Wireless Sensor Network Routing Protocols Using Machine Learning

Shivalingagowda, Chaya; Ahmad, Hifzan; Jayasree, Puravoor; Kumar, Sunil

doi:10.1007/978-981-16-0386-0_7

Cited by 4 publications

(2 citation statements)

References 33 publications

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“…Shivalingagowda's work demonstrates this in developing the routing protocols. Shivalingagowda et al (2017) employed machine learning strategies for WSN routing in their unique routing strategy. The routing protocol was created using distributed regression, Self‐organizing maps, and reinforcing learning.…”

Section: Literature Reviewmentioning

confidence: 99%

Energy optimization in path arbitrary wireless sensor network

et al. 2023

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A network of wireless sensors is a self‐infrastructure approach with many sensory nodes. The distributed sensory nodes communicate with each other via sensory points. In wireless sensor network (WSN), the sensory nodes collect information for healthcare, military and monitoring systems. Such networks require an exclusive arrangement of the nodes to challenge inherent limitations and energy deficiency. The conventional design of a communication system consumes more energy with high latency causing degraded performance. This study provided a machine learning‐based path optimization mechanism using the least energy resources in designing an effective wireless network system with enhanced three measures of network performance, including throughput, packet delivery efficiency and energy usage. The proposed methodology is validated through network simulation tools.

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Section: Literature Reviewmentioning

confidence: 99%

Energy optimization in path arbitrary wireless sensor network

et al. 2023

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“…The increasing development of internet of things (IOT) technology in various applications means that devices are always connected to internet media, in the process IoT changes physical devices into shortcut devices that provide intelligent services [1][2][3]. The IoT application framework generally consists of various layers such as the perceptual layer, network side layer, data processing layer, and application side which are integrated with each other [4,5]. In its development, IoT technology requires a wireless sensor network which is used to carry out observation processes over large areas using small computer devices.…”

Section: Introductionmentioning

confidence: 99%

Optimization of the LEACH algorithm in the selection of cluster heads based on residual energy in wireless sensor networks

Fachrizal,

Zarlis,

Sihombing

et al. 2024

EEJET

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This research has a research object, namely the optimization of the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm in the context of wireless sensor networks. The problem in this research is the imbalance in energy consumption across clusters, which has an impact on battery life and affects network performance. Other problems include selecting a cluster head that is not focused so that it is difficult to balance network performance as well as computational limitations that require optimization. The results obtained from this research are in the form of optimizing the leaching algorithm by modifying the clustering-based leaching algorithm that will be used in wireless sensor networks. In carrying out modifications, this research uses several stages in the process of selecting sensor nodes that will become members who function as cluster heads in a cluster that will be used in a wireless sensor network. In the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm the cluster head will be selected based on the modified probability value. Modifying the algorithm by considering two factors, namely distance and remaining energy used in the Cluster Head selection process on the network and increasing network usage time must be based on the energy consumption used and then compared with the remaining energy. When modifying the LEACH (Low-Energy Adaptive Clustering Hierarchy) algorithm, it is necessary to pay attention to the distance factor between the nodes on a sensor and the selected cluster so that it can result in increased network performance. Network lifetime is indicated by the average death time of the first Node in the network. This research is novel in producing a modified leaching algorithm by improving network performance and extending battery life so that it can be used for wireless sensor networks in the context of natural disaster mitigation

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