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The overlap between edge computing and unmanned aerial systems enables Unmanned Aerial Vehicles (UAV) to quickly offload image processing tasks onto edge devices, avoiding the transmission of images over long distances. To improve the speed and efficiency of UAV image stitching in an edge computing environment, this paper proposes an adaptive UAV image parallel stitching algorithm in an edge computing environment. The algorithm incorporates both route-based parallel processing and inverted binary tree-based parallel processing, dividing the image stitching task into multiple processes and allocating them to different cores based on the CPU core count, number of flight routes, and number of images, thereby enhancing computational efficiency in edge scenarios. The experimental results indicate that, when the number of flight routes is greater than or equal to the number of CPUs, the adaptive algorithm will employ the more efficient route parallelism. Conversely, when the number of flight routes is less than the number of CPUs, the efficiency of inverted binary tree parallelism is higher. In the same experimental environment and dataset, the adaptive image stitching algorithm demonstrates an efficiency improvement of approximately 2–10 times compared to other algorithms, with no significant degradation in image quality. This demonstrates that in edge environments, the utilization of multi-threaded adaptive route and inverted binary tree-based parallel approaches can effectively harness the computing resources of edge devices, significantly improving the stitching speed of UAV images and providing technical support for rapid real-time monitoring by UAV. [ABSTRACT FROM AUTHOR]

AbstractFacilitated by modern sensing and integrated circuit technology, Internet of Things (IoT) systems now have the ability to intelligently and automatically collect data, perform computational tasks on an edge, and efficiently make decisions locally or remotely. However, existing multivariate statistical process control methods commonly assume that sensors directly transmit the raw measurements to the central server, and thus the edge computing capability of IoT systems is not effectively harnessed. To fill this literature gap, we propose a monitoring scheme tailored for IoT systems by leveraging their edge computational power. In particular, inspired by the inherent hierarchical structure of the IoT system, we construct the monitoring statistics hierarchically. At each edge device, we first construct a nonparametric stream-level statistic for each data stream and aggregate them to derive the device-level statistics. To eliminate the inconsistency in device-level statistics across different edge devices, the device-level statistics are transformed into time-related statistics, which are then sent to the central server. At the central server, a system-level monitoring statistic is constructed, and the most informative edge devices that should transmit statistics are intelligently determined. Theoretical analyses are carried out to justify the effectiveness of the data transmission strategy. Both simulations and a case study, which is based on an IoT system we built, are conducted to evaluate the performance and validate the superiority of the proposed monitoring scheme. [ABSTRACT FROM AUTHOR]

Multi-output Gaussian process (MGP) regression plays an important role in the integrative analysis of different but interrelated systems/units. Existing MGP approaches assume that data from all units is collected and stored at a central location. This requires massive computing and storage power at the central location, induces significant communication traffic due to raw data exchange, and comprises privacy of units. However, recent advances in Internet of Things technologies, which have tremendously increased edge computing power, pose a significant opportunity to address such challenges. In this article, we propose FedMGP, a general federated analytics (FA) framework to learn an MGP in a decentralized manner that uses edge computing power to distribute model learning efforts. Specifically, we propose a hierarchical modeling approach where an MGP is built upon shared global latent functions. We then develop a variational inference FA algorithm that overcomes the need to share raw data. Instead, collaborative learning is achieved by only sharing global latent function statistics. Comprehensive simulation studies and a case study on battery degradation data highlight the superior predictive performance and versatility of FedMGP, achieved while distributing computing and storage demands, reducing communication burden, fostering privacy, and personalizing analysis. [ABSTRACT FROM AUTHOR]

Large amounts of processing resources are required for the sensed raw big data processing during the data generation process. Furthermore, as sensed data are typically privacy sensitive, blockchain technology can be used to ensure the privacy concerns. This study examines a multiuser mobile offloading network that consists of a cloud server located remotely and an edge node. We formulate the offloading problem as the joint optimization of task offloading decision making of all users, the computation resource allocation among the edge executing applications, and the radio resource assignment among all the remote-processing applications. The goal is to minimize the maximum weighted cost of all users. When compared to other benchmark approaches, the simulation results show that the proposed algorithm achieves optimal results in terms of both energy consumption and delay as a result of collaboration. Finally the resource allocation and optimal offloading strategy with 93% efficiency is obtained. [ABSTRACT FROM AUTHOR]

As edge devices become increasingly powerful, data analytics are gradually moving from a centralized to a decentralized regime where edge computing resources are exploited to process more of the data locally. This regime of analytics is coined as Federated Data Analytics (FDA). Despite the recent success stories of FDA, most literature focuses exclusively on deep neural networks. In this work, we take a step back to develop an FDA treatment for one of the most fundamental statistical models: linear regression. Our treatment is built upon hierarchical modeling that allows borrowing strength across multiple groups. To this end, we propose two federated hierarchical model structures that provide a shared representation across devices to facilitate information sharing. Notably, our proposed frameworks are capable of providing uncertainty quantification, variable selection, hypothesis testing, and fast adaptation to new unseen data. We validate our methods on a range of real-life applications, including condition monitoring for aircraft engines. The results show that our FDA treatment for linear models can serve as a competing benchmark model for the future development of federated algorithms. [ABSTRACT FROM AUTHOR]

Federated learning enables a large number of clients (such as edge computing devices) to learn a model jointly without data sharing. However, the high amount of communication of the federated learning aggregation algorithm hinders the realisation of artificial intelligence in the last mile. Although FederatedAveraging (FedAvg) is the leading algorithm, its communication cost is still high. The method of communication delay and gradient sparse can reduce the communication cost, but there is no previous work to analyse the relationship and common effects of these two dimensions. Aiming at the problems that federated learning communication is expensive and it has become a training bottleneck, we improve the FedAvg algorithm and propose an adaptive communication frequency FederatedAveraging algorithm (AFedAvg). The gradient sparse operation in the algorithm reduces the quantity of parameters for a single communication, while the communication delay operation allows training to converge faster and obtain smaller losses. The number of sparse parameters is used to select the communication frequency of next round dynamically. Experimental results prove that, the AFedAvg algorithm is superior to the FedAvg and its variants in terms of communication cost. It achieves 2.4X–23.1X communication compression in different data distributions with minimal communication rounds required by the algorithm to converge. [ABSTRACT FROM AUTHOR]

Deep learning solutions in big data applications can benefit cloud centres and can also lead to network communication overhead. Typically, data collected from traffic are sent to the traffic management centre for analysis. However, this process can worsen the network route to the traffic management centre. A two-tier mechanism has been developed to address this issue, which performs vehicle speed estimation and traffic congestion detection for efficient traffic management. The real-time traffic video data are captured and the video frames are initially processed through a foreground extraction process, which extracts the temporarily stopped vehicles on the road by removing background pixels from the frames. The video frames are then wrapped in an up-down view to remove the influence of the observation angle. The traffic congestion is then detected accurately based on the traffic characteristics using the proposed Ensemble Random Forest-based Gradient Optimization (ERF-GO) algorithm. The generalization error occurs when learning complex features on frames is minimized using a gradient-based optimization (GO) algorithm. Finally, the learned information on traffic conditions is forwarded to the cloud and edge computing environments based on network connection speed. The efficiency of the proposed ERF-GO is investigated in terms of performance metrics, namely root mean square error, speed detection error, execution time, computational cost, accuracy, latency, workload balance, precision, recall, f-measure, and congestion detection error rate. The analytic result displays that the proposed ERF-GO algorithm attains a greater accuracy rate of about 98.65% in detecting traffic congestion which is comparably higher than state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The communication services of multimedia, such as live online and short videos, gained significant attention and have been prominent in daily social interactions due to the fast development of 5G technology. However, the increase in multimedia communication requirements presents further difficulties for both the communication capacity of the wireless network and its processing capability. Mobile Edge Computing (MEC) is considered an effective technology to overcome the mentioned challenges. The subject of long-term task allocation including resource coordination is the primary focus of this study, with a special emphasis on multimedia services that must be processed, uploaded, and shared in network. The optimization issue is formulated as a stochastic optimization issue to reduce the system's time-average energy consumption. An online energy-efficient task allocation and computing offloading method is proposed to determine task allocation, coordinate and optimize wireless, computing resource allocation, considering dynamic wireless state and service delay constraints. The proposed method is implemented in MATLAB and the efficacy of the MCO-VEA framework is estimated and performance metrics, such as minimum user rate, sum rate, energy, and spectral performance are analyzed. Then, the performance of MEC-MCO-VEA provides 22.35%, 25.84% and 36.58% higher throughput and 25.45%, 41.28%, and 28.56% higher energy efficiency compared to the existing models, such as an energy-effective multimedia task assignment with computing offloading for mobile edge computing (BA-LO-EE), energy efficiency using a communication deep neural network optimized with power allocation optimization (EE-CDNN-PAO), and energy efficiency using deep transfer deterministic policy gradient with deep deterministic policy gradient algorithm (DT-DPG-EE), respectively. [ABSTRACT FROM AUTHOR]

This study presents an innovative architecture leveraging Deep Reinforcement Learning (DRL) to optimize online service delivery in Mobile Edge Computing (MEC) networks. Our proposed method, the DRL-based dynamic Service Function Chain (SFC) placement with parallelized Virtual Network Functions (VNFs), aims to maximize Long-Term Cumulative Reward (LTCR). By parallelizing VNFs, our approach achieves computational acceleration, enhancing the efficiency of online service provisioning. Furthermore, we enhance future request processing capabilities by extracting the distribution of initialized VNFs. Through comprehensive simulations, our proposed architecture demonstrates a notable improvement, with approximately a 9.5% increase in the average number of accepted requests. [ABSTRACT FROM AUTHOR]

In response to the issues of low detection accuracy, slow speed, excessively large models, and difficult deployment in existing coal gangue recognition algorithms, a coal gangue target detection network based on an inverted residual structure is proposed. By conducting in-depth research on advanced edge computing networks, DPsP structure and DsP structure have been devised in this paper, while incorporating GhostModule to construct the GDPs-YOLO network within the YOLOv8s. The experimental results demonstrate the superior performance of the GDPs-YOLO network compared to both the baseline network and the control network. In comparison with the YOLOv5 series, YOLOv8 series, and four advanced edge computing networks, an increase in detection accuracy for coal gangue targets by a maximum of 2.5%, 1.6%, and 3.3% is observed, respectively. The model simultaneously exhibits enhanced speed and reduced model size, with a speed increase ranging from 12.5% to 86.85% compared to the control network. The compressibility of the model ranges from 24.07% to 94%. The inference latency measures approximately 2.8 ms, while the image processing speed reaches around 357 images per second, thereby satisfying the requirements for real-time detection. [ABSTRACT FROM AUTHOR]

Analog computing is often considered to be an outdated technology only to be found and cared for in museums but not as the basis of modern cutting edge computing. Nothing could be further from reality. Together with quantum computers, analog computers complement today's stored program digital computers, acting as co-processors for certain types of computationally intensive tasks. They are highly energy efficient and exhibit a degree of parallelism that by far surpasses classic digital computers. In contrast to quantum computers, analog computers do not require exotic technologies, no intricate cooling systems, no elaborate shielding systems, etc. In addition to this there already exists a plethora of proven programs for analog computers to solve a wide variety of problems. This paper describes the necessity of analog computing for the twenty-first century, gives a short introduction to the structure and programming of analog computers, before discussing future application areas. Analog computing will drastically alter the way we think about computing in the years to come as it will become an integral part of every high performance computer. It will be an indispensable technology for future AI systems, it will revolutionize many medical applications, it might even replace or at least complement, certain types of quantum computers, etc. [ABSTRACT FROM AUTHOR]

With the continuous development of wind energy resources, wind turbines have become a crucial element in the field of green energy. However, various malfunctions may occur during their operation, affecting performance and lifespan. To ensure the safety and efficiency of wind turbines, regular inspections and maintenance are essential. In recent years, rapid advancements in unmanned aerial vehicle (UAV) technology have opened new possibilities for the inspection and maintenance of wind turbines. The blades of wind turbines are the most critical components, and this review paper provides a comprehensive analysis of UAV applications in the detection, inspection, and diagnosis of wind turbine blades. It focuses on key UAV inspection technologies, highlighting their advantages and disadvantages in both static and rotating wind turbine blades. Additionally, the paper analyses control algorithms used by UAVs for blade inspection, verifying their accuracy and practicality in this field. Based on these findings, future technological trends are proposed, including UAV autonomous navigation, edge computing-based online monitoring, and intelligent fault diagnosis. It is anticipated that UAV inspection technology will increasingly play a significant role in the inspection and maintenance of wind turbine blades in the future. [ABSTRACT FROM AUTHOR]

Accurately identifying all objects of interest inside the specified frame of reference is crucial for object identification techniques to allow machine vision to interpret images successfully. Theoretical frameworks from computer vision and deep learning have informed many potential solutions to this problem. However, current approaches frequently fail when faced with objects going through random geometric changes and continually show shortcomings when identifying small, dense objects. This research looks at state-of-the-art object detection methods, compares them, and then suggests a convolutional object detection network that has been tweaked to fix the problems with the existing methods. Comparing our research to existing approaches, we find that they perform better. We accomplish this by training deep convolutional networks to detect geometric transformations and adjusting the networks to handle multi-scaled features. The results achieved after the optimization of the You only look once (YOLO) V5 & V7 are stated in this paper. The experiments demonstrated that YOLO v8 achieved higher accuracy than the hyper-tuned YOLO V5 and V7, with an average Precision (mAP) score of 52.8% on the customized military dataset running at 60 Frames Per Second (FPS). Object detection using deep neural networks (DNNs) poses a significant challenge due to the high computational and power requirements, mainly when deployed on general-purpose platforms such as CPUs and GPUs. However, addressing these limitations becomes crucial for efficient edge computing tasks like object detection, where faster, smaller, and energy-efficient solutions are essential. To overcome these challenges, system-on-chip (SoC) designs emerge as a promising solution. [ABSTRACT FROM AUTHOR]

Job scheduling is an important part of production management and an effective method for increasing productivity, improving enterprise competitiveness, and reducing production energy consumption. However, in traditional scheduling, some problems, such as low information transparency, response lag, poor accuracy, and poor optimisation, affect the scheduling performance. Therefore, this study presents a digital-twin-based (DT-based) job shop scheduling strategy to address these problems. First, energy consumption is introduced, and a job shop scheduling model that minimises the completion time, tardiness, and energy consumption is established. Then, a scheduling decision framework based on cloud-edge computing is proposed, and the DT-based job shop system composition and operating mechanism are described. In addition, a DT-based shop scheduling strategy, comprising an overall scheduling mechanism, an accurate processing time determination method that uses a time and space compression ratio simulation, and a data comparison and abnormality determination method, is designed. Moreover, the non-dominated sorting genetic algorithm (NSGA-II) is improved by incorporating a multi-mode crossover and random variation method, along with a variable proportion elite retention strategy. Finally, the effectiveness of the improved algorithm and proposed strategy is verified using a standard dataset and practical processing problem. [ABSTRACT FROM AUTHOR]

This article has developed a novel dual-data sanitization and dual-data restoration model for privacy preservation in mobile cloud computing (MCC) environment. The original medical data have been sanitized at the transmitter end utilizing the newly suggested dual-data sanitization technique. The wireless channel transports the sanitized data from the transmitter to the receiver. The dual-sanitized input has been recovered at the other end. More significantly, the exact key generation, which has been found by a novel hybrid optimization model, is essential to both operations (dual-data cleaning and dual-data restoring). Electric fish customized dolphin swarm optimization (ECDO) is a suggested hybrid optimization model that is a conceptual merger of the electric fish optimization (EFO) and the dolphin swarm method. Ultimately, to confirm the effectiveness of the anticipated strategy, a study has been made. The present architecture has been contrasted against the previous models, including the DSOA, EFO, SMO, WOA, and J-SSO, respectively. The restoration effectiveness of the projected model is 57.84%, 26.3%, 5.26%, 31.5%, and 15.7% improved over DSOA, EFO, SMO, WOA, and J-SSO, respectively. As a result of the overall assessment, the proposed model appears to be extremely secure during data transfer. [ABSTRACT FROM AUTHOR]

One of the common techniques to reduce the scalability problem in collaborative filtering (CF)-based recommender systems is the clustering technique, which accelerates finding the nearest neighbor users in the recommendation process. Different clustering algorithms lead to improved accuracy and diversity in recommender systems. It is challenging to develop recommender systems based on clustering with decreasing scalability and simultaneously increasing accuracy. This article proposes a new clustering-based recommender system that takes into account the theoretical properties of the Learning Automata (LA) technique. The presented clustering novelty lies in the fact that employing LA technique for the user clustering in a CF-based recommender system has not been set forth so far in a way that addresses the scalability while improving accuracy. In addition, a novel similarity metric is embedded in the proposed algorithm to measure the similarity value between users. This metric is developed as like/dislike (LD) which can significantly improve the accuracy by reducing the computational cost. Extensive simulations have been performed on real-world datasets such as MovieLens and FilmTrust, which confirm the effectiveness of the proposed algorithm. In this regard, the proposed algorithm has improved the precision between 5 and 16% on average compared to the existing state-of-the-art methods such as GA-GELS, NUSCCF, NNMF, and KL-KM. [ABSTRACT FROM AUTHOR]

Prediction using computer vision is getting prevalent nowadays because of satisfying results. The vision of Internet of Vehicles (IoV) expedites Vehicle to everything (V2X) communications by implementing heterogeneous global networks. Road crack is one of the major factors that causes road mishaps and damage to vehicles. To ensure smooth and safe driving, avoiding road crack in transportation planning and navigation is significant. To address this issue, we proposed a novel convolutional neural network (CNN)-based smart transportation system. We showed how to quantify the severity of the cracks. We proposed a post-processing algorithm to provide option to the driver to select the safest road toward the destination. The communication system for the proposed smart transportation system has also been introduced. The performance comparison of a few popular CNN architectures has been investigated. Simulation results showed that Resnet50 algorithm provides significantly high accuracy compared with SqueezeNet and InceptionV3 algorithm in order to detect road cracks for the proposed transportation system. We demonstrated high accuracy of measuring the crack severity via numerical analysis. The integration of the proposed system in next generation smart vehicles can ensure accurate detection of road cracks earlier enough providing the option to select alternate safe route toward a destination as advanced driver assistance service. Moreover, the proposed system can also play a key role in order to reduce road mishaps notably by warning the driver about the updated road surface conditions. [ABSTRACT FROM AUTHOR]

This article proposes a set of information guidance models and corresponding algorithms which are embedded in a traffic information guidance system. Through a systematic analysis, the following results were obtained. When vehicles have different degrees of rationality, it is better to use a differentiation strategy in terms of traffic rewards and punishments; in particular, in a situation in which the strength of interactions between vehicles is different, the platform can guide vehicles to reduce overall transport costs by changing the information of rewards and punishments. The related stability analysis verifies that the information induction can induce the behavioural state of vehicles to stabilize, and the targeted information induction can help the whole system to stabilize. Finally, based on these findings, the traffic inducement strategy of the platform is proposed under different road conditions. [ABSTRACT FROM AUTHOR]

In Asian countries, rice is the major agricultural product. Hence, the initial identification of plant disorder via the IoT (Internet of Things) offers to ignore the rice from critical disorders. To increase crop production, the measurements should be considered to completely destroy the rice plant disorders by an effective system. This paper proposed an Intelligent IoT-aided deep learning model for detecting rice blast fungal along with a hybrid heuristic algorithm. The proposed work encompasses with multiple stages that are explained as follows. At first, the required paddy images are gathered from online data resources. Next, the pre-processing of the collected images is made by adaptive mean filtering and contrast enhancement. Further, the adaptive thresholding and morphological operation are adopted for leaf segmentation purposes, where the threshold value is tuned by a Fitness-based Billiards-inspired Rat Swarm Optimizer (FBRSO). Consequently, from the segmented image, the Region of Interest (ROI) is cropped. Finally, the cropped ROI is subjected to the Optimized MobileNetv2 and Multiscale Residual Attention Network (OMMRAN), where it includes MobileNetV2 and Multiscale Residual Attention Network, in which some of the hyperparameters are tuned by FBRSO approach. The performance is validated and compared with other existing approaches for detecting rice diseases effectively. [ABSTRACT FROM AUTHOR]

Time-consuming ocean transportation for international trades requires enterprises to advance payments for goods, which can be fatal for small and medium enterprises with insufficient self-owned capital. Logistics finance is an effective solution for tackling this problem. However, traditional logistics finance has long been plagued by information asymmetry, tremendous paperwork, and long financing cycles. As a distributed database with characteristics of transparency, traceability, and immutability, blockchain technology shows good potential in addressing pain points on traditional logistics financing platforms. This paper focuses on the operational mechanism and value analysis of the blockchain-based port logistics finance platform. The conceptual architecture of the blockchain-based port logistics finance platform is constructed, and a value analysis model based on the Newsboy Model is established for quantitatively analysing the platform's value. The results revealed that the introduction of the blockchain into port logistics financing can significantly improve small to medium-sized enterprises' profitability by providing reliable financing channels. The results also indicate that participants' profit on the blockchain-based platform may be influenced by the speed of market expansion, initial capital size, and financing cost. This paper also discusses theoretical and managerial implications by providing insights into the participants' decision-making on the platform under different constraints. [ABSTRACT FROM AUTHOR]

In this paper, an attempt is made to extend a nonlinear two-dimensional model for large oscillation and chaotic characteristics of the annular plate coupled with a piezoelectric patch subject to external excitation. For improving the mechanical properties, the core of the electrically structure is reinforced with graphene nanoplatelets. Von-Karman nonlinear theory and Hamilton's principle are taken into consideration for the exact derivation of the general nonlinear governing equations and boundary conditions of the axisymmetric annular plate coupled with a piezoelectric patch. For developing a precise solution approach, the generalized differential quadrature element method, and Multiple scales technique, are eventually used. The results demonstrate that radius ratio, applied voltage, thickness to radius ratio, and harmonic load have an important role in the nonlinear large oscillation, and chaotic motion of the graphene platelets reinforced composite (GPLRC) annular plate coupled with a piezoelectric patch. The golden and fundamental result of the current research is that, as the thickness of the piezoelectric patch increases, the system's nonlinear frequency decreases, and the area of instability in responses and maximum amplitude or the peak point of the backbone curve of the smart annular plate increases. [ABSTRACT FROM AUTHOR]

One of the most important sectors of the world economy is the mining and metals sector. Although, many operational and commercial procedures continue to be ineffective and out-of-date, which results in crucial data omissions, security flaws, and sometimes corruption. Given that the mining industry wants to increase the emphasis on ethical and open mining practices, the industry has been looking for ways to incorporate these practices. It is envisioned that such practices will contribute to the modernisation of supply chains in addition to helping to reduce risks related to sustainability and reputation. The application of blockchain technology in the minerals industry capable of tracking natural resources has been discussed in this study, giving a much-needed layer of transparency of this technology. However, there are a lot of difficulties and problems that come up when thinking about blockchain technology; if it is to advance as an industry standard, stakeholders will need to evaluate the usefulness and scalability of the technology. While blockchain technology can be applied to a wide range of areas in the minerals industry, this study shines more light on the application of this technology to conflict minerals tracking, mineral resources reporting cheating scandals, rock mechanics designs and monitoring strategy, blasting designs and operation, mine ventilation designs and applications, mining machinery maintenance and management, and mining surveying. The study reveals that blockchain technology is starting to be seriously evaluated and used by a variety of stakeholders, even though broad acceptance has not yet been attained. [ABSTRACT FROM AUTHOR]

Introduction: The metaverse is a new digital world powered by cutting-edge technologies that offers tremendous potential for healthcare. The metaverse is anticipated to experience enormous growth and adoption with a rising need for digital healthcare over the next decade. This article offers a comprehensive analysis of the integration of metaverse technologies into healthcare. Areas covered: This article delves into the multifaceted realm of metaverse technologies within healthcare, offering insights into its diverse contexts. Central to its exploration are the significant contributions, such as examining the current landscape of metaverse technologies in healthcare, offering a nuanced understanding of their applications. The article meticulously explores the array of tools and platforms available for surgical procedures, highlighting their efficacy and it also delves into how these technologies illuminate the evolving landscape of healthcare delivery. Through thorough analysis, this article elucidates the transformative potential of metaverse technologies in revolutionizing healthcare practices. Expert opinion: The integration of metaverse technologies in healthcare signifies a transformative shift in service delivery. The metaverse, driven by advanced technology, promises substantial changes, enhances medical education, patient care, and research through immersive experiences. This article aims to explore its various contexts heralding a new era in healthcare. [ABSTRACT FROM AUTHOR]

A mixed-signal binarized neural network (BNN) processor for the MNIST image classification is demonstrated based on analogue circuit networks. BNN algorithm for training neural networks with binary weights and activations reduces power consumption and memory size. This algorithm is a design to perform core operations of multi-layer perceptron (MLP) to reduce complexity and power consumption using analogue circuits. The mixed-signal BNN processor employs a current mirror neuron to perform multiply-and-accumulate (MAC) operations and sign activation functions. The near-threshold current mirror neuron that computes the key operations of the BNN algorithm is used to achieve low power consumption. The design occupies 0.065 mm2 in 28-nm CMOS with 560B of on-chip SRAM. The 28-nm CMOS test-chip achieves energy efficiency of 24.1 TOPS/W and 94% accuracy on the MNIST image classification. This design with binary weights and activations exhibits only 5% degradation in accuracy compared with the models with floating-point precision. [ABSTRACT FROM AUTHOR]

Short term forecasting is essential and challenging in time series data analysis for traffic flow research. A novel deep learning architecture on short-term traffic flow prediction was presented in this work. In conventional model-driven prediction method, a critical deviation in prediction accuracy was occurred in face of large fluctuations in traffic flow, while machine and deep learning-based approaches performed well in accuracy study than conventional regression-based models. Moreover, a fusion attention mechanism bidirectional long short-term memory model (ATT-BiLSTM) was proposed due to its bidirectional LSTM (BiLSTM) and attention mechanism units. The model not only dealt with forward and backward dependencies in time series data, but also integrated the attention mechanism to improve the ability on key information representation. The BiLSTM layer was exploited to capture bidirectional temporal and spatial features dependencies from historical data. The proposed model was also trained and validated using freeway toll datasets from Humen Bridge. The results showed that compared with ARIMA and SVR models, the indicators of the proposed model have been significantly improved. The ablation experiments were conducted to evaluate the role of the attention mechanism module. Compared with BiLSTM, CNN and 1DCNN-ATT-BiLSTM models, the MAE, RMSE and MAPE indexes of proposed model were reduced by 0.6–5.9%, 1.6–4.7% and 0.6–22.8%, respectively. More accurate predictions were obtained by the proposed model. The research results are of great significance to improve the level of traffic management. [ABSTRACT FROM AUTHOR]

The purpose of this study is to evaluate how Blockchain Technology (BCT) can support the implementation of Lean Automation. We conducted a systematic literature review to understand how BCT is being implemented in the supply chain management (SCM) domain and to evaluate how this technology can be used to reduce inefficiencies in supply chains. Firstly, we developed a holistic taxonomy of wastes to identify the most common non-value activities. Then, both inductive and deductive content analyses were performed, the latter being coded using the taxonomy. Our findings identified the most common BCT-based application themes in SCM and ways that this technology can be used to support future implementation of Blockchain-enabled Lean Automation (B-eLA). Additionally, we proposed a future research agenda. The study provides important contributions at the intersection between BCT, lean production, and Industry 4.0 within the context of SCM and seeks to exploit BCT's potential to improve businesses' efficiency, effectiveness and productivity. [ABSTRACT FROM AUTHOR]

Due to increase in pollution and greenhouse gas emissions from Internal Combustion Engines (ICEs), researchers and governments are looking forward for pollution-free, eco-friendly and efficient vehicle called Electric Vehicles (EVs), which is a best alternative for fossil fuel-based transportation systems. Limited battery capacity and frequent charging are essential for EVs. There are unscheduled EVs existed owing to the scarcity of energy and charging plugs. This research designs a multi aggregator-based charge scheduling strategy in Internet of Electric Vehicles (IoEVs) using proposed Fractional African Vulture Sail Fish Optimization (FrAVSO) algorithm. The FrAVSO is obtained by integrating Fractional Calculus (FC) with African Vulture Optimization (AVO) and Sail Fish Optimization (SFO). The energy is predicted utilizing Deep Maxout Network (DMN) and path selection is accomplished utilizing Fractional African Vulture Optimization (FrAVO). The FrAVSO achieved minimum distance of 15.313 Km, maximum energy of 0.252 J, minimum waiting time of 2.546 sec, and maximum profit of 94.978%. [ABSTRACT FROM AUTHOR]

The Physical Internet (PI) is a paradigm-changing and technology-driven vision, which is expected to significantly impact the development of the freight transport and logistics (FTL) system of today. However, the development of the FTL system towards the PI creates much uncertainty for its current stakeholders. Ports are one of those stakeholders that are expected to be profoundly affected by these developments. However, research that focuses on port policy, under the uncertain developments towards the PI, is still lacking. By providing port authorities with insights and recommendations on robust policy areas, we address this void in literature. We conduct a scenario analysis in combination with multi-criteria decision analysis (MCDA) to determine the importance of port performance indicators and policy areas in different scenarios. The most significant, uncertain, and orthogonal factors for the development of the PI are technological development and institutional development. We find that for a proper alignment with the PI vision, in three out of four scenarios, ports should prioritize the implementation of digital solutions and standards, as opposed to an infrastructure focused policy. [ABSTRACT FROM AUTHOR]

Data security in autonomous vehicles is of primary importance due to the possibility of cyberattacks on these interconnected and complex systems. As these vehicles are equipped with a number of sensors, software, and communication infrastructure, they are more vulnerable to different types of security issues. In this article. blockchain-supported QoS-based secure data communication (QoS-SDC) is proposed for high-speed, software-defined autonomous vehicles. This proposed mechanism will provide a promising solution for delivering protected data among autonomous vehicles. This model is evaluated by using the NS3 simulator, and the simulation results exhibit satisfactory secure data transactions. [ABSTRACT FROM AUTHOR]

Blockchains are a secure alternative for long-length data storage & security deployments. Though blockchains are unbounded, their quality of service (QoS) performance reduces after adding a certain number of blocks. Thus, sidechains have become essential for making the system decentralized, secure, and effective for use thereby improving their scalability & QoS performance. Sidechains reduce data storage and extraction delays. However, all sidechains of a single blockchain are the same in size, capacity, and security. This limits their application to real-world use cases that require dynamic security. To overcome this limitation, the authors propose a meta-heuristic approach to design a system that produces customized sidechains based on the quantity and quality of data stored on the chain. The model uses a machine-learning approach to find the best possible sidechain configuration for different data types. It makes the system fast and scalable and improves storage & memory efficiency. The proposed model is tested on multiple data sets and compared with various state-of-art sidechain deployments. An improvement of 18% in terms of mining speed, 27% in terms of energy efficiency, 10% with regards to throughput, and 8% concerning packet delivery ratio is observed. The model is tested under various attacks and faulty nodes to validate its security performance. A consistent QoS performance is observed for the proposed model under these attack types, thereby validating its resiliency for different attacks. This enhancement in performance makes the proposed model eligible for deployment in high-speed, low-energy, and high-security applications like IoT, mobile ad-hoc networks, and sensor networks. [ABSTRACT FROM AUTHOR]

Maritime shipping is considered the backbone of the global supply chain. It is expected that future fully automated smart ports will be data-driven, harvesting power from advanced analytics and artificial intelligence. Under the hood, Internet of Things (IoT) technologies are considered as an enabling foundation of new data sources for innovations and high-level automation. Thus, enterprise data management becomes critical when implementing IoT projects. However, the implementation of data management needs to be governed. Although the IoT adoption in smart ports receives significant interest from the port authority, the effectiveness of the IoT adoption lacks proper evaluation and support of comprehensive data management policy and guidelines. By using the systematic literature review and a case study to analyze the current gaps in IoT data governance research, this research shows that IoT data governance still needs to catch up in smart ports. In particular, compared to other industries, data lifecycle management and data quality management are two key data governance areas that should receive increasing attention from the port authorities. It is believed that with adequate data governance, smart ports can truly harvest the power of IoT to improve operational efficiency and sustainability with an increasing level of automation. [ABSTRACT FROM AUTHOR]

The detailed, high-resolution images captured by drones pose challenges to target detection algorithms with complex scenes and small-sized targets. Moreover, targets in unmanned aerial vehicle images are usually affected by factors such as viewing perspective, occlusion, and light, which increase the difficulty of target detection. In response to the above issues, we propose an improved SDMSEAFYOLOv8 for target detection based on YOLOv8, combined with a Bidirectional Feature Pyramid Network, to improve the sensing ability of the model for multiscale targets. A Space-to-depth layer replaces the traditional strided convolution layer to enhance the extraction of fine-grained information and small-sized target features. A Multi-Separated and Enhancement Attention module enhances the feature learning ability of the occluded target region, thus reducing missed and false detections. Four detection heads are employed for tiny target detection, each responsible for different size ranges, so as to improve the accuracy and robustness of small target detection. The conventional non-maximum suppression algorithm is improved, so as to reduce the problem of missed detections under a densely occluded scene by setting the attenuation function to adjust the confidence of the treated box based on the overlap between it and the highest-scoring box. Experiments demonstrate that the accuracy of SDMSEAF-YOLOv8 exceeds that of state-of-the-art models on the VisDrone2019-DET-val dataset, with a mAP of 42.9% at 640-pixel resolution, 14.8% over the baseline YOLOv8-x algorithm model, and 6.0% over the known state-of-the-art Fine-Grained Target Focusing Network model and with twice as fast detection. [ABSTRACT FROM AUTHOR]

Elliptic Curve Cryptography (ECC) is a promising cryptosystem as it presents the ideal choice for implementing public-key cryptography in resource-limited devices, such as those found in envisioned Internet of Things applications, e.g. wireless sensors. Elliptic Curve ElGamal (ECEG) is a renowned member of the ECC family. However, it requires encoding the message before encryption and decoding it after decryption. To better improve the processing time and to avoid sharing a lookup table, we propose, in this document, a new method that exchanges encoding parameters and authenticates endpoints at the same time. So, our new protocol ensures the authentication of communication parties and integrity of ephemeral encoding parameters. The robustness of our solution is proven by the Scyther tool. Experimental tests have been carried out, and the performance of the new methods is provided and compared with RSA and related work. Security and performance analysis show that our new scheme has achieved improved functionality and higher security while maintaining low computational. [ABSTRACT FROM AUTHOR]

This research introduces innovative approaches to enhance intrusion detection systems (IDSs) by addressing critical challenges in existing methods. Various machine-learning techniques, including nature-inspired metaheuristics, Bayesian algorithms, and swarm intelligence, have been proposed in the past for attribute selection and IDS performance improvement. However, these methods have often fallen short in terms of detection accuracy, detection rate, precision, and F-score. To tackle these issues, the paper presents a novel hybrid feature selection approach combining the Bat metaheuristic algorithm with the Residue Number System (RNS). Initially, the Bat algorithm is utilized to partition training data and eliminate irrelevant attributes. Recognizing the Bat algorithm's slower training and testing times, RNS is incorporated to enhance processing speed. Additionally, principal component analysis (PCA) is employed for feature extraction. In a second phase, RNS is excluded for feature selection, allowing the Bat algorithm to perform this task while PCA handles feature extraction. Subsequently, classification is conducted using naive bayes, and k-Nearest Neighbors. Experimental results demonstrate the remarkable effectiveness of combining RNS with the Bat algorithm, achieving outstanding detection rates, accuracy, and F-scores. Notably, the fusion approach doubles processing speed. The findings are further validated through benchmarking against existing intrusion detection methods, establishing their competitiveness. [ABSTRACT FROM AUTHOR]

This study examines the effect of key big data analytics capabilities (data, skills, technology, and culture) on innovation performance and business performance. Data were gathered from 91 companies and analyzed to determine correlations in the proposed model. The results find that big data analytics capabilities (BDAC) partially mediate the effect between innovative performance and business performance. Further, as a company's performance is a multidimensional element, was necessary to analyze more than one attribute to evaluate the relationship with BDAC through a canonical correlation analysis. The results in this sense reveal that the four big data capabilities increase the growth of sales, revenue, the number of workers, the net profit margin, innovation management, the development of new products and services, and the adoption of new information technologies. [ABSTRACT FROM AUTHOR]

Multi-criteria decision-making (MCDM) entails a heterogeneous decision-making problem, which poses challenges for decision-makers (DMs) in generating an optimal solution. To address this, we have proposed a heterogeneous evaluation framework. First, a novel picture fuzzy cross-entropy measure was defined with the simultaneous consideration of uncertainty and hesitancy of picture fuzzy information, overcoming the shortcomings of the existing cross-entropy measure in relation to its validity and properties. Next, an optimisation-model for determining the objective weights of criteria was constructed based on the proposed closeness measurement and the step-wise weight assessment ratio analysis (SWARA) method. This model was constituted from both objective and subjective perspectives under the circumstance of completely unknown criterion information. Additionally, the conventional Measurement of Alternatives and Ranking according to Compromise Solution (MARCOS) method was extended to the picture fuzzy environment, and a normalisation method was developed to transform heterogeneous judgments, including linguistic terms, interval numbers, and picture fuzzy numbers, into a unified representation form. A heterogeneous picture fuzzy SWARA-MARCOS evaluation framework was then established and used to solve a military equipment supplier selection problem. The results demonstrated the validity and feasibility of the proposed evaluation framework, while sensitivity, comparative, and complexity analyses demonstrated the robustness and superiority of it. [ABSTRACT FROM AUTHOR]

VANET performs analysis of patient records and sends security warnings to the carer through VANET so that they may provide urgent assistance while keeping patient data safe. Threat actors might potentially access data packets that go between biological sensors and smart ambulances in the vehicular network. When healthcare professionals use a VANET, they run the risk of receiving inaccurate or delayed data as a result of assaults like the Sybil attack, denial of service attacks, and impersonation attacks. As a result, protecting people's personal information and data has become an important task for academics. In this research, we propose the use of an Urbanized Block Chain Key Agreement Protocol (UB-KAP) based on a one-way hash chain model to increase protections for individual privacy and data integrity in VANET-based healthcare networks. The messages include timestamps in case the messages that have already expired cause excessive delays. The beacons' packet classification mechanism separates them into benign and harmful emergency signals to speed up response times. The suggested model is tested by road traffic simulation using SUMO (Simulation of Urban Mobility). Data collection per user, message delivery ratio, total data gathered, delay, and energy usage are all included in the model's overall evaluation. [ABSTRACT FROM AUTHOR]

This paper takes an interdisciplinary approach, combining legal studies, ethics and technical insights to shed light on the complex issues surrounding the regulation of predictive analytics. We argue that the individualised concept of regulation, shaped by the dogma of fundamental rights, is unable to adequately capture the implications of predictive analytics. We show that predictive analytics is a problem of collective privacy and informational power asymmetries, and conceptualise the form of data power at work in predictive analytics as 'prediction power'. The unregulated prediction power of certain actors poses societal risks, especially if this form of informational power asymmetry is not normatively represented. The article analyses this legal lacuna in the light of recent case law of the European Court of Justice and new legislation at the EU level. To address these challenges, we develop the concept of 'predictive privacy' as a protected good based on collective interests. [ABSTRACT FROM AUTHOR]

With an increase in supply chain disruptions, managing supply chain vulnerability has become a key factor to build resilient supply chains. Although supply chain vulnerability and associated factors are mostly studied individually or with selected groups, the supply chain literature lacks comprehensive and systematic studies. The current study aims to identify and segregate the supply chain vulnerability factors for manufacturing enterprises based on causal–effect relationships that exist between them. The supply chain vulnerability factors are identified from the extant literature review. Its applicability in Indian manufacturing enterprises is discussed with experts drawn from industry and academia. The cause–effect relationships of selected supply chain vulnerability factors are analysed using the fuzzy Decision Making Trial and Evaluation Laboratory (DEMATEL) approach. The obtained results indicate that supply design and supply chain efficiency-related factors are effect factors, whereas supply chain collaboration and information technology-related factors are causal factors. Complexity, centralisation, supplier concentration and low-cost sourcing are found to be the most prominent factors. This research contributes to the literature on supply chain vulnerability by describing the causal relationships among key factors impacting it. It would help managers to develop appropriate disruptions mitigation strategy(s) to make a resilient and robust supply chain. [ABSTRACT FROM AUTHOR]

SCADA systems are used in industries to perform control and monitoring of industrial processes in real-time. The advent of the Internet of Things (IoT), and the Industrial IoT (IIoT) in particular, has brought new disruptive technology that has the potential to drive the industrial digitization agenda well beyond the capabilities demonstrated by conventional SCADA systems. Industries, particularly manufacturing industries and utilities keep thriving to be competitive and the adoption of new technologies in the form of IIoT is set to improve efficiency and productivity through enhanced real-time data analytics and production availability. This paper examines the opportunities and challenges presented by integrating IIoT into existing SCADA systems. Potential solutions to the challenges are presented together with future research outlooks that have a bearing on the SCADA/IIoT integration efforts. [ABSTRACT FROM AUTHOR]

During the transition period when connected and automated vehicles (CAVs) and human-driven vehicles (HDVs) coexist on the roadway, miscommunication and improper interactions may lead to accidents due to lack of awareness of each other's intentions. The most promising approach to this problem is to view roadway transportation as a cyber-physical-social system consisting of CAV, HDV, and infrastructure subsystems. Although adaptations of infrastructure are as critical as the technological advances of vehicles, the role of infrastructure in CAV and HDV interactions has not been fully acknowledged. We consider the roadway transportation system from the system-of-systems perspective, taking a human-centred approach that integrates the behaviours of human drivers and CAVs with the design and enhancement of transportation infrastructure. We provide an overview of prior studies regarding information-processing and communication of the subsystems. Interactions between HDVs and infrastructure are summarised by human driving behaviours and HDV crash analysis. Interactions between HDVs and CAVs focus on how they perceive and predict actions of each other. Interactions between CAVs and infrastructure are characterised by possible adaptations of infrastructure to support CAV navigation. Lastly, we propose a human-centred framework to provide guidance for research on and design of next-generation transportation infrastructure with CAVs and HDVs. Implementation of CAVs on the roadways with HDVs requires a system-of-systems approach that includes the CAVs, HDVs, and transportation infrastructure. Humans must be at the center of this approach because their actions are central to the success of the entire system. [ABSTRACT FROM AUTHOR]

The concept of lean management has been of interest to researchers and practitioners in the construction industry and is implemented in most countries. While achieving waste mitigation in developed countries, lean is still in its elementary stages in the Middle East. Moreover, the implementation in the Middle East poses more challenges than benefit when choosing the right lean tools and techniques for projects. This study will critically investigate the factors affecting the process of adopting lean tools and techniques, which are directed towards technology, artificial intelligence and IoT in the construction industry in the public sector in the four governates of the Kingdom of Bahrain. Thereafter, the investigation will determine the level of lean construction management implementation and consequently its effect on sustainability using a quantitative method. The result from the questionnaire is hoped to shed a light on the poorly researched topic in Bahrain and benefit academics and practitioners. [ABSTRACT FROM AUTHOR]

This paper proposes an unconventional architecture and algorithm for implementing reservoir computing on FPGA. An architecture-oriented algorithm with improved throughput and architecture designed to reduce memory and hardware resource requirements are presented. The proposed architecture exhibits good performance in terms of benchmarks for reservoir computing. A prediction accelerator for reservoir computing that operates on 55.45 mW at 450 K fps with <3000 LEs is realized by implementing the architecture on FPGA. The proposed approach presents a novel FPGA implementation of reservoir computing focussing on both algorithms and architecture that may serve as a basis for applications of AI at network edge. [ABSTRACT FROM AUTHOR]

This essay defines the metaverse and its many uses. It then draws parallels to adventure and examines the elements that make adventure a unique learning medium. Next, it predicts the future based on the present and the past and discusses the drawbacks of this process and its outcomes. Finally, it concludes with a few observations and expectations around adventure not being easily replaced by the metaverse in the near future, but long term advances might tell a different story. [ABSTRACT FROM AUTHOR]

Ubiquitous music (ubimus) frameworks imply a flexible approach to the design, implementation and deployment of networked resources, featuring characteristics that challenge current conceptual groundings of musicmaking. As a potentially unifying place-holder for post-2020 musical practices enabled by ubiquitous technologies, musical stuff presents specific potentialities and caveats. We discuss the requirements for ubimus technological designs and analyze the possibilities and problems of adopting ‘stuff’ as a theoretical construct for ubimus endeavours. Several case scenarios tied to specific qualities are described, addressing aspects such as volatility, antirivalry, semantic strategies and fluidity, targeting the deterritorialization of resources and the application of aesthetic pliability. We highlight the challenges presented by the disruptive tendencies of fast or socially brittle technological change, with particular attention to the monetization of network assets. Stuff is proposed as nonrival, genre-neutral and pliable material for the development of a community-oriented musical internet. [ABSTRACT FROM AUTHOR]

This study combined the Human-centered design (HCD) approach with the immersive VR techniques to design and evaluate a new evacuation alarm system with improved warning efficiency for building emergency evacuation, considering the negative influence of pre-emergency activity. Four main HCD design activities were performed, the context-of-use, user requirements were defined through a theoretical perspective, technology-based solution research, expert group meeting, and focus group were used to produce the final concept. A smart phone-based evacuation alarm system concept, consisting of a control center and a smartphone-based user interface, featuring warning redundancy and multimodality was proposed. A developed virtual environment (VE) was used to evaluate and refine the final design in an iteration manner. This study widened the application of the HCD approach by using alternative simulator methods for evaluation, and added a step for the validation of VR as an adequate tool for designing and evaluating products. [ABSTRACT FROM AUTHOR]

The sparrow search algorithm (SSA) is an efficient swarm-intelligence-based algorithm that has made some significant advances since its introduction in 2020. A detailed overview of the basic SSA and several SSA-based variants is presented in this paper. To be specific, first, the principle of the basic SSA is introduced including its search mechanism and implementation process. Second, many improved SSAs are reviewed including the hybrid, chaotic, adaptive, binary and multi-objective SSAs. In addition, the applications of the SSAs are presented in some real scenarios such as the machine learning areas, energy systems, path planning and image processing. Finally, further research directions of the SSAs are discussed. This survey paper aims to provide a timely review on the latest developments of the SSAs. [ABSTRACT FROM AUTHOR]

A desirable array of electrical generating techniques reserves, utilizing renewable energy sources such as solar power, wind, and tidal energy. Many research companies have thought about primitive power generation technology, but electricity produced from these resources is not adequate and satisfactory and the power demands are that effective and intelligent systems are required to deliver the high energy level and the proper power delivery. A variety of other improved approaches can be used for successful manufacturing and delivery processes, although certain additional functionality would be needed to improve the operation. This article, thus, introduces an uncommon management framework and discusses efficiently the effects of the control of energy generation and delivery from renewable resources. Mainly for enriching the features Internet of Things (IoT) gets implemented for doubling the process output. IoT is a class of worldwide global neural networks in the cloud that acquaintances various things. The IoT is an elegantly connected device and framework which contain gleaming machine connecting and communicating with different machines, environments, and matter and will rise to meet a new challenge. Furthermore, this investigation here depends on the empowered solar panel monitoring and search for the proficient way to expand more power from solar radiation. [ABSTRACT FROM AUTHOR]

While a wealth of potentially valuable data is generated and stored every year, many businesses suffer from inefficiencies, information asymmetries, and high storage costs, and lack knowledge on how to monetize their data assets. Blockchain is said to offer crucial building blocks to enable a verified, traceable exchange and trading with sensitive data goods and to address current challenges. While the technology's potentials for decentralized data markets have been discussed, the question of how to realize it to optimize trading and welfare remains open. Applying design-science research methods and computational simulation to a real-world business-oriented blockchain project, this study proposes a market model. By adopting the consortium blockchain, we are thinking outside the confines of tokens tied to a blockchain when applying blockchain to the data trading market. Our marketplace is designed outside the speculative tokens space and can focus on the data trading marketplace. We evaluate the effects of different pricing functions on market welfare and trading in on-chain data goods. The results indicate that data trading and welfare can be maximized through a logarithmic pricing function. Further, in a market of heterogeneous agents, unexpectedly, we observe a tipping point in transaction fees above which market operations collapse. Monitoring the market's consumer price elasticity helps us to avoid this collapse node, and we can also impact it by controlling transaction costs. Academics and practitioners can learn about the idiosyncrasies of blockchain in market design and operation. [ABSTRACT FROM AUTHOR]

Cooperation is an emerging paradigm for improving spatial differentiation in Vehicular Ad-Hoc Networks (VANETs). Hence, many network solutions are motivated through cooperative communications for boosting VANET efficiency regarding specific network constraints like energy efficiency, network capacity, and outage probability. The main intention of this paper is to design and develop an optimization model for selecting the minimum number of multi-hops between the source and destination for the cooperative VANET networks. The first phase has a first-time slot, where a signal is transmitted by the source to the cooperative nodes (its relays) and their equivalent destination. The main problem considered here is to optimally select the number of hops or relays that are adaptable for communication, to solve the multi-objective functions concerning the target throughput and outage probability. The adoption of new Dimension-based Cat Swarm Optimization (D-CSO) is the main contribution here to optimally select the multi-hops among source and destination. Through the performance evaluation, it is observed that the designed model has achieved networks-wide fairness performance and a good convergence rate. [ABSTRACT FROM AUTHOR]