FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks

Chowdhury, Aparajita; De, Debashis

doi:10.1016/j.comcom.2020.09.002

Cited by 22 publications

(10 citation statements)

References 45 publications

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“…In 2020, Chowdhury and De 26 had developed a new model called “dynamic fuzzy inference system based reverse glowworm swarm optimization (FIS‐RGSO)” in smart green Mobile Wireless sensor Networks (MWSNs) by considering the coverage and energy constraints. The major aim of this developed model was to attain minimal energy consumption through the sensors based on the optimal movement of sensors to maximize the lifetime and area of networks.…”

Section: Literature Surveymentioning

confidence: 99%

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A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

Akhtar

Ahamad²,

Abdalrahman

et al. 2022

Int J Communication

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Summary Nowadays, there is an emerging need for applications based on the Internet of Things (IoT). The sensor nodes present in the IoT network produce data constantly, which directly influences the durability of the network. Therefore, two major challenges while designing IoT systems are network lifetime and energy consumption. Although the ability of IoT applications is huge, there are several limitations such as energy optimization, heterogeneity of devices storage, load balancing, privacy, and security that have to be addressed. These constraints have to be optimized for improving the efficiency of the networks. Hence, the main intention of this paper is to develop the intelligent‐based cluster head selection model for accomplishing green communication in IoT. The two famous algorithms like spotted hyena optimization (SHO) and sun flower optimization (SFO) are integrated to form sun flower‐spotted hyena optimization (SF‐SHO) by utilizing the hybrid meta‐heuristic concept for the optimal cluster head selection. The most significant parameters in IoT networks like delay, distance, energy, temperature, and load are considered for deriving a multi‐objective function to offer optimal clustering. The cluster head of the model is optimally tuned based on the hybrid SF‐SHO, to solve the multi‐objective problem, thus showing the enhanced green communication performance. The proposed model is analyzed and evaluated over different approaches in terms of energy‐specific factors, and the attained results confirm the efficiency of the developed method.

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Section: Literature Surveymentioning

confidence: 99%

“…The sensing area of the sensors is greatly utilized. In FIS‐RGSO, 26 the lifetime of the sensor depends on the energy consumed while the sensor moves when an obstacle is sensed. FLC‐based FODPSO 27 is highly reliable and accurate in its performance.…”

Section: Literature Surveymentioning

confidence: 99%

A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

Akhtar

Ahamad²,

Abdalrahman

et al. 2022

Int J Communication

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“…is type of approach uses statically deployed sensors to achieve full coverage. is method can also improve the sensing efficiency of coverage with a smaller number of sensors [7]. However, the deterministic strategy of WSN placement (peer-to-peer) can be used for small-scale deployments [10].…”

Section: Related Workmentioning

confidence: 99%

“…Besides, the deployment method only focuses on the coverage performance of the event and does not consider the connectivity of the network. Ghowdhury and De proposed an energy-efficient self-deployment scheme that takes advantage of the attractiveness of the centroid of the sensor's local VORONOI polygon and the repulsive force often used in self-deployment scenarios using the potential field [7]. In contrast to existing self-deployment solutions, we also provide the design and implementation of a simulator for analyzing the performance of the proposed method.…”

Section: Related Workmentioning

confidence: 99%

“…e typical representative of this strategy is the Dynamic Virtual Force-Based Algorithm (VFE) proposed by Luo and Cai for data aggregation, which can adapt to different scene changes [4,5]. e energy-saving self-deployment scheme proposed by Han et al utilizes the attractive force generated by the centroid of the local Voronoi polygon of the sensor and the repulsive force often used in the selfdeployment scheme using the potential field [6,7]. ese networks using virtual power ideas have significant improvements in latency and packet loss due to dynamic changes in sensor or node locations.…”

Section: Introductionmentioning

confidence: 99%

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Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

Liu

et al. 2021

Mathematical Problems in Engineering

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In a wireless sensor network, the sensor nodes transmit the acquired information to the server through the data transmission link. On the serverside, the data are processed, fused, and expressed to serve the user. Sensor deployment is a key factor related to the stability and security of wireless networks. This article uses environmental changes to drive related technologies to deploy wireless sensors. In this article, environmental change-driven means that through certain deployment cost model assumptions and problem descriptions, network deployment is artificially divided into two stages: initial deployment and redeployment. In the deployment phase, by referring to the idea of virtual force, a new sensor deployment algorithm is proposed in the redeployment phase, which can well solve the stability- and security-related issues encountered in agricultural wireless sensor networks. In this algorithm, the moving distance of the mobile receiver and the average coverage of the network are calculated based on the virtual force, the direction, and the number of adjacent clusters. Finally, the algorithm model was simulated in MATLAB, and the feasibility of the algorithm was verified by analyzing the event coverage and the moving distance of nodes. The final simulation results show that the algorithm proposed in this paper can achieve better performance than existing algorithms in terms of average coverage and moving distance.

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Wireless sensor network coverage of improved sea lion algorithm

Kagi,

Mallapur

2024

Int J Communication

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SummaryThe base station receives the environmental data from a predetermined field that is collected and transferred by the sensors for processing and analysis. However, coverage maximization is the major issue that requires the deployment of varied sensor nodes (SNs), in such a way that optimizes network coverage while enduring practical limitations. This is pointed out to be a significant challenge in constructing WSNs. Since this is considered to be a well‐known NP‐hard issue, metaheuristic methods must be used for solving the realistic problem sizes. Hence, our work considers the problem of finding the best placement to ensure good network coverage in WSN. Accordingly, the solution to the above‐mentioned problem is modeled by covering a new 2‐D distance evaluation based on weighted Minkowski. Further, we deploy the Self Improved Sealion with Opposition Behavior (SI‐SLOB) algorithm for determining the optimal placement of given sensor nodes. In the end, we perform varied evaluations on distance and coverage area to ensure the enhancement of the SI‐SLOB scheme over the other state‐of‐the‐art algorithms. The proposed method achieves minimum distance mean value in target node 25, which is 4.1%, 4.0%, 2.3%, 5.1%, 3.5%, 3.0%, and 4.1% better than the other methods such as SLO, GWO, PSO, BMO, BOA, RHSO, and WOA, respectively. Thus the proposed WSN node coverage models have diverse applications across various domains, contributing to improved efficiency, safety, and resource management.

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FIS-RGSO: Dynamic Fuzzy Inference System Based Reverse Glowworm Swarm Optimization of energy and coverage in green mobile wireless sensor networks

Cited by 22 publications

References 45 publications

A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

A novel hybrid meta‐heuristic concept for green communication in IoT networks: An intelligent clustering model

Event-Sensitive Network: A Construction Algorithm of Agricultural Sensor Network Driven by Environmental Change

Wireless sensor network coverage of improved sea lion algorithm

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