Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks

Sampathkumar, A.; Mulerikkal, Jaison; Sivaram, M.

doi:10.1007/s11276-020-02336-w

Cited by 39 publications

(13 citation statements)

References 33 publications

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“…Most of the papers prioritize the following elements in their experiments: task scheduling [96], [123], [124], biometric identification [112], [125], network optimization [60], [126]- [129], and network stability [123], [130], [131]. ACO may be very promising when implemented to single-goal optimization, even though more common GA have somewhat overtaken.…”

Section: Discussion and Potential Remarksmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature's ecosystems. Bio-inspired algorithms' adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges,

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Section: Discussion and Potential Remarksmentioning

confidence: 99%

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

et al. 2020

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“…Moreover, an efficient data aggregation model was involved to improve the network throughput, wherein the redundant data packets were eliminated. An efficient load balancing optimization algorithm for the employment of energy effectual routing as well as a load balancing protocol were used to enhance the network lifetime [19,20]. The authors in [21] explored the cluster-based backpressure routing algorithm for IoTs, which discusses the energy and congestion issues in IoT environment.…”

Section: Related Workmentioning

confidence: 99%

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Nagaraju¹,

Venkatesan²,

Kalaivani

et al. 2022

Energies

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Wireless sensor networks (WSNs) and the Internet of Things (IoT) are increasingly making an impact in a wide range of domain-specific applications. In IoT-integrated WSNs, nodes generally function with limited battery units and, hence, energy efficiency is considered as the main design challenge. For homogeneous WSNs, several routing techniques based on clusters are available, but only a few of them are focused on energy-efficient heterogeneous WSNs (HWSNs). However, security provisioning in end-to-end communication is the main design challenge in HWSNs. This research work presents an energy optimizing secure routing scheme for IoT application in heterogeneous WSNs. In our proposed scheme, secure routing is established for confidential data of the IoT through sensor nodes with heterogeneous energy using the multipath link routing protocol (MLRP). After establishing the secure routing, the energy and network lifetime is improved using the hybrid-based TEEN (H-TEEN) protocol, which also has load balancing capacity. Furthermore, the data storage capacity is improved using the ubiquitous data storage protocol (U-DSP). This routing protocol has been implemented and compared with two other existing routing protocols, and it shows an improvement in performance parameters such as throughput, energy efficiency, end-to-end delay, network lifetime and data storage capacity.

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“…Various surveys on energy use, energy gaps and attacks on RPL and LLP systems can be found in [27][28][29][30]. The glowworm swarm-based approach cast-off energy-based transmission strategy has been presented to decrease energy consumption caused by control overhead [31]. A least-square support vector machine (LS-SVM) based on modified particle swarm optimization (MPSO) is developed.…”

Section: Literature Reviewmentioning

confidence: 99%

“…In this, D a × D b denotes the grid cell dimension where the length is determined by D a and width is determined by X b . Equation (1) [31] indicates the grid cell generation process. The grid-building procedure is completed by…”

Section: Grid Formationmentioning

confidence: 99%

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

et al. 2022

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The goal of today’s technological era is to make every item smart. Internet of Things (IoT) is a model shift that gives a whole new dimension to the common items and things. Wireless sensor networks, particularly Low-Power and Lossy Networks (LLNs), are essential components of IoT that has a significant influence on daily living. Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard protocol for IoT and LLNs. It is not only used widely but also researched by various groups of people. The extensive use of RPL and its customization has led to demanding research and improvements. There are certain issues in the current RPL mechanism, such as an energy hole, which is a huge issue in the context of IoT. By the initiation of Grid formation across the sensor nodes, which can simplify the cluster formation, the Cluster Head (CH) selection is accomplished using fish swarm optimization (FSO). The performance of the Graph-Grid-based Convolution clustered neural network with fish swarm optimization (GG-Conv_Clus-FSO) in energy optimization of the network is compared with existing state-of-the-art protocols, and GG-Conv_Clus-FSO outperforms the existing approaches, whereby the packet delivery ratio (PDR) is enhanced by 95.14%.

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Glowworm swarm optimization for effectual load balancing and routing strategies in wireless sensor networks

Cited by 39 publications

References 33 publications

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Applications and Evaluations ofBio-InspiredApproaches in Cloud Security: A Review

Secure Routing-Based Energy Optimization for IoT Application with Heterogeneous Wireless Sensor Networks

Clustering Based Optimal Cluster Head Selection Using Bio-Inspired Neural Network in Energy Optimization of 6LowPAN

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