Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

Al-Mousawi, Ali Jameel

doi:10.1007/s11276-019-02008-4

Cited by 20 publications

(9 citation statements)

References 54 publications

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“…Figure 9 depicts that the proposed solution maintains stability and gives an immense packet received ratio when compared to the previous algorithms. On the whole, this proposed work conveys that, in compare to the other application-oriented algorithms [28][29][30], RSC outperforms the time-critical as well as event -based applications.…”

Section: Comparison Of Rsc and Fan Shaped Clustering (Fsc)mentioning

confidence: 78%

A geodesic deployment and radial shaped clustering (RSC) algorithm with statistical aggregation in sensor networks

Krishnasamy¹,

Ramasamy²,

Dhanaraj³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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Wireless Sensor Networks (WSN) comprise a large number of connected tiny or small sensor devices to sense physical phenomenon. In WSN, prolonging the network's lifetime is a biggest challenge due to absence of power harvesting facility and irreplaceable batteries of the sensor devices. Clustering is one of the widely accepted and standard technique to solve the energy issues faced in WSN. In addition to clustering, the shape of the deployment area also plays the major role especially for large scale sensor deployment. This paper proposes a Radial Shaped Clustering (RSC) algorithm with angular inclination routing. The radial shaped deployed area is divided into virtual concentric layers and each layer is further divided into a set of sectors called clusters. Angular routing is applied to achieve multi-hop routing of packets towards the Sink node. In comparison to Fan Shaped Clustering (FSC), RSC performs better in terms of residual energy and packet received ratio.

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Section: Comparison Of Rsc and Fan Shaped Clustering (Fsc)mentioning

confidence: 78%

A geodesic deployment and radial shaped clustering (RSC) algorithm with statistical aggregation in sensor networks

Krishnasamy¹,

Ramasamy²,

Dhanaraj³

et al. 2021

Turkish Journal of Electrical Engineering and Computer Sciences

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“…Most of the power is consumed when the data is transfer in between the CHs and the BS. To turn down the power utilization in the clustered wireless sensor network, routing mechanism is used in between the CHs and the BS [24][25][26][27]. However, none of the mechanisms deliver serious effects of the problem in the wireless sensor network to cover the optimal solution.…”

Section: Literature Surveymentioning

confidence: 99%

Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm

Mishra

Yadav

2023

Wireless Pers Commun

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WSN consist of tiny sensors which are distributed over the entire network having capabilities of sensing the data, processing it and convey it from one node to another node. The purpose of the study is to minimize the power utilization per round and elevate the network lifespan. In the present case, the nature inspired mechanisms are used to minimize the power utilization of the network. In the proposed study Butterfly Optimization Mechanism is used to choose the optimum quantity of CH from the dense nodes. The parameter is to be considered for the choosing of the CH is the remaining power of the node, interspace from the other nodes in the network, interspace from the BS, node centrality and node degree. The PSO is used to form the CH by choosing certain parameters such as interspace from the CH and the BS. The path is choosing by means of the Ant Colony Optimization (ACO) Mechanism. The route is optimized by the interspace, node degree and the choose remaining power. The inclusive performance of the projected protocol is measured in terms of stability period, quantity of active nodes, data acknowledged by the BS and the overall power utilization of the network. The results of the put redirect methodology are correlated with the extant mechanisms such as LEACH, DEEC, DDEEC, EDEEC [50-53] and also correlated with the swarm mechanisms such as CRHS, BERA, FUCHAR, ALOC, CPSO, FLION. This review will help investigators to discover the applications in this arena.

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“…The simulation to evaluate the performance of the EEG algorithm was implemented using MATLAB R2018b. The EEG was compared with the GECR using a similar configuration used in [7], and the Lambda's equal value in [20] was used to form the comparisons with the network lifetime, defined as first node death (FND), half node alive (HNA), and last node death (LND).…”

Section: Simulation Setup and Configurationmentioning

confidence: 99%

“…The sensor nodes have been used in diverse tangible applications (e.g., electronic, biotechnology, chemical, and so forth) to accomplish many tasks, such as detecting, discovering, processing, and transmitting sensitive data to the knowledge station, referred to as a sink node [3], [4]. Furthermore, energy proficiency, precision, robustness, reliability, and data throughput make the WSN widely functional in healthcare, traffic controls, home appliances control, industrial diagnostic, natural disaster prevention, surveillance, and precision agriculture [5]- [7]. Figure 1 depicts the elemental architecture of a WSN.…”

Section: Introductionmentioning

confidence: 99%

Fine-tuning approach in metaheuristic algorithm to prolong wireless sensor networks nodes lifetime

Rahiman

Williams

Zakaria

2022

IJEECS

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Wireless sensor networks (WSN) have evolved a vibrant and lively research field. It comprises numerous wise and low-power consumption devices for gathering the contiguous atmosphere's data. However, the energy dissipation matter that distorts network lifetime remains the challenge since the sensor node battery is non-rechargeable and irreplaceable. Clustering and routing protocol has become the furthermost solutions and invariably minimizes depletion and prolongs the sensor node lifetime. Such protocols have adopted metaheuristic algorithms to secure the efficiency of the clustering and routing protocols. However, the cluster head's extensive task favors consuming and draining more energy. This study proposed a fine-tuning solution for the sensor node's population and generation sizes. It benefits from the modified problem-oriented genetic algorithm parameters in securing the sensor node lifetime. Besides, the solution works effectively to balance the load of the cluster head nodes. A set of simulations has been performed using MATLAB R2018b on the proposed solution, namely the energy efficient of genetic (EEG) algorithm and has revealed that the solution outperforms the network lifetime and cluster head load of the existing solution.

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Evolutionary intelligence in wireless sensor network: routing, clustering, localization and coverage

Cited by 20 publications

References 54 publications

A geodesic deployment and radial shaped clustering (RSC) algorithm with statistical aggregation in sensor networks

A geodesic deployment and radial shaped clustering (RSC) algorithm with statistical aggregation in sensor networks

Energy Efficient Cluster-Based Routing Protocol for WSN Using Nature Inspired Algorithm

Fine-tuning approach in metaheuristic algorithm to prolong wireless sensor networks nodes lifetime

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