Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs

Rambabu, Bandi; Ananthula, Venugopal Reddy; Janakiraman, S.

doi:10.1007/s11277-021-08821-5

Cited by 17 publications

(11 citation statements)

References 24 publications

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“…14 Some of the well-known techniques used for CH selection are Tabu search, simulated annealing, differential evolution, ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and bacterial foraging optimization (BFO). 15 Thus, the proposed IBEABCCR aided in determining optimized number of CHs in the network with maximized energy stability along with network lifetime. It helped in determining the optimal points on the route over which base station (BS) can be deployed for achieving maximized delivery of packets to the BS.…”

Section: Introductionmentioning

confidence: 99%

“…In specific, optimization in WSNs is necessary for achieving desired objectives based on balanced energy consumption, prolonged network lifespan, and increased throughput 14 . Some of the well‐known techniques used for CH selection are Tabu search, simulated annealing, differential evolution, ant colony optimization (ACO), particle swarm optimization (PSO), artificial bee colony (ABC), firefly algorithm (FA), and bacterial foraging optimization (BFO) 15 . Thus, the proposed IBEABCCR aided in determining optimized number of CHs in the network with maximized energy stability along with network lifetime.…”

Section: Introductionmentioning

confidence: 99%

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Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Janakiraman¹

2023

Int J Communication

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Summary In this paper, improved bat and enhanced artificial bee colony optimization algorithm‐based cluster routing (IBEABCCR) scheme is proposed for optimal cluster head (CH) selection with the merits of global diversity and improved convergence rate. It is proposed for achieving optimal CH selection by balancing the tradeoff between the phases of exploration and exploitation. It specifically targeted on the formulation of an ideal CH selection scheme using improved bat optimization algorithm (IBOA) for minimizing the energy depletion rate. It also focuses on the design of an enhanced artificial bee colony (EABC)‐based sink node mobility scheme for determining the optimal points of deployment over which sink nodes can be moved to achieve better delivery of packets from CH to sink node. This CH selection and sink node mobility schemes are contributed for extending the network lifespan using the fitness function, which adopted the factors of node centrality, node degree, distance amid CH and base station (BS), distance among sensor nodes, and residual energy during CH selection process. The simulation experiments were performed using MATLAB version 2018, which confirmed that the number of alive nodes realized in the network is enhanced by 39.21% with the location of BS positioned at (100, 100). The number of rounds (network lifetime) is enhanced by 23.84% with different BS locations in the network. Furthermore, the packets received at the BS are also realized to be enhanced by 26.32% on an average in contrast to the baseline CH schemes used for investigation.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Janakiraman¹

2023

Int J Communication

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“…Then the learning algorithm will makes integrates with feature subset selection. Proposed a malware detection model associated with cloud computing based on packet networking [10,34]. The identification of packets, which is considered as the input uses data mining technique to reduce the packet knowledge and this helps to validate whether malware detection or not.…”

Section: Introductionmentioning

confidence: 99%

Auto Deep Learning-based Automated Surveillance Technique to Recognize the Activities in the Cyber-Physical System

Archana

Kavitha

Vathsala³

2023

IJRITCC

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In recent days, the Internet of Things (IoT) plays a significant role and increasing in rapid usage in various applications. As IoT is being developed for cyber-physical systems in the specific domain of e-health care, military, etc. Based on real-time applications, security plays a vital role in certain activities in educational institutions. In the institutions, there are multiple videos are collected and stored in the data repositories. Those datasets are developed specifically for certain activities and no other datasets are developed for academic activities. As there is a large number of videos and images are collected and considered, advanced technologies like, deep learning and IoT are used to perform certain tasks. In this paper, a Auto Deep learning-based Automated Identification Framework (DLAIF) is proposed to consider and reconsider the activities based on image pre-processing, model can be trained through the proposed GMM model and then predication to make an effective surveillance process based on HMM. This proposed process makes to recognize the activities through EM and log Likelihood for cyber-physical systems. In the performance analysis, the proposed model efficiency can be determined through Accuracy detection, False Positive rate and F1 Score requirement. Then calculating the accuracy is more effective for the proposed model compared to other existing models such as BWMP and LATTE.

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“…Wireless sensor networks (WSNs) represent a network of numerous sensor nodes that operates together into a package for the objective of monitoring an area of interest. These sensor nodes in WSNs possess multi‐functional, efficient, and cost‐effective potentiality for achieving monitoring process 1 . They are responsible for gathering data from the specified area of interest under monitoring and forward them back to the base station (BS) for the purpose of data processing and decision making 2 .…”

Section: Introductionmentioning

confidence: 99%

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

Pratha

Asanambigai

Mugunthan³

2023

Int J Communication

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Summary Designing an energy efficient and durable wireless sensor networks (WSNs) is a key challenge as it personifies potential and reactive functionalities in harsh antagonistic environment at which wired system deployment is completely infeasible. Majority of the clustering mechanisms contributed to the literature concentrated on augmenting network lifetime and energy stability. However, energy consumption incurred by cluster heads (CHs) are high and thereby results in minimized network lifetime and frequent CHs selection. In this paper, a modified whale‐dragonfly optimization algorithm and self‐adaptive cuckoo search‐based clustering strategy (MWIDOA‐SACS) is proposed for sustaining energy stability and augment network lifetime. In specific, MWIDOA‐SACS is included for exploiting the fitness values that aids in determining two optimal nodes that are selected as optimal CH and cluster router (CR) nodes in the network. In MWIDOA, the search conduct of dragon flies is completely updated through whale optimization algorithm (WOA) for preventing load balancing at CHs. It minimized the overhead of CH by adopting CHs and CR for collecting information from cluster members and transmitting the aggregated data from CHs to the base station (BS). It included self‐adaptive cuckoo search (SACS) for achieving sink mobility using radius, energy stability, received signal strength, and throughput for achieving optimal data transmission process after partitioning the network into unequal clusters. Simulation experiments of the proposed MWIDOA‐SACS confirmed better performance in terms of total residual energy by 21.28% and network lifetime by 26.32%, compared to the competitive CH selection strategies.

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Self Adapting Differential Search Strategies Improved Artificial Bee Colony Algorithm-Based Cluster Head Selection Scheme for WSNs

Cited by 17 publications

References 24 publications

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Improved bat optimization algorithm and enhanced artificial bee colony‐based cluster routing scheme for extending network lifetime in wireless sensor networks

Auto Deep Learning-based Automated Surveillance Technique to Recognize the Activities in the Cyber-Physical System

A modified whale‐dragonfly algorithm and self‐adaptive cuckoo search‐based clustering strategy for augmenting network lifetime in wireless sensor networks

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