A survey on the utilization of Ant Colony Optimization (ACO) algorithm in WSN

Gajalakshmi, G.; Srikanth, G. Umarani

doi:10.1109/icices.2016.7518949

Cited by 15 publications

(6 citation statements)

References 6 publications

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“…The designed protocol is simulated in Network Simulator (NS-2) and output results are compared with our previous work ESNA [16], GAECH [14], and NETCRP [11]. The parameters considered for simulations are given below.…”

Section: Resultsmentioning

confidence: 99%

“…Shortest path to the food was achieved by forming clusters. The arbitrary shaped clusters and problem of outliers from the monitoring area are addressed in ESNA-ACO [16]. PSO [17] is famous among many researchers.…”

Section: Literature Surveymentioning

confidence: 99%

“…If the network concentration is scarce all the above schemes are avoided to save unwanted energy consumption. The simple ACO(implemented in ESNA) [16] is considered and the shortest path is formed by high concentration pheromone value of ANTS.…”

Section: Alternate Path Building By Acomentioning

confidence: 99%

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QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

Halkai¹,

Terdal²

2021

IJIES

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A sensor network operates wirelessly and transmits detected information to the base station. The sensor is a small sized device, it is battery-powered with some electrical components, and the protocols should operate efficiently in such least resource availability. Here, we propose a novel improved framework in large scale applications where the huge numbers of sensors are distributed over an area. The designed protocol will address the issues that arise during its communication and give a consistent seamless communication system. The process of reasoning and learning in cognitive sensors guarantees data delivery in the network. Localization in Scarce and dense sensor networks is achieved by efficient cluster head election and route selection which are indeed based on cognition, improved Particle Swarm Optimization, and improved Ant Colony Optimization algorithms. Factors such as mobility, use of sensor buffer, power management, and defects in channels have been identified and solutions are presented in this research to build an accurate path based on the network context. The achieved results in extensive simulation prove that the proposed scheme outperforms ESNA, NETCRP, and GAECH algorithms in terms of Delay, Network lifetime, Energy consumption.

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Section: Resultsmentioning

confidence: 99%

Section: Literature Surveymentioning

confidence: 99%

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QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

Halkai¹,

Terdal²

2021

IJIES

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“…After all ants achieve the goal to reach the destination, each individual entity ant compares to a path of routing. For optimum route determination, a fitness function is provided where the fitness value can be computed for the path as follows: (8) where the residual energy level of a sensor node n i for instance is . refers to the length of the route for m th ant and k th iteration.…”

Section: Fitness Functionmentioning

confidence: 99%

“…The primary aim of appropriateness of ACO in WSNs is the comparative attributes of both where ACO can essentially perform in unique topology, neighbourhood tasks, hubs with bounds, multiway and connection excellence [7]. ACO is motivated from the conduct of genuine ants searching for nourishment and this attribute of ant colonies is exploited in ACO algorithm [8]. ACO is a meta-heuristic method and can solve for instance discrete optimization problems.…”

Section: Introductionmentioning

confidence: 99%

A novel bio-inspired routing algorithm based on ACO for WSNs

2019

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The methods to achieve efficient routing in energy constrained wireless sensor networks (WSNs) is a fundamental issue in networking research. A novel approach of ant colony optimization (ACO) algorithm for discovering the optimum route for information transmission in the WSNs is proposed here for optimization and enhancement. The issue of path selection to reach the nodes and vital correspondence parameters, for example, the versatility of nodes, their constrained vitality, the node residual energy and route length are considered since the communications parameters and imperatives must be taken into account by the imperative systems that mediate in the correspondence procedure, and the focal points of the subterranean insect framework have been utilized furthermore. Utilizing the novel technique and considering both the node mobility and the existing energy of the nodes, an optimal route and best cost from the originating node to the target node can be detected. The proposed algorithm has been simulated and verified using MATLAB and the simulation results demonstrate that new ACO based algorithm achieved improved performance, about 30% improvement compared with the traditional ACO algorithm, and faster convergence to determine the best cost route, and recorded an improvement in the energy consumption of the nodes per transmission.

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Energy‐aware intelligent hybrid routing protocol for wireless sensor networks

Ibrahim

Ahmed

2021

Concurrency and Computation

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Designing a wireless sensor network (WSN) energy-aware routing protocol is a thought-provoking mission. Thus, this article presents an energy-saving routing protocol for WSNs. The proposed protocol considers the energy level of sensor nodes and the distance to the base station to optimally determine the best route. It also takes advantage of the inherent complementarity of clustering techniques. The proposed routing scheme also exploits data aggregation to improve energy utilization and reduce communication costs. To choose the flawless route between a source node and the base station, ant colony optimization and A* algorithms are integrated. Finally, the performance of the proposed routing technique is compared with those of other existing up-to-date protocols. The results show that the proposed routing protocol outperforms them in terms of total energy consumption, network lifetime, stability period, and goodput.

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A survey on the utilization of Ant Colony Optimization (ACO) algorithm in WSN

Cited by 15 publications

References 6 publications

QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

QALPA- An Efficient QoS Aware Hybrid Localization Model Using Particle Swarm Optimization and Ant Colony Optimization for Cognitive Wireless Sensor Networks

A novel bio-inspired routing algorithm based on ACO for WSNs

Energy‐aware intelligent hybrid routing protocol for wireless sensor networks

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