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Special Issue "Advanced Applications of Sensor Network and Wireless Communication"

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A special issue of Electronics (ISSN 2079-9292). This special issue belongs to the section "Computer Science & Engineering".

Deadline for manuscript submissions: closed (31 March 2023) | Viewed by 17765

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Special Issue Editors

Dr. Dinh-Thuan Do

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Guest Editor

School of Engineering, University of Mount Union, Alliance, OH 44601-3993, USA
Interests: ML/federated learning in wireless systems; heterogeneous networks; massive MIMO; reconfigurable intelligent surface-assisted networks; mmWave communication networks; energy harvesting; full-duplex communications; cognitive radio; small cell; non-orthogonal multiple access (NOMA); physical layer security; UAV networks; visible light communication; IoT system
Special Issues, Collections and Topics in MDPI journals

Dr. Samarendra Nath Sur

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Guest Editor

Department of Electronics and Communication Engineering, Sikkim Manipal Institute of Technology, Sikkim Manipal University, Gangtok 737136, India
Interests: wireless communication; radar; signal processing; MIMO; OFDM

Dr. Mostafa Fouda

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Guest Editor

Department of Electrical and Computer Engineering, Idaho State University, Pocatello, ID 83209, USA
Interests: cybersecurity; artificial intelligence (AI); internet of things (IoT); smart grids; 5G/6G networks; vehicular networks; communication networks; image processing; signal processing; smart healthcare
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Adão Silva

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Guest Editor

Instituto de Telecomunicações (IT) and Departamento de Eletrónica, Telecomunicações e Informática (DETI), University of Aveiro, 3810-193 Aveiro, Portugal
Interests: cooperative communications; massive MIMO; millimeter wave communications; interference management; precoding and equalizer design
Special Issues, Collections and Topics in MDPI journals

Dr. Yawen Chen

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Guest Editor

Department of Computer Science, University of Otago, Dunedin 9054, New Zealand
Interests: resource optimization in computer architecture and computer networking, with a current focus on network-on-chips; optical networks; interconnection networks; green computing and cloud computing

Prof. Dr. Lingling Lv

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Guest Editor

School of Electric Power, North China University of Water Resources and Hydropower, Zhengzhou 450053, China
Interests: industrial Internet; edge computing; information physics system; wireless communication; smart grid and sustainable energy

Dr. Abbas Arghavani

E-Mail Website
Guest Editor

Department of Electrical Engineering, Signals and Systems, Uppsala University, 75236 Uppsala, Sweden
Interests: wireless communication; signal processing; sensor networks

Special Issue Information

Dear Colleagues,

Wireless Sensor Network (WSN) build up with spatially distributed sensor nodes, which are interconnected by using wireless communication. Over the past several decades, WSNs have enabled extraordinary advances in diverse fields. With the advancement of the wireless communication, WSN becoming more reliable, robust and secure networks with ultra-low latency support. The benefits of advanced sensing and communication technologies along with soft/cloud computing, will lead to ground-breaking novel applications in military, health, autonomous vehicles, telemedicine, environmental, framing, industrial and urban. With the persistent effort various applications of WSNs are either matured or still at the initial stage. Therefore, it is required to explore the possibilities of applying advanced WSNs and communication technologies over the diverse field for the betterment of the society.

In this special issue, we invite researchers and practitioners to submit their latest results and developments for the following topics: (but are not limited to)

Novel theories, concepts, for the Convergence of AI, IoT, and Edge-Cloud
Application of WSNs and Communication technology for Battlefield Surveillance, Healthcare, Smart Home, Smart Cities, Environment monitoring, Farming, Vehicular network, Greenhouse monitoring, Industrial application etc.
Emerging communications techniques applied in WSNs
Integration of artificial intelligence and WSNs
Vehicle networks based on WSNs
Unmanned aerial vehicle (UAV) assisted communication and its application.
Cognitive radio-inspired WSNs and applications
Development of Energy efficient communication technologies/WSNs and related applications.
Security and privacy issues in WSNs
Study on robustness, latency, tolerance in WSNs with communication protocols
Application related to Body area sensor networks
Advancement of the communication/Networks protocols to support diverse application

We look forward to receiving your contributions.

Dr. Dinh-Thuan Do
Dr. Samarendra Nath Sur
Dr. Mostafa Fouda
Prof. Dr. Adão Silva
Dr. Yawen Chen
Prof. Dr. Lingling Lv
Dr. Abbas Arghavani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Electronics is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2200 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

WSN
Healthcare
Smart City
Industry 4.0
Vehicular Network
Body Area Network

Published Papers (14 papers)

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Research

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Open AccessArticle

Blind Matching Filtering Algorithm for Spectrum Sensing under Multi-Path Channel Environment

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Electronics 2023, 12(11), 2499; https://doi.org/10.3390/electronics12112499 - 01 Jun 2023

Viewed by 400

Abstract

Matching filtering has been proven to be the optimal spectrum sensing algorithm under Gaussian white noise. However, the application of this algorithm is limited because of its dependence on prior information. In this paper, we propose a spectrum sensing algorithm based on blind matching filtering (BMF) by using the correlation between adjacent received signals under dispersive channels. Theoretical analysis shows that the proposed algorithm can achieve a performance comparable to that of the matching filtering algorithm without requiring the prior information of the primary user. Thus, this algorithm shows superior detection performance. Moreover, an improved BMF (IBMF) algorithm is proposed on the basis of the correlation between different time-delay signals. IBMF utilizes more comprehensive correlation information of the received signals and achieves better detection performance compared to BMF. Furthermore, the two proposed algorithms have lower computational complexity than the classical approaches based on the covariance matrix of the received signals. Numerical simulations confirm the superior performance of the proposed detectors and validate the theoretical analysis. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Deep Reinforcement Learning-Based Adaptive Beam Tracking and Resource Allocation in 6G Vehicular Networks with Switched Beam Antennas

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Electronics 2023, 12(10), 2294; https://doi.org/10.3390/electronics12102294 - 19 May 2023

Viewed by 844

Abstract

In this paper, we propose a novel switched beam antenna system model integrated with deep reinforcement learning (DRL) for 6G vehicle-to-vehicle (V2V) communications. The proposed system model aims to address the challenges of highly dynamic V2V environments, including rapid changes in channel conditions, interference, and Doppler effects. By leveraging the beam-switching capabilities of switched beam antennas and the intelligent decision making of DRL, the proposed approach enhances the performance of 6G V2V communications in terms of throughput, latency, reliability, and spectral efficiency. The proposed work develops a comprehensive mathematical model that accounts for 6G channel modeling, beam-switching, and beam-alignment errors. The Proposed DRL framework is designed to learn optimal beam-switching decisions in real time, adapting to the complex and varying V2V communication scenarios. The integration of the proposed antenna system and DRL model results in a robust solution that is capable of maintaining reliable communication links in a highly dynamic environment. To validate the proposed approach, extensive simulations were conducted and performance analysis using various performance metrics, such as throughput, latency, reliability, energy efficiency, resource utilization, and network scalability, was analyzed. Results demonstrate that the proposed system model significantly outperforms conventional V2V communication systems and other state-of-the-art techniques. Furthermore, the proposed approach shows that the beam-switching capabilities of the switched beam antenna system and the intelligent decision making of the DRL model are essential in addressing the challenges of 6G V2V communications. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

MC-ISA: A Multi-Channel Code Visualization Method for Malware Detection

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Electronics 2023, 12(10), 2272; https://doi.org/10.3390/electronics12102272 - 17 May 2023

Viewed by 696

Abstract

Malware detection has always been a hot topic in the cyber security field. With continuous research over the years, many research methods and detection tools based on code visualization have been proposed and achieved good results. However, in the process of code visualization, the existing methods have some issues such as feature scarcity, feature loss and excessive dependence on manual analysis. To address these issues, we propose in this paper a code visualization method with multi-channel image size adaptation (MC-ISA) that can detect large-scale samples more quickly without manual reverse analysis. Experimental results demonstrate that MC-ISA achieves both higher accuracy and F1-score than the existing B2M algorithm after introducing three mechanisms including image size adaptive, color enhancement and multi-channel enhancement. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

An Enhanced LBPH Approach to Ambient-Light-Affected Face Recognition Data in Sensor Network

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Electronics 2023, 12(1), 166; https://doi.org/10.3390/electronics12010166 - 30 Dec 2022

Viewed by 1618

Abstract

Although combining a high-resolution camera with a wireless sensing network is effective for interpreting different signals for image presentation on the identification of face recognition, its accuracy is still severely restricted. Removing the unfavorable impact of ambient light remains one of the most difficult challenges in facial recognition. Therefore, it is important to find an algorithm that can capture the major features of the object when there are ambient light changes. In this study, face recognition is used as an example of image recognition to analyze the differences between Local Binary Patterns Histograms (LBPH) and OpenFace deep learning neural network algorithms and compare the accuracy and error rates of face recognition in different environmental lighting. According to the prediction results of 13 images based on grouping statistics, the accuracy rate of face recognition of LBPH is higher than that of OpenFace in scenes with changes in ambient lighting. When the azimuth angle of the light source is more than +/−25° and the elevation angle is +000°, the accuracy rate of face recognition is low. When the azimuth angle is between +25° and −25° and the elevation angle is +000°, the accuracy rate of face recognition is higher. Through the experimental design, the results show that, concerning the uncertainty of illumination angles of lighting source, the LBPH algorithm has a higher accuracy in face recognition. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Performance Analysis of Dual-Hop AF Cognitive Relay Networks with Best Selection and Interference Constraints

Moawiah Alhulayil

Mamoun F. Al-Mistarihi

and

Mohammad M. Shurman

Electronics 2023, 12(1), 124; https://doi.org/10.3390/electronics12010124 - 28 Dec 2022

Cited by 1 | Viewed by 935

Abstract

In this paper, a dual-hop underlay cognitive relay network (CRN) with a best-relay selection (BRS) scheme under spectrum-sharing constraints from the primary user (PU) is investigated. The system model in this work consists of one PU, one secondary user (SU) and a few SU relays. Both users exchange the information using a half-duplex mode through amplify-and-forward (AF) SU relays. Moreover, all channels are modelled using the Nakagami-m fading model. In this work, the outage probabilities (OPs) are derived for the proposed system model under several scenarios to investigate the network performance under interference power constraint

I_{p}

at the PU receiver. In addition, the impacts of the number of relays and the channel fading severity parameters are investigated as well. Furthermore, the system performance is investigated for several PU locations. The various numerical results are verified using a Monte Carlo simulation. Overall, the obtained results show that several factors such as the number of relays, channel fading severity parameters and PU location have a major impact on the outage performance of the SU. The simulation and analytical results are perfectly matched, confirming the accuracy of the analytical derivations. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Pretrained Configuration of Power-Quality Grayscale-Image Dataset for Sensor Improvement in Smart-Grid Transmission

Yeong-Chin Chen

Mariana Syamsudin

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Sunneng S. Berutu

Electronics 2022, 11(19), 3060; https://doi.org/10.3390/electronics11193060 - 26 Sep 2022

Cited by 1 | Viewed by 985

Abstract

The primary source of the various power-quality-disruption (PQD) concerns in smart grids is the large number of sensors, intelligent electronic devices (IEDs), remote terminal units, smart meters, measurement units, and computers that are linked by a large network. Because real-time data exchange via a network of various sensors demands a small file size without an adverse effect on the information quality, one measure of the power-quality monitoring in a smart grid is restricted by the vast volume of the data collection. In order to provide dependable and bandwidth-friendly data transfer, the data-processing techniques’ effectiveness was evaluated for precise power-quality monitoring in wireless sensor networks (WSNs) using grayscale PQD image data and employing pretrained PQD data with deep-learning techniques, such as ResNet50, MobileNet, and EfficientNetB0. The suggested layers, added between the pretrained base model and the classifier, modify the pretrained approaches. The result shows that advanced MobileNet is a fairly good-fitting model. This model outperforms the other pretraining methods, with 99.32% accuracy, the smallest file size, and the fastest computation time. The preprocessed data’s output is anticipated to allow for reliable and bandwidth-friendly data-packet transmission in WSNs. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Secure Mimo Communication System with Frequency Hopping Aided OFDM-DCSK Modulation

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Electronics 2022, 11(19), 3029; https://doi.org/10.3390/electronics11193029 - 23 Sep 2022

Cited by 1 | Viewed by 748

Abstract

In this paper, a multiple-input multiple-output (MIMO) communication system with frequency hopping (FH) aided orthogonal frequency division multiplexing differential chaotic shift keying (OFDM-DCSK) modulation is proposed. Our objective is to improve the security of MIMO communication system which is encoded by space time block coding (STBC). In order to combat the eavesdropping or malicious attacks due to the broadcast characteristics of wireless communication system, we propose to use DCSK and FH modules to encrypt the information, and hide the user data in the chaotic sequences, where the initial value of chaotic sequences and the method of generating FH module are only shared among legitimate users. Moreover, we derive the bit error rate (BER) and the secrecy capacity of the scheme in additive white Gaussian noise (AWGN) channel and Rayleigh fading channel. Simulation results show that the proposed scheme can effectively improve the security of MIMO communication system, which can be seen from the BER of eavesdroppers and legitimate users, and the secrecy capacity of the proposed scheme and the benchmark schemes. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessFeature PaperArticle

Clustering Algorithm Based on the Ground-Air Cooperative Architecture in Border Patrol Scenarios

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Electronics 2022, 11(18), 2876; https://doi.org/10.3390/electronics11182876 - 11 Sep 2022

Cited by 1 | Viewed by 884

Abstract

The border security situation is complex and severe, and the border patrol system relying on the ground-air cooperative architecture has been paid attention to by all countries as an important means of protecting national security. In the flying ad-hoc network (FANET), under the ground-air cooperative architecture, an unmanned aerial vehicle (UAV) uses a patrol mobility model to improve patrol efficiency. Since the patrol mobility model leads to frequent changes in UAV movement direction to improve patrol efficiency, selecting some clustering utility factors and calculating utility factors in previous clustering algorithms do not apply to this scenario. To solve the above problems, in this paper, we propose a border patrol clustering algorithm (BPCA) based on the ground-air cooperative architecture, which is based on the existing weighted clustering algorithm and improved in terms of the selection of utility factors and calculations of utility factors in cluster head selection. This algorithm comprehensively considers the effects of relative speed, relative distance, and the movement model of the UAV on the network topology. Extensive simulation results show that this algorithm can extend the duration time of cluster heads and cluster members and improve the stability of clusters and the reliability of links. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Secure Performance Analysis of Aerial RIS-NOMA-Aided Systems: Deep Neural Network Approach

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Electronics 2022, 11(16), 2588; https://doi.org/10.3390/electronics11162588 - 18 Aug 2022

Cited by 1 | Viewed by 1245

Abstract

The next generation of wireless systems has benefits in terms of spectrum and energy inefficiencies by exploiting two promising techniques including Non-Orthogonal Multiple Access (NOMA) and Reconfigurable Intelligent Surfaces (RIS). The scenario of two legitimate users existing together with an eavesdropper is worth examining in terms of secure matter while enabling machine learning tools at the base station for expected improvement. The base station deals with a highly complicated algorithm to adjust parameters against the attack of eavesdroppers and to better improve the secure performance of mobile users. This paper suggests a better solution to allow the base station to predict performance at destinations to adjust necessary parameters such as power allocation coefficients properly. To this end, we propose a deep neural network (DNN)-based approach which also leverages the benefits of aerial RIS to achieve predictable performance and significant secure performance improvement could be enhanced. We first derive the formulations for security outage probability (SOP) in closed-form expressions and analyze the strictly positive secrecy capacity (SPSC), which are crucial performance metrics to determine how the systems are against the existence of eavesdroppers. Such eavesdroppers intend to overhear signal transmission dedicated to intended users and incur degraded system performance. The numerical simulations are expected to evaluate how the machine learning tool works with the traditional computation of system performance metrics which is able to be verified by comparing with the Monte-Carlo method. Our numerical simulations demonstrate that the design of a higher number of meta-surface elements at the RIS, as well as a higher signal-to-noise ratio (SNR) levels at the base station, are key parameters to achieving improved security performance for users. For detailed guidelines of the RIS-NOMA aided system, we provide a table of parameters samples resulting in secure performance as expected. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

Hybrid Precoding Algorithm for Millimeter-Wave Massive MIMO-NOMA Systems

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Electronics 2022, 11(14), 2198; https://doi.org/10.3390/electronics11142198 - 13 Jul 2022

Cited by 2 | Viewed by 1350

Abstract

In this paper, the performance of the millimeter-wave (mmWave) massive multiple-input multiple-output (mMIMO) non-orthogonal multiple access (NOMA) systems is investigated under multiple user scenarios. The performance of the system has been analyzed in terms of spectral efficiency (SE), energy efficiency (EE), and computational complexity. In the case of the mMIMO system, the linear precoder with matrix inversion becomes less efficient due to its high computational complexity. Therefore, the design of a low-complex hybrid precoder (HP) is the main aim of this paper. Here, the authors have proposed a symmetric successive over-relaxation (SSOR) complex regularized zero-forcing (CRZF) linear precoder. Through simulation, this paper demonstrates that the proposed SSOR-CRZF-HP performs better than the conventional linear precoder with reduced complexity. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

An Enhanced DV-Hop Localization Scheme Based on Weighted Iteration and Optimal Beacon Set

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Electronics 2022, 11(11), 1774; https://doi.org/10.3390/electronics11111774 - 02 Jun 2022

Cited by 3 | Viewed by 1042

Abstract

Node localization technology has become a research hotspot for wireless sensor networks (WSN) in recent years. The standard distance vector hop (DV-Hop) is a remarkable range-free positioning algorithm, but the low positioning accuracy limits its application in certain scenarios. To improve the positioning performance of the standard DV-Hop, an enhanced DV-Hop based on weighted iteration and optimal beacon set is presented in this paper. Firstly, different weights are assigned to beacons based on the per-hop error, and the weighted minimum mean square error (MMSE) is performed iteratively to find the optimal average hop size (AHS) of beacon nodes. After that, the approach of estimating the distance between unknown nodes and beacons is redefined. Finally, considering the influence of beacon nodes with different distances to the unknown node, the nearest beacon nodes are given priority to compute the node position. The optimal coordinates of the unknown nodes are determined by the best beacon set derived from a grouping strategy, rather than all beacons directly participating in localization. Simulation results demonstrate that the average localization error of our proposed DV-Hop reaches about 3.96 m, which is significantly lower than the 9.05 m, 7.25 m, and 5.62 m of the standard DV-Hop, PSO DV-Hop, and Selective 3-Anchor DV-Hop. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessArticle

AAPFE: Aligned Assembly Pre-Training Function Embedding for Malware Analysis

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Electronics 2022, 11(6), 940; https://doi.org/10.3390/electronics11060940 - 17 Mar 2022

Cited by 1 | Viewed by 1531

Abstract

The use of natural language processing to analyze binary data is a popular research topic in malware analysis. Embedding binary code into a vector is an important basis for building a binary analysis neural network model. Current solutions focus on embedding instructions or basic block sequences into vectors with recurrent neural network models or utilizing a graph algorithm on control flow graphs or annotated control flow graphs to generate binary representation vectors. In malware analysis, most of these studies only focus on the single structural information of the binary and rely on one corpus. It is difficult for vectors to effectively represent the semantics and functionality of binary code. Therefore, this study proposes aligned assembly pre-training function embedding, a function embedding scheme based on a pre-training aligned assembly. The scheme creatively applies data augmentation and a triplet network structure to the embedding model training. Each sub-network extracts instruction sequence information using the self-attention mechanism and basic block graph structure information with the graph convolution network model. An embedding model is pre-trained with the produced aligned assembly triplet function dataset and is subsequently evaluated against a series of comparative experiments and application evaluations. The results show that the model is superior to the state-of-the-art methods in terms of precision, precision ranking at top N (p@N), and the area under the curve, verifying the effectiveness of the aligned assembly pre-training and multi-level information extraction methods. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Open AccessFeature PaperArticle

Android-SEM: Generative Adversarial Network for Android Malware Semantic Enhancement Model Based on Transfer Learning

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Electronics 2022, 11(5), 672; https://doi.org/10.3390/electronics11050672 - 22 Feb 2022

Cited by 2 | Viewed by 1517

Abstract

Currently, among the millions of Android applications, there exist numerous malicious programs that pose significant threats to people’s security and privacy. Therefore, it is imperative to develop approaches for detecting Android malware. Recently developed malware detection methods usually rely on various features, such as application programming interface (API) sequences, images, and permissions, thereby ignoring the importance of source code and the associated comments, which are not usually included in malware. Therefore, we propose Android-SEM, which is an Android source code semantic enhancement model based on transfer learning. Our proposed model is built upon the Transformer architecture to achieve a pretraining framework for generating code comments from malware source code. The performance of the pretraining framework is optimized using a generative adversarial network. Our proposed model relies on a novel regression model-based filter to retain high-quality comments and source code for feature fusion pertinent to semantic enhancement. Creatively, and contrary to conventional methods, we incorporated a quantum support vector machine (QSVM) for classifying malicious Android code by combining quantum machine learning and classical deep learning models. The results proved that Android-SEM achieves accuracy levels of 99.55% and 99.01% for malware detection and malware categorization, respectively. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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Review

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Open AccessReview

A Comprehensive Survey of Energy-Efficient MAC and Routing Protocols for Underwater Wireless Sensor Networks

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Electronics 2022, 11(19), 3015; https://doi.org/10.3390/electronics11193015 - 22 Sep 2022

Cited by 7 | Viewed by 1829

Abstract

Underwater wireless sensor networks (UWSNs) have become highly efficient in performing different operations in oceanic environments. Compared to terrestrial wireless sensor networks (TWSNs), MAC and routing protocols in UWSNs are prone to low bandwidth, low throughput, high energy consumption, and high propagation delay. UWSNs are located remotely and do not need to operate with any human involvement. In UWSNs, the majority of sensor batteries have limited energy and very difficult to replace. The uneven use of energy resources is one of the main problems for UWSNs, which reduce the lifetime of the network. Therefore, an energy-efficient MAC and routing techniques are required to address the aforementioned challenges. Several important research projects have been tried to realize this objective by designing energy-efficient MAC and routing protocols to improve efficient data packet routing from Tx anchor node to sensor Rx node. In this article, we concentrate on discussing about different energy-efficient MAC and routing protocols which are presently accessible for UWSNs, categorize both MAC and routing protocols with a new taxonomy, as well as provide a comparative discussion. Finally, we conclude by presenting various current problems and research difficulties for future research. Full article

(This article belongs to the Special Issue Advanced Applications of Sensor Network and Wireless Communication)

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