Cyclic redundancy check (CRC) is a well-known error detection code that is widely used in Ethernet, PCIe, and other transmission protocols. The existing FPGA-based implementation solutions are faced with the problem of excessive resource utilization in high-performance scenarios. The padding zeros problem and the introduction of programmability further exacerbate this problem. In this brief, the stride-by-5 algorithm is proposed to achieve the optimal utilization of FPGA resources. The pipelining go back algorithm is proposed to solve the padding zeros problem. The method of reprogramming by HWICAP is proposed to realize programmability with a small and constant resource utilization. The experimental results show that the resource utilization of proposed non-segmented architecture is 80.7%-87.5% and 25.1%-46.2% lower than those of two state-of-the-art FPGA-based CRC implementations, and the proposed segmented architecture has a lower resource utilization by 81.7%-85.9% and 2.9%-20.8% compared wtih the two state-of-the-art architectures; meanwhile, the throughput and programmability are guaranteed. We made the source code available on GitHub.

Given the increasing number of space-related applications, research in the emerging space industry is becoming more and more attractive. One compelling area of current space research is the design of miniaturized satellites, known as CubeSats, which are enticing because of their numerous applications and low design-and-deployment cost. The new paradigm of connected space through CubeSats makes possible a wide range of applications, such as Earth remote sensing, space exploration, and rural connectivity. CubeSats further provide a complementary connectivity solution to the pervasive Internet of Things (IoT) networks, leading to a globally connected cyber-physical system. This paper presents a holistic overview of various aspects of CubeSat missions and provides a thorough review of the topic from both academic and industrial perspectives. We further present recent advances in the area of CubeSat communications, with an emphasis on constellation-and-coverage issues, channel modeling, modulation and coding, and networking. Finally, we identify several future research directions for CubeSat communications, including Internet of space things, low-power long-range networks, and machine learning for CubeSat resource allocation.

In body area networks (BANs), a node placed near the hub has a significant signal strength advantage. This issue, known as near-far problem, indicates that a near node may serve as a relay for a far node. Currently, the established orthogonal multiple access (OMA) requires the allocation of a specific resource block to a single node. In contrast, the emerging non-orthogonal multiple access (NOMA) allows simultaneous transmissions and uses successive interference cancellation (SIC) to recover the signals. For the first time in the literature, we introduce NOMA in BANs, to allow simultaneous transmissions from two nodes at a time, with distinguishable power levels. The crucial part of this technique is the dynamic pairing of the nodes by the hub. First, we review the fundamentals of uplink NOMA and we propose an access protocol in compliance with IEEE 802.15.6-2012, which combines random OMA with scheduled NOMA. Next, we propose three node pairing strategies, namely a random, a correlative and a conditional one, to meet the requirements of several applications. These strategies are evaluated with packet level simulations in OMNeT++. Our results show that the proposed scheme outperforms the basic OMA scheme at high packet rates and provides a feasible direction for novel relay-based applications.

Among radiological imaging data, chest X-rays are of great use in observing COVID-19 mani- festations. For mass screening, using chest X-rays, a computationally efficient AI-driven tool is the must to detect COVID-19 positive cases from non-COVID ones. For this purpose, we proposed a light-weight Convolutional Neural Network (CNN)-tailored shallow architecture that can automatically detect COVID-19 positive cases using chest X-rays, with no false positive. The shallow CNN-tailored architecture was designed with fewer parameters as compared to other deep learning models, which was validated using 130 COVID-19 positive chest X-rays. In this study, in addition to COVID-19 positive cases, another set of non-COVID-19 cases (exactly similar to the size of COVID-19 set) was taken into account, where MERS, SARS, Pneumonia, and healthy chest X-rays were used. In experimental tests, to avoid possible bias, 5-fold cross validation was followed. Using 260 chest X-rays, the proposed model achieved an accuracy of an accuracy of 96.92%, sensitivity of 0.942, where AUC was 0.9869. Further, the reported false positive rate was 0 for 130 COVID-19 positive cases. This stated that proposed tool could possibly be used for mass screening. Note to be confused, it does not include any clinical implications. Using the exact same set of chest X-rays collection, the current results were better than other deep learning models and state-of-the-art works.

The discussion concerning programmable MAC in Body Area Networks (BANs) is due to the demand for simple and low-power sensor nodes. Additionally, the diverse applications in BANs require low-level modifications to support adaptive services or custom functions. In this work, we propose a novel scheme for programmable MAC, which requires a minor modification to the beacon frame of IEEE 802.15.6-2012. Specifically, our main contribution is the attachment of a command to the beacon, which is broadcasted at the beginning of each superframe by the hub. The hub requests an action, typically a modification of a MAC capability field, by the nodes with a metric which satisfies a constraint. Thus, one command at a time, the proposed scheme is applied with a negligible overhead. Two adaptive use cases, based on signal strength, are implemented to demonstrate this scheme. Firstly, the hub requests the nodes with high signal strength to enable relay support and secondly, the hub requests the nodes with low signal strength to set a sleeping pattern. In the first case packet delivery increases significantly, while in the second case each node saves an amount of energy.

Communication in Body Area Networks (BANs) involves extremely weak signals because of safety regulations. Human mobility adds one more layer of complexity as it has an effect on path loss depending on the activity. In this paper, we improve the quality of service (QoS) by searching for the lost nodes. Specifically, an Emergency Phase (EP) is added after RAP1 of IEEE 802.15.6-2012 superframe. The connected nodes transmit rescue beacons to reach distressed nodes, i.e. nodes that are disconnected. If a distressed node receives a rescue beacon, it participates in the current EP. The packets are buffered and relayed to the hub by the connected nodes. Our results show that when EP is enabled it is feasible to reach more nodes.

The success of blockchain as the underlying technology for cryptocurrencies has opened up possibilities for its use in other application domains as well. The main advantages of blockchain for its potential use in other areas are its inherent security mechanisms and immunity to data manipulation attacks. A blockchain relies on a consensus method for agreeing on any new data. Most of the consensus methods which are currently used for the blockchain of different cryptocurrencies require high computational power and thus are not suitable for resource-constrained systems. In this article, we survey the various blockchain-based consensus methods that are applicable to resource-constrained IoT devices and networks. In a typical IoT network, there exist several devices with limited computational and communication capabilities. Most often, these devices cannot perform intensive computations and are starved for bandwidth. Therefore, we discuss the possible measures that can be taken to reduce the computational power and convergence time for the underlying consensus methods. We also talk about some of the alternatives to the public blockchain, such as private blockchain and tangle, along with their potential adoption for IoT networks. Furthermore, we review the existing consensus methods that have been implemented and explore the possibility of utilizing them to realize a blockchainbased IoT network. Some of the open research challenges including AI-enabled blockchains are also put forward.

With high paced growth in biometrics, and its easy availability to capture various biometric features, it is emerging as one of the most valuable technologies for multifactor authentication to verify a user’s identity, for data security.Organizations encourage their members to use biometrics, but they are hesitant to use due to perceived security risks. Because of its low usage rate, many medium and small segment organizations find it unfeasible to deploy robust biometric systems.We propose a “server-specific add-on biometric security layer model,” to enhance confidence in the usage of biometrics. We tested this model via a biometric mobile app, and the survey showed a favorable response of 80%. The innovative mobile app was tested for its usability and got a score of more than 71%. For test tool reliability, we examined the equal error rate (EER) of the app and got a reasonably low score of 6%. The results show a good potential of this framework to enhance users’ confidence-level in the usage of biometrics. Higher usage rates may make deployment of biometrics more cost-effective for many organizations to decrease their information security risk.

Communication in Body Area Networks (BANs) involves weak signals, due to safety regulations, huge pathloss from the absorption and usually high mobility. In this work, we introduce an improved mobility aware relaying scheme for BANs, as an alternative to the two-hop star topology extension of IEEE 802.15.6-2012, in order to enhance packet delivery. Specifically, an emergency phase (EP) is added after the regular random access phase (RAP1) of the superframe and the connected nodes transmit rescue beacons to reach disconnected nodes. When a disconnected node receives a rescue beacon, it participates in the current EP. The packets are buffered and relayed to the hub by the connected nodes. Simulation results show that it is feasible to receive more packets compared to the standard with a justified increase of energy consumption due to random access which is compensated with increased packet delivery.

Non-orthogonal multiple access (NOMA) based wireless caching network (WCN) is considered as one of the most promising technologies for next-generation wireless communications since it can significantly improve the spectral efficiency. In this paper, we propose a quality of service (QoS)-oriented dynamic power allocation strategy for NOMA-WCN. In content stack phase, the base station sends multiple files to the content servers by allocating different powers according to the different QoS targets of files, for ensuring that all content servers can successfully decode the two most popular files. In content deliver phase, the content servers serve two users at the same time by allocating the minimum power to the far user according to the QoS requirement, and then all the remaining power is allocated to the near user. Hence, the proposed power allocation scheme is able to increase the hit probability and drop the outage probability compared with conventional method. Simulation results confirm that the proposed power allocation method can significantly improve the caching hit probability and reduce the user outage probability. It is also shown that this strategy can reduce the user delay time, improve the system efficiency and the capacity.

In the fifth-generation (5G) wireless communications system, various service requirements under different communication environments are expected to be satisfied. As a new evolution network structure, heterogeneous networks (HetNet) have been fully studied in recent years. In contrast to conventional homogeneous networks, the key feature of HetNet is to increase the opportunity of spatial resource reuse and improve the quality of service of users by allowing small cells to cooperate in macrocell networks. However, since the mutual interference among different users and the limited resource are existing in HetNets, efficient resource allocation (RA) schemes are very important to reduce the interference and achieve spectrum sharing. In this paper, we provide a comprehensive survey on RA in HetNets for 5G communications. Specifically, we first introduce the definition and different network scenarios of HetNet. Second, RA models are discussed. Then, we present classification to analyze current RA schemes in the existing references. Finally, some challenging open issues and future research trends are addressed in this field. We also provide two effective approaches for the sixth-generation (6G) communications to solve the RA problems of future HetNets, namely, a learning-based approach and a control theory-based approach. This paper provides important information on HetNets, which can be used to guide the development of more efficient RA schemes in this area.

As Internet of Things (IoT) and Cyber-Physical systems become more ubiquitous in our daily lives, it necessitates the capability to measure the trustworthiness of the aggregate data from such systems to make fair decisions. However, the interpretation of trustworthiness is contextual and varies according to the risk tolerance attitude of the concerned application. In addition, there exist varying levels of uncertainty associated with an evidence upon which a trust model is built. Hence, the data integrity scoring mechanisms require some provisions to adapt to different risk attitudes and uncertainties. In this paper, we propose a prospect theoretic framework for data integrity scoring that quantifies the trustworthiness of the collected data from IoT devices in the presence of adversaries who try to manipulate the data. In our proposed method, we consider an imperfect anomaly monitoring mechanism that tracks the transmitted data from each device and classifies the outcome (trustworthiness of data) as not compromised, compromised, or undecided. These outcomes are conceptualized as a multinomial hypothesis of a Bayesian inference model with three parameters. These parameters are then used for calculating a utility value via prospect theory to evaluate the reliability of the aggregate data at an IoT hub. In addition, to take into account different risk attitudes, we propose two types of fusion rule at IoT hub– optimistic and conservative. Furthermore, we put forward asymmetric weighted moving average (AWMA) scheme to measure the trustworthiness of aggregate data in presence of On-Off attacks. The proposed framework is validated using extensive simulation experiments for both uniform and On-Off attacks. We show how trust scores vary under a variety of system factors like attack magnitude and inaccurate detection. In addition, we measure the trustworthiness of the aggregate data using the well-known expected utility theory and compare the results with that obtained by prospect theory. The simulation results reveal that prospect theory quantifies trustworthiness of the aggregate data better than expected utility theory.

Manuscript draft of a privacy-preserving system for detecting and predicting occupancy in a building, deployed in a real application

Internet of Things (IoT) is one of the fastest emerging networking paradigms enabling a large number of applications for the benefit of mankind. Advancements in embedded system technology and compressed IPv6 have enabled the support of IP stack in resource constrained heterogeneous smart devices. However, global connectivity and resource constrained characteristics of smart devices have exposed them to different insider and outsider attacks, which put users’ security and privacy at risk. Various risks associated with IoT slow down its growth and become an obstruction in the worldwide adoption of its applications. In RFC 6550, the IPv6 Routing Protocol for Low Power and Lossy Network (RPL) is specified by IETF’s ROLL working group for facilitating efficient routing in 6LoWPAN networks, while considering its limitations. Due to resource constrained nature of nodes in the IoT, RPL is vulnerable to many attacks that consume the node’s resources and degrade the network’s performance. In this paper, we present a study on various attacks and their existing defense solutions, particularly to RPL. Open research issues, challenges, and future directions specific to RPL security are also discussed. A taxonomy of RPL attacks, considering the essential attributes like resources, topology, and traffic, is shown for better understanding. In addition, a study of existing cross-layered and RPL specific network layer based defense solutions suggested in the literature is also carried out.

The beginning of 2020 has seen the emergence of coronavirus outbreak caused by a novel virus called SARS-CoV-2. The sudden explosion and uncontrolled worldwide spread of COVID-19 show the limitations of existing healthcare systems to timely handle public health emergencies. In such contexts, innovative technologies such as blockchain and Artificial Intelligence (AI) have emerged as promising solutions for fighting coronavirus epidemic. On the one hand, blockchain can combat pandemics by enabling early detection of outbreaks, protecting user privacy, and ensuring reliable medical supply chain during the outbreak tracking. On the other hand, AI provides intelligent solutions for identifying symptoms caused by coronavirus for treatments and supporting drug manufacturing. Motivated by these, in this paper we present an extensive survey on the use of blockchain and AI for combating coronavirus (COVID-19) epidemics based on the rapidly emerging literature. First, we introduce a new conceptual architecture which integrates blockchain and AI specific for COVID-19 fighting. Particularly, we highlight the key solutions that blockchain and AI can provide to combat the COVID-19 outbreak. Then, we survey the latest research efforts on the use of blockchain and AI for COVID-19 fighting in a wide range of applications. The newly emerging projects and use cases enabled by these technologies to deal with coronavirus pandemic are also presented. Finally, we point out challenges and future directions that motivate more research efforts to deal with future coronavirus-like epidemics.

This study presented a new multi-species binary coded algorithm, Mendelian Evolutionary Theory Optimization (METO), inspired by the plant genetics. This framework mainly consists of three concepts: First, the “denaturation” of DNA’s of two different species to produce the hybrid “offspring DNA”. Second , the Mendelian evolutionary theory of genetic inheritance, which explains how the dominant and recessive traits appear in two successive generations. Third, the Epimuation, through which organism resist for natural mutation. The above concepts are reconfigured in order to design the binary meta-heuristic evolutionary search technique. Based on this framework, four evolutionary operators – 1) Flipper, 2) Pollination, 3) Breeding, and 4) Epimutation – are created in the binary domain. In this paper, METO is compared with well-known evolutionary and swarm optimizers 1) Binary Hybrid GA (BHGA), 2) Bio-geography Based Optimization (BBO), 3) Invasive Weed Optimization (IWO), 4) Shuffled Frog Leap Algorithm (SFLA), 5) Teaching-Learning Based Optimization (TLBO), 6) Cuckoo Search (CS), 7) Bat Algorithm (BA), 8) Gravitational Search Algorithm (GSA), 9) Covariance Matrix Adaptation Evolution Strategy(CMAES), 10) Differential Evolution (DE), 11) Firefly Algorithm (FA) and 12) Social Learning PSO (SLPSO). This comparison is evaluated on 30 and 100 variables benchmark test functions, including noisy, rotated, and hybrid composite functions. Kruskal Wallis statistical rank-based non-parametric H-test is utilized to determine the statistically significant differences between the output distributions of the optimizer, which are the result of the 100 independent runs. The statistical analysis shows that METO is a significantly better algorithm for complex and multi-modal problems with many local extremes.

We investigate the performance of a dual-hop intervehicular communications (IVC) system with relay selection strategy. We assume a generalized fading channel model, known as cascaded Rayleigh (also called n*Rayleigh), which involves the product of n independent Rayleigh random variables. This channel model provides a realistic description of IVC, in contrast to the conventional Rayleigh fading assumption, which is more suitable for cellular networks. Unlike existing works, which mainly consider double-Rayleigh fading channels (i.e, n = 2); our system model considers the general cascading order n; for which we derive an approximate analytic solution for the outage probability under the considered scenario. Also, in this study we propose a machine learning-based power allocation scheme to improve the link reliability in IVC. The analytical and simulation results show that both selective decode-and-forward (S-DF) and amplify-and-forward (S-AF) relaying schemes have the same diversity order in the high signal-to-noise ratio regime. In addition, our results indicate that machine learning algorithms can play a central role in selecting the best relay and allocation of transmission power.

In this paper, we demonstrate, for the first time, an isolating bandpass filter with low-loss forward transmission and high reverse isolation by modulating its constituent resonators. To understand the operating principle behind the device, we develop a spectral domain analysis method and show that the same-frequency nonreciprocity is a result of the nonreciprocal frequency conversion to the intermodulation (IM) frequencies by the time-varying resonators. With appropriate modulation frequency, modulation depth, and phase delay, the signal power at the IM frequencies is converted back to the RF frequency and adds up constructively to form a low-loss forward passband, whereas they add up destructively in the reverse direction to create the isolation. To validate the theory, a lumped-element three-pole 0.04-dB ripple isolating filter with a center frequency of 200 MHz and a ripple bandwidth of 30 MHz is designed, simulated, and measured. When modulated with a sinusoidal frequency of 30 MHz, a modulation index of 0.25, and an incremental phase difference of 45°, the filter achieves a forward insertion loss of 1.5 dB and a reverse isolation of 20 dB. The measured nonmodulated and modulated results agree very well with the simulations. Such nonreciprocal filters may find applications in wideband simultaneous transmit and receive radio front ends.