An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection

Abraham, Robin; Vadivel, M.

doi:10.1007/s11277-023-10342-2

Cited by 12 publications

(3 citation statements)

References 31 publications

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“…However, it considered a number of simulation rounds to choose optimal CH. Abraham et al [27] proposed a Flamingo Search Algorithm-based energy-efficient CH selection in WSNs (FSA-CHS). The ultrascalable ensemble technique was adapted for clustering nodes and handling extensive data in the network.…”

Section: Related Workmentioning

confidence: 99%

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SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network’s lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

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Section: Related Workmentioning

confidence: 99%

“…Samiayya et al [24] Hybrid Snake Whale Optimization (HSWO) Takes more time to converge. Abraham et al [27] Flamingo Search Algorithm (FSA)…”

Section: Snomentioning

confidence: 99%

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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“…Durham et al [13] proposed a quasi-opposition-based Flamingo Search Algorithm and integrated a generalized cyclic crossover model to achieve more effective feature selection. Abraham et al [14] proposed an energy-efficient cluster head selection for wireless sensor networks based on the Flamingo Search Algorithm to improve the energy efficiency of wireless sensor networks. Srinivasarao et al [15] proposed an effective Flamingo Search Algorithm based on a multi-objective cost model to optimize the efficiency of instantiation view selection in data warehouse management.…”

Section: Introductionmentioning

confidence: 99%

Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

et al. 2023

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To overcome the limitations of the Flamingo Search Algorithm (FSA), such as a tendency to converge on local optima and improve solution accuracy, we present an improved algorithm known as the Multi-Strategy Improved Flamingo Search Algorithm (IFSA). The IFSA utilizes a cube chaotic mapping strategy to generate initial populations, which enhances the quality of the initial solution set. Moreover, the information feedback model strategy is improved to dynamically adjust the model based on the current fitness value, which enhances the information exchange between populations and the search capability of the algorithm itself. In addition, we introduce the Random Opposition Learning and Elite Position Greedy Selection strategies to constantly retain superior individuals while also reducing the probability of the algorithm falling into a local optimum, thereby further enhancing the convergence of the algorithm. We evaluate the performance of the IFSA using 23 benchmark functions and verify its optimization using the Wilcoxon rank-sum test. The compared experiment results indicate that the proposed IFSA can obtain higher convergence accuracy and better exploration abilities. It also provides a new optimization algorithm for solving complex optimization problems.

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Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

Srivastava,

Mishra

2023

Concurrency and Computation

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SummarySensors plays an important role in day‐to‐day life as they sense and transfer data on the cloud and servers. These sensors have limited battery power due to which their optimized use is preferred with efficient energy consumption. As sensors were deployed almost everywhere under‐earth, underwater, electronic devices, and mobiles thus their enhanced performance is really important. In WSN, clustering is considered to be an essential approach that gives various advantages such as efficient energy, stability period, network lifetime, less delay, and scalability but has an issue of hot‐spot or energy‐hole problems. For this Un‐Equal Clustering is proposed where the size of clusters varies directly to the distance of the B.S. (Base Station). The main objective of Un‐Equal clustering is energy consumption, hot‐spot problem, and load balance among the cluster heads. In this paper, we have proposed an improved fuzzy‐based multi‐attributes un‐equal clustering to overcome the problem of hot‐spot problem. Earlier, researchers considered two attributes for selecting cluster heads (CHs), but later some of the MADM approaches were used that considered multiple attributes for optimal cluster head selection. In this paper, we have applied the fuzzy‐based TOPSIS method for optimized Un‐Equal clustering where the optimal node deployments with load balance among sensor nodes having optimal coverage and connectivity is considered. The results of the proposed method validate that it is one of the effective ways for selecting optimal cluster heads using fuzzy‐based multi‐attributes. Fuzzy‐based TOPSIS for cluster heads selection method shows 37% enhanced network performance with other compared existing algorithms. The performance of the proposed method is evaluated in terms of network stability, packet delivery, energy consumption, and network lifetime and F‐MAUC (Fuzzy based multi‐attributes un‐equal clustering) outperforms all other compared algorithms.

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An Energy Efficient Wireless Sensor Network with Flamingo Search Algorithm Based Cluster Head Selection

Cited by 12 publications

References 31 publications

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Multi-Strategy Improved Flamingo Search Algorithm for Global Optimization

Fuzzy based multi‐criteria based cluster head selection for enhancing network lifetime and efficient energy consumption

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