Çok Atlamalı İletim İçeren Bir Telsiz Duyarga Ağında Hedef Takibi için Uyarlı Duyarga Nicemleme Eşiklerinin Çok lı Eniyileme ile Belirlenmesi

Bu çalışmada, enerji yayan bir hedefi takip etmekle görevli bir telsiz duyarga ağı (TDA) modellenmektedir. Duyargaların hedeften aldıkları ölçümler, ikili nicemlendikten sonra son istatiksel çıkarım için tümleştirme merkezine (TM'ye) tek ya da iki atlamalı olarak iletilirler. Hedef takibinin her bir adımında, duyargaların yerel karar eşikleri eniyi ve uyarlanır olarak iki işlevli bir Çok-amaçlı Eniyileme Problemi (ÇEP) ile elde edilmektedir. Dikkate alınan ÇEP, hem hedef konumu kestirim hatasını en azaltmak için Fisher Bilgisini ençoklamakta, hem de TDA'da harcanan toplam enerjiyi en azaltmaya çalışmaktadır. Benzetim sonuçlarımıza göre iki işlev arasındaki ödünleşme cephesi üzerinden ile elde edilen duyarga karar eşikleri çözümü ile Fisher Bilgisini en çoklayan duyarga karar eşikleri çözümü benzer kestirim hatasını vermektedir. Öte yandan, ödünleşme cephesi üzerinden elde edilen duyarga karar eşikleri çözümü, Fisher Bilgisini en çoklayan duyarga karar eşikleri çözümüne göre TDA'da harcanan toplam enerjiyi önemli ölçüde azaltmıştır. Bununla birlikte duyargalar ve TM arası kanallarda yol kaybının yüksek olduğu durum altında tek atlamalı iletim yerine iki atlamalı iletim yapılması kestirim hatasından ödün vermeden TDA'da harcanan toplam enerjiyi daha da azaltmaktadır.

Evalulation of Adaptive Sensor Quantization Thresholds Using Multiobjective Optimization For Target Tracking in a Wireless Sensor Network Involving Multihop Transmissions

In this work, a wireless sensor network (WSN) whose task is to track a target emitting energy is considered. Received sensor measurements observed from the target are first binary quantized, and then transmitted to a fusion center over one or two hop links for the final statistical inference. At each time step of tracking, the sensor decision thresholds are obtained optimally and dynamically as a result of a Multiobjective Optimization Problem (MOP). The proposed MOP jointly maximizes the Fisher Information to decrease the estimation error in tracking and minimizes the total transmission energy consumption of the WSN. Simulation results show that the solution of sensor decision thresholds obtained from the Pareto-Optimal front between the two objectives, yields similar estimation performance with that of the solution of sensor decision thresholds obtained by maximizing the Fisher Information. On the other hand, the solution of sensor decision thresholds obtained from the Pareto-Optimal front significantly reduces the total energy consumption of WSN significantly as compared to the solution of sensor decision thresholds obtained by maximizing the Fisher Information. Furthermore, when the channels between sensors and the fusion center undergo high path loss, using two-hop transmission instead of single-hop further reduces the total energy consumption in the WSN without sacrificing from the estimation error.

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