Immune System Based Distributed Node and Rate Selection in Wireless Sensor Networks

Wireless sensor networks (WSNs) are event-based systems that rely on the collective effort of dense deployed sensor nodes. Due to the dense deployment, since sensor observations are spatially correlated with respect to spatial location of sensor nodes, it may not be necessary for every sensor node to transmit its data. Therefore, due to the resource constraints of sensor nodes it is needed to select the minimum number of sensor nodes to transmit the data to the sink. Furthermore, to achieve the application-specific distortion bound at the sink it is also imperative to select the appropriate reporting frequency of sensor nodes to achieve the minimum energy consumption. In order to address these needs, we propose the new distributed node and rate selection (DNRS) method which is based on the principles of natural immune system. Based on the B-cell stimulation in immune system, DNRS selects the most appropriate sensor nodes that send samples of the observed event, are referred to as designated nodes. The aim of the designated node selection is to meet the event estimation distortion constraint at the sink node with the minimum number of sensor nodes. DNRS enables each sensor node to distributively decide whether it is a designated node or not. In addition, to exploit the temporal correlation in the event data DNRS regulates the reporting frequency rate of each sensor node while meeting the application-specific data bound at the sink. Based on the immune network principles, DNRS distributively selects the appropriate reporting frequencies of sensor nodes according to the congestion in the forward path and the event estimation distortion periodically calculated at the sink by adaptive LMS filter. Performance evaluation shows that DNRS provides the minimum number of designated nodes to reliably detect the event properties and it regulates the reporting frequency of designated nodes to exploit the temporal correlation in the event data whereby it provides the significant energy saving

[1]  N. K. Jerne,et al.  Idiotypic Networks and Other Preconceived Ideas , 1984, Immunological reviews.

[2]  Baltasar Beferull-Lozano,et al.  On network correlated data gathering , 2004, IEEE INFOCOM 2004.

[3]  Kannan Ramchandran,et al.  Distributed source coding using syndromes (DISCUS): design and construction , 2003, IEEE Trans. Inf. Theory.

[4]  Jon Timmis,et al.  Data analysis using artificial immune systems, cluster analysis and Kohonen networks: some comparisons , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[5]  Alan S. Perelson,et al.  The immune system, adaptation, and machine learning , 1986 .

[6]  Gaetano Borriello,et al.  Location Systems for Ubiquitous Computing , 2001, Computer.

[7]  Alan S. Perelson,et al.  Self-nonself discrimination in a computer , 1994, Proceedings of 1994 IEEE Computer Society Symposium on Research in Security and Privacy.

[8]  C. Guestrin,et al.  Distributed regression: an efficient framework for modeling sensor network data , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[9]  Kay Römer,et al.  An Adaptive Strategy for Quality-Based Data Reduction in Wireless Sensor Networks , 2006 .

[10]  Kannan Ramchandran,et al.  Distributed compression for sensor networks , 2001, Proceedings 2001 International Conference on Image Processing (Cat. No.01CH37205).

[11]  J. Berger,et al.  Objective Bayesian Analysis of Spatially Correlated Data , 2001 .

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

[13]  Özgür B. Akan,et al.  Spatio-temporal Characteristics of Point and Field Sources in Wireless Sensor Networks , 2006, 2006 IEEE International Conference on Communications.

[14]  Roger Wattenhofer,et al.  Gathering correlated data in sensor networks , 2004, DIALM-POMC '04.

[15]  I.F. Akyildiz,et al.  Spatial correlation-based collaborative medium access control in wireless sensor networks , 2006, IEEE/ACM Transactions on Networking.

[16]  J Timmis,et al.  An artificial immune system for data analysis. , 2000, Bio Systems.

[17]  Baltasar Beferull-Lozano,et al.  Power-efficient sensor placement and transmission structure for data gathering under distortion constraints , 2006, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

[18]  Martin Vetterli,et al.  On the optimal density for real-time data gathering of spatio-temporal processes in sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[19]  Kwee-Bo Sim,et al.  Realization of cooperative strategies and swarm behavior in distributed autonomous robotic systems using artificial immune system , 1999, IEEE SMC'99 Conference Proceedings. 1999 IEEE International Conference on Systems, Man, and Cybernetics (Cat. No.99CH37028).

[20]  Özgür B. Akan,et al.  Spatio-temporal correlation: theory and applications for wireless sensor networks , 2004, Comput. Networks.

[21]  Vishnu Navda,et al.  Efficient gathering of correlated data in sensor networks , 2008, TOSN.