ACO-Discreet: An Efficient Node Deployment Approach in Wireless Sensor Networks

Qasim, Tehreem; Minhas, Qurrat ul Ain; Mujahid, Alam; Bhatti, Naeem; Mushtaq, Muhammad; Saleem, Khalid; Mahmood, Hasan; Sameem, M. Shujah Islam

doi:10.1007/978-3-319-54978-1_6

Cited by 7 publications

(4 citation statements)

References 9 publications

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“…Network lifetime and coverage of the area are considered as objectives of the algorithm, which use the circle point concept. In [14] ACO-Discreet, a modification of Ant Colony Optimization is proposed which reduces the sensing cost with efficient deployment and enhanced connectivity. The algorithm includes two phases: at the first phase, the ordinary ACO is used for the grid-based problem coverage with low cost and connectivity-guarantee.…”

Section: Related Workmentioning

confidence: 99%

Ant Colony based Coverage Optimization in Wireless Sensor Networks

Khoshrangbaf¹,

Akram²,

Challenger³

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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Maximizing the covered area of wireless sensor networks while keeping the connectivity between the nodes is one of the challenging tasks in wireless sensor networks deployment. In this paper, we propose an ant colony-based method for the problem of sensor nodes deployment to maximize the coverage area. We model sensor locations as a graph and use an adapted ant colony optimization-based method to find the best places for each sensor node. To keep the connectivity of the sensor network, every sensor must be covered by the other sensors; this is a hard constraint that is applied to the cost function as a penalty. The proposed algorithm is evaluated with different numbers of sensor nodes and sensing ranges. The simulation results showed that increasing the number of iterations in the algorithm generates a better coverage ratio with the same number of nodes. Index Terms4Wireless sensor networks, node deployment, coverage maximization, ant colony optimization.

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Section: Related Workmentioning

confidence: 99%

Ant Colony based Coverage Optimization in Wireless Sensor Networks

Khoshrangbaf¹,

Akram²,

Challenger³

2022

Communication Papers of the 17th Conference on Computer Science and Intelligence Systems

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“…The second step of the approach is the removal of redundant sensors which enables the network to ensure the connectivity constraint with a minimized deployment cost. The same authors proposed a WSN deployment approach in a 2D environment in [123]. As for the 3D environment, this approach applies at the first step the ordinary ACO with the greedy migration scheme presented in [121] to expand coverage.…”

Section: Swarm Intelligence Optimization Algorithmsmentioning

confidence: 99%

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Zaimen¹,

Brahmia²,

Moalic

et al. 2022

IEEE Access

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Recent advances in hardware and communication technologies have accelerated the deployment of billions of wireless sensors. This transformation has created a wide range of applications adapted to the evolving trends of our daily life requirements. Wireless sensor networks (WSNs) could be deployed in several target areas including buildings, forests, oceans, and smart cities. Nevertheless, finding the optimal location for each sensor node is a challenging task, typically when the environment involves heterogeneous obstacles. Many approaches and methods have been proposed to deal with the problem of WSN deployment, each addressing one or more objectives and constraints, such as network coverage, lifetime, connectivity, and energy consumption. The purpose of this survey paper is to provide the needed background to understand and study the WSNs deployment problem with a focus on its two key aspects: the optimization model and the solving methods based on artificial intelligence (AI). Additionally, it covers recent works on WSNs deployment and identifies their advantages and limitations. Furthermore, simulation experiments were carried out to compare the performance of widely used algorithms in the context of WSNs deployment problem, primarily genetic algorithm, particle swarm optimization, flower pollination, and ant colony optimization. Finally, this paper discusses and highlights several open challenges and research issues that must be explored in the future.

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“…The proposed protocol identifies the backbone nodes and turns of the other nodes to increase the network lifetime. The proposed algorithm in [ 23 ] uses ant colony optimization to reduce the sensing cost while maintaining the network’s coverage and connectivity. A sleep/awake schema based on redundant data and duty cycling has been proposed in [ 24 ] that tries to reduce the overall energy consumption of nodes and increase the network lifetime.…”

Section: Related Workmentioning

confidence: 99%

“…A sleep/awake schema based on redundant data and duty cycling has been proposed in [ 24 ] that tries to reduce the overall energy consumption of nodes and increase the network lifetime. Generally, the proposed methods in [ 20 , 21 , 22 , 23 , 24 ] focus on discovering the redundant data or redundant covered area.…”

Section: Related Workmentioning

confidence: 99%

Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications

Aljubori

Akram

Challenger

2022

Sensors

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Wireless sensor networks can be used as cost-effective monitoring and automation platforms in smart manufacturing and Industry 4.0. Maximizing the covered area and increasing the network lifetime are two challenging tasks in wireless sensor networks. A feasible solution for maximizing the coverage area and network lifetime is detecting and relocating the covered redundant nodes. A covered redundant node is a node whose covered area is also covered by the other active nodes in the network. After identifying the covered redundant nodes, putting them in sleep mode can increase the network lifetime. In addition, moving the detected redundant nodes to the uncovered locations can improve the overall covered area by the sensor nodes. However, finding the redundant nodes is an NP-complete problem. In this paper, we propose a localized distributed algorithm for identifying the redundant nodes based on the 2-hop local neighborhood information of the nodes. The proposed algorithm uses the existing connections between the neighbors of each sensor node to decide the redundancy of the node. The algorithm is localized and does not need the entire topology of the network or the coordinates of the nodes.

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ACO-Discreet: An Efficient Node Deployment Approach in Wireless Sensor Networks

Cited by 7 publications

References 9 publications

Ant Colony based Coverage Optimization in Wireless Sensor Networks

Ant Colony based Coverage Optimization in Wireless Sensor Networks

A Survey of Artificial Intelligence Based WSNs Deployment Techniques and Related Objectives Modeling

Improving the Deployment of WSNs by Localized Detection of Covered Redundant Nodes in Industry 4.0 Applications

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