Data detection and fusion in decentralized sensor networks

Decentralized sensor networks are collections of individual local sensors that observe a common phenomenon, quantize their observations, and send this quantized information to a central processor (fusion center) which then makes a global decision about the phenomenon. Most of the existing literature in this field consider only the data fusion aspect of this problem, i.e., the statistical hypothesis testing and optimal combining of the information obtained by the local sensors. In this thesis, we look at both the data detection and the data fusion aspects of the decentralized sensor networks. By data detection, we refer to the communication problem of transmitting quantized information from the local sensors to the fusion center through a multiple access channel. This work first analyzes the data fusion problem in decentralized sensor network when the sensor observations are corrupted by additive white gaussian noise. We optimize both local decision rules and fusion rule for this case. After that, we consider same problem when the observations are corrupted by correlated gaussian noise. We propose a novel parallel genetic algorithm which simultaneously optimizes both the local decision and fusion rules and show that our algorithm matches the results from prior work with considerably less computational cost. We also demonstrate that, irrespective of the fusion rule, the system can provide equivalent performance with an appropriate choice of local decision rules. The second part of this work analyzes the data detection problem in distributed sensor networks. We characterize this problem as a multiple input multiple output (MIMO) system problem, where the local sensors represent the multiple input nodes and the fusion center(s) represent the output nodes. This set up, where the number of input nodes (sensors) is greater than the number of output nodes (fusion center(s)), is known as an overloaded array in MIMO terminology. We use a genetic algorithm to solve this overloaded array problem.

[1]  H. Vincent Poor,et al.  Signal detection in the presence of weakly dependent noise--I: Optimum detection , 1982, IEEE Trans. Inf. Theory.

[2]  M. J. Gans,et al.  On Limits of Wireless Communications in a Fading Environment when Using Multiple Antennas , 1998, Wirel. Pers. Commun..

[3]  John N. Tsitsiklis,et al.  Decentralized detection by a large number of sensors , 1988, Math. Control. Signals Syst..

[4]  Krishna R. Pattipati,et al.  An algorithm for determining the decision thresholds in a distributed detection problem , 1989, Conference Proceedings., IEEE International Conference on Systems, Man and Cybernetics.

[5]  H. Vincent Poor,et al.  Memoryless discrete-time detection of a constant signal in m-dependent noise , 1979, IEEE Trans. Inf. Theory.

[6]  T. Willink,et al.  Genetic algorithm assisted array processing in overloaded systems , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[7]  Balasubramaniam Natarajan,et al.  Impact of Local Decision Rules in Distributed Sensor Networks , 2006, 2006 IEEE 63rd Vehicular Technology Conference.

[8]  José M. F. Moura,et al.  Fusion in sensor networks with communication constraints , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[9]  Rohit U. Nabar,et al.  Introduction to Space-Time Wireless Communications , 2003 .

[10]  E. Drakopoulos,et al.  Optimum multisensor fusion of correlated local decisions , 1991 .

[11]  N. Mansouri,et al.  Simple counting rule for optimal data fusion , 2003, Proceedings of 2003 IEEE Conference on Control Applications, 2003. CCA 2003..

[12]  H. Vincent Poor,et al.  Signal detection in the presence of weakly dependent noise--II: Robust detection , 1982, IEEE Trans. Inf. Theory.

[13]  Pramod K. Varshney,et al.  Distributed detection with multiple sensors I. Fundamentals , 1997, Proc. IEEE.

[14]  Venugopal V. Veeravalli,et al.  Decentralized detection in sensor networks , 2003, IEEE Trans. Signal Process..

[15]  K. Khalil.  On the Complexity of Decentralized Decision Making and Detection Problems , 2022 .

[16]  Ramanarayanan Viswanathan,et al.  Optimal distributed decision fusion , 1989 .

[17]  R. Cristi,et al.  Optimal data fusion strategies using multiple-sensor detection systems , 1997, Conference Record of the Thirty-First Asilomar Conference on Signals, Systems and Computers (Cat. No.97CB36136).

[18]  Emad K. Al-Hussaini,et al.  Decentralized CFAR signal detection , 1995, Signal Process..

[19]  Haifeng Wang,et al.  Equalized parallel interference cancellation for MIMO MC-CDMA downlink transmissions , 2003, 14th IEEE Proceedings on Personal, Indoor and Mobile Radio Communications, 2003. PIMRC 2003..

[20]  John H. Holland,et al.  Adaptation in Natural and Artificial Systems: An Introductory Analysis with Applications to Biology, Control, and Artificial Intelligence , 1992 .

[21]  Rick S. Blum,et al.  Optimum distributed detection of weak signals in dependent sensors , 1992, IEEE Trans. Inf. Theory.

[22]  Luc Vandendorpe,et al.  A fractionally spaced DF equalisation scheme performing joint detection for MC-CDMA transmissions , 1999, Gateway to 21st Century Communications Village. VTC 1999-Fall. IEEE VTS 50th Vehicular Technology Conference (Cat. No.99CH36324).

[23]  A. Goldsmith,et al.  Capacity of Rayleigh fading channels under different adaptive transmission and diversity-combining techniques , 1999, IEEE Transactions on Vehicular Technology.

[24]  Ian F. Akyildiz,et al.  Wireless sensor networks , 2007 .

[25]  Y. Chau,et al.  Distributed detection of weak signals from multiple sensors with correlated observations , 1988, Proceedings of the 27th IEEE Conference on Decision and Control.

[26]  Emre Telatar,et al.  Capacity of Multi-antenna Gaussian Channels , 1999, Eur. Trans. Telecommun..

[27]  S. Lyengar,et al.  Distributed sensor networks-introduction to the special section , 1991 .

[28]  B. Natarajan,et al.  Parallel genetic algorithm based optimal fusion in sensor networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[29]  Y.L.C. de Jong,et al.  Iterative trellis search detection for asynchronous MIMO systems , 2003, 2003 IEEE 58th Vehicular Technology Conference. VTC 2003-Fall (IEEE Cat. No.03CH37484).

[30]  Weixian Liu,et al.  Data fusion of multiradar system by using genetic algorithm , 2002 .

[31]  B. Natarajan,et al.  Analysis of the Performance of Decentralized Sensor Network with Correlated Observations , 2007, 2007 2nd International Symposium on Wireless Pervasive Computing.

[32]  Peter Willett,et al.  Parley as an approach to distributed detection , 1995 .

[33]  W. Gray,et al.  Optimal data fusion of correlated local decisions in multiple sensor detection systems , 1992 .

[34]  Ramanarayanan Viswanathan,et al.  Asymptotic performance of a distributed detection system in correlated Gaussian noise , 1992, IEEE Trans. Signal Process..

[35]  S.K. Jayaweera,et al.  MIMO capacity of an OFDM-based system under Ricean fading , 2004, 2004 IEEE 59th Vehicular Technology Conference. VTC 2004-Spring (IEEE Cat. No.04CH37514).

[36]  John N. Tsitsiklis,et al.  Some properties of optimal thresholds in decentralized detection , 1992, Other Conferences.

[37]  Matt Welsh,et al.  Integrating wireless sensor networks with the grid , 2004, IEEE Internet Computing.

[38]  Ming Xiao,et al.  A new energy-efficient MIMO-sensor network architecture M-SENMA , 2004, IEEE 60th Vehicular Technology Conference, 2004. VTC2004-Fall. 2004.

[39]  Tim C. W. Schenk,et al.  Implementation of a MIMO OFDM-based wireless LAN system , 2004, IEEE Transactions on Signal Processing.

[40]  José M. F. Moura,et al.  Detection in decentralized sensor networks , 2004, 2004 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[41]  Lang Tong,et al.  Sensor networks with mobile agents , 2003, IEEE Military Communications Conference, 2003. MILCOM 2003..

[42]  Edgar H. Callaway,et al.  Wireless Sensor Networks: Architectures and Protocols , 2003 .

[43]  Pravin Varaiya,et al.  Capacity of fading channels with channel side information , 1997, IEEE Trans. Inf. Theory.

[44]  Ian F. Akyildiz,et al.  Wireless sensor networks: a survey , 2002, Comput. Networks.

[45]  V. Aalo,et al.  On distributed detection with correlated sensors: two examples , 1989 .

[46]  H. Vincent Poor,et al.  An Introduction to Signal Detection and Estimation , 1994, Springer Texts in Electrical Engineering.

[47]  Pramod K. Varshney,et al.  Distributed Detection and Data Fusion , 1996 .