Wireless Sensor Networks Fundamentals: A Review

Heydarishahreza, Navid; Ebadollahi, Saeed; Vahidnia, Reza; Dian, F. John

doi:10.1109/iemcon51383.2020.9284873

Cited by 7 publications

(4 citation statements)

References 37 publications

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“…The sensor node architecture can be seen in Figure 3, which consists of the sensing unit that can be either the sensor or actuator, which senses the environment and take measurements of things such as temperature, humidity, sound, or vibration, thereby converting the collected analog signals into digital signals through the analog to digital converter. Then, it sends these signals or measurements to the processing unit to be processed and stored until sent through the communication unit to a relay node or a sink node [7,8]. The WSN consists of all, or parts, of the following components [9]:…”

Section: Wireless Sensor Network Components and Node Architecturementioning

confidence: 99%

“…Different types of WSN are used for different applications, as shown in Figure 4 , the first use of WSNs was for military applications, where a system of an acoustic sensor on the ocean bottom was developed for monitoring the soviet submarines in the cold war; later on, in 1980, the work started on developing distributed sensor network (DSN). Now, WSNs are used in different areas, such as agriculture, bio-medical, health, traffic, industry, environment, and so on [ 7 , 11 ]. The work of WSN can be divided into two dimensions.…”

Section: Introductionmentioning

confidence: 99%

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Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Abdulwahid

Mishra

2022

Sensors

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In recent years, different types of monitoring systems have been designed for various applications, in order to turn the urban environments into smart cities. Most of these systems consist of wireless sensor networks (WSN)s, and the designing of these systems has faced many problems. The first and most important problem is sensor node deployment. The main function of WSNs is to gather the required information, process it, and send it to remote places. A large number of sensor nodes were deployed in the monitored area, so finding the best deployment algorithm that achieves maximum coverage and connectivity with the minimum number of sensor nodes is the significant point of the research. This paper provides a systematic mapping study that includes the latest recent studies, which are focused on solving the deployment problem using optimization algorithms, especially heuristic and meta-heuristic algorithms in the period (2015–2022). It was found that 35% of these studies updated the swarm optimization algorithms to solve the deployment problem. This paper will be helpful for the practitioners and researchers, in order to work out new algorithms and seek objectives for the sensor deployment. A comparison table is provided, and the basic concepts of a smart city and WSNs are presented. Finally, an overview of the challenges and open issues are illustrated.

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Section: Wireless Sensor Network Components and Node Architecturementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Abdulwahid

Mishra

2022

Sensors

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“…Opportunistic relay selection (ORS) [11], on the other hand, is a single-relay strategy that does not need topological knowledge. Tis approach selects a single relay with the best channel condition based on local channel measurements (in accordance to a given selection criterion [12]. When compared to more complicated protocols such as DSTC, ORS has no performance penalty in terms of the multiplexingdiversity tradeof.…”

Section: Cooperative Communicationsmentioning

confidence: 99%

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

Rathika

Poruran

Kumar

et al. 2022

Mathematical Problems in Engineering

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In recent years, cooperative communication (CC) technology has emerged as a hotspot for testing wireless communication networks (WCNs), and it will play an important role in the spectrum utilization of future wireless communication systems. Instead of running node transmissions at full capacity, this design will distribute available paths across multiple relay nodes to increase the overall throughput. The modeling WCNs coordination processes, as a recurrent mechanism and recommending a deep learning-based transfer choice, propose a recurrent neural network (RNN) process-based relay selection in this research article. This network is trained according to the joint receiver and transmitter outage likelihood and shared knowledge, and without the use of a model or prior data, the best relay is picked from a set of relay nodes. In this study, we make use of the RNN to do superdimensional (high-layered) processing and increase the rate of learning and also have a neural network (NN) selection testing to study the communication device, find out whether or not it can be used, find out how much the system is capable of, and look at how much energy the network needs. In these simulations, it has been shown that the RNN scheme is more effective on these targets and allows the design to keep converged over a longer period of time. We will compare the accuracy and efficiency of our RNN processed-based relay selection methods with long short-term memory (LSTM), gated recurrent units (GRU), and bidirectional long short-term memory (BLSTM),which are all acronyms for long short-term memory methods.

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“…WSNs have emerged as transformative systems combining advancements in wireless communication, miniaturized sensor technology, and low-power computing. Comprising small, autonomous nodes equipped with sensing, processing, and communication capabilities, WSNs are designed for collaborative data collection from the environment [1]. Deployed in different applications, including monitoring the environment, as well as automation in industry [2], healthcare [3], and smart cities [4], WSNs provide real-time data on diverse parameters.…”

Section: Introductionmentioning

confidence: 99%

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

Ibraheem,

Al-Abadi,

Mohamed

et al. 2024

Applied Sciences

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Wireless sensor networks (WSNs) play a pivotal role in diverse applications such as environmental monitoring, industrial automation, healthcare, and smart cities. The motivation behind the development of WSNs stems from their impact in providing real-time data on various environmental parameters. The challenge for WSNs is to achieve strong security and efficient energy saving together. Traditional methods sought to find solutions either through security or energy. In response, this study proposed a secure and energy-efficient framework for enhancing security measures in WSNs while minimizing the impact on energy resources by using the Enhanced Consumed Energy Leach (ECP-LEACH) protocol and the Enhanced Random Forest Classifier for Low Execution Time (ERF-LET) algorithm for attack detection named Security-Enhanced Energy Conservation with ERF-LET (S-2EC-ERF). The integration of the detection algorithm at the node level played a pivotal role in fortifying the security posture of individual nodes by detecting and mitigating potential security threats. Leveraging a comprehensive dataset obtained from NS3 simulations, the ERF-LET algorithm demonstrated its proficiency in differentiating between normal and attack packets, thereby laying a strong foundation for subsequent evaluations, where it achieved an accuracy of 98.193%. The proposed methodology was further validated through real-time simulations conducted on the NS3. The results demonstrated the superiority of the proposed S-2EC-ERF in terms of the packet delivery ratio (PDR), average throughput, end-to-end delay, and mean energy consumption compared to the Security-Enhanced Energy Conservation with Logistic Regression (S-2EC-LR), Security-Enhanced Energy Conservation with Decision Tree (S-2EC-DT), and Security-Enhanced Energy Conservation with AdaBoost (S-2EC-Ada) algorithms.

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Wireless Sensor Networks Fundamentals: A Review

Cited by 7 publications

References 37 publications

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Deployment Optimization Algorithms in Wireless Sensor Networks for Smart Cities: A Systematic Mapping Study

Cooperative Communications Based on Deep Learning Using a Recurrent Neural Network in Wireless Communication Networks

A Security-Enhanced Energy Conservation with Enhanced Random Forest Classifier for Low Execution Time Framework (S-2EC-ERF) for Wireless Sensor Networks

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