An optimal model for enhancing network lifetime and cluster head selection using hybrid snake whale optimization

Samiayya, Duraimurugan; Radhika, S.; Chandrasekar, A.

doi:10.1007/s12083-023-01487-9

Cited by 6 publications

(5 citation statements)

References 26 publications

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“…Samiayya et al [ 24 ] proposed hybrid snake whale optimization (HSWO) for optimal CH selection for enhancing network lifetime in WSNs. It had three phases: CH selection, initialization, and route selection.…”

Section: Related Workmentioning

confidence: 99%

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SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network’s lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

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Section: Related Workmentioning

confidence: 99%

“…Energy consumption increases during the process of CH selection. Samiayya et al [24] Hybrid Snake Whale Optimization (HSWO) Takes more time to converge. Abraham et al [27] Flamingo Search Algorithm (FSA)…”

Section: Snomentioning

confidence: 99%

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Somula,

Cho,

Mohanta

2024

Sensors

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“…Use of formal techniques in this study: Formal techniques used in this study are the Energy-Efficient Cluster Head Selection Mechanism for Livestock Industry using Artificial Rabbits Optimization (EECHS-ARO) [33], the Energy-Efficient Cluster Head Selection in Wireless Sensor Networks Using an Improved Grey Wolf Optimization (EECHIGWO) [22], the Osprey Optimization Algorithm based on Energy-Efficient Cluster Head Selection (SWARAM) [15] and Hybrid Snake Whale Optimization (HSWO) [34]. However, previously, there were a number of models that focused on minimization of energy consumption in WSN.…”

Section: Dataset Descriptionmentioning

confidence: 99%

“…Based on these statistics, the table shows the training time and execution time of each attack based on its features, which is tabulated. The simulation results for the WSN's performance, taking into account dead nodes, are shown in Figure 5a, with existing techniques like EECHS-ARO [33], HSWO [34] and SWARAM. When a node's energy ran out throughout the simulation, that node counted as a dead node.…”

Section: Performance Analysis Of Eecmcmmentioning

confidence: 99%

Energy-Efficient Secure Routing for a Sustainable Heterogeneous IoT Network Management

Thangavelu,

Rajendran

2024

Sustainability

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The Heterogeneous Internet of Things (H-IoT) is considered as the upcoming industrial and academic revolution in the technological world, having billions of things and devices connected to the Internet. This H-IoT has a major issue of energy consumption during data transmission which leads to low scalability. Additionally, anomalies in the data create a serious threat to energy in H-IoT. To overcome these issues, a novel approach has been proposed in this study termed as the Energy-Efficient Memetic Clustering Method (EEMCM), which combines the Parallelized Memetic Algorithm (PMA) with the AlexNet architecture to improve anomaly detection efficiency in IoT WSNs. Initially, cluster formation and CH selection are carried out using PMA. This is followed by routing path generation, and the data are prepared for high-level feature extraction. The extracted features are classified to identify anomalies. For anomaly detection, high-level features were collected that contain data relevant to the model given as input into the AlexNet architecture, which detects anomalies and identifies normal or potential attacks within the IoT WSNs. The proposed EEMCM model has been implemented in the MATLAB platform and obtained an accuracy of 99.11%. As a result, the overall performance of the network is improved.

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“…Currently, delay-sensitive control systems have made significant progress in multiple industries including transportation [ 5 , 6 ], autonomous vehicle control [ 7 ], wireless sensor networks [ 8 ], and power systems [ 9 , 10 ]. In recent years, stability analysis and controller integration have been used in linear systems with measurement delays or actuator delays.…”

Section: Introductionmentioning

confidence: 99%

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

Wang,

Zhao

et al. 2023

Sensors

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The uncertain delay characteristic of actuators is a critical factor that affects the control effectiveness of the active suspension system. Therefore, it is crucial to develop a control algorithm that takes into account this uncertain delay in order to ensure stable control performance. This study presents a novel active suspension control algorithm based on deep reinforcement learning (DRL) that specifically addresses the issue of uncertain delay. In this approach, a twin-delayed deep deterministic policy gradient (TD3) algorithm with system delay is employed to obtain the optimal control policy by iteratively solving the dynamic model of the active suspension system, considering the delay. Furthermore, three different operating conditions were designed for simulation to evaluate the control performance: deterministic delay, semi-regular delay, and uncertain delay. The experimental results demonstrate that the proposed algorithm achieves excellent control performance under various operating conditions. Compared to passive suspension, the optimization of body vertical acceleration is improved by more than 30%, and the proposed algorithm effectively mitigates body vibration in the low frequency range. It consistently maintains a more than 30% improvement in ride comfort optimization even under the most severe operating conditions and at different speeds, demonstrating the algorithm’s potential for practical application.

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An optimal model for enhancing network lifetime and cluster head selection using hybrid snake whale optimization

Cited by 6 publications

References 26 publications

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things

Energy-Efficient Secure Routing for a Sustainable Heterogeneous IoT Network Management

Research on Deep Reinforcement Learning Control Algorithm for Active Suspension Considering Uncertain Time Delay

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