An energy efficient clustering using firefly and HML for optical wireless sensor network

Goswami, Pratik; Yan, Ziwei; Mukherjee, Amrit; Yang, Lixia; Routray, Sidheswar; Palai, G.

doi:10.1016/j.ijleo.2018.12.191

Cited by 46 publications

(19 citation statements)

References 4 publications

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“…In 2019, Goswami et al [28] introduced a cluster-based model by deploying HML and FF model in OWSN for improving the EE and minimizing the costs. Here, the issues in FF model were prevailed over by integrating the theory of HML with it.…”

Section: A Related Workmentioning

confidence: 99%

“…Numerous methods have been focused on energy-aware CHS models in WSN. But still, the existing models like FF-PUD [24], BOA + ACO [25], MOFPL [26], Taylor KFCM model [27], FF [28] have some common problems like high convergence, local search issues in FF, high-cost efficiency, there is a need of standard optimizations and need consideration on constraints like security and trust.…”

Section: B Problem Formulationmentioning

confidence: 99%

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Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

Alghamdi¹

2021

IJACSA

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Clustering is one of the significant techniques for expanding the lifetime of networks in wireless sensor networks (WSNs). It entails combining of sensor nodes (SNs) into clusters and electing cluster heads (CHs) for each and every cluster. CH collects the information from particular cluster nodes and passes the cumulative data to the base station (BS). However, the most important requirement in WSN is to choose a suitable CH with an increased network life span. This work introduces a new CHS model in WSN. The optimal CH is elected by a new hybridized model termed as "Lion Updated Dragonfly Algorithm (LU-DA) that hybrid the concepts of Dragonfly Algorithm (DA) and Lion Algorithm (LA)". Moreover, the optimal selection of CH is done depending upon constraints like "energy, delay, distance, security (risk) and trust (direct and indirect trust)". This optimal CH ensures the network lifetime enhancement. At last, the superiority of the developed approach is proved on varied measures like energy and alive node analysis. Accordingly, the proposed model has accomplished higher energy of 0.55 at 1 st round, whereas at the 2000 th round, the normalized energy value has been dropped to 0.1.

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Section: A Related Workmentioning

confidence: 99%

Section: B Problem Formulationmentioning

confidence: 99%

Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

Alghamdi¹

2021

IJACSA

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“…All cluster heads will dynamically maintain their cluster table with schedules communicated by nodes.During the data transmission, the node priority is validated by extracting the details from the Packet Header. The packet header contains the information about the parameters such as the average number of nodes distributed in the clusters, number of packets in the buffer, sleep time for each sensor, sensors connectivity with its neighbors and the mobility of the sensors [20] [21]. The weighted fair queuing model is applied to decide the priority based on the uniformly distributed weight values.…”

Section: P-macron Algorithmmentioning

confidence: 99%

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Bhandari¹,

Sekhar²

2021

IJACSA

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Wireless sensor network with mobility is rapidly evolving and increasing in the recent decade. The cluster and hierarchical routing strategy demonstrates major changes in the lifespan of the network and the scalability. The latency, average energy consumption, packet distribution ratio is highly impacted due to a lack of coordination between cluster head and extreme mobile network nodes. Overall efficiency of highly mobile wireless sensor network is reduced by current techniques such as mobilityconscious media access control, sleep/wakeup scheduling and transmission of real-time services in wireless sensor network. This paper proposes a novel Priority-Mobility Aware Clustering Routing algorithm (p-MACRON) for high delivery of packets by assigning fair weightage to each and every packet of node. To automatically decide the scheduling policy, reinforcement learning approach is integrated. The mixed approach of priority and selflearning results into better utilization of energy. The experimental result shows comparisons of slotted sense multiple access protocol, AODV, MEMAC and P-MACRON, in which proposed algorithm delivered better results in terms of interval, packet size and simulation time.

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“…For the conventional error function, let G is the effective radius of the network and considering noise variance N 0. The M-ARY modulation, E b as energy per bit, the expression for energy-tonoise ratio can be expressed in dynamic WSN applications [46][47][48]:…”

Section: Error Analysismentioning

confidence: 99%

Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks

et al. 2020

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An energy efficient clustering using firefly and HML for optical wireless sensor network

Cited by 46 publications

References 4 publications

Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

Hybrid Metaheuristic Aided Energy Efficient Cluster Head Selection in Wireless Sensor Network

Priority-Mobility Aware Clustering Routing Algorithm for Lifetime Improvement of Dynamic Wireless Sensor Network

Adaptive Particle Swarm Optimisation based Energy Efficient Dynamic Correlation Behavior of Secondary Nodes in Cognitive Radio Sensor Networks

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