A Hybrid Swarm Intelligence Algorithm for Clustering-Based Routing in Wireless Sensor Networks

Barzin, Amirhossein; Sadegheih, Ahmad; Zare, Hassan Khademi; Honarvar, Mahbooeh

doi:10.1142/s0218126620501637

Cited by 27 publications

(19 citation statements)

References 33 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fuzzy-based clustering protocol is proposed in [30] which determines the cooperative node (CN) that joins a cluster and establishment of a communication path between a CN and CH is done using PSO. Shuffled Frog-leaping and Firefly Algorithms (SFFA) [31] is a clustering protocol for WSNs. SFFA considers different criteria s(CHs' distances from the BS, residual energy of nodes, inter and intra-cluster distances and a load of clusters) as the multi-objective fitness function to select the most proper CHs at each round.…”

Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

See 1 more Smart Citation

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

View full text Add to dashboard Cite

The rapid advancement in Wireless Sensor Network (WSN) technology has enabled smart environments to provide ubiquitous real-time applications in various fields such as industry, smart city, transport, health and Internet of Things (IoT). Energy is the most significant resource in WSNs as it has a direct effect on their lifetime. The efficient use of energy is required for the lifetime extension of WSNs. One of the well-known methods for achieving high scalability and efficient resource allocation in WSN is a clustering of sensor nodes. In this paper, the Chicken Swarm Optimization based Clustering Algorithm (CSOCA) is proposed to improve energy efficiency in WSNs. The chicken swarm optimization is discretized by applying a sigmoid function to individuals. Moreover, we proposed CSOCA with Genetic Algorithm (CSOCA-GA) which is an improvement to CSOCA by employing the Genetic Algorithm's processes in CSOCA. CSOCA-GA utilizes crossover and mutation processes for individuals with low fitness value to extend the population diversity. CSOCA and CSOCA-GA are tested and compared with other similar algorithms to confirm their effectiveness in terms of extending WSN lifetime and reducing energy consumption. INDEX TERMS Wireless sensor network, clustering, Internet of Things, genetic algorithm, chicken swarm optimization.

show abstract

Section: B Ai-based Clustering Methodsmentioning

confidence: 99%

“…For the next tests, we consider one point crossover and mutation rate of 0.05% for CSOCA-GA. The performance results of COSCA, and CSOCA-GA are compared with LEACH-MS [25], LEACH-PSO [24], EODC [32], GCDC [33] and SFFA [31] algorithms.…”

Section: B Cosca and Cosca-ga Evaluationsmentioning

confidence: 99%

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

2020

View full text Add to dashboard Cite

show abstract

“…The LEACH protocol is completely a clustering routing protocol. Clustering is the most common technique to balance energy consumption among nodes, while minimizing traffic and overhead during the data transmission phases [ 24 ]. The lifetime of LEACH consists of many rounds, and each round consists of a set-up phase and steady-state phase.…”

Section: Related Workmentioning

confidence: 99%

“…Most of the multi-hop protocols improved for LEACH mainly focus on intercluster multi-hop. Barzin et al [ 24 ] proposed a multiobjective nature-inspired algorithm based on the shuffled frog-leaping algorithm and firefly algorithm (MOSFA) as an adaptive application-specific clustering-based multi-hop routing protocol. MOSFA’s multiobjective function regards different criteria (inter- and intracluster distances, the residual energy of nodes, distances from the sink, overlap, and load of clusters) to select appropriate cluster heads at each round.…”

Section: Related Workmentioning

confidence: 99%

Centralized Multi-Hop Routing Based on Multi-Start Minimum Spanning Forest Algorithm in the Wireless Sensor Networks

Jin

Fan

Sun

2021

Sensors

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) are widely applied in environmental monitoring, target tracking, military, and industrial fields. However, the battery energy of sensor nodes in WSNs is limited, which limits its development. Previous studies have shown that clustering protocols and multi-hop communication are beneficial to reduce nodes energy consumption. The multi-hop protocol based on low energy adaptive clustering hierarchy (LEACH) has been proven to significantly reduce energy dissipation. However, LEACH-based multi-hop protocols generally have the problem of unbalanced energy dissipation and data conflicts. In this paper, we propose a centralized multi-hop routing based on multi-start minimum spanning forest (LEACH-CMF) to optimize LEACH. In order to realize multi-hop communication, we introduced a multi-start minimum spanning tree algorithm to select relay nodes with the minimum relay cost and generate appropriate multi-hop paths. To avoid data collision in multi-hop communication and make nodes including the cluster heads sleep as much as possible in the non-working state, we design a bottom-up continuous time slot allocation method to improve the time division multiple access (TDMA) cycle. We performed simulation in NS2. The simulation results show that the network lifetime is approximately doubled compared to LEACH and centralized low energy adaptive clustering hierarchy (LEACH-C). The simulation results show that the proposed protocol can effectively balance the energy dissipation of nodes and prolong network lifetime.

show abstract

“…There are seven objective functions [38,39]. These seven objective functions are covering all important aspects needed for energy-efficient clustering such as saving the energy by minimizing the number of CHs, finding the current energy ratio, enhancing the link quality in clusters, minimizing the distance between CHs and BS, reducing the intra-cluster distances, maximizing the inter-cluster distance between CHs, and finally balancing the load between CHs.…”

Section: Objective Functionmentioning

confidence: 99%

Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

Rathore

Sangwan

Prakash

et al. 2020

J Wireless Com Network

View full text Add to dashboard Cite

The energy harvesting methods enable WSNs nodes to last potentially forever with the help of energy harvesting subsystems for continuously providing energy, and storing it for future use. The energy harvesting techniques can use various potential sources of energy, such as solar, wind, mechanical, and variations in temperature. Energy-constrained sensor nodes are small in size. Therefore, some mechanisms are required to reduce energy consumption and consequently to improve the network lifetime. The clustering mechanism is used for energy efficiency in WSNs. In the clustering mechanism, the group of sensor nodes forms the clusters. The performance of the clustering process depends on various factors such as the optimal number of clusters formation and the process of cluster head selection. In this paper, we propose a hybrid whale and grey wolf optimization (WGWO)-based clustering mechanism for energy harvesting wireless sensor networks (EH-WSNs). In the proposed research, we use two meta-heuristic algorithms, namely, whale and grey wolf to increase the effectiveness of the clustering mechanism. The exploitation and exploration capabilities of the proposed hybrid WGWO approach are much higher than the traditional various existing metaheuristic algorithms during the evaluation of the algorithm. This hybrid approach gives the best results. The proposed hybrid whale grey wolf optimization-based clustering mechanism consists of cluster formation and dynamically cluster head (CH) selection. The performance of the proposed scheme is compared with existing state-of-art routing protocols.

show abstract

A Hybrid Swarm Intelligence Algorithm for Clustering-Based Routing in Wireless Sensor Networks

Cited by 27 publications

References 33 publications

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

CSOCA: Chicken Swarm Optimization Based Clustering Algorithm for Wireless Sensor Networks

Centralized Multi-Hop Routing Based on Multi-Start Minimum Spanning Forest Algorithm in the Wireless Sensor Networks

Hybrid WGWO: whale grey wolf optimization-based novel energy-efficient clustering for EH-WSNs

Contact Info

Product

Resources

About