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

Öz

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 hop transmission.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

Anahtar Kelimeler

• Wireless sensor networks, Target tracking, Multiobjective optimization problem, Quantizer design, Single and two-hop transmission

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

Öz

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

Anahtar Kelimeler

• Telsiz duyarga ağları, Hedef takibi, Çok amaçlı eniyileme problemi, Nicemleme tasarımı, Tekli ve ikili atlamalı iletim

Kaynakça

1. Chong CY, Kumar SP. Sensor networks: evolution, opportunities and challenges. Proceedings of the IEEE, Vol.91, No.8, pp.1247-1256, 2003.
2. Joshi S, Boyd S. “Sensor selection via convex optimization,” IEEE Transactions on Signal Processing, vol. 57, no. 2, pp. 451 – 462, Feb. 2009.
3. Masazade E, Niu R, Varshney PK. “Dynamic bit allocation for object tracking
4. networks,” IEEE Transactions on Signal Processing, vol. 60, no. 10, pp. 5048–5063, 2012.
5. sensor [4] Niu R, Varshney PK. “Target location estimation in sensor networks with quantized data,” IEEE Transactions on Signal Processing, vol. 54, no. 12, pp. 4519–4528, Dec. 2006.
6. Ozdemir O, Niu R, Varshney PK. “Adaptive local quantizer design for tracking in a wireless sensor network,” in 42nd Asilomar Conference on Signals, Systems and Computers, Oct 2008, pp. 1202– 1206.
7. Vemula M, Bugallo M, Djuric P. “Particle filtering-based target tracking in binary sensor networks using adaptive thresholds,” in 2nd IEEE International Workshop on Computational Advances in Multi- Sensor Adaptive Processing, 2007., Dec 2007, pp. 17–20.
8. Liu S, Masazade E, Shen X, Varshney PK. “Adaptive non- myopic quantizer design for target tracking

9. networks,” in 2013 Asilomar Conference on Signals, Systems and Computers, Nov 2013, pp.1085– 1089. sensor [8] Kose A, Masazade E.
10. Optimization Multiobjective
11. Approach for Adaptive Binary Quantizer Design for Target Tracking in Wireless Sensor Networks", Proc. IEEE 2015 International
12. Multisensor Fusion and Integration for Intelligent Systems, San Diego, USA, September 2015.
13. on [9] Mukherjee K, Ray A, Wettergren T, Gupta S, Phoha S. “Realtime adaptation of decision thresholds in sensor networks for detection of moving targets,” Automatica, vol. 47, no. 1, pp. 185–191, 2011.
14. Min R, Chandrakasan AP. “Top five myths consumption communication”,
15. Comp. and Comm. Rev., vol. 6, no. 4, pp. 65-67, 2002.
16. energy wireless Mobile ACM
17. Yang X, Niu R, Masazade E, Varshney PK. “Channel-Aware Tracking in Multi-Hop Wireless Sensor Networks with Quantized Measurements” IEEE Transactions on Aerospace and Electronic Systems, 2353 – 2368, October 2013.
18. Aeron S, Saligrama V, Castanon DA. “Energy Efficient Policies for Distributed Target Tracking in Multihop Sensor Networks” 45th IEEE Conference on Decision and Control, 380 – 385, Dec 2006.
19. Huang Y, Hua Y. “Energy Planning for Progressive Estimation in Multihop Sensor Networks” IEEE Transactions on Signal Processing, 4052 – 4065, October 2009.
20. Zhong LC, Rabaey JM, Wolisz A. “Does proper coding make single hop wireless sensor networks reality: the power consumption perspective”, Proc. IEEE Wireless Comm. and Networking Conf., vol. 2, pp. 664-669, Mar. 2005.
21. Cao, N., Masazade, E., and Varshney, P. K., “A Multiobjective Optimization
22. Selection Method for Target Tracking in Wireless Sensor Networks,” in Proc. International Conference on Information Fusion (FUSION), Istanbul, Turkey, July 2013.
23. Sensor [16] Masazade E, Rajagopalan R, Varshney P, Mohan C, Sendur G, Keskinoz M. “A multiobjective optimization approach to obtain decision thresholds for distributed detection in wireless sensor networks,” IEEE Transactions on Systems, Man, and Cybernetics, Part B: Cybernetics,vol. 40, no. 2, pp. 444–457, April 2010.
24. Heinzelman WR, Chandrakasan A, Balakrishnan H. “Energy efficient communication
25. wireless microsensor networks,” in Proc. 33rd Int. Conf. Syst. Sci., Jan. 2000, p. 8020.
26. for [18] Masazade, E., Fardad M., and P. Varshney, Extended Promoting
27. Filtering for Target Tracking in Wireless Sensor Networks," in IEEE Signal Processing Letters, vol. 19, no. 12, pp. 845-848, Dec. 2012. [19] Deb, K., Pratap, A., Agarwal, S., and Meyarivan, T., "A fast and elitist multiobjective genetic algorithm: NSGA-II," in IEEE Transactions on Evolutionary Computation, vol. 6, no. 2, pp. 182-197, Apr 2002.
28. Das I, Dennis J. “Normal-Boundary Intersection: A new method for generating the pareto surface in nonlinear optimization
29. Journal of Optimization, vol. 8, pp. 631–657, 1998.
30. SIAM [21] Niu R, Varshney PK. “Distributed detection and fusion in a large wireless sensor network of random size,” EURASIP Journal on Wireless Communications and Networking, vol. 2005, no. 4, pp. 462–472, 2005. [22] Arulampalam M, Maskell S, Gordon N, Clapp T. “A tutorial on particle filters for online nonlinear/non- Gaussian Bayesian tracking,” IEEE Transactions on Signal Processing, vol. 50, no. 2, pp. 174-188, Feb 2002
31. Jiang R, Chen B. "Fusion of censored decisions in wireless sensor Transactions
32. Communications, vol. 4, no. 6, pp. 2668-2673, Nov. 2005. in IEEE on
33. Wireless [24] Rigoni E, Poles S. “NBI and MOGA- II, two complementary algorithms for Multi-Objective optimizations,” in Practical Approaches to Multi- Objective
34. Dagstuhl Seminar Proceedings, J. Branke, K. Deb, K. Miettinen, and R. E. Steuer, Eds., no. 04461. Dagstuhl, Germany: Begegnungs
35. Forschungszentrum für Informatik (IBFI), Schloss Dagstuhl, Germany, 2005.
36. http://drops.dagstuhl.de/opus/vol ltexte/2005/272
37. ser. Internationales und [Online]. Available: