Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications

Mishra, Mukesh; Gupta, Gourab Sen; Gui, Xiang

doi:10.3390/s21227439

Cited by 19 publications

(8 citation statements)

References 44 publications

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“…Firstly, we allocated an initial energy, denoted as "E 0 ", to each sensor node. Secondly, when focusing on transmission dynamics, the energy, represented as "E T X ", required by a sensor node to broadcast a data packet containing "k" bits over a distance "d" is shown in Equation 7 [50], [51], [52], [53], [54], [55], [46].…”

Section: F Energy Consumption Modelmentioning

confidence: 99%

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

Haidar Hari,

Sholihul Hadi,

Sujito

2024

IJCDS

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Wireless Sensor Networks (WSNs) stand as a vital component in contemporary wireless technology. The endurance of nodes within WSNs significantly influences system efficiency. This study delves into five energy management and node longevity strategies: Low Energy Adaptive Clustering Hierarchy (LEACH), LEACH-C, TS-I-LEACH, LEACH-Enh-DVHOP, and an original method proposed herein. Graphical analysis underscores the marked enhancement of the proposed method in sustaining active nodes over rounds, notably in initial phases compared to other methods. A pivotal innovation lies in employing angular area-based routing, augmenting resource allocation and energy efficiency. Moreover, the study introduces a node's probability to serve as a Cluster Head (CH), leveraging the refined threshold formula "T A(i)," contingent on its distance from the base station and the midpoint of the represented zone, denoted by "δ(i)". This approach offers a novel means to bolster node longevity in WSNs while optimizing energy usage. Furthermore, the study evaluates the time required to reach the point where all nodes are depleted of energy, referred to as the "All Node Die" metric, against the number of rounds (r). LEACH-Enh-DVHOP (the previous study) exhibits a 63.14% increase in energy efficiency over LEACH Original, while in this study, Proposed (This Paper) demonstrates a 3.10% improvement over LEACH-Enh-DVHOP. Additionally, an enhancement in Quality of Service (QoS) is observed. The packet delivery ratio increases by 200% compared to LEACH Original for LEACH-Enh-DVHOP and by 383.3% for Proposed (This Paper) compared to LEACH-Enh-DVHOP (previous study). The study's outcomes are poised to advance WSN technology in the future significantly, redefining energy management paradigms and enriching node sustainability in wireless networks.

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Section: F Energy Consumption Modelmentioning

confidence: 99%

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

Haidar Hari,

Sholihul Hadi,

Sujito

2024

IJCDS

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“…An Energy-Efficient Hybrid Routing Protocol was suggested by Mishra et al 21 for the improvement of the Network lifetime of IoT applications. A multi-objective optimization strategy is proposed in this work.…”

Section: Related Workmentioning

confidence: 99%

Hybrid deep marine reinforcement learning-based routing protocol in wireless sensor networks

Kumar

2023

International Conference on Mathematical and Statistical Physics, Computational Science, Education, and Communication (ICMSCE 2

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The Wireless Sensor Network (WSN) is a network with limited resources that uses more energy to transmit and receive data. The small sensor nodes are typically difficult to recharge after deployment so that, the routing protocol's energy consumption can have an impact on the lifespan of a WSN. In general, data aggregation is used to lower the quantity of data transferred in a WSN by minimizing and/or eliminating data redundancy at each node. This paper proposes a new energy-efficient hybrid routing protocol to enhance the network lifetime in WSN model. This protocol accomplishes the best data transmission through the selected path. The clustering approach used to distribute WSN nodes must be effective in order for the complete structure to achieve a longer network lifespan. Using the Fuzzy Density Peak Clustering (FDPC) Method, the whole network is divided into multiple clusters during clustering. Furthermore, the cluster head (CH) selection procedure requires careful consideration in order to provide effective data transfer to the sink node via the chosen CH and to improve node accessibility inside the cluster. An Adaptive Donkey Theorem Optimization (ADTO) technique is used to identify the CH necessary for effective data transfer. The data is sent to the sink node over the chosen CH using an efficient Hybrid Deep Marine Reinforcement Learning (H-DMRL) approach. The proposed hybrid routing protocol attains an improved throughput, network lifetime, and to reduce the energy consumption by nodes and CH, death of sensor nodes, routing overhead. The proposed scheme will be implemented in MATLAB platform and the performance is evaluated and compared with earlier clustering and routing scheme with respect to Quality of service parameters such as network lifetime, energy consumption, packet delivery ratio, routing overhead, packet loss, bit rate, throughput, transmission delay, and jitter.

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“…Table 4 provides further information on each of these potential outcomes. The hybrid routing protocol, it is parameter settings and network structure form the basis for different scenarios as reported in [ 47 ].…”

Section: Network Setup and Scenario Analysismentioning

confidence: 99%

Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

Mishra

Gupta

Gui

2022

Sensors

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The exponential growth in remote sensing, coupled with advancements in integrated circuits (IC) design and fabrication technology for communication, has prompted the progress of Wireless Sensor Networks (WSN). WSN comprises of sensor nodes and hubs fit for detecting, processing, and communicating remotely. Sensor nodes have limited resources such as memory, energy and computation capabilities restricting their ability to process large volume of data that is generated. Compressing the data before transmission will help alleviate the problem. Many data compression methods have been proposed but mainly for image processing and a vast majority of them are not pertinent on sensor nodes because of memory impediment, energy utilization and handling speed. To overcome this issue, authors in this research have chosen Run Length Encoding (RLE) and Adaptive Huffman Encoding (AHE) data compression techniques as they can be executed on sensor nodes. Both RLE and AHE are capable of balancing compression ratio and energy utilization. In this paper, a hybrid method comprising RLE and AHE, named as H-RLEAHE, is proposed and further investigated for sensor nodes. In order to verify the efficacy of the data compression algorithms, simulations were run, and the results compared with the compression techniques employing RLE, AHE, H-RLEAHE, and without the use of any compression approach for five distinct scenarios. The results demonstrate the RLE’s efficiency, as it surpasses alternative data compression methods in terms of energy efficiency, network speed, packet delivery rate, and residual energy throughout all iterations.

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Network Lifetime Improvement through Energy-Efficient Hybrid Routing Protocol for IoT Applications

Cited by 19 publications

References 44 publications

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

LEACH Protocol with Angular Area Routing: Boosting Energy Efficiency and QoS in Wireless Sensor Networks

Hybrid deep marine reinforcement learning-based routing protocol in wireless sensor networks

Investigation of Energy Cost of Data Compression Algorithms in WSN for IoT Applications

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