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The communication among smart meters (SMs) and neighborhood area network (NAN) gateways is a fundamental requisite for managing the energy consumption at the consumer site. The bidirectional communication among SMs and NANs over the insecure public channel is vulnerable to impersonation, SM traceability, and SM physical capturing attacks. Many existing schemes’ insecurities and/or inefficiencies call for an efficient and secure authentication scheme for smart grid infrastructure. In this article, we present a privacy preserving and lightweight authentication scheme for smart grid (LAS-SG) using elliptic curve cryptography. The proposed LAS-SG is proved as secure under the standard model. Moreover, the efficiency of the LAS-SG is extracted through a real-time experiment, which attests that proposed LAS-SG completes a round of authentication in 20.331 ms by exchanging only two messages and 192 B. Due to the adequate efficiency and ample security, the proposed LAS-SG is more appropriate for SG environments.

In this paper, we propose a novel adaptive decoding mechanism (ADM) for the unmanned aerial vehicle (UAV)-enabled uplink (UL) non-orthogonal multiple access (NOMA) communications. Specifically, considering a harsh UAV environment, where ground-to-ground links are regularly unavailable, the proposed ADM overcomes the challenging problem of conventional UL-NOMA systems whose performance is sensitive to the transmitter's statistical channel state information and the receiver's decoding order. To evaluate the performance of the ADM, we derive closed-form expressions for the system outage probability (OP) and system throughput. In the performance analysis section, we provide novel expressions for practical air-to-ground and ground-to-air channels, while taking into account the practical implementation of imperfect successive interference cancellation (SIC) in UL-NOMA. Moreover, the obtained expression can be adopted to characterize the OP of various systems under a Mixture of Gamma (MG) distribution-based fading channels. Next, we propose a sub-optimal Gradient Descent-based algorithm to obtain the power allocation coefficients that result in maximum throughput with respect to each location on UAV's trajectory. To determine the significance of the proposed ADM in nonstationary environments, we consider the ground users and the UAV to move according to the Random Waypoint Mobility (RWM) and Reference Point Group Mobility (RPGM) models, respectively. Accurate formulas for the distance distributions are also provided. Numerical solutions demonstrate that the ADM-enhanced NOMA not only outperforms Orthogonal Multiple Access (OMA), but also improves the performance of UAV-enabled UL-NOMA even in mobile environments.

The Internet of Things (IoT) is a transformative technology marking the beginning of a new era where physical and digital worlds are integrated by connecting a plethora of uniquely identifiable smart objects. Although the Internet of terrestrial things (IoTT) has been at the center of our IoT perception, it has been recently extended to different environments, such as the Internet of underWater things (IoWT), the Internet of Biomedical things (IoBT), and Internet of underGround things (IoGT). Even though radio frequency (RF) based wireless networks are regarded as the default means of connectivity, they are not always the best option due to the limited spectrum, interference limitations caused by the ever-increasing number of devices, and severe propagation loss in transmission mediums other than air. As a remedy, optical wireless communication (OWC) technologies can complement, replace, or co-exist with audio and radio wave-based wireless systems to improve overall network performance. To this aim, this paper reveals the full potential of OWC-based IoT networks by providing a top-down survey of four main IoT domains: IoTT, IoBT, and IoGT. Each domain is covered by a dedicated and self-contained section that starts with a comparative analysis, explains how OWC can be hybridized with existing wireless technologies, points out potential OWC applications fitting best the related IoT domain, and discusses open communication and networking research problems. More importantly, instead of presenting a visionary OWC-IoT framework, the survey discloses that OWC-IoT has become a reality by emphasizing ongoing proof-of-concept prototyping efforts and available commercial off-the-shelf (COTS) OWC-IoT products.

In this paper, we aim to improve the connectivity, scalability, and energy efficiency of machine-type communication (MTC) networks with different types of MTC devices (MTCDs), namely Type-I and Type-II MTCDs, which have different communication purposes. To this end, we propose two transmission schemes called connectivity-oriented machine-type communication (CoM) and quality-oriented machine-type communication (QoM), which take into account the stochastic geometry-based deployment and the random active/inactive status of MTCDs. Specifically, in the proposed schemes, the active Type-I MTCDs operate using a novel Bernoulli random process-based simultaneous wireless information and power transfer (SWIPT) architecture. Next, utilizing multi-user power-domain non-orthogonal multiple access (PD-NOMA), each active Type-I MTCD can simultaneously communicate with another Type-I MTCD and a scalable number of Type-II MTCDs. In the performance analysis of the proposed schemes, we prove that the true distribution of the received power at a Type-II MTCD in the QoM scheme can be approximated by the Singh-Maddala distribution. Exploiting this unique statistical finding, we derive approximate closed-form expressions for the outage probability (OP) and sum-throughput of massive MTC (mMTC) networks. Through numerical results, we show that the proposed schemes provide a considerable sum-throughput gain over conventional mMTC networks.

Dynamic and non-linear systems are emerging as potential candidates for random bit generation. In this context, chaotic systems, which are both dynamic and stochastic, are particularly suitable. This paper introduces a new continuous chaotic system along with its corresponding implementation, which targets field-programmable gate arrays (FPGAs). This chaotic system has five dimensions, which exhibit complex chaotic dynamics, thus enabling the utilization of chaotic signals in cryptography. A mathematical analysis is presented to demonstrate the dynamic characteristics of the proposed hyperchaotic system. A novel digital implementation of the proposed system is presented. Moreover, a data scrambling circuit is implemented to eliminate the bias effect and increase the randomness of the bitstream generated from the chaotic signals. We show that the proposed random bit generator has high randomness. The generated bits successfully pass well-known statistical randomness test-suites, i.e., NIST SP800-22, Diehard, and TestU01. The ready-to-use random bit generator is deployed on a Xilinx Zynq-7000 SoC ZC702 Evaluation Kit. Experimental results show that the proposed random bit generator can achieve a maximum throughput of 6.78 Gbps, which is over 3.6 times greater than state-of-the-art designs while requiring under 4% of the resources available on the targeted FPGA.

Semantic communication (SemCom) aims to convey the meaning behind a transmitted message by transmitting only semantically-relevant information. This semantic-centric design helps to minimize power usage, bandwidth consumption, and transmission delay. SemCom and goal-oriented SemCom (or effectiveness-level SemCom) are therefore promising enablers of 6G and developing rapidly. Despite the surge in their swift development, the design, analysis, optimization, and realization of robust and intelligent SemCom as well as goal-oriented SemCom are fraught with many fundamental challenges. One of the challenges is that the lack of unified/universal metrics of SemCom and goal-oriented SemCom can stifle research progress on their respective algorithmic, theoretical, and implementation frontiers. Consequently, this survey paper documents the existing metrics -- scattered in many references -- of wireless SemCom, optical SemCom, quantum SemCom, and goal-oriented wireless SemCom. By doing so, this paper aims to inspire the design, analysis, and optimization of a wide variety of SemCom and goal-oriented SemCom systems. This article also stimulates the development of unified/universal performance assessment metrics of SemCom and goal-oriented SemCom, as the existing metrics are purely statistical and hardly applicable to reasoning-type tasks that constitute the heart of 6G and beyond.

Ultra-reliable and low-latency (URLLC) traffic in the upcoming sixth-generation (6G) systems utilize short packets, signalling a distancing from traditional communication systems devised only to support long data packets based on Shannon's capacity formula. This poses a formidable challenge for system designers and network operators. Many URLLC scenarios involve infrastructure-less unmanned aerial vehicle (UAV)-assisted communications. One of the biggest challenges with UAVs is their limited battery capacity, which can cause abrupt disruption of UAV-assisted communications. To overcome these limitations, we consider URLLC-enabled over-the-air charging of UAV relay system using a laser transmitter. Furthermore, we formulate a non-convex optimization problem to minimize the total decoding error rate subject to optimal resource allocation, including blocklength allocation, power control, trajectory planning, and energy harvesting to facilitate URLLC in such systems. In this regard, given its lower complexity, a novel perturbation-based iterative method is proposed to solve the optimization problem. The proposed method yields optimal blocklength allocation and power control for the two transmission phases, i.e., from the source node to the UAV and from the UAV to the robot acting as a ground station. It also maps the UAV trajectory from the initial position to the final position, and the UAV completes the flight using the laser's harvested energy. It is shown that the proposed algorithm and fixed baseline scheme, named fixed blocklength (FB), yield a similar performance as the exhaustive search in terms of UAV energy consumption. In contrast, fixed trajectory (FT) delivers the worst performance. Simultaneously, the proposed method yields the best performance in terms of the lowest average overall decoding error compared to fixed baseline schemes, including FB and FT, showing the efficacy of the proposed technique.

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.

Non-orthogonal multiple access (NOMA) was recently proposed as a promising multiple access technique that can enable improved spectral efficiency, massive connectivity, and low latency. In this letter, we analyze the performance of power domain NOMA in the presence of impulsive noise, which is encountered in various practical applications, such as smart-grid communications. Such noise is known to degrade overall system performance. Moreover, since the noise in question is bursty, it is an arduous task to detect the signals with conventional receivers. Therefore, this letter sheds light on the performance degradation and mitigation of bursty impulsive noise, modeled by a two-state Markov-Gaussian process, in the context of uplink NOMA systems. More specifically, we propose a maximum a posteriori (MAP) receiver combined with successive interference cancellation to mitigate the effect of impulsive noise on the detection of the different users’ signals. Simulation results prove the effectiveness of the proposed MAP-based bursty impulsive noise mitigation scheme in terms of bit error rate.

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.

Internet-of-drones (IoD) systems require enhanced data transmission security and efficient interference management to accommodate the rapidly growing drone-based and rate-intensive applications. This paper develops a novel resource allocation scheme to jointly manage interference and enhance the physical layer security of cellular-connected IoD networks in the presence of a multi-band eavesdropping drone. Our envisioned cellular-connected IoD network has multiple full-duplex cellular base stations (CBSs), where each CBS reserves an orthogonal cellular radio resource block (RRB) for the aerial communication links. To efficiently utilize the cellular RRBs, each CBS is connected to a cluster of data transmitting drones using uplink non-orthogonal multiple access (NOMA) scheme. In addition, all the CBSs simultaneously transmit artificial noise signals to weaken the eavesdropper links. A joint optimization problem, considering the transmit power allocation and clustering of the legitimate drones, and the jamming power allocation of the CBSs, is formulated to maximize the worst-case average sum-secrecy-rate of the network. The joint optimization problem is decomposed into drone-clustering and power allocation sub-problems to obtain an efficient solution. A multi-agent reinforcement-learning framework is devised to solve the drone-clustering sub-problem. Meanwhile, the transmit and jamming power allocation sub-problem is solved by employing fractional programming, successive convex approximation, and alternating optimization techniques. By iteratively solving these two sub-problems, a convergent resource allocation algorithm, namely, security and interference management with reinforcement-learning and NOMA (SIREN), is proposed. The superiority of SIREN over several benchmark schemes is verified via extensive simulations.

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.

Breathing rate (BR) is one of the vital signs used in physiological monitoring. Conventional BR monitoring requires attaching wired canula/thermistor on the buco-nasal area to measure air-flow, inducing discomfort to the subject. Abdominal/thoracic belts are also used to detect breathing movements whereas esophageal pressure is the gold standard to measure breathing effort. In this paper, we aim to validate the consistency of using only bed-sheet pressure sensors to monitor the BR in healthy adults. We propose a method and demonstrate that it could be used interchangeably with respiratory belts which were approved for medical use by the American Association of Sleep Medicine (AASM). We build a ten-sinusoidal model-based extended Kalman Filter to adaptively estimate the breathing movements’ signal from the body pressure distribution data. The model is posture-specific, I.e., parameters are optimized based on the detected posture. An artificial neural network (ANN) model was used to detect four bed postures to perform the kalman filter parameters’ optimization step. The BRs of 12 healthy adults are recorded using the pressure mattress and a reference respiratory belt. To validate the method as a surrogate measure, a Bland-Altman (BA) analysis was performed on both pressure and belt data, and the linear relationship is evaluated using Pearson Correlation Coefficient (PCC). Interestingly, a high inter-rater agreement, an average maximum difference of 1.93 Breaths Per Minute (BrPM), a confidence interval of 95%, along with a strong linear relationship of 95.8% on average between the two methods were interestingly obtained. The presented results show the suitability of the proposed solution in medical applications requiring respiration monitoring.

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.

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.

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.

The 5th Generation (5G) New Radio (NR) and beyond technologies will support enhanced mobile broadband, very low latency communications, and huge numbers of mobile devices. Therefore, for very high speed users, seamless mobility needs to be maintained during the migration from one cell to another in the handover. Due to the presence of a massive number of mobile devices, the management of the high mobility of a dense network becomes crucial. Moreover, a dynamic adaptation is required for the Time-to-Trigger (TTT) and hysteresis margin, which significantly impact the handover latency and overall throughput. Therefore, in this paper, we propose an online learning-based mechanism, known as Learning-based Intelligent Mobility Management (LIM2), for mobility management in 5G and beyond, with an intelligent adaptation of the TTT and hysteresis values. LIM2 uses a Kalman filter to predict the future signal quality of the serving and neighbor cells, selects the target cell for the handover using state-action-reward-state-action (SARSA)-based reinforcement learning, and adapts the TTT and hysteresis using the epsilon-greedy policy. We implement a prototype of the LIM2 in NS-3 and extensively analyze its performance, where it is observed that the LIM2 algorithm can significantly improve the handover operation in very high speed mobility scenarios., 16 pages

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.