Energy Efficiency Performance Improvements for Ant-Based Routing Algorithm in Wireless Sensor Networks

Abstract

The main problem for event gathering in wireless sensor networks (WSNs) is the restricted communication range for each node. Due to the restricted communication range and high network density, event forwarding in WSNs is very challenging and requires multihop data forwarding. Currently, the energy-efficient ant based routing (EEABR) algorithm, based on the ant colony optimization (ACO) metaheuristic, is one of the state-of-the-art energy-aware routing protocols. In this paper, we propose three improvements to the EEABR algorithm to further improve its energy efficiency. The improvements to the original EEABR are based on the following: (1) a new scheme to intelligently initialize the routing tables giving priority to neighboring nodes that simultaneously could be the destination, (2) intelligent update of routing tables in case of a node or link failure, and (3) reducing the flooding ability of ants for congestion control. The energy efficiency improvements are significant particularly for dynamic routing environments. Experimental results using the RMASE simulation environment show that the proposed method increases the energy efficiency by up to 9% and 64% in converge-cast and target-tracking scenarios, respectively, over the original EEABR without incurring a significant increase in complexity. The method is also compared and found to also outperform other swarm-based routing protocols such as sensor-driven and cost-aware ant routing (SC) and Beesensor.

Prolonging the Lifetime of the Wireless Sensor Network Based on Blending of Genetic Algorithm and Ant Colony Optimization

“…The proposed algorithm was simulated using MATLAB R2009b and compared with Genetic Algorithm Based Energy Efficient Clusters (GABEEC) [5] and Energy Efficiency Performance Improvements for Ant-Based Routing Algorithm (EEABR) [12]. Figure 4 and Figure 5 shows the simulation parameters along with their values.…”

“…The BS is placed in the location (120, 50) away from the sensor field. We have compared our results with GABEEC [5] and EEABR [12] protocols. Energy is one of the major issues in wireless sensor network.…”

Concepts And Evolution Of Research In The Field Of Wireless Sensor Networks

“…Moreover, bio-inspired solutions are progressively applied in WSNs [50,51]. Swarms intelligence, artificial immune system, genetic algorithm and other nature-inspired phenomenon in which self-organization and collective intelligence can emerge, are increasingly used in the fields of sensor networks in order to optimize the whole management of WSNs [56,57,58].…”

Implementation of Bio Inspired Algorithm in Identification of Best Route via Ant Colony Optimization Energy Level and Throughput with Encryption

“…Designing such QoS based routing protocol to meet the reliability and delay guarantee of critical events while preserving the energy efficiency is a challenging task.In paper[6] presents a proactive routing method that is aware of the node's destruction threat and adapts the routes accordingly, before node failure results in broken routes, delay and power consuming route re-discovery. The performance of the presented routing scheme is evaluated and compared to OLSR based routing in the same scenario.In paper [7] Smart sensor nodes are low power devices equipped with one or more sensors, a processor, memory, a power supply, a radio, and an actuator.1 A variety of mechanical, thermal, biological, chemical, optical, and magnetic sensors may be attached to the sensor node to measure properties of the environment.In paper [8] proposes an enhanced real time with load distribution (ERTLD) routing protocol for MWSN which is based on our previous routing protocol RTLD. ERTLD utilized corona mechanism and optimal forwarding metrics to forward the data packet in MWSN.…”