A Hybrid Ant Colony and Artificial Bee Colony Optimization Algorithm-based Cluster Head Selection for IoT

Janakiraman, Sengathir

doi:10.1016/j.procs.2018.10.407

Cited by 60 publications

(36 citation statements)

References 8 publications

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“…The simulation of the proposed IBkd‐Tree‐IEFCRP scheme is facilitated in the deployment area of 100 m × 100 m area in a random area that sustains constant density of nodes in the network. In addition, the potential simulation parameters 40–42 used in the implementation of the proposed IBkd‐Tree‐IEFCRP scheme is highlighted in Table 2.…”

Section: Resultsmentioning

confidence: 99%

An Energy‐Proficient Clustering‐Inspired Routing Protocol using Improved Bkd‐tree for Enhanced Node Stability and Network Lifetime in Wireless Sensor Networks

Janakiraman¹,

Priya

2020

Int J Communication

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Energy conservation in Wireless Sensor Networks (WSNs) is essential for improving the stability of sensor nodes and network lifetime. An energyproficient clustering-based routing protocol is necessary for efficient cluster formation and Cluster Head (CH) selection to sustain Quality of Service (QoS). In this paper, an Improved Bkd-Tree-Inspired Energy-eFficient Clusteringbased Routing Protocol (IBkd-Tree-IEFCRP) is propounded as a potential strategy for guaranteeing significant cluster formation and management. It is proposed as an effective strategy in CH node allocation that handles the impacts of QoS during the process of data dissemination. This IBkd-Tree-IEFCRP is proposed to deal with energy saving with respect to data traffic by including a strategy of cluster formation which incorporates the benefits of partition data structure that incorporates animprovedBkd-treealgorithm.Italsoconcentrates ontheprocessofCHselection by integrating a reactive approach that enhances throughput with minimized energy, delay and jitter. The simulation experiments of the proposed IBkd-Tree-IEFCRP confirm a better performance in improving the average throughput by 13.21%, reducing the energy consumptions by 12.96%, minimizing the delay by 11.98% and reducing the jitter by 14.24% when compared to the baseline kdimensional tree algorithm (kd-tree algorithm), Weighting and Parameter Optimization-based Energy-Efficient Clustering Routing Protocol (WPO-EECRP), Improved Clustering by Fast Search and Finding of Density Peaks (ICFSFDP), and Energy-based Cluster Centered Routing Protocol (ECCRP) taken for comparison. The proposed IBkd-Tree-IEFCRP is also identified to reduce delay by 8.42% and jitter by 7.18% and increase throughput by 6.84% when compared to the benchmarked protocols for different number of rounds.

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Section: Resultsmentioning

confidence: 99%

An Energy‐Proficient Clustering‐Inspired Routing Protocol using Improved Bkd‐tree for Enhanced Node Stability and Network Lifetime in Wireless Sensor Networks

Janakiraman¹,

Priya

2020

Int J Communication

Self Cite

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“…In this layer, the input weight and deviation will be randomly generated and least square method will be deployed to determine output weight analytically [17], which differentiates this method from traditional methods. In this phase, learning happens followed by finding transformation matrix [93][94][95][96][97][98][99][100][101][102][103]. It is deployed to minimize the sum-of-squares error function.…”

Section: Machine Learning and Classification Methodsmentioning

confidence: 99%

Literature Review on Big Data Analytics Methods

Vanani¹,

Majidian²

2020

Social Media and Machine Learning

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Companies and industries are faced with a huge amount of raw data, which have information and knowledge in their hidden layer. Also, the format, size, variety, and velocity of generated data bring complexity for industries to apply them in an efficient and effective way. So, complexity in data analysis and interpretation incline organizations to deploy advanced tools and techniques to overcome the difficulties of managing raw data. Big data analytics is the advanced method that has the capability for managing data. It deploys machine learning techniques and deep learning methods to benefit from gathered data. In this research, the methods of both ML and DL have been discussed, and an ML/DL deployment model for IOT data has been proposed.

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“…An integrated ABC and monarchy optimization algorithm-based optimal cluster head selection scheme was proposed for ensuring energy stability in the network. 35 This integrated clustering scheme included a dynamic process of butterfly adjustment rate that determine a reliable attempt to balance the trade-off between the exploitation and exploration. It was confirmed to be excellent in throughput, percentage of alive nodes, and residual energy by 14.21%, 16.58%, and 17.95% compared to the EPSO-CHSS and FC-ABCAICHSS used for investigation.…”

Section: Related Workmentioning

confidence: 99%

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

Murugadass

Poruran

2020

Int J Communication

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Summary Clustering‐based optimal cluster head selection in wireless sensor networks (WSNs) is considered as the efficient technique essential for improving the network lifetime. But enforcing optimal cluster head selection based on energy stabilization, reduced delay, and minimized distance between sensor nodes always remain a crucial challenge for prolonging the network lifetime in WSNs. In this paper, a hybrid elephant herding optimization and cultural algorithm for optimal cluster head selection (HEHO‐CA‐OCHS) scheme is proposed to extend the lifetime. This proposed HEHO‐CA‐OCHS scheme utilizes the merits of belief space framed by the cultural algorithm for defining a separating operator that is potent in constructing new local optimal solutions in the search space. Further, the inclusion of belief space aids in maintaining the balance between an optimal exploitation and exploration process with enhanced search capabilities under optimal cluster head selection. This proposed HEHO‐CA‐OCHS scheme improves the characteristic properties of the algorithm by incorporating separating and clan updating operators for effective selection of cluster head with the view to increase the lifetime of the network. The simulation results of the proposed HEHO‐CA‐OCHS scheme were estimated to be superior in percentage of alive nodes by 11.21%, percentage of dead nodes by 13.84%, residual energy by 16.38%, throughput by 13.94%, and network lifetime by 19.42% compared to the benchmarked cluster head selection schemes.

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A Hybrid Ant Colony and Artificial Bee Colony Optimization Algorithm-based Cluster Head Selection for IoT

Cited by 60 publications

References 8 publications

An Energy‐Proficient Clustering‐Inspired Routing Protocol using Improved Bkd‐tree for Enhanced Node Stability and Network Lifetime in Wireless Sensor Networks

An Energy‐Proficient Clustering‐Inspired Routing Protocol using Improved Bkd‐tree for Enhanced Node Stability and Network Lifetime in Wireless Sensor Networks

Literature Review on Big Data Analytics Methods

A hybrid elephant herding optimization and cultural algorithm for energy‐balanced cluster head selection scheme to extend the lifetime in WSNs

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