An Ant Colony Optimization Based Approach for Minimum Cost Coverage on 3-D Grid in Wireless Sensor Networks

Qasim, Tehreem; Muhammad, Zia; Minhas, Qurratul-Ain; Bhatti, Naeem; Saleem, Khalid; Qasim, Tooba; Mahmood, Hasan

doi:10.1109/lcomm.2018.2819643

Cited by 47 publications

(40 citation statements)

References 7 publications

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“…However, there are many disadvantages such as too many nodes participating in the movement and too many redundant nodes. Therefore, Qasim et al [21] proposed a three-dimensional grid minimum cost deployment algorithm based on ant colony optimization. The algorithm modifies the standard ACO algorithm to achieve node sparse deployment, but it is easy to fall into local optimization in the late iteration, while the algorithm execution time is longer.…”

Section: Related Workmentioning

confidence: 99%

“…In the MCC-ACO, the movement probability of the k node from the grid point i to the grid point j is defined as shown in Equation (10) [20,21,25]:…”

Section: Design Of the Mcc-aco Algorithmmentioning

confidence: 99%

“…First, we simulated the node deployment optimization process for a three-dimensional underwater sensor network and analyzed the MCC-ACO algorithm when one instance was run on the node deployment platform. Subsequently, The NDSBAalgorithm [20] and the ACO-MCC3D algorithm [21] are typical ACO-based deployment algorithms, so these two algorithms were chosen for comparisons. The performance of the NDSBA algorithm, the ACO-MCC3D algorithm, and the MCC-ACO algorithm were compared using the following evaluation indicators: moving distance, number of node deployments, and node distribution density.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

“…The computing platform for the operating algorithm simulation was an Intel(R) Core(TM) i7-3770 CPU @ 3.40 GHz (4CPUs) processor, 8 GB RAM, and Windows 7 operating system. During the simulation, we referred to [20,21], and the parameters of this algorithm and the experimental parameters were set as: λ = 1, γ = 0.2, Ψ = 86, c = 5, α = 1, β = 5, r s = 15 m, r c = 15 m, ρ = 0.5, and N max = 50.…”

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

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A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

Wang

Li-liang

et al. 2019

Applied Sciences

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The self-deployment of nodes with non-uniform coverage in underwater acoustic sensor networks (UASNs) is challenging because it is difficult to access the three-dimensional underwater environment. The problem is further complicated if network connectivity needs to be considered. In order to solve the optimization problem of sensor network node deployment, we propose a maximum coverage and connectivity self-deployment algorithm that is based on ant colony optimization (MCC-ACO). We carry out the greedy strategy, improve the path selection probability and pheromone update system, and propose a self-deployment algorithm based on the foundation of standard ant colony optimization algorithms, so as to achieve energy-saving optimization coverage of target events. The main characteristic of the MCC-ACO algorithm is that it fully considers the effects of the changes in the event quantities and the random distribution of the nodes on the deployment effect of the nodes, ensures that every deployed node can be connected to the sink, and achieves the matching of node distribution density and event distribution. Therefore, the MCC-ACO algorithm has great practical value. A large number of comparative simulation experiments show that the algorithm can effectively realize the self-deployment problem of underwater sensor nodes. In addition, the paper also gives the impact of changes in the number of events in the network on the deployment effect.

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Section: Related Workmentioning

confidence: 99%

“…In the MCC-ACO, the movement probability of the k node from the grid point i to the grid point j is defined as shown in Equation (10) [20,21,25]:…”

Section: Design Of the Mcc-aco Algorithmmentioning

confidence: 99%

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

Section: Simulation and Performance Evaluationmentioning

confidence: 99%

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A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

Wang

Li-liang

et al. 2019

Applied Sciences

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“…The hot spot problems can be mitigated using special clustering and well-designed topology controls, but they cannot solve the issue fundamentally. Recent research on sink mobility technology provides a series of novel ideas to address the problem of hot spots and has achieved great results [12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29].…”

Section: Introductionmentioning

confidence: 99%

A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Gao

Wang

et al. 2019

Sensors

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Wireless Sensor Networks (WSNs) are usually troubled with constrained energy and complicated network topology which can be mitigated by introducing a mobile agent node. Due to the numerous nodes present especially in large scale networks, it is time-consuming for the collector to traverse all nodes, and significant latency exists within the network. Therefore, the moving path of the collector should be well scheduled to achieve a shorter length for efficient data gathering. Much attention has been paid to mobile agent moving trajectory panning, but the result has limitations in terms of energy consumption and network latency. In this paper, we adopt a hybrid method called HM-ACOPSO which combines ant colony optimization (ACO) and particle swarm optimization (PSO) to schedule an efficient moving path for the mobile agent. In HM-ACOPSO, the sensor field is divided into clusters, and the mobile agent traverses the cluster heads (CHs) in a sequence ordered by ACO. The anchor node of each CHs is selected in the range of communication by the mobile agent using PSO based on the traverse sequence. The communication range adjusts dynamically, and the anchor nodes merge in a duplicated covering area for further performance improvement. Numerous simulation results prove that the presented method outperforms some similar works in terms of energy consumption and data gathering efficiency.

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Revised beaconing glowworm swarm optimization ant colony optimization algorithm to localize nodes and optimize the energy consumed by nodes in wireless sensor networks

Reddy

Gayathri

2020

Concurrency and Computation

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Summary In the wireless sensor networks energy consumption is broadest and widely explored area of research. The solution for energy optimization encompasses various techniques such as efficient routing protocols, data scheduling, clustering, hardware redesigning, supervised and unsupervised network learning algorithm, and so forth. Compared with all the methods that has been so far discussed, swarm intelligence (SI) is considered to be the optimal way to find solution for reducing energy consumption as it is simple and the network formation is understood by the natural mechanism present in nature. SI approaches include ant colony optimization (ACO), particle swarm optimization, glowworm swarm optimization (GSO), and so forth. In this article, the authors provide the solution for the energy conservation problem through efficient GSO methods combined ACO. The revised beaconing glowworm swarm optimization ant colony optimization algorithm will be applied on the sensor network divided into swarms based on glowworms, the ants are introduced in the network that would parse the network by visiting the swarm heads with the principle of ACO behind it. The algorithm is tested on MATLAB 2015a for performance comparison with the HM‐ACOPSO method with depicts energy conservation and efficiency in data collection.

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An Ant Colony Optimization Based Approach for Minimum Cost Coverage on 3-D Grid in Wireless Sensor Networks

Cited by 47 publications

References 7 publications

A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

A Self-Deployment Algorithm for Maintaining Maximum Coverage and Connectivity in Underwater Acoustic Sensor Networks Based on an Ant Colony Optimization

A Hybrid Method for Mobile Agent Moving Trajectory Scheduling using ACO and PSO in WSNs

Revised beaconing glowworm swarm optimization ant colony optimization algorithm to localize nodes and optimize the energy consumed by nodes in wireless sensor networks

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