Event Based Clustering Localized Energy Efficient Ant Colony Optimization (EBC_LEE-ACO) for Performance Enhancement of Wireless Sensor Network

Abstract

The main challenge of a wireless sensor network (WSN) in disaster situations is to discover efficient routing, to improve quality of service (QoS) and to reduce energy consumption. Location awareness of nodes is also useful or even necessary. Without knowing the position of sensor nodes, collected data is insignificant. Ant colony optimization (ACO) is a unique form of optimization method, which is highly suitable for adaptive routing and guaranteed packet delivery. The primary drawbacks of ACO are data flooding, huge overhead of control messages and long convergence time. These drawbacks are overcome by considering location information of sensor nodes. An event-based clustering localized energy efficient ant colony optimization (EBC_LEE-ACO) algorithm is proposed to enhance the performance of WSN. The main focus of the proposed algorithm is to improve QoS and minimize the network energy consumption by cluster formation and selecting the optimal path based on the biological inspired routing-ACO and location information of nodes. In clustering, data is aggregated and sent to the sink (base station) through cluster head (CH) which reduces overheads. EBC_LEE-ACO is a scalable and energy efficient reactive routing algorithm which improves QoS, lifetime and minimizes energy consummation of WSN as compared to other routing algorithms like AODV, ACO, ACO using RSSI. The proposed algorithm reduces energy consumption by approximately 7%, in addition to improvement in throughput, packet delivery ratio and increase in packet drop which has been observed in comparison with other algorithms, i.e. autonomous localization based eligible energetic Path_with_Ant Colony optimization (ALEEP with ACO) of the network. Use of IEEE 802.11 standard in proposed work increased packet drop.

Increased Efficiency of the Artificial Bee Colony Algorithm Using the Pheromone Technique

“…1 @ < A If edge , ' is used by the ant 0 Otherwise (6) where L K(t) is the length of the K th ant's trip. Equation (6) shows that adding more pheromones to an edge gives a better ant trip, and edges with high pheromones are more likely to be visited in the next iteration. In AS, all ants add a certain quantity of pheromone on the visited edge.…”

“…After the onlooker has explored the food source, each bee will deposit the pheromone on the link using (6). Then, the pheromones on all edges are reduced to prevent stagnation.…”

“…ABC integrates grenade explosion and the Cauchy operator to avoid random searches and solve Dynamic Economic Emission Dispatch (DEED) problems [5]. In [6,7], methods were used for routing and improving the performance of Wireless Sensor Networks. The memory mechanism plays a crucial role in the guide of the search process by memorizing valuable information and exploring new search spaces.…”

R-SOR: Ranked Social-based Routing Protocol in Opportunistic Mobile Social Networks

“…Routing in mobile networks is a challenging issue [11], networks such as the Vehicular Ad-hoc Network (VANET) and the Wireless Sensor Network (WSN) require efficient routing protocols due to their dynamic topology [12,13]. Exploiting long-term social features in a disruptive and varied network environment for routing is a fruitful approach [14][15][16].…”

Artificial Bee Colony with Crossover Operations for Discrete Problems

“…`ABC integrating grenade explosion and Cauchy operator to avoid random search and solving the Dynamic Economic Emission Dispatch (DEED) problem was established in [2]. Authors in [3,4] used ABC for routing and performance enhancement of wireless sensor networks. Authors in [5] modified ABC to solve constrained optimization problems.…”