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The traditional healthcare is overwhelmed by the processing and storage of massive medical data. The emergence and gradual maturation of Internet of Things (IoT) technologies bring the traditional healthcare an excellent opportunity to evolve into the IoT-enabled healthcare of massive data storage and extraordinary data processing capability. However, in IoT-enabled healthcare, sensitive medical data are subject to both privacy leakage and data tampering caused by unauthorized users. In this paper, an attribute-based Secure Access Control Mechanism, coined SACM, is proposed for IoT-Health utilizing the federated deep learning. Specifically, we manage to discover the relationship between users’ social attributes and their trusts, which is the trustworthiness of users rely on their social influences. By applying graph convolutional networks to the social graph with the susceptible infected recovered model based loss function, users’ influences are obtained and then are transformed to their trusts. For each occupation, users’ trusts allow them to access specific medical data only if their trusts are higher than the corresponding threshold. Then, the federated deep learning is applied to obtain the optimal threshold and relevant access control parameters for the improvement of access control accuracy and the enhancement of privacy preservation. The experiment results show that the proposed SACM achieves accurate access control in IoT-enabled Healthcare with high data integrity and low privacy leakage.

Artificial Intelligence of Things (AIoT), as a fusion of AI and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest, named D2MIF, for the federated learning empowered AIoT. In D2MIF, an isolation forest is constructed to compute the malicious score for each model uploaded by the corresponding participant, then the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public datasets Mnist and Fashion_Mnist. And the experiment results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning empowered AIoT.

With the rapid development of the Internet of Things (IoT), more and more data are generated by smart devices to support various edge services. Since these data may contain sensitive information, security and privacy of data aggregation has become a key challenge in IoT. To tackle this problem, a Blockchain based Secure Data Aggregation strategy, namely (BSDA), is proposed for edge computing empowered IoT. Specifically, in order to restrict task receivers (i.e., Mobile Data Collectors (MDC)) to search and accept tasks, the block header is intergraded with a security label including task security level and task completion requirement. Accordingly, new block generation rules are developed to improve system performance in throughput and transaction latency. Furthermore, BSDA decomposes both sensitive tasks and task receivers into groups against privacy disclosure. On the other hand, a deep reinforcement learning method, the Improved double bootstrapped Deep Deterministic Policy Gradient (IDDPG), is developed to design energy efficient MDC routes under the constrains that the security levels of MDCs should be higher than the security levels of data aggregation tasks. Simulation results indicate that (i) as a privacy-preserving strategy, BSDA obtains high throughput and low transaction latency; (ii) BSDA outperforms certain contemporary strategies in aggregation ratio and energy cost.

Industrial Internet of Things (IIoT) is an emerging technology that can promote the development of industrial intelligence, improve production efficiency, and reduce manufacturing costs. However, anomalies of IIoT devices might expose sensitive data about users of high authenticity and validity, resulting in security and privacy threats to the IIoT applications. That suggests the significance of anomaly detection executed by proper authorities. To address these problems, we propose a reliable anomaly detection strategy for IIoT using federated learning. Specifically, we apply the federated learning technique to build an universal anomaly detection model with each local model trained by the deep reinforcement learning algorithm. Since local datasets are not required during the federated learning, the chance of privacy leakage is reduced. In addition, by introducing privacy leakage degree and action relation to anomaly detection design, we can greatly improve the detection accuracy. The validation experiments indicate that the proposed strategy achieves high throughput, low latency, and high anomaly detection accuracy for privacy preservation in various IIoT scenarios.

This work revolves around lightwave power transfer networks in which we aim to maximize the number of users served while simultaneously minimizing the transmit power. By formulating the problem as a reinforcement learning (RL) problem, we propose the use of the evolution strategies (ES) method as a novel solution. In this context, ES is a heuristic search method inspired from the biological evolution of nature and it is used to solve complex machine learning problems. Hence, a learning scenario and an ES-based algorithm are devised to solve the RL problem. The results demonstrate that the proposed approach can achieve considerable performance gains compared to the conventional Q-learning method.

In a multi-user wireless network under jamming attacks, self-interested users trying to learn their best anti-jamming strategy encounter a major problem, which is interference. An interesting solution is for users to learn anti-jamming techniques cooperatively, which can be achieved using a distributed learning algorithm. In this context, works proposing distributed learning algorithms to overcome jamming in multi-user applications, rely on the availability of a safe communication link to exchange information between users. However, if this communication link wireless, assuming that this link is safe against jamming attacks would not be accurate. Consequently, we propose a novel distributed multi-agent reinforcement learning algorithm for anti-jamming, namely Cross-Check Q-learning, where users build estimates of each other’s decision-making policies and adapt to them, therefore eliminating their need to communicate. When applied against both sweeping and smart jammers, our algorithm provides users with a better understanding of their environment and helps them learn the attacker’s policy and effectively avoid mutual interference. The proposed method’s transmission rates and interference levels is compared with the standard Q-learning, the collaborative multi-agent algorithm, and random policy. Simulation results show that our algorithm improves the users’ transmission rates, eliminates mutual interference, and has the highest convergence speed under the two considered jamming attacks.

Sixth generation (6G) network is a revolutionary technology to satisfy the ever-growing demands from the sustainable development of emerging industrial applications and services. Due to its high flexibility, convenient and rapid deployment, self-organization capability, and outstanding expansibility, network-in-box (NIB) represents a promising approach for future networks. The integration of NIB with 6G can lead to many new applications in geoscience, robotics, and industrial automation. For 6G-enabled NIB, services are deployed directly on the NIB, which increases the fault tolerance and reduces the traffic volume on the backhaul link. As more and more data are processed and shared in industrial applications and services, the security of data aggregation becomes a key challenge for 6G-enabled NIB. To address this challenge, in this article, we propose a blockchain based privacy-aware distributed collection (BPDC) oriented strategy for data aggregation. In BPDC, an improved blockchain with a new block header structure and two different block generation rules are designed and introduced, which restricts the task receivers to search and receive the tasks beyond their levels of security permission. While guaranteeing the data aggregation performance, BPDC can also achieve privacy protection by decomposing sensitive tasks and task receivers into multiple groups. Validation experiments show that the BPDC accomplishes low overhead, high throughput, and privacy preservation in various industrial applications.

The fifth-generation wireless technology (5G) has been developed with an aim to provide ubiquitous and scalable connectivity for Internet-of-Things (IoT) nodes. Likewise, the cloud radio access network (C-RAN) architecture can be exploited to enable efficient network access to IoT nodes. Nevertheless, the 5G C-RAN architecture is based on large data centers geographically located far apart, which introduces an inevitable overhead. Therefore, to supply real-time data services near by the data terminals, fog computing emerges as a promising solution. However, constrained physical fog resources and delay-sensitive services hinder the application of new virtualization technologies in the baseband unit (BBU) task allocation management of the fog network. To tackle these challenges, a task allocation framework for hierarchical software-defined fog virtual radio access networks (v-RANs) is proposed in this article. Precisely, we apply an enhanced ant colony optimization (ACO) in combination with a max–min algorithm to efficiently determine the optimal path for BBU task allocation management, while minimizing the transmission time for parallel task execution scheduling. Experimental results demonstrate that the queue delay in our approach is 98.38% and 98.82% lower than the round-robin (RR) algorithm and least connection technique (LCT), respectively.

In this paper, we study the channel access problem of vehicles in a cognitive radio vehicular network, where each vehicle opportunistically accesses the channel resources of the primary network in order to successfully receive the necessary data packets within a time deadline. Given the access priority constraint and the limited bandwidth of the primary network, a smart channel connection scheme is indispensable to ensure a decent quality of service (QoS) at the vehicles’ side. Due to the competitive nature of vehicles, the vehicle access control is formulated as a multi-agent access problem that comes with an intrinsic challenge, i.e. the partial observation of the information about the environment dynamics. On top of that, considering the temporal usage profile of the primary network, the environment dynamics are also time-dependant, and hence making the aforementioned access control a non-Markovian problem. Consequently, the estimation of the system states, which are used for the decision making process of a vehicle, is very challenging. To deal with the issues arising from such non-Markovian problem, we propose a vehicle connection algorithm based on a deep recurrent Q-learning network. With the aid of a recurrent Long Short Term Memory (LSTM) layer integrated into a deep Q-network, the time-correlated system states can be properly estimated, thereby improving the vehicle channel access policy. Besides, we introduce novel reward quantities that help improving the network performance and the capability to flexibly adapt to unexplored scenarios. A new structure of the cumulative reward function is also presented to balance the performance trade off between the cooperative and competitive objectives. Simulation results are provided to verify the advantage and the stability of our proposed algorithm over the benchmark schemes.

The exponential increase in the number of connected devices in the network led to a paradigm shift from the traditional host-centric IP-based network architecture to a content-based paradigm, which eliminates the address-content bindings in traditional IP-based architectures. In content-centric networking (CCN) architecture, contents are accessed by name rather than the physical location where these are stored, which results in more flexible and robust content-based architecture. However, the broadcast nature of devices in CCN causes network congestion and latency. The Internet of Healthcare Vehicles (IoHV) is an evolving paradigm that relies on smart transportation involving ambulances and other healthcare vehicles (for rapid COVID testing) to connect with each other through the Internet, especially in case like COVID 19 testing and contact tracing. However, the applications of IoHV are not similar to other networks and therefore bring new technical challenges due to high mobility and rapid changes of topology. Therefore, to handle these challenges, in this article, we propose a novel scheme that finds an optimal path. Using the optimal path, the interest packet is forwarded among healthcare vehicles in a smart city scenario. The proposed scheme maximizes the probability of finding the requested data while minimizing latency. As the proposed scheme does not involve broadcasting of interest packets, the problem of network congestion in CCN can be solved to a large extent. In order to facilitate the interoperability of heterogeneous healthcare devices in the network, and to improve the routing flexibility, an SDN controller is deployed in the IoHV scenario. It maintains the global information of the network in its flow tables. Moreover, to achieve faster response time and lower latency in the IoHV environment, edge devices are utilized instead of central cloud. Keeping in view the memory constraints of edge devices, deletable Bloom filters are used for storage and caching. Finally, the open issues and challenges related to the proposed solution for the IoHV scenario are also presented.

With the exponential growth in the number of connected devices, in recent years there has been a paradigm shift toward mobile edge computing. As a promising edge technology, it pushes mobile computing, network control, and storage to the network edges so as to provide better support to computation-intensive Internet of Things (IoT) applications. Although it enables offloading latency-sensitive applications at the resource-limited mobile devices, decentralized architectures and diversified deployment environments bring new security and privacy challenges. This is due to the fact that, with wireless communications, the medium can be accessed by both legitimate users and adversaries. Though cloud computing has helped in substantial transformation of global business, it falls short in provisioning distributed services, namely, security of IoT systems. Thus, the ever-evolving IoT applications require robust cyber-security measures particularly at the network's edge, for widespread adoption of IoT applications. In this vein, the classic machine learning models devised during the last decade, fall short in terms of low accuracy and reduced scalability for real-time attack detection across widely dispersed edge nodes. Thus, the advances in areas of deep learning, federated learning, and transfer learning could mark the evolution of more sophisticated models that can detect cyberattacks in heterogeneous IoT-driven edge networks without human intervention. We provide a SecEdge-Learn Architecture that uses deep learning and transfer learning approaches to provided a secure MEC environment. Moreover, we utilized blockchain to store the knowledge gained from the MEC clusters and thereby realizing the transfer learning approach to utilize the knowledge for handling different attack scenarios. Finally, we discuss the industry relevance of the MEC environment.

The ever-growing trend of making the traditional power grids smarter than before has resulted in their gradual evolution to more sophisticated grids, referred to as Smart Grids (SGs) Cyber-Physical Systems with complex networking technologies. The integration of Information and Communication Technologies with power grids fosters seamless data sharing between different SG entities, which supports effective and smart governance in terms of demand response management, frequency support, and voltage stabilization. Nonetheless, this integration opens up several security and privacy concerns, namely, electricity theft, power loss, battery exhaustion, infrastructure mapping, etc . These issues become even more important with the addition of distributed energy sources, e.g. electric vehicles (EVs), battery energy storage systems, and renewable energy sources, into the SGs. We present a framework based on Software Defined Networking (SDN) and BlockChain (BC) to address two challenging issues of EV-aided SG ecosystems, namely, privacy assurance and power security. We leverage the capabilities of SDN to handle the complex interactions between different subsystems of the SG. Furthermore, we also employ BC and smart contracts’ properties to secure energy transactions and data communications. We design a secure and efficient mutual authentication protocol based on Elliptic Curve Cryptography (ECC) and BC for privacy preservation during smart energy trading. We also proposed a BC-based smart contract for effective Demand Response Management (DRM) during bidirectional energy transfer between EVs and SG. Finally, we present experimental evaluations to validate the proposed framework’s performance. The results obtained demonstrate the improved performance of the proposed scheme compared with current state-of-the-art approaches. The mutual authentication protocol designed is not only secure against major attack vectors (namely, session key security, message integrity, anonymity, forward secrecy, and so on), but it is also cost-efficient in terms of communication and computational costs. Additionally, the SC designed assures power security and maintains an adequate balance between demand and supply.

Owing to its benefits such as flexibility, scalability, and interoperability, Software-Defined Networking (SDN) has been incorporated into Internet of Vehicles (IoV) to cope with the increasing demands of vehicular applications. The integration of SDN and IoV, namely SDN-IoV, can enrich many new applications for intelligent transportation such as traffic monitoring, smart navigation, and self-driving. The spatial crowdsourcing technology has been adopted as an effective data collection and processing method that is the premise of various SDN-IoV applications. However, as huge amounts of data are generated in spatial crowdsourcing services, the data privacy and security has become a key challenge for SDN-IoV. To overcome abovementioned challenge, a Deep Reinforcement Learning (DRL) and Blockchain empowered Spatial Crowdsourcing System (DB-SCS) is proposed. In DB-SCS, we design an improved multi-blockchain structure and a blockchain-based hierarchical task management method, which divide the spatial tasks into different categories according to the privacy requirements and the areas of the task and then decompose different categories of tasks and task receivers into sub-blockchains. While guaranteeing the data privacy, DB-SCS can also enhance the spatial crowdsourcing performance by using the proposed DRL-based management strategy to dynamically select the consensus algorithm, block size, and block generation rule. Extensive simulation experiments demonstrate that the DB-SCS can obtain high throughput, low overhead, and data privacy under various SDN-IoV scenarios.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide quick and immediate identification of infection, proper and immediate clinical support is needed. Researchers have proposed various machine learning and smart IoT-based schemes for categorizing COVID-19 patients. Artificial neural networks (ANNs), which are inspired by the biological concept of neurons, are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing healthcare information. The aim of this article is to provide secure COVID-19 vaccine distribution through IoT-based systems. The level-wise blockchain network is used to ensure security among IoT devices while distributing the vaccines. The proposed phenomenon is analyzed and verified over synthesized data where vaccine units are supplied by various distributors. The proposed approach is validated over accurate report generation and data alteration parameters against existing methods.

The industrial Internet-of-Things (IIoT) is a powerful Internet of Things (IoT) application that enables industrial growth by ensuring transparent communication among the various entities of a company such as the manufacturing locations, design hubs, and packaging units. However, current industrial architectures are unable to efficiently deal with advanced security issues that come with this communication due to the distributed and expandable nature of IIoT networks. Furthermore, from a security perspective, malicious devices with the objective of modifying data from within the premises of the network pose a high risk for the IIoT. Therefore, introducing intelligent decision-making models to the IIoT can enhance our ability to examine any collected data in a more structured, efficient, and secure manner. In this article, we provide a decision-making model for securing IIoT data. The proposed model, based on the Technique for Order Preference by Similarity to the Ideal Solution , can provide secure information transmission and recording/storage using various communicating parameters. The degree of trust of the IoT devices is analyzed using these parameters. Simple additive weighting is integrated into the proposed model to remove inefficient and ill-structured parameters. The proposed model is validated using various spectrum sensing and security parameters against a baseline method for the IIoT. Simulation results show that the proposed model is approximately 85% more efficient in identifying malicious nodes and denial-of-service threats compared to the baseline method.

Energy consumption minimization of cloud data centers (DCs) has attracted much attention from the research community in the recent years; particularly due to the increasing dependence of emerging Cyber-Physical Systems on them. An effective way to improve the energy efficiency of DCs is by using efficient job scheduling strategies. However, the most challenging issue in selection of efficient job scheduling strategy is to ensure service-level agreement (SLA) bindings of the scheduled tasks. Hence, an energy-aware and SLA-driven job scheduling framework based on MapReduce is presented in this article. The primary aim of the proposed framework is to explore task-to-slot/container mapping problem as a special case of energy-aware scheduling in deadline-constrained scenario. Thus, this problem can be viewed as a complex multi-objective problem comprised of different constraints. To address this problem efficiently, it is segregated into three major subproblems (SPs), namely, deadline segregation, map and reduce phase energy-aware scheduling. These SPs are individually formulated using Integer Linear Programming. To solve these SPs effectively, heuristics based on Greedy strategy along with classical Hungarian algorithm for serial and serial-parallel systems are used. Moreover, the proposed scheme also explores the potential of splitting Map/Reduce phase(s) into multiple stages to achieve higher energy reductions. This is achieved by leveraging the concepts of classical Greedy approach and priority queues. The proposed scheme has been validated using real-time data traces acquired from OpenCloud. Moreover, the performance of the proposed scheme is compared with the existing schemes using different evaluation metrics, namely, number of stages, total energy consumption, total makespan, and SLA violated. The results obtained prove the efficacy of the proposed scheme in comparison to the other schemes under different workload scenarios.

We develop a deep learning (DL) based Gaussian source estimation technique when the source is impaired by bursty impulsive noise. This noise is correlated in time, and hence estimating the source with a low-complexity algorithm is a challenging task. To address this challenge, we train a long short term memory (LSTM) based deep neural network (DNN) model offline with different bursty noisy observations and deploy the trained model in real-time. The trained model detects the noise state online and thus applies a linear minimum mean square error (LMMSE) method to estimate the source signal. To demonstrate the effectiveness of the proposed scheme, we compare its performance with baseline schemes. Simulation results reveal the effectiveness of the proposed estimation technique in terms of mean square error and computational complexity.

COVID-19 is an extremely dangerous disease because of its highly infectious nature. In order to provide a quick and immediate identification of infection, a proper and immediate clinical support is needed. Researchers have proposed various Machine Learning and smart IoT based schemes for categorizing the COVID-19 patients. Artificial Neural Networks (ANN) that are inspired by the biological concept of neurons are generally used in various applications including healthcare systems. The ANN scheme provides a viable solution in the decision making process for managing the healthcare information. This manuscript endeavours to illustrate the applicability and suitability of ANN by categorizing the status of COVID-19 patients’ health into infected (IN), uninfected (UI), exposed (EP) and susceptible (ST). In order to do so, Bayesian and back propagation algorithms have been used to generate the results. Further, viterbi algorithm is used to improve the accuracy of the proposed system. The proposed mechanism is validated over various accuracy and classification parameters against conventional Random Tree (RT), Fuzzy C Means (FCM) and REPTree (RPT) methods.

Online social networking is expanding gradually in our professional as well as personal life in a variety of natures, beliefs, attitudes, and personalities. During communicating through the networks, the trust plays a very significant role while undertaking the communication process. This article proposes a secure trusted hypothetical mathematical model for ensuring secure communication among devices by computing the individual trust of each node. In addition, a decision making model is integrated with the hypothetical model for further speeding up the real time communication decision within the network. The proposed phenomenon is validated against variety of security threats by considering both ideal and adversarial models. Furthermore, the proposed framework is compared to a baseline approach against various security threats such as system accuracy, DDoS attack, data falsification threat,and number of processed requests. The proposed scheme is verified by simulating over synthesized data-set.

The advances in the Internet of Things (IoT) provide several chances to develop a variety of innovations supporting smart home users in several industries including healthcare, energy management, etc. Ubiquitous support by intelligent appliances at modern homes, which constantly work to gather information can help us to solve everyday issues. In this article, we present a comparative study of recent advances in smart home development. The study aims to present the main trends in this field. During the analysis of the research reports and patents, we identify the propositions that constitute the main research streams. Through extensive analysis, we provide an outlook on the wide spectrum of the proposed solutions. We also analyze the main market to present which publishers are leading with the innovative science in this field. We also show the leaders of science and technology in the World. Finally, we define the ratio of the developments and outline the next stage of the development in the smart home industry.

Energy Internet (also referred to as Smart Grid 2.0) is another promising application of the Industrial Internet of Things (IIoT), for example, in the way energy is being produced, traded, distributed, and consumed. This is partly due to the lowering of barriers (e.g., costs and Internet connectivity) and advances in the underlying technologies, such as smart meters, electric vehicles, and actuators. This has also resulted in significant growth in the volume, velocity, variety, veracity, and value of data (i.e., the 5 Vs of big data). However, efficiently and effectively handling such big data remains challenging. One solution currently being explored in the literature (including industry) to cope with the increasing network traffic is to use conventional cloud infrastructure, but the key limitations of such an approach include long response time and high bandwidth consumption. Therefore, there have been attempts to introduce next-generation Internet of Things networks to satisfy (real-time) network service demands and guarantee quality of service, for example, by pushing computing capabilities closer to the users (e.g., edge of the network). However, computation-intensive energy analytics can be challenging to perform at the network edge by edge or fog computing devices. Hence, there have also been attempts to utilize software defined networking (SDN) and network function virtualization (NFV) in order to improve network functionality while adding programmability and flexibility features to the network infrastructure. Specifically, NFV facilitates virtual network function (VNF) deployment and orchestration, while SDN controls them for specific use cases. However, with the rise of next-generation mobile networks (i.e., 5G), applications and services require fast and smooth operations with greater flexibility, efficiency, and scalability. In order to align with 5G and leverage potential benefits of edge computing, VNFs should possess critical processing requirements (e.g., high throughput, low latency, and minimal computation overheads). In other words, virtualization plays an important role. To date, several techniques have been introduced in order to achieve the desired objective using virtual machines (VMs) and containers in isolation. However, a hybrid approach using VMs and containers is likely to bring potential benefits for the large-scale deployment of VNFs across the heterogeneous edge and cloud platform. Thus, in this article, a novel architecture for SDN integrated with NFV, specifically for the Energy Internet ecosystem, is presented by leveraging the advantagesof edge computing. In the considered setup, the deployment of VNFs is achieved using a mix of both virtualization and containerization. Findings from our evaluation demonstrate the potential for VNF placement across a hybrid execution setup powered by VMs, as well as the benefits of using containers.

Due to the limited bandwidth of Roadside Units (RSUs) deployed in drive-thru networks, vehicles entering the network coverage with data requests have to contend for the access to the data service provided by RSUs. In order to maximize the vehicle utility, efficient access schemes are indispensable at the vehicles’ side. This paper studies the optimal access control of vehicles in multi-agent drive-thru systems. In such networks, each vehicle, acting as an independent agent, can take an access decision that could potentially maximize the individual utility based on its own observations of the instantaneous environment states. Consequently, the decision of one vehicle will influence those of others, making environment states only partially observable at the vehicles’ side and complicating the optimal access design. To tackle this coupling decision issue, we first formulate the optimization problem as a finite Markov Decision Process (MDP). Then, we propose a distributed access algorithm that combines the statistic learning method and the dynamic programming technique. With the proposed algorithm, missing vehicle states and related transition probabilities will be estimated by vehicles. The optimization problem is recursively solved by applying the dynamic programming technique. Simulation results are provided to show the significant improvement achieved by the proposed algorithm on multiple performance metrics. The convergence of the algorithm is numerically confirmed, verifying the stability of our approach.

The rise of the Internet of Things (IoT) presents important challenges for future radio networks. Non-orthogonal multiple access (NOMA), which allows the network to support more than one user per orthogonal resource element, was recently proposed as a promising solution that can ultimately support the daunting requirements of such networks including massive connectivity, high spectral efficiency, and low latency. Nevertheless, numerous ultra-high-reliability applications of IoT present environments that are hampered by impulsive electromagnetic interference, referred to as impulsive noise. Such noise is known to cause degradation to the overall system performance. Moreover, given the non-orthogonal multiplexing in NOMA, such noise is expected to have a relatively more pronounced impact on the system performance. Therefore, this article sheds light on the performance degradation and mitigation of impulsive noise in the context of NOMA-based IoT networks. It proposes a multistage nonlinear processing approach specifically designed for OFDM-based PDM-NOMA systems. To obtain the optimum threshold of the corresponding users, we propose a deep learning approach to estimate the impulsive noise parameters from the received OFDM symbol. This information can consequently be used to evaluate the corresponding optimal threshold using Siegert's ideal observer criterion. Finally, this work sheds light on potential opportunities and challenges that are expected to arise during the implementation of NOMA in impulsive environments.

Impulsive noise, a common impediment preventing the system from achieving error-free transmission, is significant in many wireless and power line communication environments. Although the performance of several mitigation techniques for orthogonal frequency division multiplexing (OFDM)-based multicarrier communication systems impaired by memoryless impulsive noise are widely acknowledged, we note that OFDM is outperformed by its single-carrier counterpart when the impulses are very strong and/or they occur frequently, which is likely to exist in contemporary communication systems including smart grid communications. On the other hand, many communication technologies used in the smart grid do not employ OFDM and likewise, the assumption of memoryless noise is not valid for such communication scenarios. Memoryless noise models cannot take into account one of the main features of the actual noise, i.e., the time-correlation among the noise samples. The aim of this article is to compare and analyze several mitigation techniques such as clipping, blanking, and combined clipping-blanking to mitigate the noxious effects of bursty impulsive noise for low-density parity-check coded single-carrier communication systems. Moreover, we propose a log-likelihood ratio (LLR)-based impulsive noise mitigation for the considered scenario. In this context, provided simulation results highlight the superiority of the LLR-based mitigation scheme over the clipping/blanking schemes.

The Internet of Things (IoT) has emerged as a revolution for the design of smart applications like intelligent transportation systems, smart grid, healthcare 4.0, Industry 4.0, and many more. These smart applications are dependent on the faster delivery of data which can be used to extract their inherent patterns for further decision making. However, the enormous data generated by IoT devices are sufficient to choke the entire underlying network infrastructure. Most of the data attributes present little or no relevance to the prospective relationships and associations with the projected benefits foreseen. Therefore, order-based generalization mechanisms, known as tensors, can be used to represent these multidimensional data, thereby minimizing the flow table (FT) lookup time and reducing the storage occupancy. So, a novel IoT-train-deep approach for intelligent software-defined networking is designed in this article. The proposed approach works in four phases: 1) tensor representation; 2) deep Boltzmann machine-based classification; 3) subtensor-based flow matching process; and 4) incremental tensor train network for FT synchronization. The proposed model has been extensively tested, and it illustrates significant improvements with respect to delay, throughput, storage space, and accuracy.

Vehicular communications, with their promise to provide drivers and passengers with a wide range of applications, are attracting significant attention from both research and industry. In this paper, we study the performance of full duplex amplify and forward (AF) relaying-based vehicle-to-vehicle (V2V) cooperative wireless communications over Nakagami- $m$ fading channels. In such systems, in practical scenarios, the communication link inevitably suffers from co-channel interference, residual self-interference, and blockage from other vehicles on the road. In this context, we consider independent and not necessarily identically distributed (i.n.i.d) Nakagami- $m$ fading channels and derive novel exact and asymptotic outage probabilities of the exact equivalent and approximated signal-to-interference-plus-noise ratio (SINR), respectively. Building on this, the end-to-end exact and asymptotic outage probabilities are expressed in terms of the blockage probability and then used to evaluate the throughput of the proposed system. In addition, a lower bound to the symbol error rate of the considered system is also derived. Monte-Carlo simulation results are provided to demonstrate the accuracy of the proposed analytical expressions. The results demonstrate the significant impact of the considered interference and blockage on the system performance. Precisely, it is shown that the system performance is degraded when the average height of the obstacles is increased. This highlights the importance of taking into account these phenomena in the performance evaluation in order to assess the practical limit of V2V cooperative wireless communications.

With the rapid explosion of Industrial Internet of Things (IIoT), the need for real-time data processing with enhanced flexibility and scalability has increased manifold. However, the newly evolved containerization technology offers lucrative advantages in comparison to the conventional virtual machines. However, management of these light-weight containers is a tedious task, but Google Kubernetes offers a consolidated container management and scheduling for successful execution of various lightweight containers. Nevertheless, the existing Kubernetes solutions fall short in efficiently handling the “interference” and “energy minimization” challenges in IIoT set-up. Hence, in this article, we present a competent controller, named Kubernetes-based energy and interference driven scheduler (KEIDS), for container management on edge-cloud nodes taking into account the emission of carbon footprints, interference, and energy consumption. The problem of task scheduling has been formulated using integer linear programming based on multiobjective optimization problem. In detail, KEIDS minimizes the energy utilization of edge-cloud nodes in IIoT for optimal green energy utilization. Henceforth, the applications are scheduled on the available nodes in less time with minimum interference from other applications, which in turn guarantees an optimal performance to the end-users. An extensive evaluation of the proposed KEIDS scheduler in comparison to the existing state-of-the-art schemes indicates its superior performance on real-time data acquired from Google compute cluster.

The Internet of Things (IoT) has many applications, including Industry 4.0. There are a number of challenges when deploying IoT devices in the Industry 4.0 setting, partly due to the low-cost IoT devices/nodes with limited capacity to run/support security solutions. Hence, there is a need for a lightweight and efficient security solution to protect the environment. Thus, in this article, we present a robust, lightweight, and provably secure authentication and key agreement protocol specifically for the IoT environment based on a hierarchical approach. The proposed protocol relies on lightweight operations, such as elliptic curve cryptography, physically unclonable functions, hash functions, concatenation, and XOR operations. We then evaluate the security of the designed protocol, including the widely used automated validation of Internet security protocols and applications (AVISPA), and demonstrate that it supports mutual authentication between IoT nodes and server, and is resilient against a number of common security attacks [denial of service (DoS), replay, spoofing, etc.]. The computational and communication overhead analysis shows that the proposed protocol is comparatively less expensive than three other recently published, competing protocols.

The rapidly evolving industry standards and transformative advances in the field of Internet of Things are expected to create a tsunami of Big Data shortly. This, in turn, will demand real-time data analysis and processing from cloud computing platforms. A substantial part of the computing infrastructure is supported by large-scale and geographically distributed data centers (DCs). Nevertheless, these DCs impose a substantial cost in terms of rapidly growing energy consumption, which in turn adversely affects the environment. In this context, efficient resource utilization is seen as a potential candidate to enhance energy efficiency and minimize the load on the power sector. Nevertheless, in the majority of the public clouds, the resources are idle most of the time (i.e., under-utilized) as the load of the servers is unpredictable; thereby leading to a lofty increase in the energy utilization index and wastage of resources. Thus, it is highly essential to devise a precise and efficient resource management technique. Therefore, in this article, we leverage the advantages of software defined data centers (SDDCs) to minimize energy utilization levels. Precisely, SDDC refers to the process of programmatically abstracting the logical computing, network, and storage resources; and configuring them in real-time based on workload demands. In detail, we demonstrate the possibility of 1) designing a consolidated SDDC-based model to jointly optimize the process of virtual machine (VM) deployment and network bandwidth allocation for reduced energy consumption and guaranteed quality of service (QoS), particularly for heterogeneous computing infrastructures; 2) formulating a multiobjective optimization problem to deduce the optimal allocation of resources for both critical and noncritical applications; and 3) designing an efficient scheme based on heuristics to provide suboptimal results for the formulated multiobjective optimization problem. The proposed article presents a suboptimal approach based on first fit decreasing algorithm. Further, our empirical evaluations suggest that the proposed framework leads to almost 27.9% savings in terms of energy consumptions against the existing schemes with negligible QoS violations (approximately 0.33).

Achieving ultra-high reliability for short packets is a core challenge for future wireless communication systems, as current systems are designed only to transmit long packets based on classical information-theoretic principles. To tackle this challenge, this letter relies on multi-hop unmanned aerial vehicle (UAV) relay links to deliver short ultra-reliable and low-latency (URLLC) instruction packets between ground Internet of Things (IoT) devices. To accomplish this task, we perform non-linear optimization to minimize the overall decoding error probability in order to find the optimal values of the distance and the blocklength. In this vein, a novel, semi-empirical based non-iterative algorithm is proposed to solve the quasi-optimization problem. The algorithm executes in quasilinear time and converges to a globally optimal/sub-optimal solution based on the chosen parameters. Simulation results demonstrate that our algorithm allows operation under the ultra-reliable regime (URR), and yields the same performance as exhaustive search algorithms.

With the advent of 5G, technologies such as Software-Defined Networks (SDNs) and Network Function Virtualization (NFV) have been developed to facilitate simple programmable control of Wireless Sensor Networks (WSNs). However, WSNs are typically deployed in potentially untrusted environments. Therefore, it is imperative to address the security challenges before they can be implemented. In this paper, we propose a software-defined security framework that combines intrusion prevention in conjunction with a collaborative anomaly detection systems. Initially, an IPS-based authentication process is designed to provide a lightweight intrusion prevention scheme in the data plane. Subsequently, a collaborative anomaly detection system is leveraged with the aim of supplying a cost-effective intrusion detection solution near the data plane. Moreover, to correlate the true positive alerts raised by the sensor nodes in the network edge, a Smart Monitoring System (SMS) is exploited in the control plane. The performance of the proposed model is evaluated under different security scenarios as well as compared with other methods, where the model’s high security and reduction of false alarms are demonstrated.

Pressure ulcer prevention is a vital procedure for patients undergoing long-term hospitalization. A human body lying posture (HBLP) monitoring system is essential to reschedule posture change for patients. Video surveillance, the conventional method of HBLP monitoring, suffers from various limitations, such as subject's privacy, and field-of-view obstruction. We propose an autonomous method for classifying the four state-of-the-art HBLPs in healthy adults subjects: supine, prone, left and right lateral, with no sensors or cables attached on the body and no constraints imposed on the subject. Experiments have been conducted on 12 healthy adults (age 27.35 $\pm$ 5.39 years) using a collection of textile pressure sensors embedded in a cover placed under the bed sheet. Histogram of oriented gradients and local binary patterns were extracted and fed to a supervised artificial neural network classification model. The model was trained based on the scaled conjugate gradient backpropagation. A nested cross validation with an exhaustive outer validation loop was performed to validate the classification's generalization performance. A high testing prediction accuracy of 97.9% with a Cohen's Kappa coefficient of 97.2% has been interestingly obtained. Prone and supine postures were successfully separated in the classification, in contrast to the majority of previous similar works. We found that using the information of body weight distribution along with the shape and edges contributes to a better classification performance and the ability to separate supine and prone postures. The results are satisfactorily promising toward unobtrusively monitoring posture for ulcer prevention. The method can be used in sleep studies, post-surgical procedures, or applications requiring HBLP identification.

Dynamic and non-linear systems have been used to generate random bits in high-security applications for decades. In this perspective, due to their stochastic characteristic, chaotic systems have been emerging as the natural choice for the generation of random bits. This paper presents the design and the implementation of a chaos-based true random number generator and a chaos-key based data encryption scheme for secure communications. The mathematical expression of the dynamic system is presented and analyzed to evaluate the possibility of chaos occurrence. Then, the chaotic system is realized at the circuit level using 130 nm CMOS technology to generate random bit sequences, which are utilized in data encryption. Chaotic signal outputs of the chaos-based random number generator circuit are sampled at a maximum frequency of 50 MHz, enabling a high throughput of random bits. The core of the chaotic circuit consumes $630~\mu \text{W}$ in static mode and a maximum of $660~\mu \text{W}$ in running mode. The chaos-based one-time pad encryption scheme using the chaos-key generator shows the advantages of using this random number generator in secure communications. In this context, the data secrecy is compared to the advanced encryption standard AES128. Moreover, the design is simulated in different working conditions such as voltage supply and temperature variations, where it is shown that the random bit output benefits from a high entropy per bit and passes the standard statistical test suite (NIST) for cryptographic applications.

In this study, we address the problem of chaotic synchronization over a noisy channel by introducing a novel Deep Chaos Synchronization (DCS) system using a Convolutional Neural Network (CNN). Conventional Deep Learning (DL) based communication strategies are extremely powerful but training on large data sets is usually a difficult and time-consuming procedure. To tackle this challenge, DCS does not require prior information or large data sets. In addition, we provide a novel Recurrent Neural Network (RNN)-based chaotic synchronization system for comparative analysis. The results show that the proposed DCS architecture is competitive with RNN-based synchronization in terms of robustness against noise, convergence, and training. Hence, with these features, the DCS scheme will open the door for a new class of modulator schemes and meet the robustness against noise, convergence, and training requirements of the Ultra Reliable Low Latency Communications (URLLC) and Industrial Internet of Things (IIoT).

This paper investigates non-orthogonal multiple access (NOMA), cooperative relaying, and energy harvesting to support device-to-device (D2D) transmission. In particular, we deploy multiple relay nodes and a cell-center D2D device which can operate in full-duplex (FD) or half-duplex (HD) mode to communicate with a cell-edge D2D device. In this context, there are two possible signal transmission paths from the base station (BS) to the far D2D user either through multiple decode-and-forward (DF) relay nodes or through a near D2D user. Consequently, we propose three schemes to support D2D-NOMA systems, namely non-energy harvesting relaying (Non-EHR), energy harvesting relaying (EHR) and quantize-map-forward relaying (QMFR) schemes. For each of the proposed schemes, closed-form expressions of the outage probabilities of both D2D users are derived. Extensive Monte-Carlo simulation results are provided to validate the derived analytical expressions. The study results show that the proposed schemes can improve the outage performance compared to conventional orthogonal multiple access (OMA) schemes. Moreover, it is shown that the Non-EHR scheme achieves the best outage performance among the three considered schemes.

Decision-making is of critical significance in Internet-of-Vehicles (IoV), where vehicles need to quickly make decisions in real-time when sharing or transferring the information. In addition, it is necessary to identify the significant factors of an entity while measuring its legitimacy or to record the real-time data generated by it. Traditional automated schemes in IoV are confronted by the issues related to real-time processing and the manner they respond, such as traffic congestion information, fastest route selection, and road accidental information. The exchange of accurate information among vehicles is critical, but the decision-making for IoV has still not been fully investigated in the literature. Further, the involvement of malicious devices in the network may disgrace the network performance by consuming network resources. In this paper, we propose a hybrid decision-making scheme in vehicular informatics for data transferring and processing through VIKOR and analytic hierarchy process (AHP) methods. The proposed model is scrutinized and verified rigorously through several sensing and decision-making metrics against a conventional solution. The simulation results depict that the proposed model leads to 93 percent competence in terms of decision-making, identification of legitimate sensors, and data alteration process when sharing the information through various sensors in IoV.

The rapid advancement of the Internet of Things is expected to play a critical role in future intelligent transportation systems. This technology utilizes advanced information and communication technologies to enhance the operational capabilities of the vehicles and is often referred to as IoAV. More importantly, the increasing usage of sensors and other technologies generates vast amounts of data and information to be exchanged between AVs. Since the wireless connectivity between AVs is constantly expanding, the transmission of data is expected to pose several challenges to the conventional wireless networks, including resource utilization, network optimization, quality of service, and so on. To overcome these challenges, software-defined networking (SDN) has emerged as a powerful technology. This article presents a composite architecture named SD-IoAV for the integration of SDN with IoAV. This integration is not straightforward because SD-IoAV is geographically dispersed by nature. An efficient technique to manage the underlying communications is to deploy multiple SDN controllers across the widely dispersed SDN domains. This is referred to as the controller placement problem (CPP). Thus, the primary focus of this work is to explore CPP in the context of SD-IoAV as a special case of energy minimization and load balancing under latency restrictions. In this context, for large networks, the number of variables increases exponentially, which in turn escalates the complexity of the problem manifold. Hence, to deduce a near optimal solution for large networks, a heuristic approach based on the incremental expansion of candidate space is proposed. The simulation results have been carried out in MATLAB, and the obtained results show that the proposed scheme attains higher energy savings and better load capacity management compared to an existing technique, that is, improved performance by 18.73 and 9.42 percent, respectively.

Rapid advancements in IIoT have paved the way to the evolution of SGs, which support many applications including DRM, energy sharing, and fault recovery. Among these, DRM is very critical since it helps to maintain adequate balance between electricity demand and supply. However, with the increasing penetration of EVs and their dynamic charging and discharging requirements, energy management in SGs is expected to face some serious challenges. Thus, to exploit the full potential of EVs in terms of DRM, cloud computing and edge computing have emerged as powerful computational models. The latter mimics the capabilities of the powerful cloud computing infrastructure but closer to the users. Hence, in this article, we present an edge-cloud framework where cooperation among cloud and edge devices is realized to make intelligent decisions related to EVs' charging and discharging in addition to achieving the expected demand-supply balance. However, one of the fundamental challenges of this setup is induced due to a large number of migrations in the edge-cloud setup, primarily due to network congestion and EVs' mobility patterns. Hence, to handle this problem, an amalgamation of software-defined networking (SDN) with opportunistic networks (OppNetworks) can be a viable solution, referred to as opportunistic SDN (Opp-SDN). Specifically, the proposed work exploits the use of EVs in two forms: as energy reservoirs for instantaneous DRM and as forwarding nodes in Opp-SDN. To realize EVs' activities in both forms, this work formulates dedicated Stackelberg game-based approaches for DRM and Opp-SDN. The obtained results indicate superior performance of the proposed scheme in contrast to the existing state-of-the-art schemes.

With the emergence of the Internet-of-Things (IoT) and seamless Internet connectivity, the need to process streaming data on real-time basis has become essential. However, the existing data stream management systems are not efficient in analyzing the network log big data for real-time anomaly detection. Further, the existing anomaly detection approaches are not proficient because they cannot be applied to networks, are computationally complex, and suffer from high false positives. Thus, in this paper a hybrid data processing model for network anomaly detection is proposed that leverages grey wolf optimization (GWO) and convolutional neural network (CNN). To enhance the capabilities of the proposed model, GWO and CNN learning approaches were enhanced with: 1) improved exploration, exploitation, and initial population generation abilities and 2) revamped dropout functionality, respectively. These extended variants are referred to as Improved-GWO (ImGWO) and Improved-CNN (ImCNN). The proposed model works in two phases for efficient network anomaly detection. In the first phase, ImGWO is used for feature selection in order to obtain an optimal trade-off between two objectives, i.e., reduced error rate and feature-set minimization. In the second phase, ImCNN is used for network anomaly classification. The efficacy of the proposed model is validated on benchmark (DARPA’98 and KDD’99) and synthetic datasets. The results obtained demonstrate that the proposed cloud-based anomaly detection model is superior in comparison to the other state-of-the-art models (used for network anomaly detection), in terms of accuracy, detection rate, false positive rate, and F-score. In average, the proposed model exhibits an overall improvement of 8.25%, 4.08%, and 3.62% in terms of detection rate, false positives, and accuracy, respectively; relative to standard GWO with CNN.

The ever-increasing demands of vehicular networks pose significant challenges such as availability, computation complexity, security, trust, authentication, etc. This becomes even more complicated for high-speed moving vehicles. As a result, increasing the capacity of these networks has been attracting considerable awareness. In this regard, the next generation of cellular networks, 5G, is expected to be a promising solution enabling high data rates, capacity, and quality of service as well as low latency communications. However, 5G networks still face challenges in providing ubiquitous and reliable connections among high-speed vehicles. Thus, to overcome the gaps in the existing solutions, we propose a software defined network (SDN)-based consolidated framework providing end-to-end security and privacy in 5G enabled vehicular networks. The framework simplifies network management through SDN, while achieving optimized network communications. It operates in two phases: first, an elliptic curve cryptographic based authentication protocol is proposed to mutually authenticate the cluster heads and certificate authority in SDN-based vehicular setups, and, second, an intrusion detection module supported by tensor based dimensionality reduction is designed to reduce the computational complexity and identify the potential intrusions in the network. In order to assess the performance of the proposed framework, an extensive evaluation is performed on three simulators; NS3, SUMO, and SPAN. To harness the potential benefits of the proposed model, the first module, is evaluated on the basis of security features, whereas the second module is evaluated, and compared with the existing state-of-the-art models, on the basis of detection rate, false positive rate, accuracy, detection time, and communication overhead. The simulation results indicate the superiority of the proposed framework as compared to the existing models.

Considering the fact that the mixture gamma (MG) distribution is a general model that can be used to elaborate most well-known distributions, including small-scale, large-scale, and composite fadings, this letter studies the security issue when the received signal-to-noise ratios (SNRs) follow MG distributions. Closed-form expressions for secrecy metrics including the secrecy outage probability (SOP), the probability of non-zero secrecy capacity (PNZ), and the average secrecy capacity (ASC), are derived. Monte-Carlo simulations are presented to corroborate the accuracy of our derived results. Our derived secrecy metrics provide a general and unified analysis framework for the quick evaluation of the secrecy issue over wireless channels, even when the main channel and wiretap channel are subject to different wireless channels.

This paper proposes a new location-based multicasting technique, for dual phase amplify-and-forward (AF) large networks, aiming to improve the security in the presence of non-colluding passive eavesdroppers. These eavesdroppers could also be part of this cooperative network as relays. In order to reduce the impact of these eavesdroppers on the network security, we propose a new transmission strategy where, for the first hop of each transmission time, while the destination is jamming, the source randomly chooses a different subset $K$ of the total $T$ relays, to transmit its message toward the destination. For practical implementation, sectoral transmission can be achieved with analog beamforming at the source's side. In the second hop, using the distributed beamforming technique, the $K$ AF relays retransmit the received signal to the destination. We analytically demonstrated that the proposed technique decreases the probability of choosing the same sector that has certain eavesdroppers again, for each transmission time, to $K/T$ . Moreover, we also show that the secrecy capacity scaling of our technique is still the same as for broadcasting. Hereafter, the lower and upper bounds of the secrecy outage probability are calculated, and it is shown that the security performance is remarkably enhanced, compared to conventional multicasting technique.

Inspired by the direct use of alternating current (AC) for computation, we propose a novel integrated information and energy receiver architecture for simultaneous wireless information and power transfer (SWIPT) networks. In this context, the AC computing method, in which wirelessly harvested AC energy is directly used to supply the computing block of receivers, enhances not only computational ability but also energy efficiency over the conventional direct current (DC) one. Further, we aim to manage the trade-off between the information decoding (ID) and energy harvesting (EH) optimally while taking imperfect channel estimation into account. It results in a worst-case optimization problem of maximizing the data rate under the constraints of an EH requirement, the energy needed for supplying the AC computational logic, and a transmit power budget. Then, we propose a method to derive closed-form optimal solutions. The numerical results demonstrate that the proposed architecture with AC computing significantly improves the rate-energy region., 11 pages, 4 figures, accepted for publication in IEEE Wireless Communications Letters

In this paper, we propose a solution for video transmission over a low-bandwidth network that enables a physician to take in charge of remote trauma patient. We propose and analyze a method based on an H.264 compression scheme that relies on transmitting high-quality video of a moving and dynamic region of interest while scarifying quality in the background. Our method is motivated by the problem of limited bandwidth usually encountered in air-to-ground communication channels. We propose to use a region of interest with smoothed edges to increase the video quality of the transition between the regions of various qualities. The moving region of interest, covering the torso and the head, is segmented and tracked by using the skeleton information provided by a Kinect camera. Our proposed compression scheme respects the real-time, low-complexity, and interoperability constraints. We have analyzed the results of our method obtained with various bit rate targets and have shown that a visual assessment of a patient is achievable over very low bandwidth.

The security issue impacting the Internet-of-Things (IoT) paradigm has recently attracted significant attention from the research community. To this end, several surveys were put forward addressing various IoT-centric topics, including intrusion detection systems, threat modeling, and emerging technologies. In contrast, in this paper, we exclusively focus on the ever-evolving IoT vulnerabilities. In this context, we initially provide a comprehensive classification of state-of-the-art surveys, which address various dimensions of the IoT paradigm. This aims at facilitating IoT research endeavors by amalgamating, comparing, and contrasting dispersed research contributions. Subsequently, we provide a unique taxonomy, which sheds the light on IoT vulnerabilities, their attack vectors, impacts on numerous security objectives, attacks which exploit such vulnerabilities, corresponding remediation methodologies and currently offered operational cyber security capabilities to infer and monitor such weaknesses. This aims at providing the reader with a multidimensional research perspective related to IoT vulnerabilities, including their technical details and consequences, which is postulated to be leveraged for remediation objectives. Additionally, motivated by the lack of empirical (and malicious) data related to the IoT paradigm, this paper also presents a first look on Internet-scale IoT exploitations by drawing upon more than 1.2 GB of macroscopic, passive measurements’ data. This aims at practically highlighting the severity of the IoT problem, while providing operational situational awareness capabilities, which undoubtedly would aid in the mitigation task, at large. Insightful findings, inferences and outcomes in addition to open challenges and research problems are also disclosed in this paper, which we hope would pave the way for future research endeavors addressing theoretical and empirical aspects related to the imperative topic of IoT security.

Adopting OFDMA and MU-MIMO techniques for both downlink and uplink IEEE 802.11ax will help next-generation WLANs efficiently cope with large numbers of devices but will also raise some research challenges. One of these is how to optimize the channelization, resource allocation, beamforming design, and MCS selection jointly for IEEE 802.11ax-based WLANs. In this paper, this technical requirement is formulated as a mixed-integer non-linear programming problem maximizing the total system throughput for the WLANs consisting of unicast users with multicast groups. A novel two-stage solution approach is proposed to solve this challenging problem. The first stage aims to determine the precoding vectors under unit-power constraints. These temporary precoders help re-form the main problem into a joint power and radio resource allocation one. Then, two low-complexity algorithms are proposed to cope with the new problem in stage two. The first is developed based on the well-known compressed sensing method while the second seeks to optimize each of the optimizing variables alternatively until reaching converged outcomes. The outcomes corresponding to the two stages are then integrated to achieve the complete solution. Numerical results are provided to confirm the superior performance of the proposed algorithms over benchmarks.

In 5G networks, the Cloud Radio Access Network (C-RAN) is considered a promising future architecture in terms of minimizing energy consumption and allocating resources efficiently by providing real-time cloud infrastructures, cooperative radio, and centralized data processing. Recently, given their vulnerability to malicious attacks, the security of C-RAN networks has attracted significant attention. Among various anomaly-based intrusion detection techniques, the most promising one is the machine learning-based intrusion detection as it learns without human assistance and adjusts actions accordingly. In this direction, many solutions have been proposed, but they show either low accuracy in terms of attack classification or they offer just a single layer of attack detection. This research focuses on deploying a multi-stage machine learning-based intrusion detection (ML-IDS) in 5G C-RAN that can detect and classify four types of jamming attacks: constant jamming, random jamming, deceptive jamming, and reactive jamming. This deployment enhances security by minimizing the false negatives in C-RAN architectures. The experimental evaluation of the proposed solution is carried out using WSN-DS (Wireless Sensor Networks DataSet), which is a dedicated wireless dataset for intrusion detection. The final classification accuracy of attacks is 94.51\% with a 7.84\% false negative rate., Comment: 6 pages, 6 figures, conference