Prolonging the lifetime of large‐scale wireless sensor networks using distributed cooperative transmissions

Monser, Malika El; Chikha, Haithem Ben; Attia, Rabah

doi:10.1049/iet-wss.2017.0139

Cited by 10 publications

(3 citation statements)

References 18 publications

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“…In each CH determination loop, one WSN node was identified as a CH node in each zone, and the role was rotated between WSN nodes that occurred in the same zone. However, this technology does not deal with a large number of WSN nodes [ 41 ]. Therefore, the LEACH protocol remains amenable to improvement through increased efficiency in selecting the appropriate CH in each loop, and that will be one of the contributions of this paper.…”

Section: Background and Related Workmentioning

confidence: 99%

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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Wireless Sensor Networks (WSNs) continue to face two major challenges: energy and security. As a consequence, one of the WSN-related security tasks is to protect them from Denial of Service (DoS) and Distributed DoS (DDoS) attacks. Machine learning-based systems are the only viable option for these types of attacks, as traditional packet deep scan systems depend on open field inspection in transport layer security packets and the open field encryption trend. Moreover, network data traffic will become more complex due to increases in the amount of data transmitted between WSN nodes as a result of increasing usage in the future. Therefore, there is a need to use feature selection techniques with machine learning in order to determine which data in the DoS detection process are most important. This paper examined techniques for improving DoS anomalies detection along with power reservation in WSNs to balance them. A new clustering technique was introduced, called the CH_Rotations algorithm, to improve anomaly detection efficiency over a WSN’s lifetime. Furthermore, the use of feature selection techniques with machine learning algorithms in examining WSN node traffic and the effect of these techniques on the lifetime of WSNs was evaluated. The evaluation results showed that the Water Cycle (WC) feature selection displayed the best average performance accuracy of 2%, 5%, 3%, and 3% greater than Particle Swarm Optimization (PSO), Simulated Annealing (SA), Harmony Search (HS), and Genetic Algorithm (GA), respectively. Moreover, the WC with Decision Tree (DT) classifier showed 100% accuracy with only one feature. In addition, the CH_Rotations algorithm improved network lifetime by 30% compared to the standard LEACH protocol. Network lifetime using the WC + DT technique was reduced by 5% compared to other WC + DT-free scenarios.

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Section: Background and Related Workmentioning

confidence: 99%

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Ahmad

Wazirali

Bsoul

et al. 2021

Sensors

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“…In this work, the main objective is to further improve existing routing protocols [21][22][23][24] for large-scale WSN through the proposition of a new routing protocol design. In contrast to typical routing WSN protocols, which only take into account homogenous patterns, this paper's main contribution is the proposal of a novel routing protocol that tries to handle the energy and traffic heterogeneity required by WSN-assisted IoT.…”

Section: Introductionmentioning

confidence: 99%

An Energy Efficient WSN-Assisted IoT Network using Heterogeneous Dynamic Multi-hop Cluster-based Routing Protocol

Elmonser

Chikha

Attia

2023

Preprint

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Heterogeneous Wireless sensor networks (WSNs) deploy hundreds to thousands of low-cost sensors in their area detection, and these heterogeneous nodes present the main parts of the Internet of Things (IoT) network. Sensor node parameters, such as initial energy and data processing capability largely influence the network's lifetime in WSN-assisted IoT. Despite the success of various hierarchical clustering strategies dedicated to WSNs, their widespread deployment makes the search for an optimal network routing protocol an open challenge. When every node starts off with the same energy, homogeneous networks are what most hierarchical protocols have in mind. Some protocols have considered heterogeneous networks, where sensor nodes have more power and traffic than others. The network lifetime can be extended as a result. In order to increase energy conservation and extend network lifetime, this research present a Heterogeneous Dynamic Multi-Hop Low-Energy Adaptive Clustering Hierarchy (HDM-LEACH) protocol for WSN-Assisted IoT. HDM-LEACH is mainly based on a heterogeneous network system model, multi-hop communication, and a hierarchical clustering routing mechanism. Our heterogeneous network system model is composed of two energy-level nodes: normal nodes and advanced nodes. The proposed hierarchical clustering mechanism calculates the CH selection probability in an efficient and dynamic way. According to the simulation results, HDM-LEACH shows performance supremacy compared to existing algorithms in terms of stable period, energy conservation, and network lifetime.

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“…In [37], an improved nearest neighbour algorithm is proposed to construct gathering paths aiming at a uniform distribution of the energy consumption among sensors. Selecting the optimal route under interference constraints is investigated in [38], while it is investigated for battery lifetime and energy efficiency maximisation in [39, 40], respectively.…”

Section: Introductionmentioning

confidence: 99%

Multi‐hop decision gathering scheme for target‐detection wireless sensor networks

Althunibat

Tarawneh

2019

IET Communications

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Prolonging the lifetime of large‐scale wireless sensor networks using distributed cooperative transmissions

Cited by 10 publications

References 18 publications

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

Feature-Selection and Mutual-Clustering Approaches to Improve DoS Detection and Maintain WSNs’ Lifetime

An Energy Efficient WSN-Assisted IoT Network using Heterogeneous Dynamic Multi-hop Cluster-based Routing Protocol

Multi‐hop decision gathering scheme for target‐detection wireless sensor networks

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