Performance Evaluation of Ant-Based Routing Protocols for Wireless Sensor Networks

High efficient routing is an important issue in the design of limited energy resource Wireless Sensor Networks (WSNs). Due to the characteristic of the environment at which the sensor node is to operate, coupled with severe resources; on-board energy, transmission power, processing capability, and storage limitations, prompt for careful resource management and new routing protocol so as to counteract the differences and challenges. To this end, we present an Improved Energy-Efficient Ant-Based Routing (IEEABR) Algorithm in wireless sensor networks. Compared to the state-of-the-art Ant-Based routing protocols; Basic Ant-Based Routing (BABR) Algorithm, Sensor-driven and Cost-aware ant routing (SC), Flooded Forward ant routing (FF), Flooded Piggybacked ant routing (FP), and Energy-Efficient Ant-Based Routing (EEABR), the proposed IEEABR approach has advantages in terms of reduced energy usage which can effectively balance the WSN node's power consumption, and high energy efficiency. The performance evaluations for the algorithms on a real application are conducted in a well known WSN MATLAB-based simulator (RMASE) using both static and dynamic scenario.

[1]  Stephen B. Wicker,et al.  Game theory in communications: motivation, explanation, and application to power control , 2001, GLOBECOM'01. IEEE Global Telecommunications Conference (Cat. No.01CH37270).

[2]  Mohamed F. Younis,et al.  A survey on routing protocols for wireless sensor networks , 2005, Ad Hoc Networks.

[3]  Kah Phooi Seng,et al.  Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison , 2012, J. Netw. Comput. Appl..

[4]  S. Wicker,et al.  Termite: ad-hoc networking with stigmergy , 2003, GLOBECOM '03. IEEE Global Telecommunications Conference (IEEE Cat. No.03CH37489).

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

[6]  Ying Zhang,et al.  Improvements on Ant Routing for Sensor Networks , 2004, ANTS Workshop.

[7]  Levente Buttyán,et al.  Enforcing service availability in mobile ad-hoc WANs , 2000, MobiHoc.

[8]  Mohamed F. Younis,et al.  An energy-aware QoS routing protocol for wireless sensor networks , 2003, 23rd International Conference on Distributed Computing Systems Workshops, 2003. Proceedings..

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

[10]  Barbara Webb,et al.  Swarm Intelligence: From Natural to Artificial Systems , 2002, Connect. Sci..

[11]  R. H. LtJTIOLD.  THE ANT , 2014 .

[12]  Muddassar Farooq,et al.  Swarm intelligence based routing protocol for wireless sensor networks: Survey and future directions , 2011, Inf. Sci..

[13]  Ke Li,et al.  Slime mold inspired routing protocols for wireless sensor networks , 2011, Swarm Intelligence.

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

[15]  Konstantinos Kalpakis,et al.  MAXIMUM LIFETIME DATA GATHERING AND AGGREGATION IN WIRELESS SENSOR NETWORKS , 2002 .

[16]  Ahmet Zengin,et al.  A survey on swarm intelligence based routing protocols in wireless sensor networks , 2010 .

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