A Comprehensive Study on the Energy Efficiency of IoT from Four Angles: Clustering and Routing in WSNs, Smart Grid, Fog Computing and MQTT &amp; CoAP Application Protocols

Almudayni, Ziyad; Soh, Ben; Li, Alice

doi:10.1007/978-3-030-95987-6_4

Cited by 2 publications

(1 citation statement)

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“…Besides security risks, naturally WSNs leads to practical issues during deployment. The low-energy and low-range devices of WSNs has to be cost effective as frequently deployed in same network [9]. Moreover, few engineers have identified that the periodic change over power on SNs extend the lifetime of nodes, but leading to latency and routing overhead [10].…”

Section: Introductionmentioning

confidence: 99%

Investigations on Iot Based Wsn With Swipt-noma Combination

Sixtus,

Muthu

2023

Preprint

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Wireless Communication provides the interconnection of different devices for the ubiquitous accessibility of intelligent capacity. Wireless Communication incorporates device interaction for the provision of sufficient capability in networking between intermediate devices. Conventionally, the Internet of Things (IoT) and Wireless Sensor Networks (WSN) offer sufficient information between intermediate devices. IoT-WSN devices are resource constraints (RC), compact devices, and limited resources of battery. The increase in the number of users leads to challenges with security in the IoT-WSN. The data transmission between the wireless communications uses the 5G communication-based NOMA communication. Due to limited RC features the computational complexity is higher with minimal consumption of space those are evaluated with embedded hardware features within the IoT – WSN. This paper aimed to develop an appropriate optimal routing simultaneous wireless information and power transfer based Long Short-Term Memory (ORSS). The proposed ORSS model uses the covariance matrix-based routing and security model. The performance of the proposed ORSS is evaluated for security analysis based on consideration of different attacks. With the ORSS model, the position of nodes is computed with the covariance matrix estimation. To identify the optimal route’s Particle Swarm Optimization (PSO) algorithm is implemented for the route scheduling for the data transmission. To minimize the energy consumption within the network SWIPT is implemented for effective energy harvesting. Based on the covariance estimation optimal routes in the network are computed for the detection of attacks. The attacks are computed based on the utilization of LSTM model for the detection and classification of attacks with use of CICIDS datasets. The comparative analysis stated that the proposed ORSS exhibits ~40% higher data transmission and ~21% reduced delay compared with state-of-techniques.

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Section: Introductionmentioning

confidence: 99%