Data fusion algorithms for wireless sensor networks based on deep learning model

Wang, Lihong; Xia, Kuiliang

doi:10.1145/3318265.3318297

Cited by 4 publications

(2 citation statements)

References 1 publication

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“…The selection of optimal path tends to enhance energy and QoS efficiency. CL‐HPWSR utilizes a deep learning model 42 to find the most appropriate route from the accessible path. The route selection process based on a deep model can establish better performance.…”

Section: Cross‐layer‐based Hybrid Pswho and Stable Routing Technique ...mentioning

confidence: 99%

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Kumar

Dhaliwal

Singh

2023

Trans Emerging Tel Tech

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In the recent days, the Internet of Things (IoT) has assumed a significant role in automized farming applications. The farmers can receive frequent information corresponding to the soil moisture, water used, humidity, temperature, moisture, etc., by utilizing actuators and sensor nodes. Various clustering techniques used in wireless sensor network (WSN) applications have achieved energy‐efficient results. However, IoT‐enabled smart farming (SF) applications require scalable networks and long‐distance communication. The existing clustering techniques cannot provide SF solutions, and work related to communication overhead, end‐to‐end delay, latency etc., has not been taken up exhaustively. Therefore, in this article, a cross‐layer‐based hybrid particle Swarm wild horse optimizer (PSWHO) and stable routing technique (CL‐HPWSR) is designed to minimize energy consumption, communication delay, latency etc. in SF applications. Initially, the network is designed to be Bi‐concentric Hexagons. The cross‐layer‐based optimal cluster head (CH) selection mechanism is designed using the hybrid optimization algorithm‐named PSWHO to mitigate excess energy consumption problems in WSN. While clustering, cross‐layer parameters from different layers such as physical, medium access control, and network are used to compute the probability of each sensor node. Moreover, the deep learning‐based routing algorithm extended deep golden eagle neural network (EDGENN) is introduced to explore the optimal route for transmitting the data. The performance of CL‐HPWSR is implemented in Matlab by focusing energy‐efficiency, computational‐efficiency and QoS‐efficiency factors. Finally, in comparison with state‐of‐the‐art‐based SF approaches such as CL‐IoT, FEEC‐IIR, BiHCLR, PSO‐ECHS, and PSO‐C, the CL‐HPWSR has performed better in terms of energy‐efficiency, QoS‐efficiency and computational‐efficiency.

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Section: Cross‐layer‐based Hybrid Pswho and Stable Routing Technique ...mentioning

confidence: 99%

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Kumar

Dhaliwal

Singh

2023

Trans Emerging Tel Tech

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“…Lee et al in their study, they improved the query process by processing data collected from four WSNs in real time using deep learning methods [30]. Wang et al proposed a data fusion algorithm based on deep learning models to reduce energy consumption and prolong the life of WSNs [31]. In another study, the detection of rust on coffee leaves was carried out using WSN, remote sensing and deep learning methods [32].…”

Section: Introductionmentioning

confidence: 99%

Deep learning techniques of losses in data transmitted in wireless sensor networks

Ersoy¹,

Aksoy²

2021

Turk J Elec Eng & Comp Sci

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Wireless sensor network systems are frequently used today along with the rapidly developing technology. Wireless sensor networks, which form the basis of the Internet of Things, have a wide user base in the world. For example, wireless sensor networks are used in many areas from education to health, from military applications to home applications. It enables the data obtained from the sensors to be transferred between nodes with the help of end-to-end wireless protocols. In parallel with the increasing number of nodes in WSN, data traffic density also increases. Due to the limitations of the WSN network, lost packet rates also increase with increasing data traffic. In this study, a data set was created by examining the data transfers of different amounts of WSN nodes placed in different places. The effects of the number of sensors and the distance between them were obtained from the data set. In this study, a data set was created by collecting the data from the sensor nodes placed at 1500m x 1500m intervals in the ns-3 discrete event emulator program. Today, with the rapid development of technology, deep learning methods which are one of the artificial intelligence methods, are also used in WSN. In this study, the loss rate in the transferred data packets was tried to be estimated with the highest accuracy by using Deep Belief Network (DBN), Recurrent Neural Network (RNN) and Deep Neural Network (DNN) deep learning techniques over the obtained dataset. Of these three deep learning methods, DNN deep learning method was found to accurately estimate the loss rate in the transferred data packets with an accuracy rate of 88.50%.

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SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

Sujanthi¹,

Kalyani²

2020

Wireless Pers Commun

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Data fusion algorithms for wireless sensor networks based on deep learning model

Cited by 4 publications

References 1 publication

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

CL‐HPWSR: Cross‐layer‐based energy efficient cluster head selection using hybrid particle swarm wild horse optimizer and stable routing in IoT‐enabled smart farming applications

Deep learning techniques of losses in data transmitted in wireless sensor networks

SecDL: QoS-Aware Secure Deep Learning Approach for Dynamic Cluster-Based Routing in WSN Assisted IoT

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