The proliferation of large-scale Internet-of-things (IoT) deployments and the emergence of 6G wireless technologies have created a pressing need for intelligent, energy-aware, and low-latency communication frameworks. In this work, we propose a novel two-phase reinforcement learning (RL)-based architecture designed to minimize the age of information (AoI) in 6G-enabled IoT networks. Our approach integrates (i) a deep deterministic policy gradient (DDPG)-driven backscatter-assisted cognitive radio non-orthogonal multiple access (CR-NOMA) scheme in the uplink, and (ii) a lightweight Q-learning-based power-domain NOMA (PD-NOMA) strategy for the downlink. In the uplink, energy harvesting (EH) sensors employ deep RL to jointly optimize backscatter reflection coefficients and transmission scheduling over shared spectrum using CR-NOMA. This enables energy-efficient communication and reduced AoI under dynamic energy and channel conditions. In the downlink, the edge node serves multiple IoT users simultaneously using PD-NOMA, where a Q-learning agent intelligently decides whether to transmit fresh or cached data to each user based on battery levels, channel quality, and information freshness. Both phases are modeled as Markov decision processes (MDPs), allowing agents to learn independently and converge toward optimal policies that balance information freshness, spectral efficiency (SE), and energy constraints. Extensive simulations demonstrate that the proposed framework effectively reduces AoI across both phases, with consistent convergence even under varying sensor densities and EH conditions. Moreover, by relying on explainable and verifiable learning mechanisms, our model addresses emerging concerns around reliability and trustworthiness in artificial intelligence (AI)-driven 6G-IoT systems. This framework represents a step toward scalable, adaptive, and responsible AI integration for future mission-critical IoT applications.

6G networks require a sustainable and dependable power supply to ubiquitous space–air–ground connectivity infrastructures. Wireless power transfer (WPT) offers a promising path to sustainable energy delivery; however, concurrent transmitters can experience destructive interference when operating asynchronously. Most existing studies focus on centralized or synchronized WPT systems, leaving the asynchronous regime largely unexplored. In contrast to 5G’s tightly coordinated and slowly varying links, 6G WPT must function under non-stationary mobility, higher carrier frequencies, and dense power transmitter deployments. To bridge this gap, we translate key 6G stressors into design laws and probability guarantees. Specifically, we present a new systematic framework for asynchronous concurrent WPT, featuring a unified kernel that captures frequency, timing, and phase dispersions as a single retention term across instantaneous, short-time, and long-time scales. Further, we provide closed-form ppm/time budgets for a target retention, a retention cumulative distribution function that tightens asO(N−2), and a Doppler time-to-null scheduler coupled to the harvester. Finally, we perform deterministic and Monte Carlo analyses, together with experimental studies.

This paper addresses the need for an explainable and generalized intrusion detection system (IDS) for the in-vehicle networks (IVNs). While machine learning (ML)-based IDS solutions show promising performance, there are still some challenges, such as the lack of trustworthiness and scarcity of attack representing data, hindering their adoption in the automotive cybersecurity. To address these issues, this paper proposes a centralized ML model training and decentralized execution-based framework, namely ACHILLES, that facilitates an explainable and generalizable automotive IDS. Under ACHILLES, different ML models can be trained centrally to enhance decentralized and onboard intrusion detection performance with multiple automotive datasets. In addition, we generate standard feature formats to assess the ML model’s generalization efficacy, where the quality of generalization and explainability is evaluated with SHapley Additive exPlanations (SHAP) by identifying the importance of the feature. We also propose a meta-learning scheme to construct suitable ML models trained by the proposed standard feature formats. The proposed feature format exhibits significant performance gain during ML model training and testing with four state-of-the-art controller area network (CAN)-bus datasets containing real, advanced attacks. The experimental results indicate that developing ML models using the generated generalized features and the meta learning-based model building process leads to enhanced performance. In particular, under the dataset cross train-test setting, the proposed feature format enhances the average accuracy by 40.1% for the baseline model, 32.4% for the meta-learned DNN, and 23.6% for the meta-learned Random Forest, compared with the baseline feature format.

Accurate mobile network traffic prediction is crucial for transit infrastructure service optimization in industrial informatization. Traditional linear models fail to capture complex non-linear dynamics, while existing deep learning methods struggle with rapid temporal changes, signal fluctuations, and diverse network conditions, limiting real-time applicability. To address these challenges, this paper proposes a hybrid model integrating Convolutional Neural Networks (CNNs) and Transformers, tailored for High-Speed Railway (HSR) environments. The proposed hybrid model is evaluated using both public datasets and a real-world HSR dataset collected through empirical field measurements, it not only achieves state-of-the-art (SOTA) predictive accuracy, reducing root mean square error by 4.7% over strong baselines in the challenging HSR environment, but also delivers this performance with superior computational efficiency, achieving over 3.6 times lower inference latency than leading SOTA models. This establishes an optimal performance-to-cost ratio, demonstrating its practical value for real-time HSR systems.

With the increasing number of Internet-of-things (IoT) devices, the need for energy-efficient and spectrum-efficient networks that can support resource-constrained devices within existing wireless infrastructures becomes critical. This paper investigates the application of deep reinforcement learning (DRL) algorithms to optimize the energy efficiency (EE) of a secondary device (SD) equipped with radio frequency energy harvesting (RF-EH) antennas. The system models a wireless powered communication network (WPCN) where the SD employs a cognitive-radio non-orthogonal multiple access (CR-NOMA) scheme to transmit data during uplink communications of neighboring primary devices (PDs). Among the DRL approaches evaluated, proximal policy optimization (PPO) emerged as the most effective, achieving the highest EE values and demonstrating its suitability for this problem. Additionally, our results show that equal gain combining (EGC) consistently achieves superior EE compared to other diversity-combining techniques, making it a favorable choice for self-sustaining IoT networks. These findings provide valuable insights into the role of diversity-combining techniques and DRL algorithms in enhancing SD performance in dynamic EH environments.

The localization of multiple signal sources using sensor arrays has been a long-standing research challenge. While numerous solutions have been developed, signal space methods like MUSIC and ESPRIT have gained widespread popularity. As sensor arrays grow in size, sources are frequently located in the near-field region. The standard MUSIC algorithm can be adapted to locate these sources by performing a 3D search over both the distance and the angles of arrival (AoA), including azimuth and elevation, though this comes with significant computational complexity. To address this, a modified version of MUSIC has been developed to decouple the AoA and distance, enabling sequential estimation of these parameters and reducing computational demands. However, this approach suffers from reduced accuracy. To maintain the accuracy of MUSIC while minimizing complexity, this paper proposes a novel method that exploits angular variation across the array aperture, eliminating the need for a grid search over distance. The proposed method divides the large aperture into smaller sections, with each focusing on estimating the angles of arrival. These angles are then triangulated to localize the sources in the near-field of the large aperture. Numerical simulations show that this approach not only surpasses the Modified MUSIC algorithm in terms of mean absolute error but also achieves accuracy comparable to standard MUSIC, all while greatly reducing computational complexity- 370 times in our simulation scenario.

Ultra-reliable low-latency communications (URLLC) require innovative approaches to modeling channel and interference dynamics, extending beyond traditional average estimates to encompass entire statistical distributions, including rare and extreme events that challenge achieving ultra-reliability performance regions. In this paper, we propose a risk-sensitive approach based on extreme value theory (EVT) to predict the signal-to-interference-plus-noise ratio (SINR) for efficient resource allocation in URLLC systems. We employ EVT to estimate the statistics of rare and extreme interference values, and kernel density estimation (KDE) to model the distribution of non-extreme events. Using a mixture model, we develop an interference prediction algorithm based on quantile prediction, introducing a confidence level parameter to balance reliability and resource usage. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. Simulation results demonstrate that the proposed method outperforms the state-of-the-art first-order discrete-time Markov chain (DTMC) approach by reducing outage rates up to 100 -fold, achieving target outage probabilities as low as 10−7). Simultaneously, it minimizes radio resource usage$\sim 15 \%$compared to DTMC, while remaining only$\sim 20 \%$above the optimal case with perfect interference knowledge, resulting in significantly higher prediction accuracy. Additionally, the method is sample-efficient, able to predict interference effectively with minimal training data.

This paper investigates the synergistic potential of reconfigurable intelligent surfaces (RIS) and non-orthogonal multiple access (NOMA) to enhance the energy efficiency and performance of next-generation wireless networks. We delve into the design of energy-efficient passive beamforming (PBF) strategies within RIS-assisted coordinated multi-point (CoMP)-NOMA networks. Two distinct RIS configurations, namely, enhancementonly PBF (EO) and enhancement & cancellation PBF (EC), are proposed and analyzed. Our findings demonstrate that RISassisted CoMP-NOMA networks offer significant efficiency gains compared to traditional CoMP-NOMA systems. Furthermore, we formulate a PBF design problem to optimize the RIS phase shifts for maximizing energy efficiency. Our results reveal that the optimal PBF design is contingent upon several factors, including the number of cooperating base stations (BSs), the number of RIS elements deployed, and the RIS configuration. This study underscores the potential of RIS-assisted CoMP-NOMA networks as a promising solution for achieving superior energy efficiency and overall performance in future wireless networks.

This paper investigates a co-design framework for wireless networked control systems (WNCSs) that integrates multi-hop IEEE 802.15.4-based links under Wi-Fi interference, addressing the challenges of signal-to-interference-plus-noise ratio (SINR) degradation in adverse industrial environments. Multihop configurations are essential for extending the operational range and improving SINR in harsh propagation conditions, but they introduce trade-offs in control stability, latency, and computational complexity. We investigate the impact of multi-hop communication on system performance, comparing Bernoulli and Markovian control strategies. Our results demonstrate that multihop links effectively extend the operational range and mitigate SINR degradation, but at the cost of increased latency and computational cost. We analyze the spectral radius of the system stability verification matrix and control costs for Bernoulli and Markovian control strategies, illustrating that network latency and hop counts can be balanced while maintaining the stability of the multi-hop WNCS. Markovian strategy, although more computationally intensive, outperforms Bernoulli strategy under high interference, offering a robust solution for industrial WNCSs. The proposed framework provides a practical approach for deploying reliable WNCSs in interference-prone environments.

Accurate traffic forecasting in high-speed railway (HSR) systems is hindered by abrupt signal fluctuations and varied mobility scenarios. Conventional approaches that rely on fixed weighted combinations of local and global features are unable to adjust rapidly to real-time changes, resulting in suboptimal performance. To address this limitation, we propose a novel gated guided serial CNN and Transformer network (GsCT) that employs a dynamic combination mechanism implemented via a multilayer perceptron (MLP). In GsCT, CNNs capture fine-grained local variations while Transformers model longrange dependencies, and the adaptive MLP-based gating module adjusts the contribution of each branch based on time-window statistics. This dynamic fusion improves prediction quality by 6.5% compared to conventional fixed weighting mechanisms. Evaluations on both public and real-world HSR datasets demonstrate that GsCT achieves a 2.4% reduction in RMSE relative to LSTM-based methods, and the learned gating coefficients offer transparent interpretability of the feature fusion process. Overall, GsCT provides an effective solution for real-time railway traffic forecasting, paving the way for next-generation HSR services.

With the rapid growth of Internet-of-Things (IoT) devices and unmanned agents (UAs), there is a rising need for energy- and spectrum-efficient wireless networks that can support large-scale, resource-constrained deployments. To meet this demand, integration of deep reinforcement learning (DRL), non-orthogonal multiple access (NOMA), and energy harvesting (EH) offers a promising approach to enhance energy efficiency (EE) and spectrum utilization in future sixth-generation (6G) networks, particularly for sustainable Internet of UA (IUA) communications. In this paper, we investigate an IUA network where multiple low-power secondary users (SUs), equipped with radio frequency energy harvesting (RF-EH) antennas, use a cognitive radio NOMA (CR-NOMA) scheme to share uplink channels with nearby primary users (PUs). We formulate a joint transmit power control and EH scheduling problem to maximize the long-term EE of the SUs and spectrum utilization of the network, subject to quality-of-service (QoS) constraints. To address the decentralized nature of the problem, we model the environment as a multi-agent system where each SU independently optimizes its transmission and EH strategies. A range of DRL and non-DRL algorithms is then applied to solve this optimization problem. We also explore different RF-EH diversity combining techniques to further boost system performance. Simulation results highlight the impact of these techniques on EE of SU, offering insights for optimizing performance under dynamic EH conditions.

With the advent of mmWave communication in next-generation wireless networks and their associated non-line-of-sight coverage constraints, Integrated Access and Backhaul (IAB) technology has become increasingly relevant. Current IAB approaches assume fixed power connections, while the next step towards total mobility involves battery-operated IAB nodes. The potential scalability benefits of nomadic 5G+ networks with fully wireless nodes can be particularly valuable in dynamic application scenarios, offering the required adaptability while satisfying stringent network utility constraints. However, managing the balance between operational costs, pertaining energy efficiency, as well as communication integrity in IAB networks is a significant challenge and an active research topic. Consequently, this work in progress paper explores recent advances in IAB network management that aim to enhance energy efficiency and network utility. It identifies new research opportunities and discusses their challenges. Additionally, a conceptual Zero-Touch Management (ZTM) framework for optimizing the operational efficiency of IAB networks is proposed. The framework considers dynamic link scheduling and resource allocation strategies for improving energy usage. This ongoing study intends to provide preliminary insights into the current state of research on IAB network management, emphasizing the topics of energy efficiency and service quality as future research directions.

This paper explores the potential of aerial reconfigurable intelligent surfaces (ARIS) to enhance coordinated multipoint non-orthogonal multiple access (CoMP-NOMA) networks. We consider a system model where a UAV-mounted RIS assists in serving multiple users through NOMA while coordinating with multiple base stations. The optimization of UAV trajectory, RIS phase shifts, and NOMA power control constitutes a complex problem due to the hybrid nature of the parameters, involving both continuous and discrete values. To tackle this challenge, we propose a novel framework utilizing the multi-output proximal policy optimization (MO-PPO) algorithm. MO-PPO effectively handles the diverse nature of these optimization parameters, and through extensive simulations, we demonstrate its effectiveness in achieving near-optimal performance and adapting to dynamic environments. Our findings highlight the benefits of integrating ARIS in CoMP-NOMA networks for improved spectral efficiency and coverage in future wireless networks.

The integration of edge controllers into smart grid infrastructures facilitates advanced functionalities and high re-sponsiveness, thereby bolstering the overall efficiency of the energy grid. The freshness of the sensing information received at the edge controller, captured by the Age of Information (AoI) metric, is vital to maintain system stability, where outdated information may lead to incorrect responses to grid conditions, potentially causing inefficiencies or system disruptions. However, the timeliness of the transmitted updates is mainly compromised by the delay and congestion within the routing links in the Neighborhood Area Network (NAN). In this work, we develop an intelligent routing strategy to improve the AoI of the Routing Protocol for Low-power and lossy networks (RPL), which is the common routing protocol for smart grids. Our proposed method is based on an AoI-aware parent selection mechanism, by which a node becomes attached to the parent with the highest probability of delivering a packet within a predefined AoI threshold. The prediction is made based on a supervised machine learning model, trained using the collection of heterogeneous routing metrics. The performance of the proposed method is evaluated via extensive discrete-event simulations and the results show its potency to improve the peak AoI and the AoI violation probability compared to the standard RPL.

With the rapid development of Internet of Vehi-cles (IoV) technology, vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) communication have become an important part of intelligent transportation systems (ITS). In V2V commu-nication, clustering is essential to optimize network efficiency and reliability. Conventional V2V clustering prioritizes the physical distance between vehicles, neglecting key attributes information of the vehicles (i.e., computational capacity, energy consumption) and behavior like relative motion states between vehicles. Thus, this paper proposes a clustering algorithm based on vehicle attributes and behavioral characteristics (C-VABC) that utilizes the Fuzzy C-Means (FCM) algorithm. Once clusters are established, intelligent data routing decisions between clusters is facilitated by a Deep Q-Network (DQN) based algorithm. This algorithm selects neighboring vehicular Cluster Heads with similar mobility states and higher computational power as the next hop vehicles for packet forwarding. The experimental results demonstrate that the proposed clustering algorithm possesses strong adaptability and reliability, while enhancing the communication quality and efficiency in vehicle networking situations. This provides invaluable support for intelligent transportation systems, as well as intelligent and autonomous driving systems.

With the development of intelligent edge computing (IEC) in industrial IoT (IIoT), there is a growing number of service providers trying to leverage computing resources in the cloud and at the edge to meet the users‘ demand for low latency and high reliability in diversified applications. This evolving landscape necessitates innovative approaches to manage and process the vast amounts of data generated by IIoT devices. Among these approaches, distributed learning frameworks, such as federated learning (FL), have emerged as popular solutions. However, compared to computing, communication remains the primary bottleneck that constrains the speed of federated model training. Most of the previous solutions have focused on reducing communication overhead. Differently, we propose a transmission-centric approach by designing an efficient communication archi-tecture for FL with cloud-edge collaboration, specifically aimed at enhancing communication capabilities through multi-path transmission. We deploy this FL system in a real environment and conduct extensive testing. The results demonstrate that the new approach can significantly reduce communication time in FL setting, thereby enhancing model aggregation efficiency and shortening the overall training duration. Compared to conventional single-path transmission, the proposed solution improves training efficiency by up to 26.4%.

Intelligent reflecting surface (IRS) is considered as a key technology for the sixth generation (6G) networks for creating a controlled environment for users to achieve higher data rates, throughput, and energy efficiency. IRS-assisted networks provide indirect line-of-sight (LoS) to non line-of-sight (NLoS) users, enabling them to achieve higher data rates. The purpose of this paper is to investigate the performance of IRS-assisted ultra-high frequency (UHF) networks in actual rural, sub-urban, and urban environment, in terms of rate coverage probability, spectral and energy efficiency. The actual building locations of all three locations with their heights are modeled as blockages. The analysis is carried out for different densities of BSs and IRS surfaces for different number of mobile users. Our analysis underlines the difference between the coverage probability of 2D versus 3D building areas. It also shows that the deployment of IRS surfaces significantly increases the rate per unit area of the conventional BS networks in practical environments. Our simulation results provide the optimal number of IRS elements and mobile users to achieve a certain rate coverage probability with maximum energy efficiency. Our results also highlight the optimal number of BSs and IRS surfaces for each location that can be deployed to achieve higher energy efficiency.

The increasing number of IoT devices in the network brings new challenges to the network carrying capacity of intelligent edge computing, and the complicated network services make the demand for network resources in industrial production scenarios or ordinary network users often exceed the carrying capacity of the edge computing network. To alleviate this problem, this paper proposes an intelligent edge computing architecture that introduces network service identification, extracts and analyses the data characteristics of network traffic, and designs appropriate algorithms to classify network traffic into six different service types. This enables real-time and computing-requiring tasks to be prioritised in the network. Using two machine learning algorithms, KNN and MLP, a model validation is carried out on the constructed dataset, and the results show the effectiveness of the method, with the correct rate of data validation reaching 85%, which is more than 5% higher than the correct rate of direct classification of the specified applications, and the accuracy can be as high as 97% in certain scenarios.

Industry 5.0 vision, a step toward the next industrial revolution and enhancement to Industry 4.0, conceives the new goals of resilient, sustainable, and human-centric approaches in diverse emerging applications such as factories-of-the-future and digital society. The vision seeks to leverage human intelligence and creativity in nexus with intelligent, efficient, and reliable cognitive collaborating robots (cobots) to achieve zero waste, zero-defect, and mass customization-based manufacturing solutions. However, it requires merging distinctive cyber–physical worlds through intelligent orchestration of various technological enablers, e.g., cognitive cobots, human-centric artificial intelligence (AI), cyber–physical systems, digital twins, hyperconverged data storage and computing, communication infrastructure, and others. In this regard, the convergence of the emerging computational intelligence (CI) paradigm and softwarized next-generation wireless networks (NGWNs) can fulfill the stringent communication and computation requirements of the technological enablers of the Industry 5.0, which is the aim of this survey. In this article, we address this issue by reviewing and analyzing current emerging concepts and technologies, e.g., CI tools and frameworks, network-in-box architecture, open radio access networks, softwarized service architectures, potential enabling services, and others, elemental and holistic for designing the objectives of CI-NGWNs to fulfill the Industry 5.0 vision requirements. Furthermore, we outline and discuss ongoing initiatives, demos, and frameworks linked to Industry 5.0. Finally, we provide a list of lessons learned from our detailed review, research challenges, and open issues that should be addressed in CI-NGWNs to realize Industry 5.0.

Age of Information (AoI) and delay are crucial performance metrics for Industrial Internet of Things (IIoT) applications not only to perform seamless actuation and control actions but also to enable self-organized and re-configurable manufacturing systems. A challenging task in heterogeneous IIoT networks is to minimize the AoI while maintaining a predefined delay constraint. In this work, we consider a Device-to-Device (D2D)-based heterogeneous IIoT network that supports two types of traffic flows, namely AoI-sensitive flow and delay-sensitive flow. First, we introduce a distributed backlog-aware random access protocol that allows the AoI-sensitive nodes to opportunistically access the channel based on the queue occupancy of the delay-sensitive node. Then, we develop an analytical framework to evaluate the average delay and the average AoI, and formulate an optimization problem to minimize the AoI under a given delay constraint. Finally, we provide numerical results to demonstrate the impact of different network parameters on the performance in terms of the average delay and the average AoI. We also give numerical solutions of the optimal parameters that minimize the AoI subject to a defined delay constraint.

While targeting the energy-efficient connectivity of the Internet-of-things (IoT) devices in the sixth-generation (6G) networks, in this paper, we explore the integration of non-orthogonal multiple access-based backscatter communication (BAC-NOMA) and simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs). To this end, first, for the performance evaluation of the STAR-RIS-assisted BAC-NOMA system, we derive the statistical distribution of the channels under Nakagami-m fading. Second, by leveraging the derived statistical channel distributions, we present the effective capacity analysis under the delay quality-of-service (QoS) constraint. In particular, we derive the closed-form expressions for the effective capacity of the reflecting and transmitting backscatter nodes (BSNs) under the energy-splitting protocol of STAR-RIS. To obtain more insight into the performance of the considered system, we provide the asymptotic analysis, and derive the upper bound on the effective capacity, which represents the ergodic capacity. Our simulation results validate the analytical analysis, and reveal the effectiveness of the STAR-RIS-assisted BAC-NOMA system over the conventional RIS (C-RIS)- and orthogonal multiple access (OMA)-based counterparts. Finally, to highlight the trade-off between the effective capacity and energy consumption, we analyze the link-layer energy efficiency. Overall, this paper provides useful guidelines for the performance analysis and design of the STAR-RIS-assisted BAC-NOMA systems.

Current industrial communication systems (ICS) are undergoing a transformation, leveraging a multitude of technologies to meet the specific needs of the manufacturing and automation industries. The convergence of these networks into edge, fog, and cloud architectures has enhanced their scalability and facilitated the deployment of advanced data-driven approaches, such as machine learning for optimizing production processes. However, ensuring proper provisioning of network and computation resources, along with delivering quality of service, is increasingly challenging in these complex communication systems. Zero-Touch Management (ZTM) frameworks promise to reduce complexity and minimize dependence on manual configuration by human experts. Successful deployment of such frameworks requires an accurate Network Digital Twin (NDT) of relevant network elements, as autonomous decision-making heavily relies on the quantity and quality of historical and real-time node and link state information provided by the NDT. However, the use of NDTs for ICS and ZTM in particular is still an emerging research topic. This paper therefore proposes a theoretical use-case for an NDT-based ZTM framework to improve resource utilization in cloud-centered networks. It presents a state-of-the-art analysis of recent NDT advances enabling the deployment of related ZTM approaches and discusses associated challenges and future research directions.

In this paper, we investigate the reconfigurable intelligent surface (RIS)-assisted non-orthogonal multiple access-based backscatter communication (BAC-NOMA) system under Nakagami-m fading channels and element-splitting protocol. To evaluate the system performance, we first approximate the composite channel gain, i.e., the product of the forward and backscatter channel gains, as a Gamma random variable via the central limit theorem (CLT) and method of moments (MoM). Then, by leveraging the obtained results, we derive the closed-form expressions for the ergodic rates of the strong and weak backscatter nodes (BNs). To provide further insights, we conduct the asymptotic analysis in the high signal-to-noise ratio (SNR) regime. Our numerical results show an excellent correlation with the simulation results, validating our analysis, and demonstrate that the desired system performance can be achieved by adjusting the power reflection and element-splitting coefficients. Moreover, the results reveal the significant performance gain of the RIS-assisted BAC-NOMA system over the conventional BAC-NOMA system.

Through the lens of the age-of-information (AoI) metric, this paper takes a fresh look into the performance of coexisting enhanced mobile broadband (eMBB) and ultra-reliable low-latency (URLLC) services in the uplink scenario. To reduce AoI, a URLLC user with stochastic packet arrivals has two options: orthogonal multiple access (OMA) with the preemption of the eMBB user (labeled as puncturing) or non-orthogonal multiple access (NOMA) with the ongoing eMBB transmission. Puncturing leads to lower average AoI at the expense of the decrease in the eMBB user's rate, as well as in signaling complexity. On the other hand, NOMA can provide a higher eMBB rate at the expense of URLLC packet loss due to interference and, thus, the degradation in AoI performance. We study under which conditions NOMA could provide an average AoI performance that is close to the one of the puncturing, while maintaining the gain in the data rate. To this end, we derive a closed-form expression for the average AoI and investigate conditions on the eMBB and URLLC distances from the base station at which the difference between the average AoI in NOMA and in puncturing is within some small gap$\beta$. Our results show that with$\beta$as small as 0.1 minislot, the eMBB rate in NOMA can be roughly 5 times higher than that of puncturing. Thus, by choosing an appropriate access scheme, both the favorable average AoI for URLLC users and the high data rate for eMBB users can be achieved.

In designing ultra-reliable low-latency communication (URLLC) services in 5G-and-beyond systems, link adaptation (LA) plays a vital role in adjusting transmission parameters under channel and interference dynamics. Without capturing such dynamics (e.g., relying on average estimates), the LA algorithms fail to simultaneously meet the strict reliability and latency bounds of mission-critical applications. To this end, this paper focuses on interference prediction-based adaptive resource allocation of one-shot URLLC transmission, wherein our solution deviates from the conventional average-based interference estimation schemes. We predict the next interference value based on the interference distribution estimation using a discrete-time Markov chain (DTMC). Further, to exploit the time correlation of each interference source, we model the correlated interference variations as a second-order DTMC to achieve higher prediction accuracy. While accounting for the risk sensitivity of interference estimates, the prediction outcome is then used for appropriate resource allocation of a URLLC transmission under link outage constraints. We evaluate the complete solution, given in the form of an algorithm, using Monte-Carlo simulations, and compare it with the first-order baseline counterpart. The analysis shows that the second-order interference estimate can fulfill the target outage as low as 10–7and improve the outage probability more than ten times in some scenarios compared to the baseline scheme while keeping the same amount of resource usage.

Targeting the delay-constrained Internet-of-Things (IoT) applications in sixth-generation (6G) networks, in this paper, we study the integration of simultaneously transmitting and reflecting reconfigurable intelligent surfaces (STAR-RISs) and non-orthogonal multiple access-based backscatter communication (BAC-NOMA) under statistical delay quality-of-service (QoS) requirements. In particular, we derive the closed-form expressions for the effective capacity of the STAR-RIS assisted BAC-NOMA system under Nakagami-m fading channels and energy-splitting protocol of STAR-RIS. Our simulation results demonstrate the effectiveness of STAR-RIS over the conventional RIS (C-RIS) and show an excellent correlation with analytical results, validating our analysis. The results reveal that the stringent QoS constraint degrades the effective capacity; however, the system performance can be improved by increasing the STAR-RIS elements and adjusting the energy-splitting coefficients. Finally, we determine the optimal pair of power reflection coefficients subject to the per-BSN effective capacity requirements.

The rise of technologies such as 6G networks, edge computing, and the Industrial Internet has led to a dramatic increase in the amount of data that needs to be transmitted over heterogeneous integrated networks. The resources of embedded devices limit the ability of the Industrial Internet to transmit data. While the multipath transmission mechanism can mitigate data transmission issues of low reliability and low real-time performance from the network-level perspective. As the complexity of industry applications increases, however, the phenomenon that the high-quality data transmission is subject to the influence of the underlying layer is becoming increasingly apparent. The paper aims to explores the possibility of multipath transmission protocol running on a real-time kernel from the perspective of the operating system, as there is a lack of research and reports in this area. Based on RT-Preempt, a real-time system RT-Linux suitable for the “NXP i.MX6Q” ARM integrated board has been proposed, which replaces the native Linux kernel to optimize and enhance its real-time performance. As described in the experiment part, the original standard Linux system OR-Linux and the new RT-Linux are tested with single-threaded and multi-threaded load experiments, respectively. The results of the analysis show that this paper provides a way of validating the trial data and ensuring its accuracy using the lognormal distribution model, which is a statistical distribution used to model variables that are positive and skewed to the right. The RT-Linux scheme has better real-time performance and is more stable than the OR-Linux scheme after real-time processing, showing the viability of the scheme.

In this work, we consider the design of a radio resource management (RRM) solution for traffic steering (TS) use-case in the open radio access network (O-RAN). The O-RAN TS deals with the quality-of-service (QoS)-aware steering of the traffic by connectivity management (e.g., device-to-cell association, radio spectrum, and power allocation) for emerging heterogeneous networks (HetNets) in 5G-and-beyond systems. However, TS in HetNets is a complex problem in terms of efficiently assigning/utilizing the radio resources while satisfying the diverse QoS requirements of especially the cell-edge users due to their poor signal-to-interference-plus-noise ratio (SINR). In this respect, we propose an intelligent non-orthogonal multiple access (NOMA)-based RRM technique for a small cell base station (SBS) within a macro gNB. A Q-learning-assisted algorithm is designed to allocate the transmit power and frequency sub-bands at the O-RAN control layer such that interference from macro gNB to SBS devices is minimized while ensuring the QoS of the maximum number of devices. The numerical results show that the proposed method enhances the overall spectral efficiency of the NOMA-based TS use case without adding to the system's complexity or cost compared to traditional HetNet topologies such as co-channel deployments and dedicated channel deployments.

With the ever-increasing use of large-scale IoT networks in different sectors of the industry, it has become critical to realise seamless and secure communication between devices in the network. Realising secure group communication in the IoT requires solving the problem of group-key establishment. In this work, we solve the problem by designing a new lattice-based Key Encapsulation Mechanism (KEM) for resource-constrained devices that enable the distribution of a symmetric key or any other data between all the devices in a given network. This is achieved by coupling multiple private keys to a unique public key. Moreover, we present a proof-of-concept implementation based on the GGH algorithm. The results show it is feasible to use lattice-based cryptography to allow for seamless and secure group communications within a decentralised IoT network. It has been bench-marked against other common post-quantum constructs and proven to be more practical with respect to memory consumption and security, although considerably slower due to lack of optimisation in the implementation.

By amalgamating recent communication and control technologies, computing and data analytics techniques, and modular manufacturing, Industry 4.0 promotes integrating cyber–physical worlds through cyber–physical systems (CPS) and digital twin (DT) for monitoring, optimization, and prognostics of industrial processes. A DT enables interaction with the digital image of the industrial physical objects/processes to simulate, analyze, and control their real-time operation. DT is rapidly diffusing in numerous industries with the interdisciplinary advances in the industrial Internet of things (IIoT), edge and cloud computing, machine learning, artificial intelligence, and advanced data analytics. However, the existing literature lacks in identifying and discussing the role and requirements of these technologies in DT-enabled industries from the communication and computing perspective. In this article, we first present the functional aspects, appeal, and innovative use of DT in smart industries. Then, we elaborate on this perspective by systematically reviewing and reflecting on recent research trends in next-generation (NextG) wireless technologies (e.g., 5G-and-Beyond networks) and design tools, and current computational intelligence paradigms (e.g., edge and cloud computing-enabled data analytics, federated learning). Moreover, we discuss the DT deployment strategies at different communication layers to meet the monitoring and control requirements of industrial applications. We also outline several key reflections and future research challenges and directions to facilitate industrial DT’s adoption.

Wireless use cases in the industrial Internet of Things networks often require guaranteed data rates ranging from a few kilobits per second to a few gigabits per second. Supporting such a requirement in a single radio access technique is difficult, especially when bandwidth is limited. Although nonorthogonal multiple access (NOMA) can improve the system capacity by simultaneously serving multiple devices, its performance suffers from strong device interference. In this article, we propose a Q-learning-based algorithm for handling many-to-many matching problems, such as bandwidth partitioning, device assignment to sub-bands, interference-aware access mode selection [orthogonal multiple access or NOMA], and power allocation to each device. The learning technique maximizes system throughput and spectral efficiency (SE) while maintaining quality-of-service (QoS) for a maximum number of devices. The simulation results show that the proposed technique can significantly increase overall system throughput and SE while meeting heterogeneous QoS criteria.

Cellular networks are envisioned to be a cornerstone in future industrial Internet of Things (IIoT) wireless connectivity in terms of fulfilling the industrial-grade coverage, capacity, robustness, and timeliness requirements. This vision has led to the design of vertical-centric service-based architecture of 5G radio access and core networks. The design incorporates the capabilities to include 5G-AI-Edge ecosystem for computing, intelligence, and flexible deployment and integration options (e.g., centralized and distributed, physical, and virtual) while eliminating the privacy/security concerns of mission-critical systems. In this article, driven by the industrial interest in enabling large-scale wireless IIoT deployments for operational agility, flexible, and cost-efficient production, we present the state-of-the-art 5G architecture, transformative technologies, and recent design trends, which we also selectively supplemented with new results. We also identify several research challenges in these promising design trends that beyond-5G systems must overcome to support rapidly unfolding transition in creating value-centric industrial wireless networks.

Wireless communications may be the key solution to satisfy mobility, flexibility, and scalability requirements of the Internet of Things. Particularly, the low power wide area networks, operating in unlicensed bands, are promising because they permit both private and public backends. Unfortunately, their target applications are situations where sporadic transmissions are tolerated. They could not fit scenarios in which high reliability and short round trip time are required. In this article, the LoRaWAN solution is considered for managing emergency and alarm communications. Reliability and timeliness limits have been overcome by jointly using a message replication scheme with variable payload length together with low-cost repeaters. Experiments have been carried out in both fixed and low-speed mobility scenarios (with an interfering traffic of 95 message/h, generated by regular LoRaWAN colocated network). Results, in the former case, show that the success probability of confirmed messages is 100%, the 99.6% of the available data are delivered to the backend, and the average delay decreases by 150 ms. When mobility is considered (e.g., for an automatic guided vehicle use case) results confirm significant improvements for both the transaction success ratio and the data extraction rate that significantly increase up to 95% and 87%, respectively. On the contrary, the overall average delay is not always reduced.

More data and information is being captured from systems, machines, and devices and made available to industrial information technology (IT) systems. The information is processed on-the-fly, enabling IT-based management systems to generate updated information for real-time control of the manufacturing processes. This data capturing and collection for IT systems is often referred to as the Internet of Things (IoT). When adopted to the industrial requirements, such as robustness, reliability, timeliness, and security, it is often termed as the industrial IoT (IIoT) [A1]. IIoT has attracted the attention of both the industry and academia since it is expected to enhance day-to-day activities, create new business models, products, and services, and as a broad source of research topics and ideas. Meanwhile, it is envisioned that the fifth-generation (5G) networks will be a cornerstone in future wireless industrial connectivity, and currently, there are multitude of ongoing research efforts in their design and optimization. Future industries willembrace use cases with numerous wireless-connected sensors and devices, and judging by the demand, massive machine-type communication and ultra-reliable low-latency communication (URLLC) in the literature and standardization activities, have been identified as two of the three main communication scenarios for 5G. These scenarios demand intelligent, scalable, and robust radio access techniques, network architectures, and deployment options to meet industrial demands [A2]. Therefore, more in-depth research is needed for IIoT and sensor networks in 5G-and-beyond wireless communication systems to address various challenges, including the following. 1)Transmit power control policy should be judiciously designed to improve both the spectrum efficiency and energy efficiency effectively; higher transmit powers can improve reliability but increase the interference and battery consumption. 2)Low-latency communication and computing is one of the significant challenges in 5G-and-beyond IIoT; uploading the device data to the cloud computing centers has high latency and resources waste issues in sensor networks. 3)Addressing privacy and security problems [A3] in the 5G-IIoT is fundamental to the further development and spread of 5G-IIoT. 4)Reliability and latency requirements of URLLC services, requiring less than 1-ms user plane latency and higher than 99.999% reliability, are demanding to meet, especially in time-varying industrial wireless channels. 5)Radio resource allocation, sharing, and isolation with performance guarantees under dynamic traffic conditions are critical issues for emerging IIoT applications requiring real-time support of massive connected devices. 6)5G-and-beyond IIoT networks must satisfy industrial-grade coverage, capacity, time-sensitive networking, and over-the-air time synchronization requirements [A4].

5G-and-beyond (B5G) networks are moving toward the higher end of the millimeter-wave (mmWave) spectrum (i.e., from 25 to 100 GHz) to support integrated communications and ranging (ICAR) services in next-generation factory deployments. The ICAR services in factory deployments require extreme bandwidth/capacity and large ranging coverage, which a mmWave-B5G system can fulfill using massive multi-input and multioutput (mMIMO), beamforming, and advanced ranging techniques. However, as mmWave signal propagation is sensitive to harsh channel conditions experienced in typical indoor factory environments, there is a growing interest in the realistic mmWave indoor channel modeling to evaluate the practical scope of the mmWave-B5G systems. In this article, we study and implement a 3-D stochastic channel model using the baseline third-generation partnership project model. Our channel model employs the time-cluster spatial-lobe (TCSL) technique and utilizes the temporal and spatial statistics to create the channel impulse response (CIR), reflecting realistic indoor factory conditions. Using the generated CIR, we present the performance analysis of an mmWave-B5G system in terms of power delay profile, path loss, communication and ranging coverage, and mMIMO channel capacity.

As important services of the future sixth-generation (6G) wireless networks, vehicular communication and mobile edge computing (MEC) have received considerable interest in recent years for their significant potential applications in intelligent transportation systems. However, MEC-enabled vehicular networks depend heavily on network access and communication infrastructure, often unavailable in remote areas, making computation offloading susceptible to breaking down. To address this issue, we propose an MEC-enabled vehicular network assisted through aerial-terrestrial connectivity to provide network access and high data-rate entertainment services to a vehicular network. We present a time-varying, dynamic system model where high altitude platforms (HAPs) equipped with MEC servers, connected to a backhaul system of low-earth orbit (LEO) satellites, are used to provide computation offloading capability to the vehicles, as well as to provide network access for vehicle-to-vehicle (V2V) communications. Our main objective is to minimize the total computation and communication overhead of the joint computation offloading and resource allocation strategies for the system of vehicles. Since our formulated optimization problem is a mixed-integer non-linear programming (MINLP) problem, which is NP-hard, we propose a decentralized value-iteration-based reinforcement learning (RL) approach as a solution. In our Q-learning-assisted analysis, each vehicle acts as an intelligent agent to form optimal strategies for offloading and resource allocation. We further extend our solution to deep Q-learning (DQL) and double deep Q-learning to overcome the issues of dimensionality and the over-estimation of the value functions, as in Q-learning. Simulation results prove the effectiveness of our solution in successfully reducing system costs compared to baseline schemes.

Fault diagnosis in Industrial Internet of Things and Cyber-Physical Systems is essential, especially for the rapidly developing 5G and coming 6G technology. Hardware working status is the basis for fault diagnosis. However, in actual engineering, accurately locating faults is not easy for hardware engineers. In this paper, a prototype based on FPGA is developed to connect with the target field device in data acquisition. This prototype aims to extract the underlying data information in a real-time and unattended manner like the mimicry of natural immunity. The preliminary experiment results from the prototype can be used to guide practice through providing real-time data support for hardware troubleshooting and spontaneous and intelligent analysis.

One of the major challenges in implementing a group key establishment and management scheme to provide security solutions for group communication in the Internet of Things (IoT) is the limited resource availability of the nodes such as memory, computation, and energy. To ensure security such as confidentiality, integrity of the transmitting messages in a certain IoT group, a feasible group key establishment and management scheme is necessary which uses minimum resources but provides high scalability and strong security. In this paper, we propose a symmetric group key establishment scheme that uses the secrecy guarantee provided by One Time Pad (OTP) and performs computations like bitwise Exclusive OR (XOR) and bit shifting of randomly generated binary vectors to produce random different keys for different sessions of message transmission. We show that our scheme is lightweight to support the resource-constrained nature of IoT nodes by using only primitive operations and scalable to support the dynamic constellation of IoT network groups where nodes can join and exit frequently. We prove that our scheme is secure under a designed threat model where a similar existing scheme fails by a detailed analysis.

With the increase of connected Internet-of-things (IoT) devices, the need for low-power wide-area networks (LP-WANs) is imminent, and LoRaWAN is one such technology that offers an elegant solution to the problem of long-range communication and battery consumption. A parameter of special interest in LoRaWANis the spreading factor (SF), and it is often assumed that communication between different SFs is independent of each other. However, this claim has been practically debunked by many works, proving that SFs have imperfect orthogonality. To maximize connectivity and throughput, several techniques have been introduced, such as non-orthogonal-multiple-access (NOMA) and dynamic resource allocation. NOMA is getting a lot of attention recently, especially for IoT networks, because it embraces interference and tries to obtain desired information packets from corrupted ones. Furthermore, NOMA can be easily implemented on the base-station side by using the principle of successive interference cancellation (SIC). In this paper, we investigate how SIC, under the assumption of imperfect orthogonality of SF channels, can be used to increase the performance of the system. We find the expressions for success and coverage probability considering various SF allocation schemes and found the most efficient scheme for different scenarios.

Many new narrowband low-power wide-area networks (LPWANs) (e.g., LoRaWAN and Sigfox) have opted to use pure ALOHA-like access for its reduced control overhead and asynchronous transmissions. Although asynchronous access reduces the energy consumption of IoT devices, the network performance suffers from high intranetwork interference in dense deployments. Contrarily, adopting synchronous access can improve throughput and fairness, however, it requires time synchronization. Unfortunately, maintaining synchronization over the narrowband LPWANs wastes channel time and transmission opportunities. In this article, we propose the use of out-of-band time dissemination to relatively synchronize the LoRa devices and thereby facilitate resource-efficient slotted uplink communication. In this respect, we conceptualize and analyze a co-designed synchronization and random access communication mechanism that can effectively exploit technologies providing limited time accuracy, such as FM radio data system (FM-RDS). While considering the LoRa-specific parameters, we derive the throughput of the proposed mechanism, compare it to a generic synchronous random access using in-band synchronization, and design the communication parameters under time uncertainty. We scrutinize the transmission time uncertainty of a device by introducing a clock error model that accounts for the errors in the synchronization source, local clock, propagation delay, and transceiver's transmission time uncertainty. We characterize the time uncertainty of FM-RDS with hardware measurements and perform simulations to evaluate the proposed solution. The results, presented in terms of success probability, throughput, and fairness for a single-cell scenario, suggest that FM-RDS, despite its poor absolute synchronization, can be used effectively to realize time-slotted communication in LoRa with performance similar to that of more accurate time-dissemination technologies.

From the perspective of the emerging Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the main goal to integrate the capabilities of the IEEE 802.15.4e time-slotted channel hopping (TSCH) with the IPv6 protocol stack. In order to support time-critical applications in IIoT, reliable real-time communication is a key requirement. Specifically, aperiodic critical traffic, such as emergency alarms, must be reliably delivered to the destination-oriented directed acyclic graph root within strict deadline bounds to avoid system failure or safety-critical situations. Currently, there is no mechanism defined in the 6TiSCH architecture for timely and reliably handling of such traffic and its prioritization over the noncritical one. In this article, we introduce REA-6TiSCH, a reliable emergency-aware communication scheme to support real-time communications of emergency alarms in 6TiSCH networks. In REA-6TiSCH, the aperiodic emergency traffic is opportunistically enabled to hijack transmission cells preassigned for the regular periodic traffic in the TSCH schedule. Moreover, we introduce a distributed optimization scheme to improve the probability that an emergency flow is delivered successfully within its deadline bound. To the best of our knowledge, this is the first approach to incorporate emergency alarms in 6TiSCH networks. We evaluate the performance of REA-6TiSCH through extensive simulations and the results show the effectiveness of our proposed method in handling emergency traffic compared to the Orchestra scheme. Additionally, we discuss the applicability of REA-6TiSCH and provide guidelines for real implementation in 6TiSCH networks.

In this article, we present a stochastic geometry-based model to investigate alternative medium access choices for LoRaWAN-a widely adopted low-power wide-area network (LPWAN) technology for the Internet-of-Things. LoRaWAN adoption is driven by its simplified network architecture, air interface, and medium access. The physical layer, known as Long Range (LoRa), provides quasi-orthogonal virtual channels through spreading factors (SFs) and time-power capture gains. However, the adopted pure ALOHA access mechanism suffers, in terms of scalability, under the same-channel same-SF transmissions from a large number of devices. In this article, our objective is to explore access mechanisms beyond-ALOHA for LoRaWAN. Using recent results on time- and power-capture effects of LoRa, we develop a unified model for the comparative study of other choices, i.e., slotted ALOHA and carrier-sense multiple access (CSMA). The model includes the necessary design parameters of these access mechanisms, such as guard time and synchronization accuracy for slotted ALOHA, carrier sensing threshold for CSMA. It also accounts for the spatial interaction of devices in annular shaped regions, characteristic of LoRa, for CSMA. The performance metrics derived from the model in terms of coverage probability, channel throughput, and energy efficiency are validated using Monte-Carlo simulations. Our analysis shows that slotted ALOHA indeed has higher reliability than pure ALOHA but at the cost of lower energy efficiency for low device densities. Whereas, CSMA outperforms slotted ALOHA at smaller SFs in terms of reliability and energy efficiency, with its performance degrading to pure ALOHA at higher SFs.

With the rapid development of artificial intelligence and industrial Internet of Things (IIoT) technologies, intelligent predictive maintenance (IPdM) has received considerable attention from researchers and practitioners. To efficiently predict impending failures and mitigate unexpected downtime, while satisfying the instant maintenance demands of industrial facilities is very important for improving the production efficiency. In this article, a self-attention based “Perception and Prediction” framework, called DeepHealth, is proposed for the instant IPdM. Specifically, the framework is composed of two submodels (i.e., DH-1 and DH-2), which are respectively utilized to perform the health perception and sequence prediction. By operating the framework, the proposed models can predict the health conditions via predicting the future signal samples, thereby completing the instant IPdM. Considering the potential temporal correlation in time series, we deploy an enhanced attention mechanism to capture global dependencies from the vibration signals, and leverage the long- and short-term sequence prediction of sensor signals to support instant maintenance decision-making. On this basis, we conduct a destructive experiment based on the IIoT-enabled rotating machinery and construct a balanced industrial dataset for model evaluations. Extensive experiment results show that the proposed solution achieves good prediction accuracy for instant IPdM on the automatic washing equipment and Case Western Reserve University datasets.

The Routing Protocol for Low power and lossy network (RPL) is designed to support communication requirements in 6TiSCH networks in Industrial Internet of Things (IIoT) applications. RPL is mostly optimized for uplink communication, however, less attention is given to maintain connectivity for downlink communications. Supporting downlink communications is non-trivial task in process automation and control scenarios within the IIoT. RPL in its current definition is inefficient to support reliable downlink communications in terms of scalability and memory requirements leading to significant degradation in network performance. This paper introduces HyS-R, a Hybrid Subscription-Recovery method to maintain downlink connectivity and mitigate memory limitations in large-scale 6TiSCH networks. The proposed method is based on a relief group that is used as alternative route to unreachable destinations in the network. An intermediate node subscribes to the relief group when it fails to advertise a destination to its next-hop node. In addition, members of the relief group keep searching for alternative forwarders to keep the communication traffic to a minimum. Performance evaluations are carried out and the results demonstrate that the proposed HyS-R attains significant improvements in downlink communications compared to RPL storing and non-storing modes with a margin of energy cost.

In the context of Industrial Internet of Things (IIoT), the 6TiSCH working group has been created with the aim to enable IPv6 over the IEEE 802.15.4e Time-Slotted Channel Hopping (TSCH) mode. The Routing Protocol for Low power and lossy networks (RPL) is introduced as the de-facto routing protocol for 6TiSCH networks. However, RPL is primarily designed to handle moderate traffic loads, whereas, during specific events in industrial applications, high traffic rates cause congestion problems at particular intermediate nodes while other nodes are underutilized. Accordingly, packets are dropped due to buffer overflow, which in turn degrades the network performance in terms of packet loss and delay. In this paper, we introduce a congestion detection and control mechanism to reliably handle high traffic load in 6TiSCH networks. The proposed method comprises two parent selection mechanisms to adapt to dynamic traffic load in the network. Congestion is detected through monitoring of the queue backlog level of each node and new parent nodes are selected accordingly to balance the load in the network. Moreover, a new routing metric is defined that considers the queue occupancy while selecting the new parent node. Performance evaluations are carried out to prove the effectiveness of the proposed method and the results show that with a marginal increase in the average delay, our proposal improves the performance of the standard RPL under heavy traffic load conditions by at least 60% and 74% in terms of the packet delivery and queue loss, respectively.

Wireless sensors and actuators networks are an essential element to realize industrial IoT(IIoT) systems, yet their diffusion is hampered by the complexity of ensuring reliable communication in industrial environments. A significant problem with that respect is the unpredictable fluctuation of a radio-link between the line-of-sight (LoS) and the non-line-of-sight (NLoS) states due to time-varying environments. The impact of linkstate on reception performance, suggests that link-state variations should be monitored at run-time, enabling dynamic adaptation of the transmission scheme on a link-basis to safeguard QoS. Starting from the assumption that accurate channel-sounding is unsuitable for low-complexity IIoT devices, we investigate the feasibility of channel-state identification for platforms with limited sensing capabilities. In this context, we evaluate the performance of different supervised-learning algorithms with variable complexity for the inference of the radio-link state. Our approach provides fast link-diagnostics by performing online classification based on the analysis of the envelope-distribution of a single received packet. Furthermore, the method takes into account the effects of the limited sampling frequency, bit-depth, and moving average filtering, which are typical to hardware-constrained platforms. The results of an experimental campaign in both industrial and office environments show promising classification accuracy of LoS/NLoS radio links. Additional tests indicate that the proposed method retains good performance even with low-resolution RSSI-samples available in low-cost WSN nodes, which facilitates its adoption in real IIoT networks.

The accelerated move toward the adoption of the Industrial Internet-of-Things (IIoT) paradigm has resulted in numerous shortcomings as far as security is concerned. One of the IIoT affecting critical security threats is what is termed as the false data injection (FDI) attack. The FDI attacks aim to mislead the industrial platforms by falsifying their sensor measurements. FDI attacks have successfully overcome the classical threat detection approaches. In this article, we present a novel method of FDI attack detection using autoencoders (AEs). We exploit the sensor data correlation in time and space, which in turn can help identify the falsified data. Moreover, the falsified data are cleaned using the denoising AEs (DAEs). Performance evaluation proves the success of our technique in detecting FDI attacks. It also significantly outperforms a support vector machine (SVM)-based approach used for the same purpose. The DAE data cleaning algorithm is also shown to be very effective in recovering clean data from corrupted (attacked) data.

This article evaluates the quality-of-service performance and scalability of the recently released Bluetooth mesh protocol and provides general guidelines on its use and configuration. Through extensive simulations, we analyze the impact of the configuration of all the different protocol's parameters on the end-to-end reliability, delay, and scalability. In particular, we focus on the structure of the packet broadcast process, which takes place in time intervals known as Advertising Events and Scanning Events. Results indicate a high degree of interdependence among all the different timing parameters involved in both the scanning and the advertising processes and show that the correct operation of the protocol greatly depends on the compatibility between their configurations. We also demonstrate that introducing randomization in these timing parameters, as well as varying the duration of the Advertising Events, reduces the drawbacks of the flooding propagation mechanism implemented by the protocol. Using data collected from a real office environment, we also study the behavior of the protocol in the presence of WLAN interference. It is shown that Bluetooth mesh is vulnerable to external interference, even when implementing the standardized limitation of using only 3 out of the 40 Bluetooth low-energy frequency channels. We observe that the achievable average delay is relatively low, of around 250 ms for over 10 hops under the worst simulated network conditions. However, results prove that scalability is especially challenging for Bluetooth mesh since it is prone to broadcast storm, hindering the communication reliability for denser deployments.

This Special Section on "Security, privacy, and trust for Industrial Internet of Things" of the IEEE Transactions on Industrial Informatics (TII) highlights the main research challenges in the industrial Internet of Things (IoT) security, privacy, and trust. The designated nine high-quality research articles cover a wide range of the special section theme, including innovative solutions and novel technologies. These articles are briefly summarized.

Internet of Things (IoT) is in the booming age of its growth, therefore a vast amount of applications, projects, hardware/software solutions, and customized concepts are being developed. The proliferation of IoT will enable location-based services to be available everywhere for everyone, and this will raise a large number of privacy issues related to the collection, usage, retention, and disclosure of the user’s location information. In order to provide a solution to this unique problem of IoT, this paper proposes Location Privacy Assured Internet of Things (LPA-IoT) scheme, which uses the concepts of Mix-Zone, location-obfuscation along with context-awareness. To the authors’ best knowledge, the proposed LPA-IoT scheme is the first location-based privacy-preserving scheme for IoT that provides flexible privacy levels associated with the present context of the user.

Localization has gained great attention in recent years, where different technologies have been utilized to achieve high positioning accuracy. Fingerprinting is a common technique for indoor positioning using short-range radio frequency (RF) technologies such as Bluetooth Low Energy (BLE). In this paper, we investigate the suitability of LoRa (Long Range) technology to implement a positioning system using received signal strength indicator (RSSI) fingerprinting. We test in real line-of-sight (LOS) and non-LOS (NLOS) environments to determine appropriate LoRa packet specifications for an accurate RSSI-to-distance mapping function. To further improve the positioning accuracy, we consider the environmental context. Extensive experiments are conducted to examine the performance of LoRa at different spreading factors. We analyze the path loss exponent and the standard deviation of shadowing in each environment.

Small cell networks (SCNs) offer a cost-effective coverage solution to wireless applications demanding high data rates. However in SCNs, a challenging problem is the proper management of backhaul links to small cell base stations (SCBSs). To make a good backhaul link, perfect line-of-sight (LoS) communication between the SCBSs and the core network plays a vital role. In this study, we use the idea of employing unmanned aerial vehicles (UAVs) to provide connectivity between SCBSs and the core network. We focus on the association of SCBSs with UAVs by considering multiple communication-related factors including data rate limit and available bandwidth resources of the backhaul. In particular, we address the optimum placement of UAVs to serve a maximum number of SCBSs while considering available resources using unsupervised \textit{k}- means algorithm. Numerical results show that the proposed approach outperforms the conventional approach in terms of associated SCBSs, bandwidth consumption, available link utilization, and sum- rate maximization.

The deployment of the Internet of Things (IoT) in industry, called the Industrial IoT (IIoT), is supporting the introduction of very desirable improvements, such as increasing production flexibility, self-organization, and real-time and quick response to events. However, security and privacy challenges are still to be well addressed. The IIoT requires different properties to achieve secure and reliable systems and these requirements create extra challenges considering the limited processing and communication power available to IIoT field devices. In this research article, we present a key distribution protocol for IIoT that is computationally and communicationally lightweight (requires a single message exchange) and handles node addition and revocation, as well as fast rekeying. The scheme can also resist the consequences of node capture attacks (we assume that captured nodes can be detected by the gateway and previous works have shown this assumption to be acceptable in practice), server impersonation attacks and provides forward/backward secrecy. We show formally the correctness of our protocol and evaluate its energy consumption under realistic scenarios using a real embedded platform compared to previous state-of-the-art key-exchange protocols, to show our protocol reliability for IIoT.

In this paper, we study cross-layer optimization of low-power wireless links for reliability-aware applications while considering both the constraints and the nonideal characteristics of the hardware in Internet-of-Things (IoT) devices. Specifically, we define an energy consumption (EC) model that captures the energy cost-of transceiver circuitry, power amplifier (PA), packet error statistics, packet overhead, etc.-in delivering a useful data bit. We derive the EC models for an ideal and two realistic nonlinear PA models. To incorporate packet error statistics, we develop a simple, in the form of elementary functions, and accurate closed-form packet error rate approximation in Rayleigh block-fading. Using the EC models, we derive energy-optimal yet reliability and hardware compliant conditions for limiting unconstrained optimal signal-to-noise ratio (SNR), and payload size. Together with these conditions, we develop a semi-analytic algorithm for resource-constrained IoT devices to jointly optimize parameters on physical (modulation size, SNR) and medium access control (payload size and the number of retransmissions) layers in relation to link distance. Our results show that despite reliability constraints, the common notion-higher-order M-ary modulations are energy optimal for short-range communication-prevails, and can provide up to 180% lifetime extension as compared to often used OQPSK modulation in IoT devices. However, the reliability constraints reduce both their range and the energy efficiency, while nonideal traditional PA reduces the range further by 50% and diminishes the energy gains unless a better PA is used.

Low-power wide-area network (LPWAN) technologies are gaining momentum for Internet-of-things applications since they promise wide coverage to a massive number of battery operated devices using grant-free medium access. LoRaWAN, with its physical (PHY) layer design and regulatory efforts, has emerged as the widely adopted LPWAN solution. By using chirp spread spectrum modulation with qausi-orthogonal spreading factors (SFs), LoRa PHY offers coverage to wide-area applications while supporting high-density of devices. However, thus far its scalability performance has been inadequately modeled and the effect of interference resulting from the imperfect orthogonality of the SFs has not been considered. In this paper, we present an analytical model of a single-cell LoRa system that accounts for the impact of interference among transmissions over the same SF (co-SF) as well as different SFs (inter-SF). By modeling the interference field as Poisson point process under duty cycled ALOHA, we derive the signal-to-interference ratio distributions for several interference conditions. Results show that, for a duty cycle as low as 0.33%, the network performance under co-SF interference alone is considerably optimistic as the inclusion of inter-SF interference unveils a further drop in the success probability and the coverage probability of approximately 10% and 15%, respectively, for 1500 devices in a LoRa channel. Finally, we illustrate how our analysis can characterize the critical device density with respect to cell size for a given reliability target.

A fundamental challenge in Mission-Critical Internet of Things (MC-IoT) is to provide reliable and timely delivery of the unpredictable critical traffic. In this paper, we propose an efficient prioritized Medium Access Control (MAC) protocol for Wireless Sensor Networks (WSNs) in MC-IoT control applications. The proposed protocol utilizes a random Clear Channel Assessment (CCA)-based channel access mechanism to handle the simultaneous transmissions of critical data and to reduce the collision probability between the contending nodes, which in turn decreases the transmission latency. We develop a Discrete-Time Markov Chain (DTMC) model to evaluate the performance of the proposed protocol analytically in terms of the expected delay and throughput. The obtained results show that the proposed protocol can enhance the performance of the WirelessHART standard by 80% and 190% in terms of latency and throughput, respectively along with better transmission reliability.

Priority-oriented packet transmission (PPT) has been a promising solution for transmitting time-critical packets in timely manner during emergency scenarios in Internet of Things (IoT). In this paper, we develop two associated discrete time Markov chain (DTMC) models to analyze performance of the PPT in an IoT network. Using the proposed DTMC models, we investigate the effect of traffic prioritization in terms of average packet delay for a synchronous medium access control (MAC) protocol. Furthermore, the results obtained from analytical models are validated via discrete-event simulations. Numerical results prove the accuracy of the models and reveal the behavior of priority based packet transmissions.

In this paper we present an overview of the various proximity based authentication mechanisms that can be used in the Industrial Internet of Things (IIoT). We seek to identify and highlight from a holistic point of view which mechanisms can enable proximity based authentication for the Industrial Internet of Things. In addition, we identify which upcoming proximity authentication mechanisms are most important for the proliferation of the Industrial Internet of Things, and highlight major obstacles that remain unsolved with regard to authentication. In answering this, we present seven mechanisms for proximity based authentication (i.e. wire, radio, acoustics, light, image, gesture and biometrics) and discuss each mechanism in perspective of their vulnerability to different kind of attacks (such as eavesdropping, impersonation and denial of service attacks etc.) and their usability (such as proximity range, hardware requirement and ease of use) in terms of the practicality in IIoT environment in the light of which we present two typical IIoT use cases that require proximity based authentication.

Group communication in the context of Internet of Things (IoT) is an efficient and fast way of broadcasting group messages. A message needs to be sent securely to maintain confidentiality of data and privacy of users. The main challenges in sharing group keys consist in designing and implementing a group key establishment scheme that is feasible for devices with limited computational capabilities. Existing group establishment schemes do not offer a good solution for resource-constrained IoT devices, a solution that provides secure group key management procedures when new nodes join or leave the group without compromising the security of the system. In this paper, we propose a lightweight and computationally secure group key establishment scheme suitable for resource constrained IoT networks. The proposed scheme is based on elliptic curve cryptography and cryptographic one-way accumulators. We show how to combine the aforementioned concepts to design a group key establishment scheme that guarantees both forward and backward secrecy. Finally, we show how the established group key is updated when the group size dynamically changes and how the proposed solution can be used with block and stream ciphers.

Machine to Machine (M2M) communication networks are expected to connect a large number of power-constrained devices in long range applications with different quality of service (QoS) requirements. Medium access control with QoS support such as the enhanced distributed channel access (EDCA) defined by IEEE 802.11e provides traffic differentiation and corresponding priority classes, which guarantees QoS according to the needs of applications. In this paper, we employ a station grouping mechanism for enhancing the scalability of EDCA to handle the massive number of access attempts expected in large M2M networks. Furthermore, we develop a discrete time Markov chain (DTMC) model to analyze the performance of EDCA with station grouping. Using the developed DTMC model, we calculate throughput for each access category as well as for different combinations of grouping and EDCA parameters. The numerical results show that the model can precisely reveal the behavior of EDCA mechanism. Moreover, it is demonstrated that employing the proposed grouping mechanism for EDCA increases the normalized throughput significantly for all classes of priority.

The vision of connecting billions of battery operated devices to be used for diverse emerging applications calls for a wireless communication system that can support stringent reliability and latency requirements. Both reliability and energy efficiency are critical for many of these applications that involve communication with short packets which undermine the coding gain achievable from large packets. In this paper, we study a cross-layer approach to optimize the performance of low-power wireless links. At first, we derive a simple and accurate packet error rate (PER) expression for uncoded schemes in block fading channels based on a new proposition that shows that the waterfall threshold in the PER upper bound in Nakagami-m fading channels is tightly approximated by the m-th moment of an asymptotic distribution of PER in AWGN channel. The proposed PER approximation establishes an explicit connection between the physical and link layers parameters, and the packet error rate. We exploit this connection for cross-layer design and optimization of communication links. To this end, we propose a semi-analytic framework to jointly optimize signal-to-noise ratio (SNR) and modulation order at physical layer, and the packet length and number of retransmissions at link layer with respect to distance under the prescribed delay and reliability constraints.

The Licensed Assisted Access (LAA) is shown asa required technology to avoid overcrowding of the licensedbands by the increasing cellular traffic. Proposed by 3GPP,LAA uses a Listen Before Talk (LBT) and backoff mechanismsimilar to Wi-Fi. While many mathematical models have beenproposed to study the problem of the coexistence of LAAand Wi-Fi systems, few have tackled the problem of QoSprovisioning, and in particular analysed the behaviour of thevarious classes of priority available in Wi-Fi and LAA. Thispaper presents a new mathematical model to investigate theperformance of different priority classes in coexisting Wi-Fi andLAA networks. Using Discrete Time Markov Chains, we modelthe saturation throughput of all eight priority classes used byWi-Fi and LAA. The numerical results show that with the 3GPPproposed parameters, a fair coexistence between Wi-Fi and LAAcannot be achieved. Wi-Fi users in particular suffer a significantdegradation of their performance caused by the collision withLAA transmissions which has a longer duration compared toWi-Fi transmissions.

As the market for low-power wide-area network (LPWAN) technologies expands and the number of connected devices increases, it is becoming important to investigate the performance of LPWAN candidate technologies in dense deployment scenarios. In dense deployments, where the networks usually exhibit the traits of an interference-limited system, a detailed intra- and inter-cell interference analysis of LPWANs is required. In this paper, we model and analyze the performance of uplink communication of a LoRa link in a multi-cell LoRa system. To such end, we use mathematical tools from stochastic geometry and geometric probability to model the spatial distribution of LoRa devices. The model captures the effects of the density of LoRa cells and the allocation of quasi-orthogonal spreading factors (SF) on the success probability of the LoRa transmissions. To account for practical deployment of LoRa gateways, we model the spatial distribution of the gateways with a Poisson point process (PPP) and Matèrn hard-core point process (MHC). Using our analytical formulation, we find the uplink performance in terms of success probability and potential throughput for each of the available SF in LoRa's physical layer. Our results show that in dense multi-cell LoRa deployment with uplink traffic, the inter-cell interference noticeably degrades the system performance.

Energy harvesting (EH) delivers a unique technique for replenishing batteries in Internet of Things (IoT) devices. Equipped with an EH accessory, EH-enabled sensor nodes/IoT devices extract energy from ambient resources, such as solar or radio frequency (RF) signals. Relying on residual battery or/and harvested energy, sensor nodes in an IoT network perform data exchange activities. Otherwise, the delivery of sensed data would be delayed until sufficient energy is harvested. In this paper, we propose an on-demand energy requesting (OER) mechanism for improving the delay performance of a wireless EH-powered IoT network. The proposed scheme acquires energy when necessary from an energy transmitter that is capable of transmitting energy via directed RF signals. Furthermore, we develop two associated discrete time Markov chain (DTMC) models to analyze the performance of the OER scheme, targeting at a generic synchronous medium access control (MAC) protocol. Using the proposed DTMC models, we evaluate OER with respect to average packet delay, network throughput, packet loss probability, and packet reliability ratio by employing a specific synchronous MAC protocol. Numerical results obtained from both analysis and discrete-event simulations coincide with each other, indicating the accuracy of the models and revealing the behavior of EH-based packet transmissions.

The papers in this special section examine the deploying of industrial wireless sensor networks as it applies to the industrial Internet of Things. Industrial networks connect sensors and actuators in various industrial facilities, such as oil and gas production facilities, paper plants, car manufactories, and underground mines. Industrial Internet of Things (IIoT) is a paradigm that involves a network of physical objects containing embedded technologies to collect, communicate, sense, and interact with their internal states or the external environment through wireless or wired connections brilliant machines, advanced analytics, and people at work and deliver valuable new insights like never before. These insights can then help drive smarter, faster business decisions for industrial companies. Since the Internet is designed for best effort services, there is a fundamental challenge to design and support applications in the industrial automation domain that demands real-time performance at different levels.

Internet of Things (IoT) is an emerging domain that promises ubiquitous connection to the Internet, turning common objects into connected devices. The IoT paradigm is changing the way people interact with things around them. It paves the way for creating pervasively connected infrastructures to support innovative services and promises better flexibility and efficiency. Such advantages are attractive not only for consumer applications, but also for the industrial domain. Over the last few years, we have been witnessing the IoT paradigm making its way into the industry marketplace with purposely designed solutions. In this paper, we clarify the concepts of IoT, Industrial IoT, and Industry 4.0. We highlight the opportunities brought in by this paradigm shift as well as the challenges for its realization. In particular, we focus on the challenges associated with the need of energy efficiency, real-time performance, coexistence, interoperability, and security and privacy. We also provide a systematic overview of the state-of-the-art research efforts and potential research directions to solve Industrial IoT challenges.

Industrial wireless sensor networks (IWSNs) have mainly been used to monitor applications, but recently an interest in control and safety applications has emerged. Functional safety and communication in open transmission systems have been laid down in the IEC 61784-3-3 standard. The standard is based on a cyclic polling mechanism, which consumes a considerable amount of bandwidth; since existing IWSNs are very resource-constrained, this becomes a major challenge. To overcome this problem, this paper proposes a novel framework that uses an event-triggered failsafe mechanism based on synchronous wired polling and wireless time-slotted time division multiple access. We analytically derive the minimum and maximum bound for the most important metric for safety-critical applications, safety function response time (SFRT). A new metric, normal state interrupt time (NSIT), is proposed in this paper. Furthermore, we also implement the proposed framework by using the WirelessHART standard. The results are compared to the classical time-triggered approach used in the IEC 61784-3-3 standard. The obtained results show that the proposed framework can reduce the bandwidth usage by 90% and support safety-critical applications that require a SFRT less or equal to 150 ms.

Energy harvesting (EH) promises an extended lifetime for wireless sensor networks (WSNs), supplying sensor nodes with accumulated energy from natural sources. Different from battery powered sensors, nodes in EH-enabled WSNs are equipped with an energy harvesting accessory in order to extract energy from surrounding sources. Based upon the harvested energy, sensor nodes perform data exchange activities. In this paper, we develop two discrete time Markov chain (DTMC) models to analyze the performance of packet transmissions in such a WSN while employing a generic synchronous medium access control (MAC) protocol. Using the proposed DTMC models, we investigate the effect of EH over a specific synchronous MAC protocol with respect to average packet delay and network throughput. Furthermore, the analytical results are validated via discrete-event simulations. Numerical results indicate the accuracy of the models and reveal the behavior of packet transmissions relying on harvested energy.

Internet-of-things (IoT), with the vision of billions of connected devices, is bringing a massively heterogeneous character to wireless connectivity in unlicensed bands. The heterogeneity in medium access parameters, transmit power and activity levels among the coexisting networks leads to detrimental cross-technology interference. The stochastic traffic distributions, shaped under CSMA/CA rules, of an interfering network and channel fading makes it challenging to model and analyze the performance of an interfered network. In this paper, to study the temporal interaction between the traffic distributions of two coexisting networks, we develop a renewal-theoretic packet collision model and derive a generic collision-time distribution (CTD) function of an interfered system. The CTD function holds for any busy- and idle-time distributions of the coexisting traffic. As the earlier studies suggest a long-tailed idle-time statistics in real environments, the developed model only requires the Laplace transform of long-tailed distributions to find the CTD. Furthermore, we present a packet error rate (PER) model under the proposed CTD and multipath fading of the interfering signals. Using this model, a computationally efficient PER approximation for interference-limited case is developed to analyze the performance of an interfered link.

The papers in this special section focus on wireless communication for use in automation of control and cyber-physical systems. Wireless communication is becoming increasingly important for industrial automation and many solutions have started to appear on the market. However, most of the available solutions target monitoring applications with slow refresh rates and with the downlink designed for best-effort traffic, thus neglecting actuators. Critical communication applications are defined as applications that require high reliability, i.e., that data packets are delivered with very high probability within a certain deadline, to avoid serious consequences (bodily injury, human losses, severe environmental impact, or great financial loss). It is challenging to simultaneously provide low latencies (end-to-end delay) and high reliability, especially over wireless links, which are inherently unpredictable. Suitable solutions exist that work well in specific contexts.

Industrial wireless sensor and actuator networks (IWSANs) offer significant advantages to industrial automation. However, high-reliability demands and hard communication deadlines pose challenges to its practical applications. To achieve this goal, flooding is considered as a promising approach due to multipath diversity and simplicity. In this paper, an enhanced version of REALFLOW, a flooding-based routing protocol for IWSANs is presented. Compared to the original REALFLOW, network management and network stability are improved. REALFLOW is compared with four other flooding protocols via simulations. The simulation results show that REALFLOW has better performance in terms of reliability and consecutive transmission errors when considering deadlines. Compared with normal flooding, REALFLOW achieves comparable reliability performance with decreased redundancy. Measurements from a prototype implementation conducted in an industrial manufacturing workshop reveal that high-reliability and low-application failure rates can be achieved, giving more confidence in providing reliable wireless sensing and actuating for industrial automation.

A growing concern of cyber threats towards industrial plants has prompted industrial practitioners to focus on secure communication solutions which can protect their systems from vulnerabilities and as well as their brand image. The security concerns and the solutions for industrial communication networks have become well-discussed topics in research communities. Despite a huge research effort in the area of industrial communication network security, there are several issues that need to be addressed properly such that a unified security solution can be adopted in the industrial domain. In this article, we aim to outline the research direction for industrial communication security. Though security is considered as an on-going process, the major issues that still need to be addressed are trust management for heterogeneous networks, managing network performance with security requirements, usable security and key management.

This work investigates various methods to expand the deployment of Wireless Sensor-Actuator Networks (WSANs) to mission critical applications. We address the neglected open issues of hard deadline and deterministic delivery for event-triggered traffic in safety and closed-loop regulatory systems. DeMAC algorithm is proposed by utilizing TDMA-based subslots in emergency access periods, group acknowledgement, alternative packet structure, and relay node. The proposed algorithm displayed promising results in comparison with contention-based methods, in improving delivery rate of critical data within the deadline, and lowering the worst-case delay.

A key aspect of realizing the future smart grid communication solution is a balanced approach between the network performance and the network security during the network deployment. A high security communication flow path is not useful when the network path cannot support capacity and reachability requirements. The deployment phase in communication network can facilitate an optimal network path by focusing on both the network performance and the network security at the same time. In this paper, we describe a use case of smart grid application where security, network capacity and reachability needs to be optimal for successful network operation. We explain our proposed balancing approach of the network performance and the network security which can be useful for the optimal smart grid secure system design.

With the uprising trend of the integration of wireless sensing systems in fields such as health care and industrial processes, where information exchange between power-limited devices is required, lower energy consumption and Quality of Service demands are becoming more stringent. Bluetooth Low Energy is a promising technology that provides, besides ultra-low energy properties, compatibility with most mobile units. However, apart from power efficiency, opportune and reliable information delivery is mandatory in time-critical applications. In this paper we propose an analytical model of the delay performance of Bluetooth Low Energy for connection-oriented applications under different bit error conditions. In addition to the physical over-the-air latency, we also analyze the effect of the occurrence time of an Application Layer event on the effective transmission delay. We highlight the impact of the device's processing speed and the timing configuration of the connection on the final measured latency. Simulation results validate the accuracy of the model for all the analyzed cases.

Communication blackout is one of the most serious pitfalls of Wireless Sensor Networks (WSN) in industrial automation context. The industrial radio channel exhibits pronounced effects of multipath fading and wireless LAN (WLAN) interference that can potentially lead to temporary communication failures, as well as complete isolation of network devices. The current IWSN standards adopt known countermeasures to cope with the harshness of the radio channel, but they lack solutions specifically oriented to detect blackouts and self-recover the communication fulfilling hard deadline constraints. In this work we focus on the problem of blackout detection with specific interest for the WirelessHART standard, introducing a Blackout Detection Service (BDS) expressly addressed to multi-hop periodic communication with sensors and actuators. The BDS monitors end-to-end acknowledgement messages and builds specific metrics to promptly identify communication outages, enabling three criticality classes. The algorithm is tested in the ns-2 network simulator and results show that the proposed system is able to detect blackout events with reaction delays of the order of 4-5 times the refresh rate of nodes and to discriminate between small and temporary network issues and serious blackout scenarios, opening the field for recovery strategies.

Wireless sensor networks (WSNs) are gaining popularity as a flexible and economical alternative to field-bus installations for monitoring and control applications. For mission-critical applications, communication networks must provide end-to-end reliability guarantees, posing substantial challenges for WSN. Reliability can be improved by redundancy, and is often addressed on the MAC layer by resubmission of lost packets, usually applying slotted scheduling. Recently, researchers have proposed a strategy to optimally improve the reliability of a given schedule by repeating the most rewarding slots in a schedule incrementally until a deadline. This Incrementer can be used with most scheduling algorithms but has scalability issues which narrows its usability to offline calculations of schedules, for networks that are rather static. In this paper, we introduce SchedEx, a generic heuristic scheduling algorithm extension, which guarantees a user-defined end-to-end reliability. SchedEx produces competitive schedules to the existing approach, and it does that consistently more than an order of magnitude faster. The harsher the end-to-end reliability demand of the network, the better the SchedEx performs compared to the Incrementer. We further show that SchedEx has a more evenly distributed improvement impact on the scheduling algorithms, whereas the Incrementer favors schedules created by certain scheduling algorithms.

In many applications of industrial sensor networks, stringent reliability and maximum delay constraints paired with priority demands on a sensor-basis are present. These quality of service (QoS) requirements pose tough challenges for industrial wireless sensor networks that are deployed to an ever larger extent due to their flexibility and extendibility. In this paper, we introduce an integrated cross-layer framework, SchedEx-GA, spanning medium access control (MAC) layer and network layer. SchedEx-GA attempts to identify a network configuration that fulfills all application-specific process requirements over a topology including the sensor publish rates, maximum acceptable delay, service differentiation, and event transport reliabilities. The network configuration comprises the decision for routing, as well as scheduling. For many of the evaluated topologies it is not possible to find a valid configuration due to the physical conditions of the environment. We therefore introduce a converging algorithm on top of the framework which configures a given topology by additional sink positioning in order to build a backbone with the gateway that guarantees the application specific constraints. The results show that, in order to guarantee a high end-to-end reliability of 99.999% for all flows in a network containing emergency, control loop, and monitoring traffic, a backbone with multiple sinks is often required for the tested topologies. Additional features, such as multichannel utilization and aggregation, though, can substantially reduce the demand for required sinks. In its present version, the framework is used for centralized control, but with the potential to be extended for decentralized control in future work.

The papers in this special section focus on industrial wireless networks. With the rapid advance of wireless technologies, numerous emerging solutions and applications of industrial wireless systems have been developed. The present development of communication in industrial environments drives the need for ubiquitous access to distributed resources and services that are connected to things, devices, and systems. Service completions are typically perfected through smart APPS on wireless data delivery, such as WiFi, Bluetooth, ZigBee, and 5G. The occurrence of Internet of Things (IoT) further catalyzes the advent of the wireless era. These papers cover the comprehensive solutions of wireless network developments, industrial applications, and wireless prototype designs.

The security in industrial automation domain using cryptography mechanisms is being discussed in both industry and academia. An efficient key management system is required to support cryptography for both symmetric key and public/private key encryption. The key management should ensure that the device is verified before distributing the initial key parameters to devices. The software/firmware used in the device comes from manufacturers, therefore the initial authenticity of the device can be easily verified with the help of manufacturers. Mobile telecommunication is an industrial segment where wireless devices are being used for a long time and the security of the wireless device management has been considered through a standard driven approach. Therefore, it is interesting to analyse the security authentication mechanisms used in mobile communication, specified in Long-Term-Evolution (LTE) standard. This paper analyses the initial device authentication using public key infrastructure in LTE standard, and discusses if, where and how the studied solutions can be tailored for device authenticity verification in industrial plant automation systems.

The requirements of safety-critical wireless sensor network (WSN) applications, such as closed-loop control and traffic safety, cannot be met by the IEEE 802.15.4-2006 standard nor its industrial WSN (IWSN) derivatives. The main problem in that respect is the communication reliability, which is seriously compromised by 2.4-GHz interference, as well as multipath fading and attenuation (MFA) at industrial facilities. Meanwhile, communication blackouts in critical WSN applications may lead to devastating consequences, including production halts, damage to production assets and can pose a threat to safety of human personnel. This work presents PREED, a method to improve the reliability by exploiting the determinism in IWSN communication. The proposed solution is based on the analysis of bit error traces collected in real transmissions at four different industrial environments. A case study on Wireless HART packet format shows that PREED recovers 42%-134% more packets than the competing approaches on links compromised by WLAN interference. In addition, PREED reduces one of the most trivial causes of packet loss in IWSN, i.e. The corruption of frame length byte, by 88% and 99%, for links exposed to WLAN interference and MFA, respectively.

In industrial plants, there is a mix of devices with different security features and capabilities. If there is a mix of devices with various degree of security levels, then this will create independent islands in a network with similar levels of security features. However, the industrial plant is interconnected for the purpose of reducing cost of monitoring with a centralized control center. Therefore, the different islands also need to communicate with each other to improve the asset management efficiency in a plant. In this work we aim to focus on the trustworthiness assessment of devices in industrial plant networks in term of node value. We study the behavior of industrial plant networks when devices with various degrees of security features communicate. We aim to identify network properties which influence the overall network behavior. From the study, we have found that the communication path, the order of different communication paths and the number of specific types of nodes affect the final trustworthiness of devices in the network.

Industrial networks have a mix of devices with different security properties. If a mix of devices with various degrees of security features and capabilities communicate, the overall network dynamics with respect to device trust and security of message exchange will be complex. Therefore, there is a need to understand the trust and risk probabilities of devices in a heterogeneous network. This is required for heterogeneous network where the network configuration has to be made based on how trustworthy they are. In this work we focus on assessing security risks for devices and message exchanges. We define the term assurance value to denote the resilience of a device to security attacks. We study the behavior of a communication network when devices with various degrees of security features exchange messages. We aim to identify the network security properties based on the network architecture. From the study, we propose a model to estimate and predict network security properties in a heterogeneous communication network.

Smart transportation is an application of intelligent system on transportation domain, expected to bring the society environmental and economic advantages. By combining with IoT techniques, the concept is being enhanced and raised to a system level. Numerous data are able to collect and effective analysis technique is needed. Here in this paper, we proposed a framework of employing IoT technique to construct a free time navigation system. The system aims at providing a real-time quantification of traffic conditions and suggests optimal route based on the information retrieved. The system can be basically separated into two major components: (i) the traffic condition estimation module and the (ii) real-time routing algorithm. In the first component, traffic conditions of roads will be estimated based the information collected from sensors installed on vehicles. Based on these location and speed information, the traffic condition can be quantified using a weighted kernel density estimation (WKDE) function. This function is a function of time and provides a real time insight of the overall traffic condition. By combining this information and the topological structure of the road network, a more accurate time consumption on each road can be estimated and hence enable a better routing.

Communication reliability is the ultimate priority in safety-critical wireless sensor network (WSN) communication. Surprisingly enough, the enormous potential of error control on direct sequence spread spectrum (DSSS) chips in IEEE 802.15.4 has been completely overlooked by the WSN community, possibly due to incorrect presumptions, such as the concerns about computational overhead. Current error-correction schemes in WSN counteract the error process once the errors have already propagated to bit- and packet-level. Motivated by the notion that errors should be confronted at the earliest stage, this work presents CLAP, a novel method that tremendously improves the error correction in WSN by fortifying the IEEE 802.15.4 Physical layer (PHY) with straightforward manipulations of DSSS chips. CLAP is implemented on a software-defined radio platform, and evaluated on real error traces from heavily WLAN-interfered IEEE 802.15.4 transmissions at 3 different environments. CLAP boosts the number of corrected packets by 1.78-6.88 times on severely interfered links, compared to two other state-of-the-art schemes. The overhead in terms of computational complexity is about 10% of execution time of the OQPSK demodulator in the legacy IEEE 802.15.4 receiver chain.

Wireless sensor networks (WSNs) are being increasingly used for monitoring and control in modern process industries, which until a few years ago were almost exclusively done by cables. In order to successfully integrate WSNs in process industries, proper understanding of the radio channel characteristics is necessary. Predominantly, in control applications, the sensors are static, and variations in the channel are caused by the objects in the region, which move to different locations every now and then. Hence, over a period of several hours, the radio channel will have completely different properties, which will have a significant impact on network performance. In this paper, we conduct and analyze channel measurements in a fully functional factory over a much longer time horizon of 20 hours, which, to the best of our knowledge, have not been undertaken before. A key observation from our analysis is that the radio channel, over a longer time scale, shows a more complicated behavior than over a relatively shorter time period. We also show that if the long-term channel variations are not effectively characterized, then we run the risk of transmitting at a rate, as low as, 94% less than what the channel could actually support.

One of the challenges for a successful use of wireless sensor networks in process industries is to design networks with energy efficient transmission, to increase the lifetime of the deployed network while maintaining the required latency and bit-error rate. The design of such transmission schemes depend on the radio channel characteristics of the region. This paper presents an investigation of the statistical properties of the radio channel in a typical process industry, particularly when the network is meant to be deployed for a long time duration, e.g., days, weeks, and even months. Using 17-20-h-long extensive measurement campaigns in a rolling mill and a paper mill, we highlight the non-stationarity in the environment and quantify the ability of various distributions, given in the literature, to describe the variations on the links. Finally, we analyze the design of an optimal received signal-to-noise ratio (SNR) for the deployed nodes and show that improper selection of the distribution for modeling of the variations in the channel can lead to an overuse of energy by a factor of four or even higher.

This paper proposes PriorityMAC, a priority-enhanced medium access control protocol, designed for critical traffic in industrial wireless sensor and actuator networks (IWSAN). A notable trend in industrial automation in recent years has been the replacement of wired communication by IWSANs. Exceeding the required delay bound for unpredictable and emergency traffic could lead to system instability, economic and material losses, system failure, and, ultimately, a threat to human safety. Guaranteeing the timely delivery of the IWSAN critical traffic and its prioritization over regular traffic (e.g., noncritical monitoring traffic) is a significant challenge. Therefore, we present the design, implementation, performance analysis, and evaluation of PriorityMAC. A series of novel mechanisms (e.g., high priority indication space) are proposed to enable high-priority traffic to hijack the transmission bandwidth of the low-priority traffic. To the best of our knowledge, this is the first priority-enhanced MAC protocol compatible with industrial standards for IWSAN. PriorityMAC is implemented in TinyOS and evaluated on a testbed of Telosb motes. The experimental results indicate that PriorityMAC efficiently handles different traffic categories with different latency requirements, thereby achieving a significant improvement in the delivery latency compared with the current industrial standards.

Security is one of the most important issues we should solve before deploying wireless sensor networks in practical applications. Compared with traditional wireless networks, the industrial environment is harsher and more complex, which has a higher requirement for network security. Although cryptographic primitives can provide the capability to resist against the attacks from the external network, they can not address the problem caused by the internal failed or compromised field devices. In this paper, we propose a trust management scheme for Industrial Wireless Sensor Networks to solve this problem. By utilizing the centralized network manager, we first provide the architecture of trust management to improve the security of the network. Then a trust computation model is proposed to evaluate the trust value of field devices. Finally, we compare the performance of our scheme with some classic trust management schemes. The simulation results show that our scheme can improve the accuracy and robustness of the trust evaluation process in industrial environments and ensure the security of the network.

Industrial device commissioning along with the initial distribution of keying material is an important step for the security of industrial plants. An efficient key management system is required in cryptography for both symmetric key or public/private key encryption. Most of the key management system use either pre-installed shared keys or install keys using out-of-band channels. In addition to that, the sensor devices both wired and wireless need to be verified whether it is connected to the correct physical entity since these devices are linked with the physical world. Therefore in industrial plants there is a requirement to automate the trust bootstrapping process, where the devices from upper level in communication network will be aware that the communication device from below level is trusted. In this work, we present a workflow that uses the existing trust mechanism on employees to enable the initial bootstrap of trust in the devices, and also optionally support the commissioning engineer to download the required configuration data in the device as well. Thus, this approach presents a unique solution to the initial trust distribution problem reusing the existing features and facilities in industrial plants.

Applying Industrial Wireless Sensor and Actuator Networks (IWSANs) in the industrial automation is a growing trend due to flexibility, mobility and low cost. According to the current standards, such asWirelessHART and ISA100.11a, multi-channel TDMA transmission is included for reliable and deterministic communication. In this paper, we clarify the dependence of TDMA scheduling for sensors and actuators and point out the low correlation between the scheduling delay and the overall quality of control, and focus on reducing jitter in scheduling for improving quality of control and system stability. We propose a scheduling algorithm, aiming for lowing jitter and compare it with two traditional real-time scheduling schemes. Our simulation results exhibit significantly lower jitters by applying our scheduling policy than those two traditional scheduling schemes.

The security of industrial plants has gained a lot of importance since the last decade. The reason is that the different components from different network layers of automation systems have become inter-connected to support fast and cost-effective decisions at the management level. This inter-connectivity has posed many security challenges in this industrial segment. To achieve effective security mechanisms in industrial plants, there is a need to learn from other existing domains, matured in terms of security, whether existing matured security solutions can be reused in the industrial automation domain. The financial sector is a segment where security has been carefully managed since a long time, as security is very important for that sector. Therefore it would be beneficial to evaluate the security mechanisms present in financial card readers which are involved in financial transactions because these card readers have many similar characteristics with industrial process control devices. In this paper, the security requirements for both the field devices of industrial plants and card reader terminals of the financial sector have been evaluated to understand the security gap so that we can identify the areas where the security needs of industrial plants must be improved and where some of the existing security features of card reader terminals can be reused in field devices of industrial plants.

Wireless sensor networks (WSN) must ensure worst-case end-to-end delay and reliability guarantees for mission-critical applications. TDMA-based scheduling offers delay guarantees, thus it is used in industrial monitoring and automation. We propose to evolve pairs of TDMA schedule and routing-tree in a cross-layer in order to fulfill multiple conflicting QoS requirements, exemplified by latency and reliability. The genetic algorithm we utilize can be used as an analytical tool for both the feasibility and expected QoS in production. Near-optimal cross-layer solutions are found within seconds and can be directly enforced into the network.

Several wireless sensor network (WSN) applications will in the future be deployed for process control purposes which typically require timely data delivery. The main drawback with current WSNs is that they cannot guarantee predictable delay which is required in many applications. In this paper, we propose WirArb which is a new medium access control (MAC) protocol suitable for time-critical applications that needs deterministic delay guarantees. Each user is pre-defined arbitration frequency which is used to distinguish in which order the users can access the channel. The arbitration frequency is determined by orthogonal sub-carriers in the physical layer. The proposed mechanism will always guarantee the user with highest priority will gain channel access and users with lower priority needs to wait for their turn. We evaluate and compare the proposed WirArb with IEEE 802.15.4 and IEEE 802.11 in terms of latency and throughput. The obtained results show that for a star network with several users the proposed WirArb is superior.

This paper presents a cross-layer distributed scheduling protocol for sensor data gathering transmission in wireless sensor actor and actuator networks. We propose the parent-dominant decision scheduling with collision free (PDDS-CF) algorithm to adapt the dynamics of links in a realistic low-power wireless network. In addition, the protocol has a light-weight mechanism to maintain the conflict links. We have evaluated the protocol and implementation in TinyOS and Telosb hardware. The experiment shows that our protocol has robustness to the topology changes and it has significant improvements to reduce the traffic load in realistic wireless networks.

In this work we propose an Adaptive Forward Error Correction (AFEC) algorithm for best effort Wireless Sensor Networks. The switching model is described in terms of a finite-state Markov model and it is based on the channel behavior, observed via Packet Delivery Ratio in the recent past. We compare the performance of AFEC with static FEC, as well as uncoded transmissions. The results demonstrate a gain in PDR achieved by introducing FEC coding in uncoded IEEE 802.15.4 transmissions, as well as the advantages over static FEC schemes, namely increased throughput and reduced energy consumption. The proposed solution is IEEE 802.15.4-compliant and requires no additional feedback channels.

With the success of wireless technologies and the number of wireless devices increasing, the wireless coverage footprint expands and removes the needs of wired networks for industrial applications. Wireless industrial sensor networks (WISNs) are a type of wireless sensor networks (WSNs), which dedicate to industrial applications. The WISNs bring several advantages over traditional wired industrial networks for detection and control, include flexibility, scalability, remote maintenance, and rapid deployment. However, some technical issues and routing design principles need to be noticed in terms of the stringent requirements of industrial applications. In this paper, we firstly introduce the characteristics and some routing issues of WISNs. Then we discuss existing routing protocols for WISNs and analysis their performances. Finally, we outline several future research directions.

High reliability and real-time performance are main research challenges in Industrial Wireless Sensor Networks (IWSNs). Existing routing protocols applied in IWSNs are either overcomplicated or fail to fulfill the stringent requirements. In this paper, we propose a reliable and flexible Received Signal Strength-based routing scheme. Our proposed solution can achieve a seamless transition in the event of topology change and can be applied in different industrial environments. The simulation results show that our solution outperforms conventional routing protocols in both reliability and latency. Furthermore, the result also proves that the changes of the network topology have no impact on data transmissions of other nodes by our scheme, whereas conventional routing protocols are shown to fail to recover the network in a short time. Finally, due to dynamic weighting mechanism, the proposed scheme is verified to achieve significantly higher reliability in scenarios with obstacles and avoid installation troubles, compared to location-based flooding scheme. Thus, our proposed scheme is considered to be more suitable for IWSNs than other routing protocols.

In this paper we address the issues of timeliness and transmission reliability of existing industrial communication standards. We combine a Forward Error Correction coding scheme on the Medium Access Control layer with a lightweight routing protocol to form an IEEE 802.15.4-conformable solution, which can be implemented into already existing hardware without violating the standard. After laying the theoretical foundations, we conduct a performance evaluation of the proposed solution. The results show a substantial gain in reliability and reduced latency, compared to the uncoded transmissions, as well as common Wireless Sensor Network routing protocols.

Contrary to the various theoretic publications on safety-critical communication over wireless, this paper will show the actual performance of safety-critical communication in a real plant with all its environmental influences. We used PROFIsafe as a functional safety profile on top of the WirelessHART protocol. Separately these technologies are widely used in industry for safety and wireless communication respectively, but it has never been shown that the combination of them is feasible for safety-critical communication. The main focus of this work is to measure and analyze the round-trip time and the bit error rate of the safety-critical communication in order to identify whether the certification requirements of PROFIsafe holds. We will show that it is technically feasible to run safety-critical data over wireless links. However, long round trip times and high noise in the channel at certain locations in the plant are unacceptable with respect to certification and need further investigations.

Cooperative transmission is an efficient technique to realize diversity gain in wireless fading channels via a distributed way. Cooperative automatic repeat request (ARQ) is known as a protocol which exploit spatial diversity by allowing the relay node to retransmit the source data packet to the destination node, when the latter is unable to decode the source node data correctly. In this short paper we derive closed form expressions for the spectral efficiency for C-ARQ scheme and we will also present a rate adaptive C-ARQ protocol. We consider a scenario in which both the source and relay nodes employ adaptive modulation. Our simulation results prove that the proposed scheme enhances the spectral efficiency noticeably when compared to other schemes.

The third generation (3G) and future wireless networks will use packet-switching technology to provide high speed data transfer aiming at a fixed broadband experience. It has been showed that Internet data traffic exhibited self-similarity and long-range dependency. Those phenomena, that may be caused by heavy-tailed packet size distributions have significant impact on the design of Internet routers; recently, such an evolution has been observed in high speed wireless channels. This article focus on radio-based schedulers and especially on High-Speed Downlink Packet Access (HSDPA) and their impact of heavy- tailed traffic. We propose two new schedulers which are designed for wireless links fed by heavy-tailed traffic and make use of the work lookahead i.e., knowledge of the remaining file size for each flow. Both proposed algorithms provide increased performance compared to traditional scheduling methods.

In this paper, we propose and investigate scheduling algorithms for multiple-input multiple-output (MIMO) wireless local area networks (WLANs) based onorthogonalfrequencydivisionmultiplexing(OFDM). We propose two new scheduling approaches which combines the channel states with queue states. The obtained results show that the proposed schemes outperform well known schemes such asproportionalfair(PF) andshortestremainingprocessingtime(SRPT) in terms of throughput and fairness.

In this paper, we derived two new decision metrics for the non-coherent maximum a posteriori (N-MAP) detector and the generalized-likelihood ratio (GLR) detector. The proposed detectors are optimally designed to make use of the available estimated channel. The obtained results show that the proposed detectors can outperform a system using classical training-based maximum-likelihood (TBML) detector. The performance is evaluated by means of computer simulations with both convolutional coded and LDPC-coded systems.

Summary form only given. In this presentation, we present a bandwidth-efficient and power-efficient packet retransmission diversity scheme suitable for improving the performance of linear multi-level modulation schemes, such as pulse amplitude modulation (PAM), phase-shift keying modulation (PSK) and quadrature amplitude modulation (QAM), in wireless systems employing (H)ARQ protocols. Our proposed retransmission diversity scheme considers the interaction between (i) the number of retransmission diversity branches and (ii) the signal space of the modulation scheme, to improve the performance. The concept is to suitably rearrange the symbols (change the bit-to-symbol mapping) between retransmissions, while employing the modulation order M as an extra dimension. In this way the modulation order and the number of retransmission diversity branches together are viewed as creating an augmented signal space of higher dimension than the modulation signal space alone, and this is in turn exploited to improve the transmission quality. Both the robustness and the throughput performance are improved, and the number of retransmissions per packet is reduced compared to conventional packet retransmission schemes. As an extension, we apply the proposed method in conjunction with both space-time block coding and trellis coded modulation. While keeping the same rate and complexity, both analytical and simulation results show a significant improvement of the system performance. Our performance results show that the proposed concept improves the system performance over conventional packet retransmission schemes in both additive white Gaussian noise and flat-fading channels, in particular at low to medium Eb/N0. This proposed scheme is suitable for wireless LANs and future cellular applications due to its low complexity, bandwidth-and power efficiency.

In this paper, we present a combined constellation rearrangement and space-time block code scheme which is bandwidth-efficient and power-efficient and suitable for improving the performance of linear multi-level modulation schemes over multiple antenna transmission. Orthogonal transmit diversity with optimum signal combining can take full advantage of fading multi-path channels but for such a scheme the bandwidth efficiency decreases proportionally with the number of transmit branches. It will be shown that using different signal mapping constellations for each transmit branch improves the system performance. While keeping the same rate and complexity, both analytical and simulation results show a significant improvement of the system performance.

In this paper, we present a combined constellation rearrangement and space-time block code scheme which is bandwidth- efficient and power-efficient and suitable for improving the performance of linear multi-level modulation schemes over multiple antenna transmission. Orthogonal transmit diversity with optimum signal combining can take full advantage of fading multi-path channels but for such a scheme the bandwidth efficiency decreases proportionally with the number of transmit branches. It will be shown that using different signal mapping constellations for each transmit branch improves the system performance. While keeping the same rate and complexity, both analytical and simulation results show a significant improvement of the system performance.

Communication in high-speed wireless local area networks such as IEEE 802.11a are known to suffer from location-dependent, time-varying, and burst errors. It is usual to implement forward error correction and automatic repeat request mechanisms in the physical and data link layer to combat this errors. In this paper we evaluate the system performance of a WLAN with the CSMA/MAC protocol with different ARQ protocols. We have implemented three different schemes: (1) selective repeat ARQ; (2) ARQ with majority voting; and (3) type II hybrid ARQ. The simulation results show that all three schemes perform well when the channel is in good condition but when the bit error rate (BER) increases the type II hybrid ARQ is superior to the other two schemes.