Hybrid model for security‐aware cluster head selection in wireless sensor networks

Shankar, Achyut; Jaisankar, N.; Khan, Mohammad S.; Patan, Rizwan; Balamurugan, B.

doi:10.1049/iet-wss.2018.5008

Cited by 57 publications

(51 citation statements)

References 34 publications

(62 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Particle swarm modeling -ECHS is talked about in [18], where PSO based CH determination is made utilizing parameters like tiny node to-tiny node separation, separation to BS and lingering vitality. Another advancement strategy known as Grouped Gray Wolf Search Optimization is utilized in [19] for safety-mindful CH choice to improve the system's lifetime. An enhancement of LEACH was planned in [20] where leftover vitality assumes a significant job in the CH political decision.…”

Section: Related Workmentioning

confidence: 99%

Enhanced Energy-Efficient Heterogeneous Routing Protocols in Wsns for IOT Application

Rana¹,

Sharma²

2019

IJEAT

View full text Add to dashboard Cite

Bunch specific transducers of Wireless sensor networks (WSN) that give detecting administrations to the Internet of Things gadgets with constrained vitality and capacity assets. Because substitution or energizing of battery in tiny sensor nodes is practically incomprehensible, control utilization winds up one of the critical structure issues in WSN for the future, we proposed a crossbreed directing convention: Advanced Zone-Stable Election Protocol (AZ-SEP) with nature of heterogeneous WSNs for IoT situations. In this convention, a few nodes transmit information legitimately to the base station while some utilization the bunching method to send information to the base station. We actualized AZ-SEP and contrasted it and the customary Low Energy adaptive clustering hierarchy (LEACH). Recreation results demonstrated that Z-SEP improved the steadiness time frame and throughput than existing conventions like LEACH. The proposed AZ-SEP convention outflanks when contrasted with the current LEACH convention with a 64% ascent in better output in the form throughput and broadening the quantity of alive tiny nodes to 2702 rounds which can be utilized to improve the IoT lifetime. At the point when contrasted and other vitality productive conventions, it is discovered that the proposed calculation performs better as far as dependability period and system lifetime in various situations of region, vitality and node density. Thus our simulation result will show enhanced energy, throughput with data aggregation

show abstract

Section: Related Workmentioning

confidence: 99%

Enhanced Energy-Efficient Heterogeneous Routing Protocols in Wsns for IOT Application

Rana¹,

Sharma²

2019

IJEAT

View full text Add to dashboard Cite

show abstract

“…In 2018, Achyut Shankar et al, [18] offered hybrid GGWSO to improve recital of CH selection in SNs; therefore networks lifetime can be enhanced. Indeed, the anticipated model is anticipated on delay, distance, security and energy to construct CH selection in SNs.…”

Section: Related Workmentioning

confidence: 99%

Effectual Clustering and Node Placement with Differential Evolution Particle Swarm Optimization using Markov Chain Clustering in FANET

2019

IJEAT

View full text Add to dashboard Cite

Flying ad hoc network (FANET) comprises of multiple unmanned aerial vehicles (UAVs) which is effectual technology for future generation. In this investigation, the specific way for constructing a FANET topology using clustering technique to achieve end-to-end communication is elaborated. For this purpose, an application that uses the meta-heuristics approach for cluster analysis is anticipated. Specifically, the parameters of differential evolution (DE) and particle swarm optimization (PSO) have gained the attention and extensive popularity in various communities based on its working effectiveness in resolving complex combinational optimization crisis. Thus, hybrid model of DE and PSO based Markov Chain Clustering Model (MCCM) is designed in this investigation to analyse the problems of clustering in FANET and reliability parameters are examined. The proposed (DEPSO-MCM) model is to enhance search capability and to attain superior flexibility in forming nodes cluster. Empirical outcomes demonstrate DEPSOMCM based clustering algorithm attains superior performance in number of epochs to acquire fitness function effectually. The simulation was carried out in NS-2 simulator, the outcomes based on the simulation shows that the proposed method works effectually and shows better trade-off than the existing techniques to provide a meaningful clustering.

show abstract

“…However, data loss was higher. A hybrid GGWSO (Grouped Grey Wolf Search Optimisation) procedure was applied [18] to obtain a cluster head selection performance in WSN. But, network lifespan was lower.…”

Section: Introductionmentioning

confidence: 99%

Deep Neural Glow Worm Swarm Optimized Soft C-Means Clustering for Energy Aware Route Discovery and Data Gathering in WSN

Ramaiah¹,

Nagraj²

2021

IJIES

View full text Add to dashboard Cite

Cluster head selection enacts a prominent role in Wireless Sensor Network to optimize the energy usage during the data collection. Few research works have been designed to choose the best cluster head in wireless network using different optimization techniques. However, cluster head selection performance of conventional algorithms was lower to extend the lifetime of network. Therefore, a Deep Neural Glowworm Swarm Optimized Soft C-Means Clustering (DNGSOSCC) model is proposed. Initially, DNGSOSCC model obtains number of sensor nodes as input at the input layer. After taking input, soft clustering process is carried in DNGSOSCC model at the first hidden layer where it groups the sensor nodes in WSN into a different cluster. Then, the glow worm population is initialized in DNGSOSCC model with the support of number of clusters formed at the second hidden layer. Next, Luciferin value is assessed in DNGSOSCC model for all sensor nodes according to their residual energy level in the third hidden layer. Followed by, the sensor node with higher luciferin value within cluster is selected as optimal cluster head at the fourth hidden layer to perform energy efficient data gathering in WSN. Finally, the output layer provides the selection result of optimal cluster heads in WSN. By using the above process, DNGSOSCC model significantly gathers the data from its cluster member through selected optimal cluster heads with minimal amount of energy. From that, DNGSOSCC model upsurges the lifespan of network through performing efficient route discovery and data collection in WSN. The DNGSOSCC model conducts the simulation work using metrics such as data deliverance ratio, data loss ratio, data transmission delay and network lifetime with reverence to number of data packets and sensor nodes. From the experiments conducted, DNGSOSCC model improves the data deliverance ratio and network lifetime by 19% and 10% as well as minimizes the data loss ratio and data transmission delay by 76% and 34% respectively than existing methods.

show abstract

Hybrid model for security‐aware cluster head selection in wireless sensor networks

Cited by 57 publications

References 34 publications

Enhanced Energy-Efficient Heterogeneous Routing Protocols in Wsns for IOT Application

Enhanced Energy-Efficient Heterogeneous Routing Protocols in Wsns for IOT Application

Effectual Clustering and Node Placement with Differential Evolution Particle Swarm Optimization using Markov Chain Clustering in FANET

Deep Neural Glow Worm Swarm Optimized Soft C-Means Clustering for Energy Aware Route Discovery and Data Gathering in WSN

Contact Info

Product

Resources

About