Improved Artificial Bee Colony Optimization-Based Clustering Technique for WSNs

Famila, S.; Jawahar, A.

doi:10.1007/s11277-019-06837-6

Cited by 25 publications

(11 citation statements)

References 18 publications

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“…The PDR is computed by using the Eq. ( 8) as follows, 𝑃𝐷𝑅 = 𝑇 𝑁𝐷 𝑇 𝑁𝑆 (8) where, the number of delivered packets is represented as 𝑇 𝑁𝐷 and the number of sent packets is referred as 𝑇 𝑁𝑆 .…”

Section: Packet Delivery Ratementioning

confidence: 99%

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Security and Energy Aware Adaptive Routing using Cost Centric Cuckoo Search Algorithm

2021

IJIES

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Section: Packet Delivery Ratementioning

confidence: 99%

“…Hence, the clustering and routing approaches help in limiting the energy utilization and in reducing the processing overhead in the network. However, the path obtained to reach the destination node from source node by using the routing approach has several security issues [8,9].…”

Section: Introductionmentioning

confidence: 99%

Security and Energy Aware Adaptive Routing using Cost Centric Cuckoo Search Algorithm

2021

IJIES

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“…This is aggregate energy in sensor nodes. Nodes with high energy will only participate in CH selection [49]…”

Section: Cluster Head Selection Criteriamentioning

confidence: 99%

“…"Bee Sensor-C" is an energy-efficient and scalable multi-channel routing protocol based on dynamic cluster and bee group behavior [49]. It adopts an improved multipath architecture strategy, achieving balanced energy depletion in the network, reducing overhead routing and refining network scalability and performance, as well as its lifetime.…”

Section: Bee-sensor-cmentioning

confidence: 99%

Machine Learning-Based Optimized Hierarchical Routing Protocols for WSN Lifetime: A Review

Prakash¹

2021

ITII

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In Wireless Sensor Networks, network lifetime optimization has challenging and significant issue. Subsequently, most of the existing works delineate several factors to improve the network lifetime: by decreasing the amount of the consumption of energy, reducing latency, load balancing, clustering, efficient data aggregating and by minimizing the data transmission delays. This paper provides a review of recent techniques and presents a Machine Learning-based Optimized Hierarchical Routing Protocols for WSN Lifetime. Research has been done, and reviews have been studied to explore the energy management schemes using optimized routing approach and Machine Learning Adaptability for WSN’s. Further, recommend future directions related to the Optimized Clustering Approaches to enhance wsn lifetime.

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“…However, data delivery rate was not higher. An enhanced Artificial Bee Colony Optimization-based Clustering Technique (IABCOCT) was presented [8] for carrying out optimal clustering process and selection of cluster heads. But, transmission delay was not reduced.…”

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

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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.

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Improved Artificial Bee Colony Optimization-Based Clustering Technique for WSNs

Cited by 25 publications

References 18 publications

Security and Energy Aware Adaptive Routing using Cost Centric Cuckoo Search Algorithm

Security and Energy Aware Adaptive Routing using Cost Centric Cuckoo Search Algorithm

Machine Learning-Based Optimized Hierarchical Routing Protocols for WSN Lifetime: A Review

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

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