MADM based Optimal Nodes Deployment for WSN with Optimal Coverage and Connectivity

Rajpoot, Prince; Dwivedi, Pragya

doi:10.1088/1757-899x/1020/1/012003

Cited by 8 publications

(3 citation statements)

References 16 publications

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“…In today's era, people were accessing various facilities such as smart home appliances, smart watches, smart TV, smart traffic systems [19], and smart healthcare systems [20] all of these with the help of emerging technologies where sensors play an important role. Considering multiattributes and making coordination among them by Multi-AttributeDecision-Making (MADM) approaches [21,22] will enhance the network lifetime of the network. Various technologies need optimal node deployment where the performance of applications increases with efficient energy consumption.…”

Section: Introductionmentioning

confidence: 99%

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Srivastava

Mishra

2023

Journal of Sensors

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In recent times, the deployment of wireless sensor networks becomes important in revolutionary areas such as smart cities, environmental monitoring, smart transportation, and smart industries. The battery power of sensor nodes is limited due to which their efficient utilization is much necessary as the battery is irreplaceable. Efficient energy utilization is addressed as one of the important issues by many researchers recently in WSN. Clustering is one of the fundamental approaches used for efficient energy utilization in WSNs. The clustering method should be effective for the selection of optimal clusters with efficient energy consumption. Extensive modification in the clustering approaches leads to an increase in the lifetime of sensor nodes which is a unique way for network lifetime enhancement. As the technologies were taken to next the level where multiparameters need to be considered in almost every application in clustering, multiple factors affect the clustering and these factors were conflicting in nature too. Due to the conflicting nature of these factors, it becomes difficult to coordinate among them for optimized clustering. In this paper, we have considered multiattributes and made coordination among these attributes for optimal cluster head selection. We have considered Multi-Attribute Decision-Making (MADM) methods for CH’s selection from the available alternatives by making suitable coordination among these attributes, and comparative analysis has been taken in LEACH, LEACH-C, EECS, HEED, HEEC, and DEECET algorithms. The experimental results validate that using MADM approaches, the proposed APRO algorithm proves to be one of the better exhibits for choosing the available CHs.

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Section: Introductionmentioning

confidence: 99%

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Srivastava

Mishra

2023

Journal of Sensors

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“…In [14], the goal was is achieve the optimal solution for network deployment to cover area 100% using optimal number of nodes and using optimal cluster head coverage. The optimal node number help to minimize total cost of deployment to use minimum number of wireless sensors.…”

Section: Related Workmentioning

confidence: 99%

Optimal Drone Nodes Deployment to Maximize Coverage and Energy in WSNs Using Genetic Algorithms

Ayad Khudhair

2024

J. Al-Qadisiyah Comp. Sci. Math.

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Increasing coverage and reducing the energy consumed in wireless sensor networks is an interesting field for researchers since the discovery of wireless sensor networks and it is an open problem. Activating the lowest connectivity range for each node individually depending on its remaining power and the place it covers, while maximizing coverage by placing it in the optimal place will reduce the energy consumed and increase the lifetime of wireless sensor networks. In this paper, we find an approach to increase coverage by optimizing correlation and residual energy using genetic algorithms by placing each node in the optimal position to maximize coverage, assuming that each node has a different energy from the other nodes. We will use drones to carry and move sensors to optimal position. The aim of the proposed work is to cover the largest area of a given region by using the least range of connection and increasing the Lifetime. We ran a succession of simulations and found the proposed model better than the strategies we found in literature.

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“…An optimal deployment of sensor nodes has two main objectives. The first aim is to maximize the coverage of a given region of interest, while the second objective consists in minimizing the number of sensor nodes to be deployed [22,23]. This type of problem is classified as an NP-hard optimization problem [24].…”

Section: Introductionmentioning

confidence: 99%

Distributed Approach for the Indoor Deployment of Wireless Connected Objects by the Hybridization of the Voronoi Diagram and the Genetic Algorithm

Abdallah¹,

Mnasri²,

Val³

2022

J. Engg. Res. & Sci.

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IoT data collection networks have recently become one of the important research areas due to their fundamental role and wide application in many domains. The establishment of networks of objects is based essentially on the deployment of connected objects to process the collected data and transmit them to the various locations. Subsequently, a large number of nodes must be adequately deployed to achieve complete coverage. This manuscript introduces a distributed approach, which combines the Voronoi Diagram and the Genetic algorithm (VD-GA), to maximize the coverage of a region of interest. The Voronoi diagram is used to divide region into cells and generate initial solutions that present the positions of the deployed IoT objects. Then, a genetic algorithm is executed in parallel in several nodes to improve these positions. The developed VD-GA approach was evaluated on an experimental environment by prototyping on a real testbed utilizing M5StickC nodes equipped with ESP32 processor. The experiments show that the distributed approach provided better degree of coverage, RSSI, lifetime and number of neighboring objects than those given by the original algorithms in terms of the suggested distributed Genetic-Voronoi algorithm outperforms the centralized one in terms of speed of computing.

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MADM based Optimal Nodes Deployment for WSN with Optimal Coverage and Connectivity

Cited by 8 publications

References 16 publications

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Load‐Balanced Cluster Head Selection Enhancing Network Lifetime in WSN Using Hybrid Approach for IoT Applications

Optimal Drone Nodes Deployment to Maximize Coverage and Energy in WSNs Using Genetic Algorithms

Distributed Approach for the Indoor Deployment of Wireless Connected Objects by the Hybridization of the Voronoi Diagram and the Genetic Algorithm

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