Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Abstract

Wireless Sensor Network (WSN) has been widely implemented in large sectors such as military, habitat, business, industrial, health and environment. WSN is part of a distributed system where elements such as routing, load balancing, energy efficiency, node localization, time synchronization, data aggregation and security need to be addressed to improve its efficiency, robustness, extendibility, applicability and reliability. Despite multiple approaches proposed to improve all these aspects, there is still room for improvement in order to enhance the capability of WSN in terms of routing and energy efficiency. Ant Colony Optimization (ACO) is one of the approaches used to extend WSN capabilities because its heuristic nature is very suitable with distributed and dynamic environments. This study covers the common WSN aspects and performance evaluation criteria in addition to the list of previous studies that have used ACO approaches in WSN.

Improved DV-Hop based on parallel and compact whale optimization algorithm for localization in wireless sensor networks

“…Where node localization is an important issue that directly affects the performance of WSN. In this issue, a robust algorithm can provide a highly accurate localization solution, and at the same time can avoid coverage holes and connection failures caused by random deployment of nodes [11]. Hence, how to design a locating algorithm to achieve a refined tradeoff between limited resources of nodes and high location accuracy of nodes has become one of the challenges in WSN.…”

Enhanced Ant Colony Optimization for Routing in WSNs An Energy Aware Approach

“…We suggested using ACO in WSN to be performed by sensors since it is a distributed algorithm [21]. We propose to utilize the information in the neighbour's table such as distance and the residual energy in the ACO probability equation along with pheromone value, which is also will be added to the table, to choose the next-hop neighbour and subsequently the best path to the destination, aiming to reduce the energy consumption with high throughput [22]. The ACO probability equation in [23]…”

Energy Efficient Ant Colony System for Packet Routing in Wireless Sensor Network

“…ACO has been successfully applied to solve many computational problems such as fault tolerance, sequential ordering, and data classification. Many researchers also applied ACO in optimizing the packet routing in WSN due to its adaptability on static, mobile, and dynamic WSN environments [7][8] [9]. Several examples of ACO application in WSN include Energy-Efficient Ant-Based Routing Algorithm (EEABR), Improved Energy-Efficient Ant-Based Routing, Fish Swarm Ant Colony Optimization (FSACO), and Enhanced Ant Colony System (EACS).…”