Tunicate swarm Grey Wolf optimization for multi-path routing protocol in IoT assisted WSN networks

Chouhan, Nitesh; Jain, Satbir

doi:10.1007/s12652-020-02657-w

Cited by 32 publications

(29 citation statements)

References 20 publications

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“…For cluster head selection and network lifetime improvement, various researchers have used metaheuristic optimization algorithms 26–28 . Vimalarani et al 26 presented an enhanced PSO‐based clustering energy optimization (EPSO‐CEO) algorithm for cluster head selection and minimize energy consumption.…”

Section: Review Of Related Workmentioning

confidence: 99%

“…In a large‐scale mobile WSN, Yinggao Yue et al 27 used the artificial bee colony algorithm to find the shortest path between the receiver node and cluster head. Nitesh Chouhan et al 28 used the Tunicate swarm grey wolf optimization (TSGWO) algorithm to act as an uninterrupted routing protocol in the internet of things (IoT) aided WSN. The fractional gravitational search algorithm (FGSA) is used for cluster head selection and the routing path is mainly selected using different parameters such as QoS and trust parameters.…”

Section: Review Of Related Workmentioning

confidence: 99%

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An energy‐efficient routing based on a hybrid improved whale artificial ecosystem optimization algorithm in WSN

Devi¹,

Sethukarasi²

2021

Concurrency and Computation

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Wireless sensor network (WSN) is an auto‐configured and baseless wireless network system to detect the physical scenarios or atmospheric situations like temperature, sound, pressure, pollutants, and so on. These investigations can be aided for the smooth transmission of data along with the networks. The current smart world exploits WSN in most applications, such as technical applications, industrial applications, low‐power embedded systems, and other smart communications. Even though it has so many applications, the available limited supply resource causes ill effects while transmitting large amounts of data. Hence, we can deem energy efficiency as a crucial factor that affects the performances of WSN. Processes such as routing, and for long‐lasting life span WSN requires more energy. Hence, to have an effective routine and enhanced life span of WSN, we propose a novel energy‐efficient routing protocol based on a hybrid improved whale artificial ecosystem optimization algorithm. To attain an energy‐efficient network it is ineluctable to choose an optimized cluster head and other nodes. These can be performed by evaluating various fitness functions of WSN. Henceforth, the nodes are organized and better routing can be obtained based on the fitness function values. This also improves the energy efficiency of the WSN and has been utilized to select the other forwarding nodes. Experimental analyses are conducted based on the deemed three scenarios which are based on the positioning of the base station (BS) and compared with other prior works. In all aspects, our proposed work has better performances than the other state‐of‐art works.

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Section: Review Of Related Workmentioning

confidence: 99%

Section: Review Of Related Workmentioning

confidence: 99%

An energy‐efficient routing based on a hybrid improved whale artificial ecosystem optimization algorithm in WSN

Devi¹,

Sethukarasi²

2021

Concurrency and Computation

View full text Add to dashboard Cite

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“…These include designing of double notched ultra-wide band antenna [14] and localization problem of wireless sensor networks (WSN) [15]. On the other hand, TSA was also used for economic load dispatch problems [16], parameter optimization of solar cells [17] and multi-path routing protocol in IOT assisted WSN [18].…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

Singh¹,

Mittal²,

Singh³

et al. 2022

Computers, Materials &Amp; Continua

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This paper provides a new optimization algorithm named as tunicate swarm naked mole-rat algorithm (TSNMRA) which uses hybridization concept of tunicate swarm algorithm (TSA) and naked mole-rat algorithm (NMRA). This newly developed algorithm uses the characteristics of both algorithms (TSA and NMRA) and enhance the exploration abilities of NMRA. Apart from the hybridization concept, important parameter of NMRA such as mating factor is made to be self-adaptive with the help of simulated annealing (sa) mutation operator and there is no need to define its value manually. For evaluating the working capabilities of proposed TSNMRA, it is tested for 100-digit challenge (CEC 2019) test problems and real multi-level image segmentation problem. From the results obtained for CEC 2019 test problems, it can be seen that proposed TSNMRA performs well as compared to original TSA and NMRA. In case of image segmentation problem, comparison of TSNMRA is performed with multi-threshold electro magnetism-like optimization (MTEMO), particle swarm optimization (PSO), genetic algorithm (GA), bacterial foraging (BF) and found superior results for TSNMRA.

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“…Chouhan et al [10] present the multipath routing protocol with the presented optimization algorithm, called TSGWO method in the IoT enabled WSN system. With the multipath routing protocol, the multi-path is developed using multi-path source nodes to many destinations.…”

Section: Introductionmentioning

confidence: 99%

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

Dutta¹,

Srinivasan²,

Rao³

et al. 2022

Computers, Materials &Amp; Continua

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In current days, the domain of Internet of Things (IoT) and Wireless Sensor Networks (WSN) are combined for enhancing the sensor related data transmission in the forthcoming networking applications. Clustering and routing techniques are treated as the effective methods highly used to attain reduced energy consumption and lengthen the lifetime of the WSN assisted IoT networks. In this view, this paper presents an Ensemble of Metaheuristic Optimization based QoS aware Clustering with Multihop Routing (EMO-QoSCMR) Protocol for IoT assisted WSN. The proposed EMO-QoSCMR protocol aims to achieve QoS parameters such as energy, throughput, delay, and lifetime. The proposed model involves two stage processes namely clustering and routing. Firstly, the EMO-QoSCMR protocol involves crossentropy rain optimization algorithm based clustering (CEROAC) technique to select an optimal set of cluster heads (CHs) and construct clusters. Besides, oppositional chaos game optimization based routing (OCGOR) technique is employed for the optimal set of routes in the IoT assisted WSN. The proposed model derives a fitness function based on the parameters involved in the IoT nodes such as residual energy, distance to sink node, etc. The proposed EMO-QoSCMR technique has resulted to an enhanced NAN of 64 nodes whereas the LEACH, PSO-ECHS, E-OEERP, and iCSHS methods have resulted in a lesser NAN of 2, 10, 42, and 51 rounds. The performance of the presented protocol has been evaluated interms of energy efficiency and network lifetime.

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Tunicate swarm Grey Wolf optimization for multi-path routing protocol in IoT assisted WSN networks

Cited by 32 publications

References 20 publications

An energy‐efficient routing based on a hybrid improved whale artificial ecosystem optimization algorithm in WSN

An energy‐efficient routing based on a hybrid improved whale artificial ecosystem optimization algorithm in WSN

A Novel Hybrid Tunicate Swarm Naked Mole-Rat Algorithm for Image Segmentation and Numerical Optimization

Design of QoS Aware Routing Protocol for IoT Assisted Clustered WSN

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