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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.

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 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.

In the context of localization over ad-hoc wireless networks, the effect of non-line-of-sight (NLoS) presents a severe performance bottleneck that causes outages in the signal-to-noise ratio (SNR) of the returns and significantly increases the location estimates’ error-floor. The non-Gaussianity and the scenario-dependent distributions/statistics of the additive noise-process caused by the NLoS returns make the naive ML-based solutions sub-optimal. In this regard, information-potential (IP) based localization algorithms have recently been found viable, especially in the presence of arbitrary additive noise-processes. This letter presents analytical insights on the error-bounds of two IP based algorithms in a cooperative-localization context, namely, maximum correntropy (MCC) and the minimum error entropy with fiducial points (MEE-FP). The presented analytical results for the considered IP based approaches are validated using relevant computer-simulations assuming typical point-processes.

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.

Reconfigurable intelligent surface (RIS) is an emerging technology that can achieve reconfigurable radio propagation environments for beyond 5G/6G wireless systems by smartly tuning the signal reflection via a large number of low-cost passive reflecting elements. In this letter, we investigate the effect of co-channel interference (CCI) on RIS-based dual-hop mixed free-space optical (FSO)-radio frequency (RF) communication systems. The source node, equipped with multiple FSO apertures, transmits subcarrier intensity modulated symbols on the FSO link subjected to Gamma-Gamma (GG) distributed atmospheric turbulence with pointing errors (PEs). It is assumed that the relay-RIS and RIS-destination links follow Rayleigh distribution. The destination node is corrupted by multiple CCI (each following Rayleigh distribution). The performance of the considered system is evaluated by deriving novel closed-form expressions for the outage probability (OP) and bit error rate (BER). It is shown through numerical results that RIS-based dual-hop mixed FSO-RF system provide a significant performance enhancement in comparison to a traditional dual-hop mixed FSO-RF system even in presence of CCI.

Cyber-attacks cyber–physical systems (CPSs) can lead to sensing and actuation misbehavior, severe damages to physical objects, and safety risks. Machine learning algorithms have been proposed for hindering cyber-attacks on CPSs, but the absence of labeled data from novel attacks makes their detection quite challenging. In this context, generative adversarial networks (GANs) are a promising unsupervised approach to detect cyber-attacks by implicitly modeling the system. However, the detection of cyber-attacks on CPSs has strict latency requirements, since the attacks need to be stopped before the system is compromised. In this article, we propose FID-GAN, a novel fog-based, unsupervised intrusion detection system (IDS) for CPSs using GANs. The IDS is proposed for a fog architecture, which brings computation resources closer to the end nodes and thus contributes to meeting low-latency requirements. In order to achieve higher detection rates, the proposed architecture computes a reconstruction loss based on the reconstruction of data samples mapped to the latent space. Other works that follow a similar approach struggle with the time required to compute the reconstruction loss, which renders them impractical for latency constrained applications. We address this problem by training an encoder that accelerates the reconstruction loss computation. Experiments show that the proposed solution achieves higher detection rates and is at least 5.5 times faster than a baseline approach in the three studied data sets.

Nonlinear device characteristics present a severe performance-bottleneck for several upcoming next-generation wireless communication systems and prevent them from delivering high data-rates to the end-users. In this context, reproducing kernel Hilbert space (RKHS) based signal processing methods have gained widespread deployment and have been found to outperform classical polynomial-filtering-based solutions significantly. Furthermore, recent RKHS based techniques that rely on explicit feature-maps called random Fourier features (RFF) have emerged. These techniques alleviate the dependence on learning a dictionary and avoid the computations and errors incurred in dictionary-based learning. However, the performance of existing RKHS based solutions depends on choosing a suitable kernel-width. For the widely-used Gaussian kernel, we propose a methodology of assigning kernel-bandwidths that capitalizes on a stochastic sampling of kernel-widths using an ensemble drawn from a pre-designed probability density function. The technique is found to deliver a comparable convergence/error-rate performance to the scenario when the kernel-width is chosen by brute-force trial and error for tuning it for best performance. The desirable properties of the proposed kernel-sampling technique are supported by analytical proofs and are further highlighted by computer-simulations presented in the form of case studies in the context of next-generation communication systems.

Non-orthogonal multiple access (NOMA) has emerged as a promising multiple-access technique capable of accommodating many users over limited time-frequency resources. Among several NOMA techniques, sparse code multiple access (SCMA) has emerged as viable due to its promise of high coding-gain by appropriate codebook-design and simplistic detection using message-passing algorithm (MPA). However, the performance of classic SCMA based systems are severely impaired by impulsive noise (IN), which causes outages and therefore non-negligible degradations to the bit-error-rate (BER) performance. Exploiting the robustness of the maximum correntropy criterion (MCC) to non-Gaussian noise processes, an MPA based detector is formulated with message-functions derived from the MCC criterion. Based on the said MCC criterion, concurrent-adaptation of the MCC’s spread-parameter is proposed in this letter. Lastly, analytical results for the proposed approach’s BER performance are presented with corresponding validation by computer-simulations.

Localization has been a well-investigated problem in the past decade for several networks and is one of the most celebrated applications of adaptive signal processing. However, existing localization algorithms exhibit a significant degradation under non-line of sight (NLOS) conditions, especially in outdoor scenarios. Due to the inherent non-Gaussianity in the NLOS returns, generic mitigation of the degradations due to NLOS remains quite an open challenge. In this regard, information-theoretic learning (ITL) criteria are attractive due to their ability to adapt to arbitrary NLOS distributions and suppress NLOS-induced non-Gaussian processes. In this regard, this letter proposes the use of minimum error entropy with Fiducial points (MEE-FP) in the particular context of round-trip time of arrival (RTTOA) based localization. From the presented simulations, it is observed that the proposed MEE-FP based localization method delivers lower variance under severe NLOS conditions and is closer to the ideal maximum-likelihood solution than contemporary ITL based approaches. Lastly, analytical variance-expressions are derived for the proposed localization technique, which is validated by computer simulations.

In this paper, an artificial noise (AN) injection technique is incorporated in a free-space optical (FSO) communication system with the aim of enhancing the secrecy performance of the system. An intensity modulated direct detection (IM/DD) FSO link which is subjected to Malaga ( $\mathcal {M}$ ) distributed turbulence with pointing errors is considered in this paper. The performance of FSO systems is evaluated by deriving novel closed-form expressions for the secrecy outage probability (SOP), strictly positive secrecy capacity (SPSC), and throughput of the system. By formulating a constrained optimization problem, we discuss an optimal power allocation strategy for throughput maximization in the considered system. It is shown through the results that the proposed technique is very effective in improving the secrecy performance of FSO systems.

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.

Upcoming fifth-generation (5G) networks need to support novel ultrareliable and low-latency (URLLC) traffic that utilizes short packets. This requires a paradigm shift as traditional communication systems are designed to transmit only long data packets based on Shannon’s capacity formula, which poses a challenge for system designers. To address this challenge, this article relies on an unmanned aerial vehicle (UAV) and a reconfigurable intelligent surface (RIS) to deliver short URLLC instruction packets between ground Internet-of-Things (IoT) devices. In this context, we perform passive beamforming of RIS antenna elements as well as nonlinear and nonconvex optimization to minimize the total decoding error rate and find the UAV’s optimal position and blocklength. In this article, a novel, polytope-based method from the class of direct search methods (DSMs) named Nelder–Mead simplex (NMS) is used to solve the optimization problem based on its computational efficiency; in terms of lesser number of required iterations to evaluate objective function. The proposed approach yields better convergence performance than the traditional gradient-descent optimization algorithm and a lower computation time and equivalent performance for the blocklength variable as the exhaustive search. Moreover, the proposed approach allows ultrahigh reliability, which can be attained by increasing the number of antenna elements in RIS as well as increasing the allocated blocklengths. Simulations demonstrate the RIS’s performance gain and conclusively show that the UAV’s position is crucial for achieving ultrahigh reliability in short packet transmission.

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.

The past few years have witnessed the compelling applications of the Internet of Things (IoT) in our daily life. Meanwhile, with the explosion of IoT devices and various applications, the expectations for the performance, reliability, and security of networks are greater than ever. The current end-host-based or centralized control framework incurs too much communication and computation overhead, therefore exhibiting tardiness and clumsiness in responding to network dynamics. Recently, with the advancement of programmable network hardware, it is possible to implement network functions inside the network. However, current in-network schemes are largely dependent on the manual process, which presents poor scalability and robustness. Therefore, in this article, we present a new intelligent network control architecture, in-network intelligence control. We design intelligent in-network devices that can automatically adapt to network dynamics by leveraging powerful machine learning adaptive abilities. In addition, to enhance the collaboration among distributed in-network devices, a centralized management plane is introduced to ease the training process of distributed switches. To demonstrate the technical feasibility and performance advantage of our architecture, we present three use cases: in-network load balance, in-network congestion control, and in-network DDoS detection.

Efficient resource allocation can maximize power efficiency, which is an important performance metric in future fifth-generation (5G) communications. The minimization of sum uplink power in order to enable green communications while concurrently fulfilling the strict demands of ultrareliability for short packets is an essential and central challenge that needs to be addressed in the design of 5G and subsequent wireless communication systems. To address this challenge, this article analyzes the joint optimization of various unmanned aerial vehicle (UAV) systems parameters, including the UAV’s position, height, beamwidth, and the resource allocation for uplink communications between ground Internet-of-Things (IoT) devices and a UAV employing short ultrareliable and low-latency (URLLC) data packets. Toward achieving the aforesaid task, we proposed a perturbation-based iterative optimization to minimize the sum uplink power in order to determine the optimal position for the UAV, its height, beamwidth of its antenna, and the blocklength allocated for each IoT device. It is shown that the proposed algorithm has lower time complexity, yields better performance than other benchmark algorithms, and achieves similar performance to exhaustive search. Moreover, the results also demonstrate that Shannon’s formula is not an optimum choice for modeling sum power for short packets as it can significantly underestimate the sum power, where our calculations show that there is an average difference of 47.51% for the given parameters between our proposed approach and Shannon’s formula. Finally, our results confirm that the proposed algorithm allows ultrahigh reliability for all the users and converges rapidly.

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.

Visible light communications (VLC) has emerged as a promising technology for the next-generation communication systems. For VLC, several non-orthogonal multiple access (NOMA) techniques have emerged, among which sparse-code multiple access (SCMA) is particularly viable due to promised shaping and coding gain, and typically minimal/no error propagation as compared to power-domain NOMA (PD-NOMA). In this work, we introduce using polychromatic light emitting diodes (LEDs) as transmitters, where several users are overlapped on each color using SCMA; consequently, the SCMA codewords on each color are overlapped in the color-domain before transmission. At the receiver, after color-filtering, the residual color-coupling is removed by inverting the coupling matrix. Finally, the users’ signals over each color-channel are recovered using the message passing algorithm. Further, asymptotic error-rate expressions are derived for the proposed color-domain SCMA. These expressions are validated by simulations assuming VLC channel-models for random waypoint user-mobility. The analytical results promise a potential gain in the sum-rate while supporting a larger number of users as compared to a monochromatic white-LED based SCMA system.

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 next-generation of communication systems must be capable of serving a significantly higher traffic generated by a large number of devices. To support massive connectivity over limited time-frequency resources, several non-orthogonal multiple access (NOMA) paradigms have emerged. In this context, sparse code multiple access (SCMA) based NOMA is particularly attractive due to its robustness to error-propagation, and its potentially high coding-gain with appropriate codebook design. However, the performance of SCMA based systems is severely degraded by distortions introduced by non-ideal hardware, e.g. power-amplifier (PA) nonlinearity. To mitigate such artefacts, in this letter, a random Fourier feature (RFF) based hybrid message passing algorithm (MPA) is proposed, and validated through computer simulations. Lastly, an analytical proof is presented that indicates that the use of RFFs significantly improves the convergence of the MPA in the presence of impairments, and renders error-rate performance equivalent to that of a linear Gaussian channel under approximate MPA.

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.

Low-power devices used in Internet-of-things networks have been short of security due to the high power consumption of random number generators. This paper presents a low-power hyperchaos-based true random number generator, which is highly recommended for secure communications. The proposed system, which is based on a four-dimensional chaotic system with hidden attractors and oscillators, exhibits rich dynamics. Numerical analysis is provided to verify the dynamic characteristics of the proposed system. A fully customized circuit is deployed using 130 nm CMOS technology to enable integration into low-power devices. Four output signals are used to seed a SHIFT-XOR-based chaotic data post-processing to generate random bit output. The chip prototype was simulated and tested at 100 MHz sampling frequency. The hyperchaotic circuit consumes a maximum of 980 $$\upmu $$ μ W in generating chaotic signals while dissipates a static current of 623 $$\upmu $$ μ A. Moreover, the proposed system provides ready-to-use binary random bit sequences which have passed the well-known statistical randomness test suite NIST SP800-22. The proposed novel system design and its circuit implementation provide a best energy efficiency of 4.37 pJ/b at a maximum sampling frequency of 100 MHz.

In this paper, we consider a three-node free space optical (FSO) cooperative network with an energy harvesting (EH) decode-and-forward (DF) relay. The relay is equipped with an energy buffer and implements the harvest-store-use (HSU) architecture where the harvested energy is accumulated and used for forwarding the information from the source node to the destination node. We propose a novel HSU-based relaying strategy based on the simultaneous lightwave information and power transfer (SLIPT) concept where the relay harvests energy from the optical signal transmitted by the source. Firstly, we analyze the performance of the considered system by modeling the energy buffer as a continuous-space Markov chain (MC) for the sake of deriving the limiting distribution of the stored energy. Secondly, we discretize the state space and use the resulting discrete-space MC to derive the steady-state distribution of the buffer content. Thirdly, we propose an approximate discrete-space MC for capturing the energy buffer dynamics in a simple manner. The third approach is useful for relating the outage probability to the channel coefficients and average amounts of harvested energy in a simple closed-form manner. Simulation results validate the presented analytical evaluation and demonstrate the performance improvement that is achieved by the proposed scheme.

In this letter, the problem of phase synchronization for a group of drones acting as relays to build a connection between a base station (BS) and a ground receiver (GR) is investigated. The received signal strength (RSS) of the GR significantly degrades due to phase-mismatch caused by mobility of the drones. To tackle this challenge, a distributed synchronization algorithm that utilizes a few feedbacks is proposed and its convergence is proved for a specified bound on the frequency deviation. To evaluate the performance of the proposed method, the received signal strength (RSS) of the GR is presented assuming different deviation frequencies and number of drones. Provided simulation results demonstrate that our proposed method is capable of synchronizing the drones’ phases for all the considered deviation frequencies and number of drones. Moreover, the proposed method outperforms the existing cumulative positive one-bit feedback based approaches in terms of convergence speed and RSS, which renders the proposed algorithm viable for practical deployment.

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.

In this article, we propose an index modulation system suitable for optical communications, where the intensity of the light is modulated along the time, space and frequency dimensions, building a Frequency-Hopping Spatial Multi-Pulse Position Modulation (FH-SMPPM). We analyze its performance from the point of view of its efficiency in power and spectrum, and its implementation complexity and required receiver latency. We analyze its error probability in the case of the non-turbulent free-space optical (FSO) channel. We derive formulas for the average symbol and bit error probabilities, and the simulation results show that they are tight enough for a wide range of signal-to-noise ratios and system parameters. We compare FH-SMPPM with other proposed index modulated systems of the same nature, and we highlight its distinctive advantages, like the flexibility to build an appropriate waveform under different constraints and objectives. Moreover, FH-SMPPM shows to be better performing in BER/SER and/or offer advantages in efficiency with respect to those alternatives, thus offering the possibility to be adopted for a variety of contexts., 14 pages, 8 figures, accepted for publication at IEEE Transactions on Communications

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.

Federated Learning (FL) has been recently presented as a new technique for training shared machine learning models in a distributed manner while respecting data privacy. However, implementing FL in wireless networks may significantly reduce the lifetime of energy-constrained mobile devices due to their involvement in the construction of the shared learning models. To handle this issue, we propose a novel approach at the physical layer based on the application of lightwave power transfer in the FL-based wireless network and a resource allocation scheme to manage the network's power efficiency. Hence, we formulate the corresponding optimization problem and then propose a method to obtain the optimal solution. Numerical results reveal that, the proposed scheme can provide sufficient energy to a mobile device for performing FL tasks without using any power from its own battery. Hence, the proposed approach can support the FL-based wireless network to overcome the issue of limited energy in mobile devices., Comment: Accepted for publication in IEEE Communications Letters

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.

The Vehicle-to-Grid (V2G) network is, where the battery-powered vehicles provide energy to the power grid, is highly emerging. A robust, scalable, and cost-optimal mechanism that can support the increasing number of transactions in a V2G network is required. Existing studies use traditional blockchain as to achieve this requirement. Blockchain-enabled V2G networks require a high computation power and are not suitable for micro-transactions due to the mining reward being higher than the transaction value itself. Moreover, the transaction throughput in the generic blockchain is too low to support the increasing number of frequent transactions in V2G networks. To address these challenges, in this paper, a lightweight blockchain-based protocol called Directed Acyclic Graph-based V2G network (DV2G) is proposed. Here blockchain refers to any Distributed Ledger Technology (DLT) and not just the bitcoin chain of blocks. A tangle data structure is used to record the transactions in the network in a secure and scalable manner. A game theory model is used to perform negotiation between the grid and vehicles at an optimized cost. The proposed model does not require the heavy computation associated to the addition of the transactions to the data structure and does not require any fees to post the transaction. The proposed model is shown to be highly scalable and supports the micro-transactions required in V2G networks.

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).

The synergy between data security and high intensive computing has envisioned the way to robust anomaly detection schemes which in turn necessitates the need for efficient data analysis. Data clustering is one of the most important components of data analytics, and plays an important role in various Internet of Things (IoT)-enabled applications such as-Industrial IoT, Smart Grids, Connected Vehicles, etc. Density-Based Spatial Clustering of Applications with Noise (DBSCAN) is one such clustering technique which is widely used to detect anomalies in large-scale data. However, the traditional DBSCAN algorithm suffers from the nearest neighbor search and parameter selection problems, which may cause the performance of any implemented solution in this environment to deteriorate. To remove these gaps, in this paper, a multi-stage model for anomaly detection has been proposed by rectifying the problems incurred in traditional DBSCAN. In the first stage of the proposed solution, Boruta algorithm is used to capture the relevant set of features from the dataset. In the second stage, firefly algorithm, with a Davies–Bouldin Index based K-medoid approach, is used to perform the partitioning. In the third stage, a kernel-based locality sensitive hashing is used along with the traditional DBSCAN to solve the problem of the nearest neighbor search. Finally, the resulting set of the nearest neighbors are used in k-distance graph to determine the desired set of parameters, i.e., E p s (maximum radius of the neighborhood) and M i n P t s (minimum number of points in E p s neighborhood) for DBSCAN. Several sets of experiments have been performed on different datasets to demonstrate the effectiveness of the proposed scheme.

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.

In this paper, we derive a closed-form expression for the evaluation of the average vectorsymbol error rate (VER) of group detection followed by maximum-likelihood (GD-ML) receivers in large multi-user multiple-input multiple-output (MU-MIMO) systems with transmit side correlated Rayleigh channels. We assume M antennas at the base station (BS), N closely-located, single-antenna user equipment (UEs) with load factor λ = N/M , and N ≫ 1; consequently, we evaluate the performance of GD-ML receivers as the load factor grows to unity. The derived expression requires a negligible correlation at the receive side of the communication channel. Hence, from a practical point-of-view, when considering scenarios with a large number of UEs, the derived analytical expression is generally more applicable for systems with a distributed massive number of BS antennas. Numerical results are provided to validate our derived expression. We observe that the GD-ML with Nu group size achieves a diversity order proportional to M - N + Nu. Moreover, we show that for small group sizes, the analytical and simulation results remain close, and at moderate to high signal-to-noise ratio (SNR), the derived expression very closely matches the simulations, whereas this match becomes perfect as the users' side correlation increases. We also demonstrate that GD-ML outperforms the zero-forcing (ZF) and minimum mean-squared error (MMSE) receivers, in terms of VER; where for high λ, GD-ML exploits the maximum spatial multiplexing gain. Moreover, in terms of floating-point operations (FLOPs), we show that GD-ML receivers have almost the same complexity as ZF and MMSE where the ML detection stage adds a negligible complexity compared to the channel matrix inversion operation.

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.

Conventional models in the intelligent transportation system (ITS) are confronted by large computational overheads and how they react during real-time scenarios. To appropriately manage the communication process in real-time, a trust-based mechanism can provide an efficient approach to acclimatize its deeds based on indecision sensory information. However, the computational models are not fully demoralized by the businesses owing to the lack of automated integration. In this study, we perform agent-based modeling (ABM) and population-based modeling (PBM) in the ITS mechanism during data transmission and record exchange for real-time communication. In addition, a trust evaluation process is performed to legitimize each device with the integration of ABM and PBM models. The simulation results show that the proposed mechanism is 89% more efficient than baseline methods in various networking scenarios, such as message alteration, distributed denial of service attacks, and information falsification threats.

The ever increasing thirst for the higher capacity demands radical changes in the design of cellular networks, such as a leap from single-tier homogeneous networks to multi-tier heterogeneous networks and the use of millimeter wave frequency band. A typical point-to-point full-duplex transmission link can double the link rate by simultaneously using the same spectrum for bidirectional traffic. However, the characterization of full-duplex two-tier heterogeneous networks is not as straightforward as that of point-to-point full-duplex systems, specially when the different tiers of the heterogeneous network use different frequency bands (millimeter wave and microwave) for their transmissions. This paper characterizes a full-duplex two-tier heterogeneous network with decoupled access, where both tiers operate on different frequency bands (millimeter wave and microwave). To evaluate the achievable spectral efficiency and association behavior of users and base stations, a two-tier heterogeneous network model is proposed in which all users and base stations are modeled using Poisson point processes. First a signal-to-interference-plus-noise-ratio optimal user association scheme is characterized. Based on the user association scheme, the spectral efficiencies of the uplink and downlink transmission links are derived. In addition, a thorough analysis of the signal-to-interference-plus-noise-ratio coverage is also provided. Moreover to render the analytical model more comprehensive and robust, i.e., different from the convention of noise-limited millimeter wave network, the interference in millimeter wave networks is also accounted for in the analytical model. Lastly, the pragmatic value of full-duplex heterogeneous networks and decoupled access is discussed in detail through numerous numerical and simulation results.

In this work, we consider a sparse code multiple access uplink system, where $J$ users simultaneously transmit data over $K$ subcarriers, such that $J > K$ , with a constraint on the power transmitted by each user. To jointly optimize the subcarrier assignment and the transmitted power per subcarrier, two new iterative algorithms are proposed, the first one aims to maximize the sum-rate (Max-SR) of the network, while the second aims to maximize the fairness (Max-Min). In both cases, the optimization problem is of the mixed-integer nonlinear programming (MINLP) type, with non-convex objective functions, which are generally not tractable. We prove that both joint allocation problems are NP-hard. To address these issues, we employ a variant of the block successive upper-bound minimization (BSUM) framework, obtaining polynomial-time approximation algorithms to the original problem. Moreover, we evaluate the algorithms’ robustness against outdated channel state information (CSI), present an analysis of the convergence of the algorithms, and a comparison of the sum-rate and Jain’s fairness index of the novel algorithms with three other algorithms proposed in the literature. The Max-SR algorithm outperforms the others in the sum-rate sense, while the Max-Min outperforms them in the fairness sense.

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.

In this article, we propose an index modulation system suitable for optical communications, based on jointly driving the time and frequency of the signal: an index-time frequency hopping (I-TFH) system. We analyze its performance from the point of view of its efficiency in power and spectrum, and its behavior in terms of error probability for the non-turbulent free-space optical (FSO) channel. We compare I-TFH with already proposed index modulated systems of the same nature, but where the amplitude or the number of transmitters are driven instead of the signal frequency. We derive and compare approximations for the average symbol and bit error probabilities of all these systems. The simulation results show that said approximations are tight enough for a wide range of signal-to-noise ratios and system parameters. Moreover, I-TFH shows to be better performing in BER and/or power efficiency than the comparative alternatives, and may offer interesting properties in a variety of contexts., 13 pages, 10 figures. IEEE Journal of Selected Topics in Signal Processing, 2019

This paper investigates a hybrid radio frequency (RF)/visible light communication (VLC) ultra-small cell network consisting of multiple optical angle-diversity transmitters, one multi-antenna RF access point (AP), and multiple terminal devices. In the network, the optical transmitters play the primary role and are responsible for delivering information and power over the visible light, while the RF AP acts as a complementary power transfer system. Thus, we propose a novel collaborative RF and lightwave resource allocation scheme for hybrid RF/VLC ultra-small cell networks. The proposed scheme aims to maximize the communication quality-of-service provided by the VLC under a constraint of total RF and light energy harvesting performance, while keeping illumination constant and ensuring health safety. This scheme leads to the formulation of two optimization problems that correspond to the resource allocation at the optical transmitters and the RF AP. Both problems are optimally solved by appropriate algorithms. Moreover, we propose a closed-form suboptimal solution with high accuracy to tackle the optical transmitters' resource allocation problem, as well as an efficient semi-decentralized method. Finally, simulation results illustrate the achievable performance of the investigated system and the effectiveness of the proposed solutions., Comment: 13 pages, 12 figures

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.