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While addressing grey fabric quality in a renowned circular weft knitting mill of Bangladesh, the authors experienced some questionable approach practiced by knitters. The subjective nature of defect detection by knitters/inspectors often time causes wrong emphasizing on frequently occurring defect(s) instead of focusing on influential defect(s) and subsequently, employing wrong quality control approach to minimize the grey fabric defects. Knit fabric defects (e.g., hole, stain, press-off, gout, miss knit, barrè, tucking, etc.) should be assessed by type, fault coverage, gravity and the frequency of occurrence instead of focusing only on frequency of occurrence in the fabric. In this study, grey weft-knitted fabric quality is investigated based on influential defects instead of frequently occurring defects. Quality data of single jersey, fleece and 1X1 rib were gathered and analyzed from an established knitting factory in Bangladesh over three months duration. A fabric inspection machine and 4-point inspection method were employed in this study. Gout was found as the most frequently occurring defect for each fabric type but not influential for rib fabric. For a significant amount of knitted fabrics, totaling of 55,524.91 m2 inspected fabric, the most occurring defects were ranked as gout, press-off, hole, miss knit, stain, and tucking and influential defects (based on inspection points) were ranked as gout, press-off, hole, stain, miss knit, and tucking (highest to lowest). In the inspection report, the knitter/inspector mistakenly categorized gout as the most occurring as well as the most influential defect for 1X1 rib fabrics and suggested remedies accordingly.

The ongoing pandemic has heightened the need for developing tools to flag COVID-19-related misinformation on the internet, specifically on social media such as Twitter. However, due to novel language and the rapid change of information, existing misinformation detection datasets are not effective for evaluating systems designed to detect misinformation on this topic. Misinformation detection can be divided into two sub-tasks: (i) retrieval of misconceptions relevant to posts being checked for veracity, and (ii) stance detection to identify whether the posts Agree, Disagree, or express No Stance towards the retrieved misconceptions. To facilitate research on this task, we release COVIDLIES, a dataset of 6591 expert-annotated tweets to evaluate the performance of misinformation detection systems on 62 different pieces of COVID-19 related misinformation. We evaluate existing NLP systems on this dataset, providing initial benchmarks and identifying key challenges for future models to improve upon. For the stance detection sub-task we provide benchmark models in zero-shot and few-shot settings. In addition to evaluation of the models using standard metrics, we also provide behaviour testing of the best models of each setting.

Recent advancements of Industrial IoT (IIoT) have revolutionized modern urbanization and smart cities. While IIoT data contains rich events and objects of interest, processing a massive amount of IIoT data and making predictions in real-time is challenging. Recent advancements in AI allow processing such a massive amount of IIoT data and generating insights for further decision-making processes. In this paper, we propose several key aspects of AI-enabled IIoT data for smart city monitoring. Firstly, we have combined a human-intelligence-enabled crowdsourcing application with that of an AI-enabled IIoT framework to capture events and objects from IIoT data in real-time. Secondly, we have combined multiple AI algorithms that can run on distributed edge and cloud nodes to automatically categorize the captured events and objects and generate analytics, reports, and alerts from the IIoT data in real-time. The results can be utilized in two scenarios. In the first scenario, the smart city authority can authenticate the AI-processed events and assign these events to the appropriate authority for managing the events. In the second scenario, the AI algorithms are allowed to interact with humans or IIoT for further processes. Finally, we will present the implementation details of the scenarios mentioned above and the test results. The test results show that the framework has the potential to be deployed within a smart city.

With the rapid progress of wireless communication technologies along with their digital revolutions, the quantity of Internet of Things (IoT) has been increased by manifolds resulting in a huge increase in data volume and network traffic. It became easier for an intruder to pretend as a valid service provider, and generate different types of network attacks. This becomes even more severe when the service involves digital financial transactions. This paper proposes an intrusion detection system (IDS) based on a stacked autoencoder (AE) and a deep neural network (DNN). The stacked AE learns the features of the input network record in an unsupervised manner to decrease the feature width. Then, the DNN is trained in a supervised manner to extract deep-learned features for the classifier. In the proposed system, the stacked AE has two latent layers and the DNN has two or three layers, where each layer has a fully-connected layer, a batch normalization, and a dropout. The system was evaluated on three publicly available datasets: KDDCup99, NSL-KDD, and AWID. Experimental results exhibited that the proposed IDS achieved 94.2%, 99.7%, 99.9% accuracy, respectively, for multiclass classification.

The 5G network presents a new horizon with tremendous opportunities for future generation wireless networks. Mobile edge computing (MEC), non-orthogonal multiple access (NOMA), and network slicing (NS) are some of the key enablers for 5G. MEC reduces the latency to a great extent for a wireless network, while NOMA gives access to more users with resource constraints. NS provides users with a better quality of service and network operators with more flexibility. In this work, we propose an NS technique enabled with NOMA for a MEC network. The proposed NS technique improves service latency for MEC users and reduces the unnecessary allocation of radio resources in NOMA. The saved resources can be leveraged to accommodate more users, thus increasing the spectral efficiency of the network. We consider different types of services based on the task completion time of users in this work. The primary focus is to optimize the total energy consumption for wireless uplink transmission for the NOMA-enabled sliced MEC network. We also propose a heuristic algorithm as an alternative to reduce the time and computational complexities of the optimization algorithm and simulate the results extensively to show the effectiveness of our proposed algorithm.

Among other motif finding algorithms Planted Motif Search (PMS) or (l, d) motif search algorithms are considered as next-generation motif finding algorithms. Here, n strings and two integer l and d are provided as input in PMS. It gives outputs of all sequences of length M in each input string, where each occurrence varies from Min at most d points. This paper proposes a new algorithm, for the (l, d) motif discovery problem in which we find all strings of length l that seems in every string of a provided set of strings with at most d mismatches has been. After proper investigation of the PMS problem, it has been shown that this is an NP-hard exact algorithm. Usually, in the worst-case scenarios, all the known exact algorithms for PMS need exponential time in some of the underlying parameters. In this paper, we proposed a faster approach that reduces the searching time in the sample-driven part of the algorithm. In particular, we used dynamic programming techniques to eliminate the recalculation of the values of some common subtree and introduced some new speedup techniques like using a linked list, reduced l-mers for making the algorithm faster. Due to the use of Linked List as a main speedup technique, we named our proposed algorithm as Linked List Implementation technique of PMS8 (LL-PMS8).

Transportation infrastructure plays a significant role in the economic growth of developing countries where heavy vehicles act as the backbone in transporting goods and people via highways. However, presence of heavy vehicles in traffic stream lowers the overall operating speed and flow in rural highways especially at the vicinity of intersections. This research aims at developing an empirical model for estimating and predicting capacity of highway roundabouts considering both geometric and traffic parameters. Based on data collected from six roundabouts along the national highways in Bangladesh, multivariate regression method has been used to develop an empirical model as a function of entry width, rotary lane width, distance between entry and nearest exit, central island diameter, and circulating traffic flow to estimate and predict the capacity of rural highway roundabouts. For incorporating the presence of heavy vehicles in the model, all vehicle types are converted to passenger car equivalent units. A comparative study with established methods (i.e., HCM 2016, TRRL, IRC and German method) proves the superiority of the developed model in terms of predictive capacity. Furthermore, a Python based program called PyNomo, has been executed with the help of several add-on packages (e.g., numpy, scipy, pyx) to generate a compound parallel scale nomograph consisting of multiple variables using the developed empirical model. Considering the impact of heavy vehicles in the socio-economic development of a country, the nomograph will aid the practitioners and engineers as a quick and easily interpretable graphical tool for planning and designing roundabouts in rural highways.

The smart power grid is a critical energy infrastructure where real-time electricity usage data is collected to predict future energy requirements. The existing prediction models focus on the centralized frameworks, where the collected data from various Home Area Networks (HANs) are forwarded to a central server. This process leads to cybersecurity threats. This paper proposes a Federated Learning (FL) based model with privacy preservation of smart grids data using Serverless cloud computing. The model considers the Blockchain-enabled Dew Servers (BDS) in each HAN for local data storage and local model training. Advanced perturbation and normalization techniques are used to reduce the inverse impact of irregular workload on the training results. The experiment conducted on benchmarks datasets demonstrates that the proposed model minimizes the computation and communication costs, attacking probability, and improves the test accuracy. Overall, the proposed model enables smart grids with robust privacy preservation and high accuracy.

Cost-friendly differential privacy (CDP) of smart meters can be preserved by an appropriate charging and discharging mechanism that uses rechargeable batteries (RBs) to generate Laplace distributed random noise. However, the existing CDP methods have several issues. Firstly, the maximum discharge rate of an RB requires to vary with the maximal consumption of houses. Secondly, the probability of an RB to charge/discharge depends on the demand, regardless of the state of charge (SoC) of an RB. Thirdly, in extreme SoC (near-empty or almost fully charged) of an RB, no noise added to the demand. To overcome these, we propose a mechanism in which a novel probability density function is designed to generate near Laplace distributed random noise. We also utilize a renewable energy source with small storage in cascade with an RB to enhance performance. Both theoretical analysis and simulations are performed to demonstrate the effectiveness of our proposed method.

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.

Measurement of human body movement is an essential step in biomechanical analysis. The current standard for human motion capture systems uses infrared cameras to track reflective markers placed on the subject. While these systems can accurately track joint kinematics, the analyses are spatially limited to the lab environment. Though Inertial Measurement Unit (IMU) can eliminate the spatial limitations of the motion capture system, those systems are impractical for use in daily living due to the need for many sensors, typically one per body segment. Due to the need for practical and accurate estimation of joint kinematics, this study implements a reduced number of IMU sensors and employs machine learning algorithm to map sensor data to joint angles. Our developed algorithm estimates hip, knee, and ankle angles in the sagittal plane using two shoe-mounted IMU sensors in different practical walking conditions: treadmill, level overground, stair, and slope conditions. Specifically, we proposed five deep learning networks that use combinations of Convolutional Neural Networks (CNN) and Gated Recurrent Unit (GRU) based Recurrent Neural Networks (RNN) as base learners for our framework. Using those five baseline models, we proposed a novel framework, DeepBBWAE-Net, that implements ensemble techniques such as bagging, boosting, and weighted averaging to improve kinematic predictions. DeepBBWAE-Net predicts joint kinematics for the three joint angles under all the walking conditions with a Root Mean Square Error (RMSE) 6.93-29.0% lower than base models individually. This is the first study that uses a reduced number of IMU sensors to estimate kinematics in multiple walking environments.

To ensure the effectiveness of the inductive power transfer (IPT) systems for high power applications, the multiple coils are being used. But the cross-couplings among the same side coils reduce the efficiency of the system. Thus, the decoupling coils are commonly used to mitigate the same side couplings in multiple transmitters and receivers system. The decoupling coils are either used outside of the main coils or concentric with the main coils. In both cases, these decoupling coils do not take part in the power transmission and have some adverse effects on the system performance. In this paper, a new IPT system with dual transmitters and dual receivers with integrated decoupling coils is introduced. Besides mitigating the mutual inductances of the same side coils, the proposed decoupling coils are also taking part in power transmission. In addition, the proposed coils also have good misalignment performance. A three-dimensional finite element analysis (FEA) tool ANSYS MAXWELL is used to investigate the proposed magnetic coupler. A scaled-down experimental setup is used to verify the feasibility of the proposed structure. The maximum dc-dc efficiency of this system is 93.14 % while delivering 700 W to the load with a 200 mm air gap.

Online news blogs and websites are becoming influential to any society as they accumulate the world in one place. Aside from that, online news blogs and websites have efficient strategies in grabbing readers’ attention by the headlines, that being so to recognize the sentiment orientation or polarity of the news headlines for avoiding misinterpretation against any fact. In this study, we have examined 3383 news headlines created by five different global newspapers. In the interest of distinguishing the sentiment polarity (or sentiment orientation) of news headlines, we have trained our model by seven machine learning and two deep learning algorithms. Finally, their performance was compared. Among them, Bernoulli naïve Bayes and Convolutional Neural Network (CNN) achieved higher accuracy than other machine learning and deep learning algorithms, respectively. Such a study will help the audience in determining their impression against or for any leader or governance; and will provide assistance to recognize the most indifferent newspaper or news blogs.

With the rapid advancement of Intelligent Transportation Systems (ITS) and vehicular communications, Vehicular Edge Computing (VEC) is emerging as a promising technology to support low-latency ITS applications and services. In this paper, we consider the computation offloading problem from mobile vehicles/users in a heterogeneous VEC scenario, and focus on the network- and base station selection problems, where different networks have different traffic loads. In a fast-varying vehicular environment, computation offloading experience of users is strongly affected by the latency due to the congestion at the edge computing servers co-located with the base stations. However, as a result of the non-stationary property of such an environment and also information shortage, predicting this congestion is an involved task. To address this challenge, we propose an on-line learning algorithm and an off-policy learning algorithm based on multi-armed bandit theory. To dynamically select the least congested network in a piece-wise stationary environment, these algorithms predict the latency that the offloaded tasks experience using the offloading history. In addition, to minimize the task loss due to the mobility of the vehicles, we develop a method for base station selection. Moreover, we propose a relaying mechanism for the selected network, which operates based on the sojourn time of the vehicles. Through intensive numerical analysis, we demonstrate that the proposed learning-based solutions adapt to the traffic changes of the network by selecting the least congested network, thereby reducing the latency of offloaded tasks. Moreover, we demonstrate that the proposed joint base station selection and the relaying mechanism minimize the task loss in a vehicular environment., Published in IEEE Transactions on Mobile Computing, Vol 21, Issue 12, Dec 2022

Due to their high maneuverability and flexible deployment, unmanned aerial vehicles (UAVs) could be an alternative option for a scenario where IoT devices consume high energy to achieve the required data rate when they are far away from the terrestrial base station (BS). Therefore, this paper has proposed an energy-efficient UAV-assisted IoT network where a low altitude quad-rotor UAV provides mobile data collection service from static IoT devices. We develop a novel optimization framework that minimizes the total energy consumption of all devices by jointly optimizing the UAV’s trajectory, devices association, and respectively transmit power allocation at every time slot while ensuring that every device should achieve a given data rate constraint. As this joint optimization problem is non-convex and combinatorial, we adopt a reinforcement learning (RL) based solution methodology that effectively decouples it into three individual optimization sub-problems. The formulated optimization problem has transformed into a Markov decision process (MDP) where the UAV learns its trajectory according to its current state and corresponding action for maximizing the generated reward under the current policy. Finally, we conceive SARSA, a low complexity iterative algorithm for updating the current policy of UAV, that achieves an excellent computational complexity-optimality tradeoff. Numerical results validate the analysis and provide various insights on optimal UAV trajectory. The proposed methodology reduces the total energy consumption of all devices by 6.91%, 8.48%, and 9.94% in 80, 100, and 120 available time slots of UAV, respectively, compared to the particle swarm optimization (PSO) algorithm.

by Suresh Velamuri, S. Hari Charan Cherukuri, Suresh Kumar Sudabattula, Natarajan Prabaharan and Eklas Hossain

This paper investigates the load frequency control (LFC) problem for smart grids with denial-of-service (DoS) attacks via a novel event-triggered control method. In order to sustain the stable operation of the smart grids, a handshake logic-based event-triggered communication scheme (HLETCS) is proposed. The targets of the designed controller are twofold. Firstly, an event-triggered mechanism with a handshake logic-based concept is proposed to save network resources. In this triggering method, a handshake protocol is used to detect DoS attacks in the communication networks. Secondly, through employing Lyapunov stability theory, sufficient stabilization criteria ensuring the uncertain LFC systems being asymptotically stable are deduced. Based on these conditions, the corresponding state feedback controller design method is given. Finally, a simulation of a two-area power system with electric vehicles is given as an example to substantiate the proposed method of this paper.

This paper proposes a fast and robust technique for instantaneous phase angle estimation of three-phase voltage systems under unbalanced and distorted conditions. It mainly relies on the Clarke transformation, Teager energy operator and delayed signal cancellation (DSC) approach. Unlike an adaptive DSC-based synchronous-reference frame phase-locked loop (PLL), the proposed technique avoids interdependent loops, thus increasing the overall stability and easing the tuning process. Moreover, it removes the phase loop of the PLL, hence eliminates the requirement of in-loop filter (low-pass filter) and/or loop filter (proportional plus integral controller). As a result, the tuning of these filters is not required and a quicker response is produced. The proposed technique also avoids inverse trigonometric function operation. In addition, it shows harmonics immunity at steady-state and can generate fast dynamic response with a settling time less than one nominal fundamental cycle. Both simulation and experimental results are presented to reveal the benefits of the technique.

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

Large-scale knowledge in the artificial intelligence of things (AIoT) field urgently needs effective models to understand human language and automatically answer questions. Pretrained language models achieve state-of-the-art performance on some question answering (QA) datasets, but few models can answer questions on AIoT domain knowledge. Currently, the AIoT domain lacks sufficient QA datasets and large-scale pretraining corpora. In this article, we propose RoBERTa_ AIoT to address the problem of the lack of high-quality large-scale labeled AIoT QA datasets. We construct an AIoT corpus to further pretrain RoBERTa and BERT. RoBERTa_ AIoT and BERT_ AIoT leverage unsupervised pretraining on a large corpus composed of AIoT-oriented Wikipedia webpages to learn more domain-specific context and improve performance on the AIoT QA tasks. To fine-tune and evaluate the model, we construct three AIoT QA datasets based on the community QA websites. We evaluate our approach on these datasets, and the experimental results demonstrate the significant improvements of our approach.

This paper proposes an open-loop based demodulation technique for the instantaneous phase angle estimation of three phase voltage systems. The proposed demodulation-based method relies on the Clarke transform, finite-impulse-response filters and algorithms for phase angle and frequency deviation estimation. When compared to a frequency-locked loop (FLL) based on dual second-order generalized integrators and a phase-locked loop (PLL) relying on adaptive delayed signal cancellation operator, the proposed one does not use any integrators, recursive signals and feedback of frequency or phase angle, thus unconditionally stable and relatively simple tuning is achieved. In addition, the proposed method is computationally efficient due to avoiding interpolation, square-root, trigonometric functions or their inverse operations. Moreover, under equal dynamic condition, the proposed one generates improved accuracy at steady-state in a range of the frequency deviation specified by standard and grid code. Simulated and experimental results are presented to verify the efficacy of the proposed method.

The fluidized bed is an essential and standard equipment in the field of process development. It has a wide application in various areas and has been extensively studied. This review paper aims to discuss computational modeling of a fluidized bed with a focus on pharmaceutical applications. Eulerian, Lagrangian, and combined Eulerian-Lagrangian models have been studied for fluid bed applications with the rise of modeling capabilities. Such models assist in optimizing the process parameters and expedite the process development cycle. This paper discusses the background of modeling and then summarizes research papers relevant to pharmaceutical unit operations.

This letter proposes a three-phase phase-locked loop (PLL) algorithm relying on an adaptive Clarke transform (CT) under amplitude and/or phase angle unbalanced condition. Analytical expressions for coefficients of matrix associated with the adaptive CT are derived and used to generate orthogonal signals. An auxiliary algorithm is also proposed to track the actual amplitudes and phase angle deviations. Unlike the conventional CT relying on constant coefficients, the adaptive CT can generate accurate orthogonal signals from the three-phase voltages including phase angle and/or amplitude imbalances. As a result, the proposed PLL is not affected by the unbalanced phase angles with/without amplitude imbalances. The advantages of the proposed adaptive CT based PLL method is illustrated by both simulation and experimental results.

An adaptive cascaded delayed signal cancellation (CDSC) method relying on a simple frequency detector is combined with a third-order function for three-phase grid synchronization application. The proposed synchronization technique shows a high degree of insensitivity to harmonics at both nominal and off-nominal frequency environments. When compared to a similar method relying on adaptive CDSC and phase-locked loop (CDSC-PLL), the proposed technique generates alike results but demands less computational effort due to avoiding real-time evaluation of computationally demanding trigonometric functions and square root operation. Moreover, unlike the CDSC-PLL method, the proposed one eliminates the interdependent loops exist between the phase and frequency estimation, thus a simpler tuning process is achieved. In addition, the proposed method stays away from the tuning of the loop filter in the PLL by removing the phase loop. The usefulness of the proposed method is established by both simulation and experimental results.

The recognition of handwritten Bangla digit is providing significant progress on optical character recognition (OCR). It is a very critical task due to the similar pattern and alignment of handwriting digits. With the progress of modern research on optical character recognition, it is reducing the complexity of the classification task by several methods, a few problems encounter during recognition and wait to be solved with simpler methods. The modern emerging field of artificial intelligence is the Deep Neural Network, which promises a solid solution to these few handwritten recognition problems. This paper proposed a fine regulated deep neural network (FRDNN) for the handwritten numeric character recognition problem that uses convolutional neural network (CNN) models with regularization parameters which makes the model generalized by preventing the overfitting. This paper applied Traditional Deep Neural Network (TDNN) and Fine regulated deep neural network (FRDNN) models with a similar layer experienced on BanglaLekha-Isolated databases and the classification accuracies for the two models were 96.25% and 96.99%, respectively over 100 epochs. The network performance of the FRDNN model on the BanglaLekha-Isolated digit dataset was more robust and accurate than the TDNN model and depend on experimentation. Our proposed method is obtained a good recognition accuracy compared with other existing available methods.

The pervasive growth in the variety of consumer electronics devices (CEDs), as well as the Internet traffic, has evolved various security risks towards their usage. This study presents the concept of communications without a centralized server to facilitate the fastest communication between end devices, e.g. user handheld devices, and edge devices. Three communication scenarios are presented to support authentication in the proposed architecture. To understand the concept, this work firstly describes the growth in the CEDs market. Afterward, the discussion on various challenges faced for incorporating security in such devices is presented along with edge-based security solutions. At last, the work presents a use-case of the content delivery network (CDN) to discuss various security solutions based on the edge with the hope that these solutions will work for future security.

Increased electrification in the residential and transport sectors is changing the energy demand profiles significantly, which results in reshaped peak demand. These changes in demand profiles can cause grid overloading and jeopardize network reliability especially when the excessive use of electricity within a network is uncoordinated. In this article, an aggregated coordination mechanism is proposed for electric vehicle (EV) charge–discharge scheduling to manage the peak demand in the low-voltage (LV) residential networks. The proposed model uses mixed-integer-programming-based optimization approach to minimize the cost of energy while managing the peak demand and complying with grid constraints. A stochastic model is presented to account for the uncertainties associated with forecast inaccuracies of the day-ahead scheduling. The proposed strategy is assessed by means of simulation studies considering an LV residential neighborhood in Sydney, Australia. The results indicate the effectiveness of the proposed strategy to minimize the cost of electricity for the EV owners while managing the peak demand for the grid operators. Comparison with the state-of-the-art EV scheduling strategies indicates that the proposed strategy can improve the load factor of the local network up to 36%, the peak-to-average ratio up to 27%, and cost reductions up to 56%.

We study the user association problem in full-duplex cellular networks, wherein base stations (BSs) are densely deployed with highly variable transmit powers and topologies. To enhance the system performance, decoupled UL-DL (DUDe) association is considered, which enables each user equipment (UE) to associate with different base stations in uplink (UL) and downlink (DL), respectively. For the DUDe in full-duplex communication (referred to as DFA) mode, the challenge arising due to the asymmetric information, which is private to the corresponding UEs but unknown to BSs and all the others (e.g. channel gains and intercell interferences), needs to be considered, To address this challenge, we propose a contract-theory based distributed user association approach. Specifically, the association process is modeled as a monopoly labor market, where the BSs act as employers and offer two-dimensional contracts to employees (i.e. UEs). Theoretical proof for contract feasibility is presented by providing sufficient and necessary conditions. To reach the optimality, a contract-theoretic decoupled user association algorithm is developed, in which a BS broadcasts the drafted contracts, and each UE self-selects the optimal contract by considering her own demands. Numerical results are presented to demonstrate the performance of proposed approach in terms of node utilities and social surplus.

Surveillance drones, called as unmanned aerial vehicles (UAV), are aircrafts that are utilized to collect video recordings, still images, or live video of the targets, such as vehicles, people or specific areas. Particularly in battlefield surveillance, there is high possibility of eavesdropping, inserting, modifying or deleting the messages during communications among the deployed drones and ground station server (GSS). This leads to launch several potential attacks by an adversary, such as main-in-middle, impersonation, drones hijacking, replay attacks, etc. Moreover, anonymity and untarcebility are two crucial security properties that need to be maintained in battlefield surveillance communication environment. To deal with such a crucial security problem, we propose a new access control protocol for battlefield surveillance in drone-assisted Internet of Things (IoT) environment, called ACPBS-IoT. Through the detailed security analysis using formal and informal (non-mathematical), and also the formal security verification under automated software simulation tool, we show the proposed ACPBS-IoT can resist several potential attacks needed in battlefield surveillance scenario. Furthermore, the testbed experiments for various cryptographic primitives have been performed for measuring the execution time. Finally, a detailed comparative study on communication and computational overheads, and security as well as functionality features reveals that the proposed ACPBS-IoT provides superior security and more functionality features, and better or comparable overheads than other existing competing access control schemes.

Internet of Things (IoT) produces massive heterogeneous data from various applications, including digital health, smart hospitals, automated pathology labs, and so forth. IoT sensor nodes are integrated with the medical equipment to enable the health workers to monitor the patients’ health condition and appliances in real-time. However, due to security vulnerabilities, an unauthorized user can access health-related information or control the IoT nodes attached to the patient’s body resulting in unprecedented outcomes. Due to wireless channels as a medium of communication, IoT poses several threats such as a denial of service attack, man-in-the-middle attack, and modification attack to the IoT networks’ security and privacy. The proposed research presents a lightweight and anonymity-preserving user authentication protocol to counter these security threats. The given scheme establishes a secure session for the legitimate user and prohibits unauthorized users from gaining access to the IoT sensor nodes. The proposed protocol uses only lightweight cryptography primitives (hash) to alleviate the node’s tiny processor burden. The proposed protocol is efficient and superior because it has low computational and communication costs than conventional protocols. The proposed scheme uses password protection to let only the legitimate user access the IoT sensor nodes to obtain the patient’s real-time health report.

Cloud radio access networks (C-RANs) are promising paradigms for the fifth-generation (5G) networks due to their interference management capabilities. In a C-RAN, a central processor (CP) is responsible for coordinating multiple Remote Radio Heads (RRHs) and scheduling users to their radio resource blocks (RRBs). In this paper, we develop a novel cross-layer network coding (CLNC) approach that proposes to optimize RRH’s transmit powers and user’s rates in making the coding decisions. As such, cross-layer throughput of the network is maximized. The joint user scheduling, file encoding, and power adaptation problem is solved by designing a subgraph for each RRB, in which each vertex represents potential user-RRH associations, encoded files, transmission rates, and power levels (PLs) for one RRB. It is then shown that the C-RAN throughput maximization problem is equivalent to a maximum-weight clique problem over the union of all such subgraphs, called herein the CRAN-CLNC graph. Numerical results revealed that the proposed joint and iterative schemes offer improved throughput performances as compared to the existing algorithms in the literature. Compared to our proposed joint scheme, our proposed iterative scheme has a certain degradation, roughly in the range of 9%–14%. This small degradation in the throughput performance of the iterative scheme comes at the achieved low computational complexity as compared to the high complexity of the joint scheme.

In the Internet of Things (IoT), a huge amount of valuable data is generated by various IoT applications. As the IoT technologies become more complex, the attack methods are more diversified and can cause serious damages. Thus, establishing a secure IoT network based on user trust evaluation to defend against security threats and ensure the reliability of data source of collected data have become urgent issues, in this paper, a Data Fusion and transfer learning empowered granular Trust Evaluation mechanism (DFTE) is proposed to address the above challenges. Specifically, to meet the granularity demands of trust evaluation, time–space empowered fine/coarse grained trust evaluation models are built utilizing deep transfer learning algorithms based on data fusion. Moreover, to prevent privacy leakage and task sabotage, a dynamic reward and punishment mechanism is developed to encourage honest users by dynamically adjusting the scale of reward or punishment and accurately evaluating users’ trusts. The extensive experiments show that: (i) the proposed DFTE achieves high accuracy of trust evaluation under different granular demands through efficient data fusion; (ii) DFTE performs excellently in participation rate and data reliability.

We demonstrate the efficacy of periodic corrugations, fabricated as part of the radar cover to serve the dual purpose of protecting the radar circuitry and maximizing transmission in millimeter-wave automotive radars. A 2-dimensional array of square-base pyramids engraved on the plastic radar cover was optimized to achieve minimum reflection for a wide range of incident angles, ± 40° and across the operating frequency band of 76 to 81 GHz. More importantly, we present an alternative radome texture, namely the inverse-pyramid corrugations that results in improved structural integrity as compared to the pyramidal corrugations. This novel inverse-pyramid radome structure is also much easier to manufacture and achieves similar performance as the more conventional pyramid structure. Both designs are shown to significantly reduce radar cover reflections, which is perhaps the most significant bottleneck in modern millimeter-wave automotive radar sensor performance.

Network and cloud service providers are facing an unprecedented challenge to meet the demand of end-users during the COVID-19 pandemic. Currently, billions of people around the world are ordered to stay at home and use remote connection technologies to prevent the spread of the disease. The COVID-19 crisis brought a new reality to network service providers that will eventually accelerate the deployment of edge computing resources to attract the massive influx of users' traffic. The user can elect to procure its resource needs from any edge computing provider based on a variety of attributes such as price and quality. The main challenge for the user is how to choose between the price and multiple quality of service deals when such offerings are changing continually. This problem falls under multi-attribute decision-making. This paper investigates and proposes a novel auction mechanism by which network service brokers would be able to automate the selection of edge computing offers to support their end-users. We also propose a multi-attribute decision-making model that allows the broker to maximize its utility when several bids from edge-network providers are present. The evaluation and experimentation show the practicality and robustness of the proposed model.

The format of transitory facilities in a building space is managed using the determination concerning the maximal proficient design of these facilities to work productively and cost successfully. The layout configuration looks for the best plan of facilities inside the accessible zone. In the plan interaction of the design, numerous goals should be considered to viably use individuals, equipment, space, and energy. This paper, along these lines, proposes a methodology dependent on the fuzzy set hypothesis and Automated Layout Design Program and Computerized Relationship Planning to improve format interaction of facilities. The principal objective of this investigation is to get the closeness rating esteems between every two facilities in the building site. To accomplish this purpose, the fuzzy set hypothesis and defuzzification measure were accustomed to research the format of transitory facilities in connection through the building(s) to be built in making space.

Reconfigurable Wireless Sensor Network (RWSN) schedules a set of devices with reconfigurable wireless interface to accomplish different data collection plans in a cost-effective way. AI technologies are applied to optimize decision making for high-level network reconfiguration. Besides, AI based data mining tools are exploited by third parities to extract useful information underlying raw data. This leads to the emergence of AI enabled RWSN. We further study a data trading market to provide the data-centric environment for large-scale applications of AI enabled RWSN. A network operator employs the devices to gather environmental data, and sells the collected data to interested third parities as data consumers. After that, two-level optimizations are performed to ensure efficient data trading. In data collection, a contract based incentive mechanism is presented for the network operator to stimulate the devices and simultaneously achieve the contractor's goal subject to feasible constraints. In data selling, a non-cooperative game is formulated among multiple data consumers. They balance the data demand since the final data price is correlated with the total data demand. Nash equilibrium is analyzed and solved under different scenarios. Finally, numerical results are provided to demonstrate the effectiveness of our scheme.

This letter proposes a method for fast and accurate estimation of three-phase grid frequency under unbalanced and distorted conditions. It includes a recursive discrete Fourier transform-based frequency adaptive bandpass filter for α – β signals generated by the Clarke transform and a two-point-based algorithm for frequency estimation. The proposed method is relatively simple to implement and can eliminate adverse influences created by the imbalances, dc offset and both odd and even harmonics. It can also provide fast estimation with a response time of almost one fundamental cycle under grid disturbances. Moreover, it can present improved performance when compared to several methods reported in the technical literature. The advantages of the proposed method are confirmed by both simulated and real-time experimental results.

The idea of cloud computing is flourished on a basic principle of reusability of IT capabilities. It is completely a web-based technology, which enhances its usefulness as well as monitors PAYG mod...

Nowadays SCADA is very important for controlling large systems like pipeline, industrial manufacture, power plant as well as in the communication system. For controlling a large system by making hu...

Power shortage is a severe problem in developing countries that are rolling to blackout, but today smart grids have the scope to avoid entire blackouts by transforming them into brownouts. A brownout is an under-voltage condition where the AC supply drops below the nominal value (120 V or 220 V) by about 10%. In a power system network, power shortages or disturbances can occur at any time, and the reliability margin analysis is essential to maintain the stability of the system. Transmission reliability margin (TRM) is a margin that keeps the network secure during any occurrence of disturbance. This paper presents a new approach to compute TRM in the case of brownout. The detailed assessment of TRM largely depends on the estimation of the available transfer power (ATC). In this method, the ATC of the system is calculated considering the effect of alternating current (AC) and direct current (DC) reactive power (Q) flow (DCQF). The entire procedure is carried out for the multi-transaction IEEE-6 bus system, and the results are compared to the current efficiency justification method. Numerical results demonstrate that the proposed technique is an effective alternative for calculating the TRM and is valid compared to the existing technique.

Neurotechnology describes as one of the focal points of today’s research of hundreds of academicians and researchers globally; particularly around the domain of Brain-Computer Interfaces (BCI) that was introduced in the 1970s and has been at the forefront of many neurotechnological discoveries. The primary attempts of BCI research are to decoding human speech from brain signals, implementing creativity by imagination, and controlling neuro-psychological patterns by utilizing billion of neural activities that would significantly benefit people suffering from neurological disorders. In this study, we illustrate the progress of BCI research and present scores of unveiled contemporary approaches. First, we explore a decoding natural speech approach that is designed to decode human speech directly from the human brain onto a digital screen introduced by Facebook Reality Lab and University of California San Francisco. Then, we study a recently presented visionary project to control the human brain using Brain-Machine Interfaces (BMI) approach. We also investigate well-known electroencephalography (EEG) based Emotiv Epoc+ Neuroheadset to identify six emotional parameters including engagement, excitement, focus, stress, relaxation, and interest using brain signals by experimenting the neuroheadset among three human subjects where we utilize two two supervised learning classifiers, Naive Bayes and Linear Regression to show the accuracy and competency of the Epoc+ device and its associated applications in neurotechnological research. We present experimental studies and the demonstration indicates 69 % and 62 % improved accuracy for the aforementioned classifiers respectively in reading the performance matrices of the participants. We envision that non-invasive, insertable, and low-cost BCI approaches shall be the focal point for not only an alternative for patients with physical paralysis but also understanding the brain that would pave us to access and control the memories and brain somewhere very near.

Introduction: * The proposed TWIST model aims to achieve a secure MANET by detecting and mitigating packet dropping attack using finite state machine based IDS model. * To determine the trust values of the nodes using context-aware trust calculation * To select the trustworthy nodes as watchdog nodes for performing intrusion detection on the network * To detect and isolate the packet dropping attackers from routing activities, the scheme uses FSM based IDS for differen-tiating the packet dropping attacks from genuine nodes in the MANET. Method: In this methodology, instead of launching an intrusion detection system (IDS) in all nodes, an FSM based IDS is placed in the trustworthy watchdog nodes for detecting packet dropping attacker nodes in the network. The proposed FSM based intrusion detection scheme has three steps. The three main steps in the proposed scheme are context- aware trust calculation, watchdog node selection, and FSM based intrusion detection. In the first process, the trust calculation for each node is based on specific parameters that are different for malicious nodes and normal nodes. The second step is the watchdog node selection based on context-aware trust value calculation for ensuring that the trust-worthy network monitors are used for detecting attacker nodes in the network. The final process is FSM based intrusion detection, where the nodes acquire each state based on their behavior during the data routing. Based on the node behavior, the state transition occurs, and the nodes that drop data packets exceeding the defined threshold are moved to the malicious state and restricted to involve in further routing and services in the network Result: The performance of the proposed (TWIST) mechanism is assessed using the Network Simulator 2 (NS2). The proposed TWIST model is implemented by modifying the Ad-Hoc On-Demand Distance Vector (AODV) protocol files in NS2. Moreover, the proposed scheme is compared with Detection and Defense against Packet Drop attack in the MANET (DDPD) scheme. A performance analysis is done for the proposed TWIST model using performance metrics such as detection accuracy, false-positive rate, and overhead and the performance result is compared with that of the DDPD scheme. After the compare result we have analyzed that the proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme. Conclusion: In the TWIST model, an efficient packet dropping detection scheme based on the FSM model is proposed that efficiently detects the packet dropping attackers in the MANET. The trust is evaluated for each node in the network, and the nodes with the highest trust value are selected as watchdog nodes. The trust calculation based on parameters such as residual energy, the interaction between nodes and the neighbor count is considered for determining watchdog node selec-tion. Thus, the malicious nodes that drop data packets during data forwarding cannot be selected as watchdog nodes. The FSM based intrusion detection is applied in the watchdog nodes for detecting attackers accurately by monitoring the neigh-bor nodes for malicious behavior. The performance analysis is performed between the proposed TWIST mechanism and existing DDPD scheme. The proposed TWIST model exhibits better performance in terms of detection accuracy, false positive rate, energy consumption, and overhead compared to the existing DDPD scheme Discussion: This work may extend the conventional trust measurement of MANET routing, which adopts only routing behavior observation to cope with malicious activity. In addition, performance evaluation of proposed work under packet dropping attack has not been performed for varying the mobility of nodes in terms of speed. Furthermore, various perfor-mance metric parameters like route discovery latency and malicious discovery ratio which can be added for evaluate the performance of protocol in presence of malicious nodes. This may be considered in future work for extension of protocol for better and efficient results. Furthermore, In the future, the scheme will focus on providing proactive detection of packet dropping attacker nodes in MANET using a suitable and efficient statistical method.

The growing use of the Internet of Things (IoT) has increased the volume of data to be processed by manifolds. Edge computing can lessen the load of transmitting a massive volume of data to the cloud. It can also provide reduced latency and real-time experience to the users. This article proposes an emotion recognition system from facial images based on edge computing. A convolutional neural network (CNN) model is proposed to recognize emotion. The model is trained in a cloud during off time and downloaded to an edge server. During the testing, an end device such as a smartphone captures a face image and does some preprocessing, which includes face detection, face cropping, contrast enhancement, and image resizing. The preprocessed image is then sent to the edge server. The edge server runs the CNN model and infers a decision on emotion. The decision is then transmitted back to the smartphone. Two data sets, JAFFE and extended Cohn–Kanade (CK+), are used for the evaluation. Experimental results show that the proposed system is energy efficient, has less learnable parameters, and good recognition accuracy. The accuracies using the JAFFE and CK+ data sets are 93.5% and 96.6%, respectively.

Artificial Intelligence of Things (AIoT) is an emerging trend that integrates artificial intelligence into the Internet of Things, which enables intelligent IoT operations and smart industrial applications. AIoT can generate a large amount of data from the network edge. Due to the concerns of bandwidth and privacy, it is often impractical to move the collected data to the cloud. To address this issue, a collaborative distributed learning has been proposed to let the clients collaboratively train a machine learning model together with their local data in a distributed manner. In this paper, we study the free market incentive mechanism for collaborative distributed learning, where multiple parameter servers (PSs) compete with each other to motivate clients to contribute model training. Specifically, the Stackelberg game with multiple leaders and multiple followers has been designed to analyze the incentive mechanism. Different experiments have been performed to illustrate the efficiency of the proposed approach. In particular, compared with the state-of-the-art under the same budget constraint, the final average utility of the PSs can be increased by at least 94.3%.

The efficacy of long-distance and bulk power transmission largely depends on the efficient control and reliable operation of a multiterminal high-voltage direct current (MT-HVdc) grid, more precisely, a meshed HVdc grid. The capability of enduring the dc grid fault eventually enhances the reliability and improves the dynamic performance of the grid. This article investigates the operation and control of an AC/multiterminal dc (MTDC) system with bipolar topology incorporating the dc grid protection schemes. Based on the scale of a circuit breaker's operating time, the performance of three different protection strategies is compared and analyzed using DIgSILENT PowerFactory. Simulation results explicitly reveal that the dynamic performance of the MTDC grid significantly deteriorates with the slow functioning of the protection schemes, followed by a dc grid fault. Besides, prolonged recovery time causes a substantial loss of power infeed and affects the ac/dc grid's stability. Finally, to assess the frailty of the MTDC grid, a transient energy stability index is proposed considering the voltage variation in the prestate and poststate fault clearing interval. Relevant case studies are performed on the MTDC grid using an analytical approach and nonlinear simulation studies to validate the effectiveness of the proposed index.