Efficient routing mechanism for neighbour selection using fuzzy logic in wireless sensor network

Ramkumar, K.; Ananthi, N.; Brabin, D. R. Denslin; Goswami, Puneet; Baskar, M.; Bhatia, Komal Kumar; Kumar, Harish

doi:10.1016/j.compeleceng.2021.107365

Cited by 18 publications

(6 citation statements)

References 14 publications

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“…The proposed FQA is simulated using MATLAB 2022a software because its Fuzzy Toolbox examines all fuzzy membership functions, hence making it suitable for use. The performance is compared with the existing protocols, including FRNSEER [13], FC-RBAT [14], BOA-ACO [22], and OAFS-IMFO [32]. The simulation parameters are presented in Table 3.…”

Section: Simulation Results and Discussionmentioning

confidence: 99%

“…Combining fuzzy logic and meta-heuristic algorithms can fully leverage their respective strengths and enhance capabilities in addressing intricate optimization problems. The generation of fuzzy rules in [13] involves considering energy and utility factors, and the fuzzy inference system is employed to identify relay nodes. The minimum length route is determined based on coverage distance, energy, utility factor, and bandwidth availability.…”

Section: Related Workmentioning

confidence: 99%

“…Unreasonable allocation will result in an unequal number of sensor nodes, resulting in uneven residual energy. Fuzzy logic performs well in optimization decision making [13][14][15], which is being used to resolve uncertainties in CH selection based on predefined fuzzy rules. Unlike the conventional technique, each variable in fuzzy logic is assigned a value between 0 and 1, signifying its correctness or incorrectness [16].…”

Section: Introductionmentioning

confidence: 99%

“…The detailed linguistic variables and parameters can be found in Table 1. The membership functions of the inputs and output can be formulated based on previous works [13,17] and experimental experiences. These are illustrated in Figure 3 and Figure 4, respectively.…”

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confidence: 99%

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Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

Wang,

Liu,

Wang

et al. 2024

Sensors

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The main limitation of wireless sensor networks (WSNs) lies in their reliance on battery power. Therefore, the primary focus of the current research is to determine how to transmit data in a rational and efficient way while simultaneously extending the network’s lifespan. In this paper, a hybrid of a fuzzy logic system and a quantum annealing algorithm-based clustering and routing protocol (FQA) is proposed to improve the stability of the network and minimize energy consumption. The protocol uses a fuzzy inference system (FIS) to select appropriate cluster heads (CHs). In the routing phase, we used the quantum annealing algorithm to select the optimal route from the CHs and the base station (BS). Furthermore, we defined an energy threshold to filter candidate CHs in order to save computation time. Unlike with periodic clustering, we adopted an on-demand re-clustering mechanism to perform global maintenance of the network, thereby effectively reducing the computation and overhead. The FQA was compared with FRNSEER, BOA-ACO, OAFS-IMFO, and FC-RBAT in different scenarios from the perspective of energy consumption, alive nodes, network lifetime, and throughput. According to the simulation results, the FQA outperformed all the other methods in all scenarios.

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Section: Simulation Results and Discussionmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

mentioning

confidence: 99%

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Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

Wang,

Liu,

Wang

et al. 2024

Sensors

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“…Ramkumar K [ 20 ] proposed fuzzy-based relay node selection and energy-efficient routing (FRNSEER) to enhance QoS performance in WSNs. The FRNSEER selects the best sink node among active relay nodes to collect data from sensor nodes; sensor hubs were applied to link sensor nodes to the sink.…”

Section: Related Workmentioning

confidence: 99%

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

Almudayni,

Soh,

2023

Sensors

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In the realm of the Internet of Things (IoT), a network of sensors and actuators collaborates to fulfill specific tasks. As the demand for IoT networks continues to rise, it becomes crucial to ensure the stability of this technology and adapt it for further expansion. Through an analysis of related works, including the feedback-based optimized fuzzy scheduling approach (FOFSA) algorithm, the adaptive task allocation technique (ATAT), and the osmosis load balancing algorithm (OLB), we identify their limitations in achieving optimal energy efficiency and fast decision making. To address these limitations, this research introduces a novel approach to enhance the processing time and energy efficiency of IoT networks. The proposed approach achieves this by efficiently allocating IoT data resources in the Mist layer during the early stages. We apply the approach to our proposed system known as the Mist-based fuzzy healthcare system (MFHS) that demonstrates promising potential to overcome the existing challenges and pave the way for the efficient industrial Internet of healthcare things (IIoHT) of the future.

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A new fuzzy logic approach for reliable communications in wireless underground sensor networks

et al. 2022

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Efficient routing mechanism for neighbour selection using fuzzy logic in wireless sensor network

Cited by 18 publications

References 14 publications

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

Energy-Efficient, Cluster-Based Routing Protocol for Wireless Sensor Networks Using Fuzzy Logic and Quantum Annealing Algorithm

Enhancing Energy Efficiency and Fast Decision Making for Medical Sensors in Healthcare Systems: An Overview and Novel Proposal

A new fuzzy logic approach for reliable communications in wireless underground sensor networks

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