Wireless Sensor Network as a Mesh: Vision and Challenges

Nurlan, Zhanserik; Zhukabayeva, Tamara; Othman, Мohamed; Adamova, Aigul; Zhakiyev, Nurkhat

doi:10.1109/access.2021.3137341

Cited by 49 publications

(20 citation statements)

References 208 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Due to the extreme environment and inaccessibility of the deployment region, these batteries are seldom rechargeable. Thus, maximizing the WSN lifetime requires saving energy for as long as feasible [20], [21], [22], and [23].…”

Section: Problems In Wsnsmentioning

confidence: 99%

A New Method Based on Machine Learning to Increase Efficiency in Wireless Sensor Networks

Alkenani

Kheerallah

2023

IJCAI

View full text Add to dashboard Cite

Wireless sensor networks (WSNs) contain many sensor nodes, and this network is used for many applications such as military, medical, and others. Accurate data aggregation and routing are critical in hostile environments, where sensors' energy consumption must be carefully monitored. There is, nevertheless, a substantial probability of duplicate data due to ambient circumstances and shortdistance sensors. Large datasets include a variety of information, some of which is useful, while others are completely superfluous. This redundancy degrades performance in terms of computing cost and redundant transmission. Data aggregation, on the other hand, may eliminate redundant data in a network. In this paper new method called Kalman filter with Support vector machine (KF-SVM) is introduced to classify and data aggregate and get rid of noise in WSNs, which enhances network efficiency and extends its lifetime. Povzetek: V prispevku je opisana izvirna metoda za agregiranje podatkov v senzorskih omrežjih, ki dela na osnovi Kalmanovega filtra in SVM.

show abstract

Section: Problems In Wsnsmentioning

confidence: 99%

A New Method Based on Machine Learning to Increase Efficiency in Wireless Sensor Networks

Alkenani

Kheerallah

2023

IJCAI

View full text Add to dashboard Cite

show abstract

Section: The Lifetime In Wsnsmentioning

confidence: 99%

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Alsawad

2022

IJICI

View full text Add to dashboard Cite

The mining and processing of data for wireless sensor networks (WSNs) have been a significant research area in a number of computer science disciplines, including distributed applications, database management systems, and information gathering. The main goal of implementing applications based on WSNs is to make real-time choices, which have proven very difficult due to the limitations of computing, communication, and data mining capacity for WSNs. Thus, due to the nature and unique characteristics of sensor data and the limitations of WSNs, traditional data mining methods cannot be easily applied to them. This paper presents a new approach, the Kalman Filter (KF) with K-Nearest Neighbors (KNN) named (KF-KNN) which is proposed for data classification and collection as well as noise elimination in WSNs, increasing the network efficiency and lifetime. The proposed technique is compared to KNN to illustrate the effectiveness of the recommended ways in increasing energy consumption and prolonging network lifetime.

show abstract

“…A middle node exists and functions as a handshake between the GW and end users. That is to forecast a path depending on the topology of a network [5], [6], [7].…”

Section: Introductionmentioning

confidence: 99%

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

et al. 2023

View full text Add to dashboard Cite

The optimal performance of a wireless mesh network (WMN) can be greatly improved by strategically placing wireless mesh routers. As a result, it is crucial to optimally locate the WMN routers for better coverage and connectivity. Besides the optimal placement, the network congestion due to overlaying routers has to be taken into consideration. These issues have become a motivation for researchers to identify a variety of approaches to optimize WMN performance. Multiple metaheuristic algorithms have been employed for identifying the trade-offs between coverage and connectivity in WMN. Consequently, a novel hybrid Harris Hawks optimization with the sine cosine algorithm (HHOSCA) is presented in this work to tackle the aforementioned WMN optimization problems. The proposed HHOSCA seeks optimal router placement that leads to significantly increased network coverage and achieves full connectivity between the mesh routers. In addition, the proposed HHOSCA produces a cost-effective WMN by reducing the congestion in the network to the minimum number of routers whilst ensuring maximum coverage and connectivity. The superiority of the proposed HHOSCA in comparison to the other algorithm was validated by using 33 benchmark functions. It was compared against four well-known algorithms including Sine Cosine Algorithm (SCA), Harris Hawks optimization (HHO), Gray Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). These algorithms are statistically analyzed and compared to the simulated results of the proposed method. In addition, the performance of HHOSCA is compared to the state-of-the-art to highlight the efficacy of the proposed algorithm. The statistical analyses and simulation findings confirm that the HHOSCA outperforms the other algorithms in terms of network connectivity, coverage, network reduction, and convergence. The experimental results reveal that the proposed HHOSCA method achieves favourable optimization results compared with other relevant methods.INDEX TERMS Optimal node placement, reliable wireless networks, network deployment optimization, particle swarm optimization, gray wolf optimization, Harris Hawks optimization, sine cosine optimization, industrial wireless mesh networks.The associate editor coordinating the review of this manuscript and approving it for publication was Xiaolong Li .

show abstract

Wireless Sensor Network as a Mesh: Vision and Challenges

Cited by 49 publications

References 208 publications

A New Method Based on Machine Learning to Increase Efficiency in Wireless Sensor Networks

A New Method Based on Machine Learning to Increase Efficiency in Wireless Sensor Networks

Increase Efficiency And Lifetime In Wsns Through Data Reduction

Hybrid Harris Hawks With Sine Cosine for Optimal Node Placement and Congestion Reduction in an Industrial Wireless Mesh Network

Contact Info

Product

Resources

About