Reliability Of Wireless Networks Research Articles

Secure Equality Test Technique Using Identity-Based Signcryption for Telemedicine Systems

For telemedicine, Wireless Body Area Network (WBAN) offers enormous benefits where a patient can be remotely monitored without compromising the mobility of remote treatments. With the advent of high capacity and reliable wireless networks, WBANs are used in several remote monitoring systems, limiting the COVID-19 spread. The sensitivity of telemedicine applications mandates confidentiality and privacy requirements. In this paper, we propose a secure WBAN-19 telemedicine system to overcome the pervasiveness of contagious deceases utilizing a novel aggregate identity-based signcryption scheme with an equality test feature. We demonstrate a security analysis regarding indistinguishable adaptive chosen-ciphertext attack (IND-CCA2), one-way security against adaptive chosen-ciphertext attack (OW-CCA2), and unforgeability against adaptive chosen-message attack (EUF-CMA) under the random oracle model. The security analysis of the scheme is followed by complexity evaluations where the computation cost and communication overhead are measured. The evaluation demonstrates that the proposed model is efficient and applicable in telemedicine systems with high-performance capacities.

RAFT Consensus Reliability in Wireless Networks: Probabilistic Analysis

The centralized system becomes less efficient, secure, and resilient as the network size and heterogeneity increase due to its inherent single point of failure issues. Distributed consensus mechanisms characterized by decentralization, autonomy, parallelism and fault-tolerance can meet the increasing demands of safety and security in critical interconnected systems. This paper establishes a Node and Link probabilistic failure model in the presence of node and communication link failures for a representative crash fault tolerant distributed consensus protocol: RAFT. The analytical results in terms of the probability density function and the mean value of consensus reliability are derived. Two important reliability performance indicators, Reliability Gain and Tolerance Gain are proposed to indicate the linear relationship between the consensus reliability and two basic parameters, i.e. the joint failure rate and the maximum number of tolerant faulty nodes, which provide the theoretical guidance for quickly deploying a RAFT system. The special case of a distributed consensus network with already a certain number of failures and its adverse impact are evaluated. The Markov probabilistic models, definitions of Reliability Gain and Tolerance Gain, and the analysis methods proposed in this paper can be extended to other consensus mechanisms.

Fibre optic-VLC versus laser-VLC: a review study

The exponential increase in the demand for a fast and reliable wireless mobile network motivates the need for researchers to engage more actively in visible light communication (VLC) technology. This increase happens in parallel with the augmentation of the population to be accommodated. As a result, the radio frequency spectrum is overcrowded. VLCs, which are part of optical wireless communications, are unregulated ultra-wideband spectrums that offer an alternative to solve RF bandwidth exhaustion. The OWC has demonstrated, among others, a throughput of 100 Gbps and is capable of offloading large data traffic from RF networks. One of the VLC main backdrops is the distance for which light signals can be transmitted, which is generally short in nature. However, this is overcome by its combination with other technologies, such as fibre optics (FO) and laser technologies, to mention only a few, which form a cascaded system with VLC. The data offloaded is fed to the cascaded system of FO-VLC and laser-VLC. In this paper, we provide a comparative review of cascaded FO-VLC and laser-VLC systems. We survey the technology that can provide optimized communication and high bandwidth. The choice of cascaded system to be used or considered is dependent on the area of application. A comparison is conducted from the simulated results of both cascaded systems considering parameters such as the gain and throughput. The FO-VLC cascaded system shows that system gain always decreases as distance increases for a range of reflection coefficient, rho, and slightly decreases with attenuation coefficient, alpha. The gain of the laser-VLC cascaded system decreases with an increase in transmission distance for a given range of floor and ceiling reflection coefficient and decreases slightly with attenuation intensity, gamma. From our investigation, it is clear that FO-VLC and laser-VLC are outstanding optical cascaded systems, which can be exploited in applications with a harsh environment. This research also highlights standardization efforts, advanced modulation schemes and coding techniques so far for VLC, FO and laser system and their promising applications.

Remote Monitoring and Controlling Electrical Devices for Industrial Application

Abstract: The advent of advanced technologies has revolutionized the industrial landscape, providing new opportunities for enhancing efficiency and productivity. In this context, remote monitoring and controlling of electrical devices have emerged as a crucial aspect of industrial automation. This paper presents a comprehensive study on the design and implementation of a remote monitoring and controlling system for electrical devices in industry. By integrating sensor networks, wireless communication protocols, and cloud-based platforms, the system establishes a seamless connection between the devices and the centralized monitoring station. This allows operators and engineers to have constant access to vital data and control functionalities, regardless of their physical location. The key components of the system include intelligent sensors deployed in the industrial environment, a reliable wireless communication network for data transmission, and a cloud-based platform for data storage and analysis. The sensors collect various parameters such as voltage, current, temperature, and power consumption, providing a comprehensive overview of the electrical devices' operational status. The wireless network facilitates real-time data transmission to the cloud platform, where the data is securely stored and made available for analysis. The cloud-based platform not only offers data storage but also supports advanced analytics and visualization techniques. This enables operators to gain valuable insights into device performance, identify potential issues, and take proactive measures to optimize operations. Furthermore, the system provides remote control capabilities, allowing operators to adjust device settings, troubleshoot problems, and initiate maintenance actions, all through a user-friendly. The benefits of implementing this remote monitoring and controlling system are numerous. It enables predictive maintenance, reducing downtime and minimizing the risk of equipment failure. Real-time monitoring helps detect anomalies and abnormal behaviours, enabling timely interventions to prevent potential hazards. Additionally, remote access to device control enhances operational flexibility and reduces the need for on-site presence, leading to significant cost savings and improved overall efficiency.

Health Care Workers' Expectations of the Mercury Advance SMARTcare Solution to Prevent Pressure Injuries: Individual and Focus Group Interview Study.

The transformation in global demography and the shortage of health care workers require innovation and efficiency in the field of health care. Digital technology can help improve the efficiency of health care. The Mercury Advance SMARTcare solution is an example of digital technology. The system is connected to a hybrid mattress and is able to detect patient movement, based on which the air pump either starts automatically or sends a notification to the app. Barriers to the adoption of the system are unknown, and it is unclear if the solution will be able to support health care workers in their work. This study aims to gain insight into health care workers' expectations of factors that could either hamper or support the adoption of the Mercury Advance SMARTcare unit connected to a Mercury Advance mattress to help prevent patients from developing pressure injuries in hospitals and long-term care facilities. We conducted a generic qualitative study from February to December 2022. Interviews were conducted, and a focus group was established using an interview guide of health care workers from both the United Kingdom and the Netherlands. Thematic analysis was performed by 2 independent researchers. A total of 14 participants took part in the study: 6 (43%) participants joined the focus group, and 8 (57%) participants took part in the individual interviews. We identified 13 factors based on four themes: (1) factors specifically related to SMARTresponse, (2) vision on innovation, (3) match with health care activities, and (4) materials and resources involved. Signaling function, SMARTresponse as prevention, patient category, representatives, and implementation strategy were identified as facilitators. Perception of patient repositioning, accessibility to pressure injury aids, and connectivity were identified as barriers. Several conditions must be met to enhance the adoption of the Mercury Advance SMARTcare solution, including the engagement of representatives during training and a reliable wireless network. The identified factors can be used to facilitate the implementation process.

FedBroadcast: Exploit Broadcast Channel for Fast Convergence in Wireless Federated Learning

With the fast development of modern networking technologies, the transmission rate and reliability of wireless networks have been greatly improved. Meanwhile, the fast-developing Internet of Things (IoT) devices provide continually increasing computation capability. Federated learning (FL) was proposed to leverage communication and computation resources to perform machine learning (ML) tasks on IoT devices. In wireless FL systems, devices train ML models with local data sets, and a base station (BS) aggregates these trained models so that data privacy is guaranteed by the isolation of data sets. However, in the existing works, the same global model is transmitted to devices individually over the wireless channel multiple times, while the updated local models are received only by the BS, which ignores the wireless broadcast channel, incurs large communication overhead, and slows down the overall training process. In this article, we propose the FedBroadcast protocol to efficiently exploit the shared wireless broadcast channel for the two-way model transmission in FL. In the downloading step, we let BS broadcast the global model once for all scheduled devices, and design a dynamic programming-based algorithm to schedule devices optimally. In the uploading step, we also leverage the wireless broadcast channel and select some devices to receive all the updated local models without waiting for model downloading in the next round. Finally, to solve the potential block-cyclic sampling problem brought by device scheduling, we adopt pluralistic averaging modification, which improves the convergence performance under extreme data distributions. Extensive experiments demonstrate that FedBroadcast outperforms the existing wireless FL under different system settings.

Intelligent Modulation Recognition of Communication Signal for Next-Generation 6G Networks

In recent years, the need for a fast, efficient and a reliable wireless network has increased dramatically. Numerous 5G networks have already been tested while a few are in the early stages of deployment. In non-cooperative communication scenarios, the recognition of digital signal modulations assists people in identifying the communication targets and ensures an effective management over them. The recent advancements in both Machine Learning (ML) and Deep Learning (DL) models demand the development of effective modulation recognition models with self-learning capability. In this background, the current research article designs a Deep Learning enabled Intelligent Modulation Recognition of Communication Signal (DLIMR-CS) technique for next-generation networks. The aim of the proposed DLIMR-CS technique is to classify different kinds of digitally-modulated signals. In addition, the fractal feature extraction process is applied with the help of the Sevcik Fractal Dimension (SFD) approach. Then, the extracted features are fed into the Deep Variational Autoencoder (DVAE) model for the classification of the modulated signals. In order to improve the classification performance ofthe DVAE model, the Tunicate Swarm Algorithm (TSA) is used to fine-tune the hyperparameters involved in DVAE model. A wide range of simulations was conducted to establish the enhanced performance of the proposed DLIMR-CS model. The experimental outcomes confirmed the superior recognition rate of the DLIMR-CS model over recent state-of-the-art methods under different evaluation parameters.

A Brief Review of Massive MIMO Technology for the Next Generation

Massive Multiple Input Multiple Output (MIMO) is an evolving technology based on the principle of spatial multiplexing winch consisting in using at the same time the same radio frequencies to send different signals. The several transmitting antennas from a base station can transmit different signals and several receiving antennas from a device can receive and divide them simultaneously. Due to the physically difficult of installing antennas close to each other, standard MIMO networks generally limit four antenna-side transmitters and receivers for data transmission while it could be more. The study aims to review the traditional MIMO different types as well as investigates the SNR between Single Input Single Output (SISO) and MIMO to ensure the best wireless connection functionality. In addition to that, a simple comparison to distinguish between SISO, SIMO, MISO, and MIMO in term of capacity and data rate to provide an indication for the quality of the wireless connection. The work's contribution is to illustrate technological benefits like MIMO, which boosts data speeds and increases the reliability of wireless networks. The outcome shows a SISO system would have a lower data rate than other systems because it only has one antenna at the transmitter and receiver, whereas a MISO system would typically have a greater Signal-to-Noise Ratio (SNR) than a SISO or SIMO system because it uses several transmit antennas. MIMO, however, took advantage of all the positive characteristics and emerged as the best solution overall.

Unobtrusive, Accurate, and Live Measurements of Network Latency and Reliability for Time-Critical Internet of Things

Time-Critical Internet of Things (TC-IoT) applications place very stringent communication requirements on underlying networks in terms of latency and reliability. To verify experimentally if wireless networks can fulfill those, an appropriate method to measure latency and reliability needs to be developed. State-of-the-art methods are either obtrusive to and dependent on application setup, or insufficient in accuracy. In this work, we propose a new solution called Unobtrusive Latency Tester (ULT) to measure the latency and reliability of wireless networks for TC-IoT applications. The ULT can be seen as unobtrusive to time-critical applications since the insertion delay is shorter than 1 µs and the accuracy of latency measurements is 40 ns. We apply the ULT in a real-life motion control testbed that uses Wi-Fi 6 and 5G networks. The presented results show the feasibility of the proposed approach. We also provide some initial observations on the latency and reliability of current Wi-Fi 6 and 5G technologies and the possibility of their use for TC-IoT applications.

Performance of Predictive Indoor mmWave Networks With Dynamic Blockers

In this paper, we consider millimeter Wave (mmWave) technology to provide reliable wireless network service within factories where links may experience rapid and temporary fluctuations of the received signal power due to dynamic blockers, such as humans and robots, moving in the environment. We propose a novel beam recovery procedure that leverages Machine Learning (ML) tools to predict the starting and finishing of blockage events. This erases the delay introduced by current 5G New Radio (5G-NR) procedures when switching to an alternative serving base station and beam, and then re-establish the primary connection after the blocker has moved away. Firstly, we generate synthetic data using a detailed system-level simulator that integrates the most recent 3GPP 3D Indoor channel models and the geometric blockage Model-B. Then, we use the generated data to train offline a set of beam-specific Deep Neural Network (DNN) models that provide predictions about the beams’ blockage states. Finally, we deploy the DNN models online into the system-level simulator to evaluate the benefits of the proposed solution. Our prediction-based beam recovery procedure guarantees higher signal level stability and up to 82% data rate improvement with respect a detection-based method when blockers move at speed of 2 m/s.

Two‐way decode and forward quadrature media‐based modulation for single‐input multiple‐output scheme

SummaryThe recently proposed quadrature spatial modulation aided single‐input multiple‐output media‐based modulation (QSM aided SIMO‐MBM) improves the spectral efficiency and achieves a high data rate and overall system error performance when compared with conventional SIMO‐MBM and SIMO schemes. Cooperative network (CN) involves collaboration among nodes to improve wireless network reliability, link quality, and spectral efficiency. This paper investigates the performance of QSM aided SIMO‐MBM in CN by employing the decode and forward relaying technique, which utilizes two source nodes that simultaneously transmit a unique message block on the same time slot to the relay node; the relay node decodes the received message block from both transmitting nodes, before re‐encoding and re‐transmitting the decoded message block in the next time slot to the destinations in order to enhance the error performance of the QSM aided SIMO‐MBM further. Each node can decode the data of the other node using network coding (NC) techniques. Algebraic algorithms are applied to the detected message to accumulate the various transmissions. The theoretical average error probability of the proposed scheme was formulated using a lower bound technique, and the Monte Carlo simulation (MCS) results obtained validated the result. The MCS results achieved exhibit a significant improvement of 8 dB at 6 b/s/Hz for a BER of and 12 dB at 8 b/s/Hz for a BER of over the conventional QSM aided SIMO‐MBM scheme. Consequently, the obtained results show that the system configuration with outperforms the system configuration with for the proposed scheme.

Wireless Network Reliability Analysis for Arbitrary Network Topologies

In this paper, the outage performance of wireless networks with unstructured topologies is investigated. The network reliability perspective of graph theory is used to obtain the network outage polynomial of generalized wireless networks for both uncorrelated and correlated wireless channels. A relationship is established between the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">max-flow min-cut</i> theorem and key communication performance indicators. The diversity order is equal to the size of the minimum cut-set between source and destination, and the coding gain is the number of cut-sets with size equal to the minimum cut. An ergodic capacity analysis of arbitrary network topologies based on the network outage polynomial is also presented. Numerical results are used to illustrate the technical definitions and verify the derivations.

Highly Accurate and Reliable Wireless Network Slicing in 5th Generation Networks: A Hybrid Deep Learning Approach

In current era, the next generation networks like 5th generation (5G) and 6th generation (6G) networks requires high security, low latency with a high reliable standards and capacity. In these networks, reconfigurable wireless network slicing is considered as one of the key element for 5G and 6G networks. A reconfigurable slicing allows the operators to run various instances of the network using a single infrastructure for better quality of services (QoS). The QoS can be achieved by reconfiguring and optimizing these networks using Artificial intelligence and machine learning algorithms. To develop a smart decision-making mechanism for network management and restricting network slice failures, machine learning-enabled reconfigurable wireless network solutions are required. In this paper, we propose a hybrid deep learning model that consists of convolution neural network (CNN) and long short term memory (LSTM). The CNN performs resource allocation, network reconfiguration, and slice selection while the LSTM is used for statistical information (load balancing, error rate etc.) regarding network slices. The applicability of the proposed model is validated by using multiple unknown devices, slice failure, and overloading conditions. An overall accuracy of 95.17% is achieved by the proposed model that reflects its applicability.

Cost-Efficient and Reliable Communication Scheme for Supporting a Mobile Device in WirelessHART of IIoT

In IIoT, WirelessHART is one of the reliable wireless network standards since it can improve the productivity and efficiency. Recently, in Industrial area, the enterprise and the researcher have been paid attention to a mobile device, which will become one of the key points in the industry due to its potential ability. However, since WirelessHART existing researches handle with a mobile device as a single point-to-point viewpoint, they will happen a disconnection and a wasteful communication resource problem when they use a mobile device. In order to solve this limitation, we propose a proactive and cost-effective scheme for the mobility by focusing on a mobility as a group viewpoint. By using onion-peeling method, the proposed scheme can select some fixed devices to communicate with a mobile device and prevent redundant communication provision for a mobile device. The simulation results show that the proposed scheme is more cost-efficient and reliable when it compares with the existing researches of WirelessHART.

Adapting Time Headway in Cooperative Adaptive Cruise Control to Network Reliability

Wireless network impairment results in adverse effect on string stability of a Cooperative Adaptive Cruise Control (CACC) platoon that relies on the wireless network, causing disturbances in the platoon that may lead to a collision. Previous work in the literature proposed fallback strategies that utilizes on-board sensors to recover in case of wireless network impairment, those methods assumes a fixed time headway value. In this paper, we study the string stability of a one-vehicle look-ahead CACC platoon under different network loss scenarios, and propose to adapt the time headway parameter of the model according to a network reliability metric that we defined based on packet burst loss length to maximize traffic flow efficiency while maintaining a string-stable platoon. Our findings show that careful adjustment of headway value according to the wireless network reliability allows the platoon to maintain string-stable operation while maximizing traffic flow.

Cooperative Rate-Splitting for Downlink Multiuser MISO Systems With Partial CSIT

Multiple antenna technology and cooperative relaying are two efficient methods to improve the capacity and reliability of wireless networks. By marrying cooperative relaying with a Rate-Splitting (RS) approach, Cooperative Rate-Splitting (CRS) shows certain benefits compared to several non-cooperative and cooperative schemes in multi-antenna Broadcast Channel (BC). In this paper, we further investigate the CRS with partial Channel State Information at the Transmitter (CSIT), as partial CSIT is an inevitable issue in practical downlink Multi-user Multiple Input Single Output (MU-MISO) systems. The proposed CRS structure includes multiple users, and one user could be selected as a relay to help the other users. With the flexible design, CRS leverages the benefits of user cooperation and relay (or user) selection for cooperative network, and also retains the superiority of RS for multi-antenna BC. Considering the Weighted Average Sum Rate (WASR) performance, we aim to solve the stochastic optimization that jointly involves the precoder design, the time resource allocation, and the relay selection issue. To address the above non-convex problem, a dedicated algorithm for CRS that exploits the Weighted Minimum Mean Square Error (WMMSE) approach and the Convex-Concave Procedure (CCP) is proposed, such that the WASR is maximized by jointly updating the precoding matrix and time allocation factor according to the incoming user channel state estimate. The proposed algorithm then enables us to solve the relay selection through an exhaustive search. Since the optimal relay selection algorithm has a high computational complexity when the number of users is large, we additionally provide a suboptimal heuristic algorithm to strike a balance between complexity and performance. Numerical results demonstrate that the proposed CRS scheme is capable of achieving a larger WASR than its non-cooperative counterpart and several existing beamforming schemes in a wide range of propagation conditions, and the heuristic algorithm achieves a performance close to the optimal one but with lower complexity.

5G Wireless Networks in the Future Renewable Energy Systems

This paper focuses on the strategies that employ the fifth generation (5G) wireless networks in the optimal management of demand-side response in the future energy systems with the high penetration of renewable energy sources (RES). It also provides a comparison between advantages and challenges of 5G networks in demand-response renewable energy grids. Large-scale renewable energy integration always leads to a mismatch between generation and load demand in the short run due to the intermittency. It is often envisioned that 5G wireless networks that were recently launched and would most likely be fully deployed worldwide by 2035 would bring many technological and economic benefits for a plethora of the future high-renewables grids featuring electric transport and heating as well as prosumers generating renewable energy and trading it back to the grid (for example, in the vehicle-to-grid (V2G) framework) and among themselves using peer-to-peer (P2P) networks. Our paper offers a comprehensive analysis of 5G architecture with the perspectives of optimal management of demand-side response in the smart grids of the future. We show that the effective deployment of faster and more reliable wireless networks would allow faster data transfers and processing, including peer-to-peer (P2P) energy trade market, Internet of Vehicles (IoV) market, or faster smart metering, and thence open the path for the full-fledged Internet of Energy (IoE). Moreover, we show that 5G wireless networks might become in the future sustainable energy systems paving the road to even more advanced technologies and the new generations of networks. In addition, we demonstrate that for the effective management of energy demand-side response with a high share of renewables, certain forms of governments funding and incentives might be needed. These are required to strengthen the support of RES and helping to shift to the green economy.

Examination of the multiple-input multiple-output space-time block-code selective decode and forward relaying protocol over non-homogeneous fading channel conditions

With the tremendous increase in wireless user traffic, investigation on the end-to-end reliability of wireless networks in practical conditions such as non-homogeneous fading channel conditions is becoming increasingly widespread. Because they fit well to the experimental data, generalized channel fading distributions like κ–μ are well suited for modeling diverse fading channels. This paper analyzes the symbol error rate (SER) and outage probability (OP) performance of multiple-input multiple-output (MIMO) space-time block-code (STBC) selective decode and forward (S-DF) network over κ–μ fading channel conditions considering the additive white Gaussian noise (AWGN). First, the closed-form (CF) analytical expressions for the probability density function (PDF) and the cumulative distribution function (CDF) of the received signal-to-noise ratio (SNR) as well as its moment generating function (MGF) are derived. Second, the OP performance is then investigated for various values of the channel fading parameter and SNR regimes. The simulation findings show an increase in SER performance with an improved line-of-sight (LOS) component. Furthermore, the results show that the S-DF relaying systems can function properly even when there is no fading or LOS component. The OP has been increasing with the increase in the value of μ and κ. In medium and high SNR regimes, simulation results exactly match with analytical results.

Distributed Deep Reinforcement Learning Computations for Routing in a Software-Defined Mobile Ad Hoc Network

The need for reliable and flexible wireless networks has significantly increased in recent years, according to the growing reliance of an enormous number of devices on these networks to establish communications and access service. Mobile Ad-hoc Networks (MANETs) allow the wireless network to establish communications without the need for infrastructure by allowing the nodes to deliver each other’s packets to their destination. Such networks increased flexibility but require more-complex routing methods. In this study, we proposed a new routing method, based on Deep Reinforcement Learning (DRL), that distributes the computations in a Software Defined Network (SDN) controller and the nodes, so that, no redundant computations are executed in the nodes to save the limited resources available on these nodes. The proposed method has been able to significantly increase the lifetime of the network, while maintaining a high Packet Delivery Rate (PDR) and throughput. The results also show that the End-to-End delay of the proposed method is slightly larger than existing routing methods, according to the need for longer alternative routes to balance the loading among the nodes of the MANET.

Binary Joint Network-Channel Coding for Reliable Multi-Hop Wireless Networks

The high and variable packet loss in wireless networks due to the unreliable nature of wireless channels, interference, link or router failure, fading, and shadowing wireless link are the main problems and challenges in implementing a reliable wireless network. Packet loss probability is the most important performance measure for real-time applications. Packet loss is due to errors, congestion in the queue because of high traffic, collision, hidden nodes, and link failures. In this paper, we focus on the links failure problem. To address this problem, improve the performance of wireless networks, and enhance and make the network survivability and availability more reliable, a new efficient binary joint network and channel coding (B-JNCC) technique is proposed. B-JNCC combines low-complexity parity-check (LCPC) channel coding and the wireless network erasure correcting with router code to detect lost packets to reduce the bit error rate and increase the packet delivery ratio over a wireless multi-access channel. B-JNCC is applied at the receiver node. Simulation results show that the proposed B-JNCC outperforms the binary symbol-wise JNCC, non-binary JNCC (NB-JNCC), and NB separate network-channel coding.