Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison

Zungeru, Adamu Murtala; Ang, Li-Minn; Seng, Kah Phooi

doi:10.1016/j.jnca.2012.03.004

Cited by 247 publications

(172 citation statements)

References 72 publications

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“…Swarm intelligence algorithms have been widely used in solving WSN issues (Kulkarni and Venayagamoorthy, 2011;Zungeru et al, 2012;Saleem et al, 2011). ACO is one of the swarm intelligence approaches that were inspired by the foraging behavior of ants that work together to find the shortest path between nest and food source (Singh et al, 2010).…”

Section: Ant Colony Optimization Approach In Wireless Sensor Networkmentioning

confidence: 99%

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Nasir¹,

Ku-Mahamud²,

Kamioka³

2017

Journal of Computer Science

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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.

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Section: Ant Colony Optimization Approach In Wireless Sensor Networkmentioning

confidence: 99%

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Nasir¹,

Ku-Mahamud²,

Kamioka³

2017

Journal of Computer Science

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“…Zungeru et al explored existing routing protocols in WSNs and categorized them based on path establishment, energy efficiency, network structure, and computational complexity [13]. Further, SI-based and classical routing protocols are compared.…”

Section: Related Workmentioning

confidence: 99%

“…The velocity and position of the selected particle is updated using Eq. (13). Then the fitness value is calculated for the new position and is compared with its previous calculated position.…”

Section: Proposed Pso-eelbmentioning

confidence: 99%

An intelligent PSO-based energy efficient load balancing multipath technique in wireless sensor networks

Randhawa¹,

Jain²

2017

Turk J Elec Eng & Comp Sci

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Abstract:To provide a reliable and efficient service, load balancing plays an important role in wireless sensor networks (WSNs). There is a need to maximize the network lifetime for WSNs applications with periodic generation of data. Due to the relationship between energy consumption and network sensor node lifetime, energy consumption in a network should be minimized and balanced in order to increase network lifetime. Energy-efficient load-balancing techniques are needed to solve this problem. In this paper, a particle swarm optimization (PSO)-based energy-efficient load-balancing technique is proposed, in which the required number of routing paths and energy consumption of different nodes and paths are calculated. Based on maximum residual energy, paths are selected and further PSO-based load balancing is performed among all the paths for data transfer at a particular point of time. The performance of the proposed technique is evaluated using real testbed and experimental results and shows that the proposed technique performs better than existing techniques in terms of network lifetime, energy consumption, throughput, number of alive nodes, number of data packets received, execution time, and convergence rate.

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“…Routing is defined as the way of finding the shortest path between two nodes. For many years ants have successfully applied routing to find the shortest paths between the food sources and their nest by means of a pheromone trail laid by the other Ants this is known Ant Colony Optimization (ACO) routing [2].…”

Section: Figure 1: Wireless Sensor Networkmentioning

confidence: 99%

Optimization of Ant based Cluster Head Election Algorithm in Wireless Sensor Networks

Kumar¹

2016

IJCA

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Wireless Sensor Networks consist of inexpensive nodes that are positioned over an area to collect the useful information and send the valuable data to the base station for further processing. In wireless sensor networks, nodes have limited power and shorter lifetime. So, it is crucial to aggregate the data in Energy Efficient manner and optimize the lifetime of the network. To solve the issue, Ant Colony Optimization, a Swarm Intelligence based Routing Technique is used. The proposed system is a Routing Approach based on ACO Algorithm along with the Clustering approach in which Cluster Head is elected based on maximum value of energy and degree to maximize Energy Efficiency of the network and to increase the lifetime of the network. Performance obtained from the simulation results shows that the proposed approach provides an Optimized solution in terms of Enhanced Network Lifetime and Efficient Energy Utilization.

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Classical and swarm intelligence based routing protocols for wireless sensor networks: A survey and comparison

Cited by 247 publications

References 72 publications

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

Ant Colony Optimization Approaches in Wireless Sensor Network: Performance Evaluation

An intelligent PSO-based energy efficient load balancing multipath technique in wireless sensor networks

Optimization of Ant based Cluster Head Election Algorithm in Wireless Sensor Networks

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