Synchronization-based data gathering scheme using chaotic pulse-coupled neural networks in wireless sensor networks

Wireless sensor networks (WSNs) have attracted significant interests of many researchers because they have great potential as a means of obtaining information of various environments remotely. WSNs have their wide range of applications, such as natural environmental monitoring in forest regions and environmental control in office buildings. In WSNs, hundreds or thousands of micro-sensor nodes with such resource limitation as battery capacity, memory, CPU, and communication capacity are deployed without control in a region and used to monitor and gather sensor information of environments. Therefore, scalable and efficient network control and/or data gathering scheme for saving energy consumption of each sensor node is needed to prolong WSN lifetime. In this paper, assuming that sensor nodes synchronize to intermittently communicate with each other only when they are active for realizing the long-term employment of WSNs, we propose a new synchronization scheme for gathering sensor information using chaotic pulse-coupled neural networks (CPCNN). We evaluate the proposed scheme using computer simulation and discuss its development potential. In simulation experiment, the proposed scheme is compared with previous synchronization scheme based on a pulse-coupled oscillator model to verify its effectiveness.

[1]  J. Rinzel,et al.  INTEGRATE-AND-FIRE MODELS OF NERVE MEMBRANE RESPONSE TO OSCILLATORY INPUT. , 1981 .

[2]  J J Hopfield,et al.  Rapid local synchronization of action potentials: toward computation with coupled integrate-and-fire neurons. , 1995, Proceedings of the National Academy of Sciences of the United States of America.

[3]  Tetsuya Asai,et al.  Frequency- and temporal-domain neural competition in analog integrate-and-fire neurochips , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[4]  Toshimichi Saito,et al.  Basic dynamics from a pulse-coupled network of autonomous integrate-and-fire chaotic circuits , 2002, IEEE Trans. Neural Networks.

[5]  Chen-Khong Tham,et al.  A novel routing protocol using mobile agents and reactive route discovery for ad hoc wireless networks , 2002, Proceedings 10th IEEE International Conference on Networks (ICON 2002). Towards Network Superiority (Cat. No.02EX588).

[6]  Nabil H. Farhat,et al.  The Bifurcating Neuron Network 2: an analog associative memory , 2002, Neural Networks.

[7]  Toshimichi Saito,et al.  Grouping synchronization in a pulse-coupled network of chaotic spiking oscillators , 2004, IEEE Transactions on Neural Networks.

[8]  Ruben Budelli,et al.  Limit cycles of a bineuronal network model , 1992 .

[9]  Naoki Wakamiya,et al.  Scalable and Efficient Ant-Based Routing Algorithm for Ad-Hoc Networks , 2006, IEICE Trans. Commun..

[10]  S. Strogatz,et al.  Synchronization of pulse-coupled biological oscillators , 1990 .

[11]  Naoki Wakamiya,et al.  Synchronization-Based Data Gathering Scheme for Sensor Networks , 2005, IEICE Trans. Commun..

[12]  Fred L. Templin,et al.  Topology Dissemination Based on Reverse-Path Forwarding (TBRPF) , 2004, RFC.

[13]  DeLiang Wang,et al.  Synchrony and Desynchrony in Integrate-and-Fire Oscillators , 1998, Neural Computation.

[14]  Eugene M. Izhikevich,et al.  Weakly pulse-coupled oscillators, FM interactions, synchronization, and oscillatory associative memory , 1999, IEEE Trans. Neural Networks.

[15]  Nabil H. Farhat,et al.  The bifurcating neuron network 3 , 2005, Proceedings. 2005 IEEE International Joint Conference on Neural Networks, 2005..

[16]  Konstantinos Kalpakis,et al.  An efficient clustering-based heuristic for data gathering and aggregation in sensor networks , 2003, 2003 IEEE Wireless Communications and Networking, 2003. WCNC 2003..

[17]  J. Broach,et al.  The dynamic source routing protocol for mobile ad-hoc networks , 1998 .

[18]  Luca Maria Gambardella,et al.  AntHocNet: An Ant-Based Hybrid Routing Algorithm for Mobile Ad Hoc Networks , 2004, PPSN.

[19]  Philippe Jacquet,et al.  Optimized Link State Routing Protocol (OLSR) , 2003, RFC.

[20]  Charles E. Perkins,et al.  Ad-hoc on-demand distance vector routing , 1999, Proceedings WMCSA'99. Second IEEE Workshop on Mobile Computing Systems and Applications.

[21]  Devika Subramanian,et al.  Ants and Reinforcement Learning: A Case Study in Routing in Dynamic Networks , 1997, IJCAI.

[22]  Wendi Heinzelman,et al.  Proceedings of the 33rd Hawaii International Conference on System Sciences- 2000 Energy-Efficient Communication Protocol for Wireless Microsensor Networks , 2022 .

[23]  Toshimichi Saito,et al.  Basic Dynamics from an Integrate-and-Fire Chaotic Circuit with a Periodic Input , 2001 .