Analysis of compressive sensing and energy harvesting for wireless multimedia sensor networks

Tekin, Nazli; Güngör, Vehbi Çağrı

doi:10.1016/j.adhoc.2020.102164

Cited by 19 publications

(6 citation statements)

References 20 publications

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“…The updated route path can be added to the current one without interruption by employing this method in order to locate and promptly report any oil traces to the washbasin. In [ 14 ], a unique ocean surface routing system that integrates two-dimensional underwater sensor networks with sleep scheduling routing was unveiled using a routing solution based on the K-NN algorithm and the clustering method to reduce end-to-end latency and energy consumption [ 15 ]. This solution provides the least number of distances through a clustering technique based on node categorization.…”

Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

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In this paper, we propose an improved clustering algorithm for wireless sensor networks (WSNs) that aims to increase network lifetime and efficiency. We introduce an enhanced fuzzy spider monkey optimization technique and a hidden Markov model-based clustering algorithm for selecting cluster heads. Our approach considers factors such as network cluster head energy, cluster head density, and cluster head position. We also enhance the energy-efficient routing strategy for connecting cluster heads to the base station. Additionally, we introduce a polling control method to improve network performance while maintaining energy efficiency during steady transmission periods. Simulation results demonstrate a 1.2% improvement in network performance using our proposed model.

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Section: Related Workmentioning

confidence: 99%

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Ullah,

Khan,

Mohy-ud-din

et al. 2024

Sensors

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“…The advantages of this technique is: less resources consuming, permit to learn from existing WSN running parameters, and predict the optimize way to securely route data [24].…”

Section: Hidden Markov Model Theorymentioning

confidence: 99%

Malicious Attack Classification Based-on Continuous Hidden Markov Models in Wireless Sensor Networks

Satori

Boutazart

Abderahim

et al. 2022

Preprint

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Alongside the rapid progress of Wireless sensor networks (WSNs) technologies, sensors and networks can rapidly be victim of distributed attacks. Attackers can perform intrusions to breakdown the network during the routing process, intercept gathered data by dropping or re-sharing them. To avoid the increasing of security issues, many attack identification models were proposed in WSNs in which detection systems are deployed to collect sensed data and categorize them using machine learning and stochastic binary-classification techniques. In this work, a new method is introduced to analyze and classify WSN dataset. We aim to design an anomaly identification approach to improve the sensor network security, it efficiency with high accuracy. To reach this goal, machine learning approaches are used to define a detection system which learn from routing dataset to identify network malicious entries. The proposed models is based on Hidden Markov Model (HMM), Gaussian Mixture Model (GMM) stochastic assumptions. Also, dimensionality reduction technique was deployed to select the most relevant features for training. The experimentation phase was realized on own made dataset reflecting different network situations of normal and attacked cases. The outcomes performances of the proposed method were obtained with classification accuracy of 92.18% using 2HMM/3GMMclassifier. This result demonstrate the quality of our proposed approach compared with existing literature and its usefulness to improve the security of WSN.

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“…In compressed sensing (CS) [ 68 ] a small number of samples of a sparse signal contains enough information to successfully recover the original signal with almost no data loss. The great advantages in terms of limitations and data reduction (part of the redundant data is never acquired) make CS the most widely used technique presently in WSNs and IoT [ 69 ], even in the case of multimedia sensor networks [ 70 ].…”

Section: Energy Conservationmentioning

confidence: 99%

Current Trends on Green Wireless Sensor Networks

López-Ardao

Rodríguez-Rubio

Suárez-González

et al. 2021

Sensors

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The issue of energy balancing in Wireless Sensor Networks is a pivotal one, crucial in their deployment. This problem can be subdivided in three areas: (i) energy conservation techniques, usually implying minimizing the cost of communication at the nodes since it is known that the radio is the biggest consumer of the available energy; (ii) energy-harvesting techniques, converting energy from not full-time available environmental sources and usually storing it; and (iii) energy transfer techniques, sharing energy resources from one node (either specialized or not) to another one. In this article, we survey the main contributions in these three areas and identify the main trending topics in recent research. A discussion and some future directions are also included.

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Analysis of compressive sensing and energy harvesting for wireless multimedia sensor networks

Cited by 19 publications

References 20 publications

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

A Hybrid Approach for Energy Consumption and Improvement in Sensor Network Lifespan in Wireless Sensor Networks

Malicious Attack Classification Based-on Continuous Hidden Markov Models in Wireless Sensor Networks

Current Trends on Green Wireless Sensor Networks

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