Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Janarthanan, Ashwinth; Srinivasan, Vidhusha

doi:10.1002/dac.5664

Cited by 8 publications

(1 citation statement)

References 43 publications

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“…The performance of deep neural networks (DNNs) in areas such as image recognition, speech recognition, and natural language processing is superior to that of traditional methods, which provides an opportunity for the development of WSNs. In [ 22 ], the authors proposed an energy-aware routing scheme based on a multi-objective cluster head for secure data aggregation in WSN. The proposed scheme outperformed the existing models in terms of network lifespan, packet transmission rate, and delay.…”

Section: Related Workmentioning

confidence: 99%

Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks

Hu,

Han,

Wang

et al. 2024

Sensors

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Energy efficiency and security issues are the main concerns in wireless sensor networks (WSNs) because of limited energy resources and the broadcast nature of wireless communication. Therefore, how to improve the energy efficiency of WSNs while enhancing security performance has attracted widespread attention. In order to solve this problem, this paper proposes a new deep reinforcement learning (DRL)-based strategy, i.e., DeepNR strategy, to enhance the energy efficiency and security performance of WSN. Specifically, the proposed DeepNR strategy approximates the Q-value by designing a deep neural network (DNN) to adaptively learn the state information. It also designs DRL-based multi-level decision-making to learn and optimize the data transmission paths in real time, which eventually achieves accurate prediction and decision-making of the network. To further enhance security performance, the DeepNR strategy includes a defense mechanism that responds to detected attacks in real time to ensure the normal operation of the network. In addition, DeepNR adaptively adjusts its strategy to cope with changing network environments and attack patterns through deep learning models. Experimental results show that the proposed DeepNR outperforms the conventional methods, demonstrating a remarkable 30% improvement in network lifespan, a 25% increase in network data throughput, and a 20% enhancement in security measures.

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Section: Related Workmentioning

confidence: 99%

Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks

Hu,

Han,

Wang

et al. 2024

Sensors

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A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Kumaragurubaran,

Vijayakumar

2024

Int J Communication

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SummaryA vehicular ad hoc network (VANET) is a wireless network composed of a collection of mobility devices that link via a wireless channel without the need for permanent infrastructure or centralized administration. Multicasting is a method of sending data from a single node to multiple recipients at the same time. Because of its energizing network topology and constricted resources, a mobile ad hoc network (MANET) presents a number of issues. Because network execution measurements such as latency, bandwidth, throughput, and energy fluctuate so rapidly owing to node movement, these network properties in a wireless mobile ad hoc network are ambiguous. Because of the influence of these uncertainty issues, determining the top‐grade path from the originating node to a set of recipient nodes is challenging. In order to preserve network resources, we used a Swarm Intelligence‐based fuzzy logic strategy in this study that includes channel caliber factor and connection termination time to try to alleviate these uncertainty issues. This strategy integrates all of the network indicators available for the routes into an individual quantity named as fuzzy cost or cost of communication. The routes with the lowest fuzzy cost will be regarded optimum, and data will be delivered along this route from the originating point to a set of receivers. The simulation was carried out using NS‐3.38 and MATLAB, and the results reveal that the suggested protocol, HACOFLM, outperforms ODMRP, OFAODV, MAODV, and DFMCRP by means of packet delivery ratio, latency, control packet overhead, and throughput. DFMCRP outperforms MAODV by 3% to 6%, OFAODV by 13% to 19%, and ODMRP by 16% to 23%. According to HACOSMO, based on the number of vehicles and velocities, routing overhead is 7% to 10% less than DFMCRP, 9% to 13% less than MAODV, 17% to 29% less than OFAODV, and 22% to 36% less than ODMRP. It improvise throughput by 4% to 6% when contrasted to DFMCRP, 5% to 9% when contrasted to MAODV, 10% to 14% when contrasted to OFAODV, and 14% to 23% when contrasted to ODMRP.

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Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

Sennan,

Ramasubbareddy,

Dhanaraj

et al. 2024

Telecommun Syst

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Multi‐objective cluster head‐based energy aware routing using optimized auto‐metric graph neural network for secured data aggregation in Wireless Sensor Network

Cited by 8 publications

References 43 publications

Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks

Security Enhancement for Deep Reinforcement Learning-Based Strategy in Energy-Efficient Wireless Sensor Networks

A novel swarm intelligence‐based fuzzy logic in efficient connectivity of vehicles

Energy-efficient cluster head selection in wireless sensor networks-based internet of things (IoT) using fuzzy-based Harris hawks optimization

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