Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

Xiu, Zhang; Lu, Xiaohui

doi:10.1016/j.adhoc.2020.102094

Cited by 24 publications

(9 citation statements)

References 25 publications

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“…The authors in the study (27,28) , have used PSO technique for clustering and communication in the wireless sensor network and it is also been highlighted that the PSO efficiently optimizes the network lifetime of WSN, however the study in (29) shows that PSO technique suffers from a problem called curse of dimensionality. The study in (30) has showed that the Differential Evolution (DE) techniques proves to be the best for clustering among all other conventional methods in WSN (31) , it also states that DE technique is simple, consisting of fewer parameters, reliable and provides the optimum solutions in each run.…”

Section: Related Workmentioning

confidence: 99%

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar¹,

Anitha²,

Mallikarjunaswamy³

2021

IJST

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Objective: Energy efficiency aspect in wireless sensor networks (WSN) can be achieved by small sized rechargeable and easily replaceable batteries. The lifetime of wireless sensor network can be improved by identifying the efficient and reliable nodes as a cluster heads using Hybrid Simulated Annealing algorithm. The proposed algorithm identifies cluster head to reduce overhead and is capable of handling high volume of nodes with minimum node death rate. Methods: This study proposed initialization of population vectors using the opposite point procedure, self-adaptive control approach by node mutation rate, crossover rate, node capacity and cluster head allocation Methods. Findings: A case study in the proposed work is found to be better in throughput, accuracy, efficiency, energy utilization, batteries recharge ability and replacement procedures compared to the conventional methods. By the analysis and comparison of the proposed method with existing methods, it is identified that the reduction of the number of dead nodes gradually increases the throughput and lifetime of the nodes with respect to the number of iterations. Novelty: To overcome the limitations of conventional Low Energy Adaptive Clustering Hierarchy (LEACH), harmony search algorithm (HSA), modified HSA and differential evolution, we propose a hybrid optimal model using simulated annealing algorithm which includes a node capability function. It is used to improve the network lifetime of the cluster heads and sensor nodes. The proposed method have capability of batteries recharge ability and replacement option to improve network throughput and reliability of network.

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Section: Related Workmentioning

confidence: 99%

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

Umashankar¹,

Anitha²,

Mallikarjunaswamy³

2021

IJST

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“…In which E Pri is primary energy and RSII is a received signal intensity identifier. Towards the starting of the complete network, values of RSSI are gained from communicating the signal testing of a base station (30)(31)(32)(33) . Consequently, cluster heads that are nearer to a base station and having a sufficient amount of energy will have a high need to turn into the root node.…”

Section: Routing Treementioning

confidence: 99%

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

Pavithra¹,

Babu²

2021

IJST

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Objectives: To efficiently monitor the highly confined area by using security measurement methods to enhance the lifetime of a large area coverage network. Methods : the modified nodes are included in the normal execution process of the network to calculate the efficiency and lifetime of the WSN. With the help of the simulation and analysis process, the proposed protocol is found to be more powerful to impact the sustainability of the network by prolonging the lifetime of the nodes. To perform this operation, the following parameters are considered: Area of the monitoring area, Number nodes, Sink portion, Energies of radio amplifier systems, Data aggregation, node electronics, and data packet length. Findings: The proposed optimal energy cluster routing protocol finds the best topological structural protocol to select the cluster head with optimal connectivity path with other nodes in a group. The data of energy utilization, data communication rate between nodes and cluster head, and analysis of the number of alive nodes concerning time are obtained. Novelty: the proposed Optimal efficient cluster routing protocol (OECRP) selects the cluster head by considering energy consumption, cluster sustainability, and its proper communication with other nodes. OECRP is used to develop the optimal topological structural protocol to connectivity path with other nodes in a group.

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“…In 2020, Xiu Zhang et al [1], studied the life of MWSNs. MWSNs was an extraordinary kind of WSN in which the SNs were changeable in a definite area.…”

Section: Literature Reviewmentioning

confidence: 99%

Sensor Activation in WSN using Improved Cuckoo Search and Squirrel Search Algorithm

Maqbali¹

2020

JNACS

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In Wireless Sensor Network (WSN), the development is augmented and an increased enormous concentration in computer vision. A huge amount of sensors are used to carry out dispersed sensing of the target field in WSN. The existing techniques exploited wireless chargers to provide energy to Sensor Networks (SNs), other than supplied energy is not adequate to control the sensor nodes. Hence, this work presents a method to reduce the energy utilization per node adopting effectual scheduling of sleep/awake of nodes. The technique experiences two stages for the sensor activation such as the initialization stage, and the activation stage. The initialization stage is proposed by a network beginning that is performed to express the network parameters to nodes or sensors. Subsequently, in every slot proposed optimization method is used to activate sensors in the activation stage. The proposed improved cuckoo search and squirrel search optimization approach named (Improved CS-SSA). Hence, the proposed technique produces control in terms of the turn-ON or OFF of sensors that represent active sensors and employ itself in sensing and monitoring distributed environment. The proposed technique performance is superior to other existing techniques through throughput, maximum energy, and alive nodes, correspondingly.

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Mobile wireless sensor network lifetime maximization by using evolutionary computing methods

Cited by 24 publications

References 25 publications

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

An efficient hybrid model for cluster head selection to optimize wireless sensor network using simulated annealing algorithm

An Optimal Energy Utilization of Cluster Routing Protocol for Wireless Sensor Network in Restricted Area

Sensor Activation in WSN using Improved Cuckoo Search and Squirrel Search Algorithm

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