A Hybrid Grey Wolf and Crow Search Optimization Algorithm-Based Optimal Cluster Head Selection Scheme for Wireless Sensor Networks

Subramanian, P.; Sahayaraj, J. Martin; Senthilkumar, S.; Alex, Doney

doi:10.1007/s11277-020-07259-5

Cited by 59 publications

(29 citation statements)

References 17 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

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“…[37][38][39][40][41][42][43][44] The simulation parameters considered for implementing the proposed HCSGWOA-NLOS-PS and the benchmarked WI-CLOSPS-DVBA, 23 CS-NLOSSL-CIC, 25 WDHP-NLOS-PS, 22 and CVN-NLOS-PS 21 schemes are kept constant throughout the simulation. 17,[45][46][47][48][49] They are implemented with the same environment, traffic condition and mobility model. The complete simulation experiments of the proposed HCSGWOA-NLOS-PS and the benchmarked schemes are conducted for a network of size 1500 × 1500 m with 500 vehicular nodes deployed in the network.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…The simulation experiments of the proposed HCSGWOA‐NLOS‐PS and the benchmarked approaches are conducted and validated using EstiNet 8.1 Simulator 37–44 . The simulation parameters considered for implementing the proposed HCSGWOA‐NLOS‐PS and the benchmarked WI‐CLOSPS‐DVBA, 23 CS‐NLOSSL‐CIC, 25 WDHP‐NLOS‐PS, 22 and CVN‐NLOS‐PS 21 schemes are kept constant throughout the simulation 17,45–49 . They are implemented with the same environment, traffic condition and mobility model.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…In addition, the possibility of hybridizing Crow Search Optimization Algorithm (CSOA) and Gray Wolf Optimization Algorithm (GWOA) contributed by Arora et al 16 and Subramanian et al 17 motivated the option of incorporating them for effective and reliable positioning of NLOS nodes in the network and ensuring a high degree of warning message delivery with balanced tradeoff between exploration and exploitation. In specific, GWOA is developed with the leadership properties and distinct hunting capability of gray wolves.…”

Section: Introductionmentioning

confidence: 99%

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A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

Malar¹,

Priya²,

Janakiraman³

2020

Int J Communication

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Summary Vehicular Ad hoc NETwork (VANET) facilitates ubiquitous connectivity for establishing Vehicle‐to‐Vehicle (V2V) communication and supporting Intelligent Transportation Systems (ITSs). This vehicle communication requires complete coverage within the target range for ensuring reliable message dissemination. High density of vehicles in the intersections introduces challenges due to obstacles such as buildings, foliage, and other moving vehicles, preventing exchange of information about location and message update between vehicles. Non‐Line‐of‐Sight (NLOS) nodes also introduce broadcasting storm problem leading to congestion that prevents emergency messages from reaching the target vehicular nodes. Integration of meta‐heuristic Crow Search Algorithm (CSA) and Gray Wolf Optimization Algorithm (GWOA) minimizes the objective function of NLOS localization problem without the solution being trapped into local optima. In this paper, a Hybrid Crow Search and GWOA‐based NLOS Positioning Scheme (HCSGWOA‐NLOS‐PS) is proposed to handle the issue of broadcast storm and facilitate reliability in emergency message delivery. The proposed HCSGWOA‐NLOS‐PS utilizes the benefits of Time of Arrival (ToA) and geographical information‐based cooperative localization agent for attaining efficient NLOS node positioning in the network. It uses the benefits of CSA and GWOA for positioning the NLOS nodes based on the intelligence derived from the crows' conduit and the social attacking behavior of gray wolves that are ideal for balancing the tradeoff between exploration and exploitation. The simulation results of the proposed HCSGWOA‐NLOS‐PS confirm a mean emergency message delivery of 11.82%, neighborhood awareness of 12.38% with reduced localization error rate of 2.36% and minimized delay of 8.64% when compared to the baseline approaches.

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“…Recently developed work is relevant to the newly deployed approach, as defined in the following. In [11,21], a hybridized grey wolf and crow search method depending on optimal CH selection (HGWCSOA-OCHS) was developed and used for enhancing network lifespan. This was achieved by reducing delays, distance between nodes, and energy utilization.…”

Section: Related Workmentioning

confidence: 99%

Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

Alkhliwi¹

2020

IJACSA

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Currently, wireless sensor networks (WSNs) and the Internet of Things (IoT) have become useful in a wide range of applications. The nodes in IoT assisted WSN commonly operate on restricted battery units, meaning energy efficiency is a major design issue. Clustering and route selection processes are commonly utilized energy-efficient techniques for WSN. Although several cluster-based routing approaches are available for homogeneous WSN, only a limited number of studies have focused on energy efficient heterogeneous WSN (HWSN). Moreover, security poses a major design issue in the HWSN. This paper introduces an energy efficient cluster-based routing protocol with a secure intrusion detection system in HWSN called EECRP-SID. The proposed EECRP-SID technique involves three main phases: cluster construction, optimal path selection, and intrusion detection. Initially, the type II fuzzy logic-based clustering (T2FC) technique with three input parameters are applied for cluster head (CH) selection. These parameters are residual energy level (REL), distance to the base station (DTBS), and node density (NDEN). In addition to CH selection, the salp swarm optimization (SSO) technique is utilized to select optimal paths for inter cluster data transmission, which results in energy efficient HWSN. Finally, to achieve security in cluster based WSN, an effective intrusion detection system (IDS) using long short-term memory (LSTM) is executed on the CHs to identify the presence of intruders in the network. The EECRP-SID method was implemented in MATLAB, and experimental outcomes indicate that it outperformed the compared methods in terms of distinct performance measures.

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A Hybrid Grey Wolf and Crow Search Optimization Algorithm-Based Optimal Cluster Head Selection Scheme for Wireless Sensor Networks

Cited by 59 publications

References 17 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

A Hybrid Crow Search and Gray Wolf Optimization Algorithm‐based Reliable Non‐Line‐of‐Sight Node Positioning Scheme for Vehicular Ad hoc Networks

Energy Efficient Cluster based Routing Protocol with Secure IDS for IoT Assisted Heterogeneous WSN

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