Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

Jothi, S.; Chandrasekar, A.

doi:10.1002/dac.4525

Cited by 16 publications

(12 citation statements)

References 55 publications

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“…In Nabavi et al, 9 the multi-objective greedy method is designed to find the optimal routing path, where each node selects another node as next hop based on several factors for transmitting information. To balance the energy consumption among all sensor nodes and further prolong the network lifetime, most approaches [10][11][12][13][14] employ a hierarchical routing protocol, where all nodes are first divided into several clusters and then a cluster head is elected to maintain other nodes in each cluster. Although those routing protocols help to reduce the energy consumption, the performance still remains inadequate.…”

Section: Related Workmentioning

confidence: 99%

End-to-end data collection strategy using mobile sink in wireless sensor networks

Chen

Zhong³

et al. 2022

International Journal of Distributed Sensor Networks

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Several data collection algorithms, which are based on the combination of using mobile sinks and multiple-hop forwarding, have been proposed to prolong the network lifetime of wireless sensor networks. However, most approaches treat the collection point selection and touring path planning as two independent problems, which leads to a sub-optimal solution for data collection. This article proposed an ant colony optimization based end-to-end data collection strategy to perform the collection point selection and the touring path planning simultaneously. The proposed algorithm first constructs a data-forwarding tree, and then heuristically selects collection points and plans a touring path at the same time. The performance evaluation shows that the end-to-end strategy can improve the network lifetime of wireless sensor network compared to other approaches, especially in the unbalanced distribution scenario of sensors. The end-to-end strategy is also capable of being integrated with other methods.

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Section: Related Workmentioning

confidence: 99%

End-to-end data collection strategy using mobile sink in wireless sensor networks

Chen

Zhong³

et al. 2022

International Journal of Distributed Sensor Networks

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“…To improve network stability and longevity. 18 Presented a genetic Spider Monkey-based Routing system. The assessment measures used for the evaluations were control overhead, throughput, end-to-end latency, and network longevity.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Justus

Muthukrishnan

Jothi

2022

Int J Communication

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Summary During the past few years, wireless sensor networks (WSNs) have created a substantial interest because of their capabilities namely, self‐healing and self‐organizing under restricted network resources. Numerous WSN‐based routing algorithms are implemented to perform well, but the energy consumption of every data packet and information loss is more. Due to the power restriction in the sensor nodes, energy consumption is regarded as the major issue in networks that need to be minimized to improve the network lifetime. Hence, to conquer these consequences, this paper aims to propose an adaptive spider monkey optimization (ASMO)‐based effective optimal routing path from various sensor nodes with enhanced network lifetime. Here an ASMO is employed in selecting an optimal routing path. In addition to this, the proposed ASMO algorithm is capable of selecting energy‐aware routes by energy consumption. It also chooses the routes with minimum end‐to‐end delay using the bare least hop count criteria, thus enhancing the network life span. The results of the proposed method are compared with other methods such as DMEERP, EGSMRP, EEIABC, and RACO. The results reveal that the proposed approach consumed less amount of energy; hence, network lifetime is more when compared with other approaches.

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“…To enhance the lifetime and stability of the sensor nodes, Soundaram et al 20 implemented an energy‐efficient genetic spider monkey base routing protocol (EGSMRP) algorithm. The EGSMRP working operation was divided into steady‐state phase and setup phase.…”

Section: Literature Surveymentioning

confidence: 99%

“…To enhance the lifetime and stability of SNs, Soundaram and Arumugam 20 implemented Energy‐Efficient Genetic Spider Monkey‐based Routing Protocol (EGSMRP). Steady state phase and setup phase were the two stages were in EGSMRP.…”

Section: Literature Surveymentioning

confidence: 99%

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

Pattnaik

Sahu

2021

Int J Communication

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Summary The selection of good rendezvous points (RPs) is a significant role for M2obile Sink (MS) in the wireless sensor network (WSN). For the mobile sink, the selection of RP is one of the major problems in WSN. The rendezvous points are only selected based on the local information, so the possibility of selecting an optimal sensor node as RP will be extremely low. The next problem is to find the mobile sink path which visits all the RPs. The above problem is comprehended by utilizing an optimization algorithm. In this paper, Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization based clustering approach and hybrid Moth‐flame cuttlefish optimization (MFO‐CFO) algorithm for efficient routing in WSN is proposed. Initially, the number of clusters is formed due to the Fuzzy C‐means (FCM) based Ant lion optimization (ALO) clustering approach. An Adaptive Neuro‐Fuzzy Inference System (ANFIS) based Particle swarm optimization (PSO) technique is presented for best cluster head (CH) selection. Here, residual energy (RE), node degree, and histories are considered as an input parameter to find the optimal cluster head. At last, we use hybrid MFO‐CFO techniques to find the minimum RPs which decreases energy consumption. The performance metrics of end to end (E2E) delay, energy consumption, channel load, throughput, bit error rate (BER), Network Life Time (NLT), packet delivery ratio (PDR), latency, jitter, and packet loss are computed using several nodes. The performance is compared with some existing algorithms such as Genetic Approach (GA), Fuzzy, PSO (Particle Swarm Optimization), Multiple Access Data Gathering using Mobile Sink for Path Constrained Environment (MADGAMSPCE).

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Genetic spider monkey‐based routing protocol to increase the lifetime of the network and energy management in WSN

Cited by 16 publications

References 55 publications

End-to-end data collection strategy using mobile sink in wireless sensor networks

End-to-end data collection strategy using mobile sink in wireless sensor networks

Energy‐efficient model using optimal route discovery based on adaptive spider monkey optimization model

Adaptive Neuro‐Fuzzy Inference System‐Particle swarm optimization‐based clustering approach and hybrid Moth‐flame cuttlefish optimization algorithm for efficient routing in wireless sensor network

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