Reduction of Power using new ACO (Ant Colony Optimization) in Wireless Sensor Network

Wireless Sensor Networks is one of the most researching topic and rising area now days. Wireless sensor network (WSN) technology has provided the availability of small and low-cost sensor nodes with capability of sensing various types of physical and environmental conditions, data processing, and wireless communication. WSN have become popular due to its wide range applications. Power optimization is the main problem constraint in WSN and this limitation combined with a typical deployment of large number of nodes has added many challenges to the design and management of wireless sensor networks. The energy of battery is limited in Wireless sensor Networks. This paper propose an energy efficient routing algorithm inspired from nature scheme, its implementation and validations are also described in this paper. The new ACO based routing algorithm is used to find the minimum route of nodes in wireless sensor networking the basis of pheromone updating. The simulations results show that our method boasts undoubtedly a number of attractive features, including power consumption, throughput, and latency performance have been impro ved.

[1]  Marco Dorigo,et al.  AntNet: Distributed Stigmergetic Control for Communications Networks , 1998, J. Artif. Intell. Res..

[2]  Marco Dorigo,et al.  Optimization, Learning and Natural Algorithms , 1992 .

[3]  Athanassios Boulis,et al.  From Simulation to Real Deployments in WSN and Back , 2007, 2007 IEEE International Symposium on a World of Wireless, Mobile and Multimedia Networks.

[4]  Yi Shang,et al.  Data dissemination based on ant swarms for wireless sensor networks , 2006, CCNC 2006. 2006 3rd IEEE Consumer Communications and Networking Conference, 2006..

[5]  T. Stützle,et al.  A Review on the Ant Colony Optimization Metaheuristic: Basis, Models and New Trends , 2002 .

[6]  M. Ismail,et al.  Security topology in wireless sensor networks with routing optimisation , 2008, 2008 Fourth International Conference on Wireless Communication and Sensor Networks.

[7]  William Agassounon.  Distributed information retrieval and dissemination in swarm-based networks of mobile, autonomous agents , 2003, Proceedings of the 2003 IEEE Swarm Intelligence Symposium. SIS'03 (Cat. No.03EX706).

[8]  Fernando Boavida,et al.  An Energy-Efficient Ant-Based Routing Algorithm for Wireless Sensor Networks , 2006, ANTS Workshop.

[9]  Roberto Montemanni,et al.  Swarm approach for a connectivity problem in wireless networks , 2005, Proceedings 2005 IEEE Swarm Intelligence Symposium, 2005. SIS 2005..

[10]  Ying-Tung Hsiao,et al.  Computer network load-balancing and routing by ant colony optimization , 2004, Proceedings. 2004 12th IEEE International Conference on Networks (ICON 2004) (IEEE Cat. No.04EX955).

[11]  K. Yamasaki,et al.  Design of energy-efficient wireless sensor networks with censoring, on-off, and censoring and on-off sensors based on mutual information , 2005, 2005 IEEE 61st Vehicular Technology Conference.

[12]  Wu Ye,et al.  A New Dynamic Routing Algorithm Based on Minimum Interference in MPLS Networks , 2008, 2008 4th International Conference on Wireless Communications, Networking and Mobile Computing.

[13]  Manuel López-Ibáñez,et al.  Ant colony optimization , 2010, GECCO '10.