan was supported in part by the Air Force Office of Scientific Research under Grant FA-9550-06-1-0277. This paper was presented in part at the Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, October 26–29, 2008. This paper was recommended by Associate Editor S. X. Yang.

Finansörler | Finansör numarası |
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TUBITAK | 105E161 |

Air Force Office of Scientific Research | FA-9550-06-1-0277 |

Türkiye Bilimsel ve Teknolojik Araştirma Kurumu |