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Patients now want a contemporary, advanced healthcare system that is faster and more individualized and that can keep up with their changing needs. An edge computing environment, in conjunction with 5G speeds and contemporary computing techniques, is the solution for the latency and energy efficiency criteria to be satisfied for a real-time collection and analysis of health data. The feature of optimum computing approaches, including encryption, authentication, and classification that are employed on the devices deployed in an edge-computing architecture, has been ignored by previous healthcare systems, which have concentrated on novel fog architecture and sensor kinds. To avoid this problem in this paper, an Optimized Deep Recurrent Neural Network (O-DRNN) model is used with a multitier secured architecture. Initially, the data obtained from the patient are sent to the healthcare server in edge computing and the processed data are stored in the cloud using the Elliptic Curve Key Agreement Scheme (ECKAS) security model. The data is pre-processed and optimal features are selected using the Particle Swarm Optimization (PSO) algorithm. O-DRNN algorithm hyper-parameters are optimized using Bayesian optimization for better diagnosis. The proposed work offers superior outcomes in terms of accuracy and encryption latency while using computational cloud services. [ABSTRACT FROM AUTHOR]

This paper mainly studies the financial data management method and edge computing platform based on intelligent edge computing and big computing. This paper first introduces the theoretical basis of edge computing and big data computing, analyzes the collaborative mode of edge computing model and cloud computing, and discusses the cloud data storage scheme. In this paper, edge computing platform is built, and EC Master and Docker private image warehouse are mainly deployed in the data center. In this paper, the edge computing platform constructed was tested. In the mobile edge computing business processing time test, when there was a big gap in computing amount between business 1 and business 2, the total service time of MEC instances was around 0.3 s, showing a good user experience. In the edge acceleration effect test, before and after the adoption of CDN edge acceleration, from the perspective of the overall web page, the average time consumed to open a web page decreased from 1615 ms to 403 ms. From requesting a single HTTP access request, the average time decreased from 520 ms to 74 ms. The financial data management edge computing platform designed in this paper can effectively manage financial data and make an effective contribution to enterprise data management. [ABSTRACT FROM AUTHOR]

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]

Due to the unprecedented growth of data, edge computing is transforming our lives in a better manner by increasing the performance and reducing the latency. It is the key framework deployed close to the data for processing effectively by connecting millions of devices together. Though it handles accelerated growth of data efficiently in real-time, data security and edge device authentication are still a nightmare. On the contrary, it is not possible to use complex protocols for authentication of edge devices due to its resource-constrained in nature. To address this issue, a state-of-art Hyperelliptic Curve Diffie–Hellman-based two-server Password-Only Authenticated Key Exchange Protocol is proposed in this paper by utilizing the properties of a geometrical shape, namely, tetrahedron. As a primary mandate, edge devices are authenticated to centralize cloud servers that store password information as tetrahedron properties using Hyperelliptic Curve Cryptography and Diffie–Hellman key exchange mechanisms. The proposed work is compared with similar existing works and proves its strength by considering communication cost, computational cost, security defense rate, and attack resistance rate as its core evaluation parameters. [ABSTRACT FROM AUTHOR]

In this paper, we consider non-clairvoyant task offloading for random tasks in mobile edge computing within the framework of combinatorial optimisation. For offline non-clairvoyant task offloading, we propose a non-clairvoyant task offloading algorithm, which is able to determine a task offloading strategy without knowing the amount of computation and communication of any task. For online non-clairvoyant task offloading, we propose a randomised online task offloading algorithm, which is able to make an offloading decision for an arrival task without knowing anything about future tasks and other tasks. For both algorithms, we analyse the probability of certain performance guarantee. We also demonstrate numerical data. To the best of the author's knowledge, this is the first paper which considers both offline and online non-clairvoyant task offloading in mobile edge computing, together with analytical results on performance guarantee with high probability. [ABSTRACT FROM AUTHOR]

Data deduplication can solve the problem of resource wastage caused by duplicated data. However, due to the limited resources of Internet of Things (IoT) devices, applying data deduplication to IoT scenarios is challenging. Existing data deduplication frameworks for the IoT are prone to inefficiency or trust crises due to the random allocation of edge computing nodes. Furthermore, side-channel attacks remain a risk. In addition, after IoT devices store data in the cloud through data deduplication, they cannot share their data efficiently. In this paper, we propose a secure and efficient data deduplication framework for the IoT based on edge computing and blockchain technologies. In this scheme, we propose a model based on parallel use of three-layer and two-layer architectures and introduce the RAndom REsponse (RARE) scheme to resist side-channel attacks. We also design a label tree to realise one-to-many data-sharing, which improves efficiency and meets the needs of the IoT. In addition, we use blockchain to resist collusion attacks. Experiments were conducted to demonstrate that our framework has advantages over similar schemes in terms of communication cost, security and efficiency. [ABSTRACT FROM AUTHOR]

Following the considerable development of big data and the need for real-time information processing, the recent computing paradigm changed from centralized intelligence on cloud computing to the distributed intelligence on Edge Computing. On the algorithm layer, Artificial Intelligence (AI) has been applied in different areas related to intelligent-edge applications. In this type of application, consumption and speed present essential challenges when designing hardware-level architecture on the embedded systems. In particular, an Edge Computing System (ECS) must be an energy efficient, compact, and fast component integrated into edge devices. Miniaturization has been happening at a steady pace for the last years. However, the MOS transistor size has been slowly reaching the physical limit. In this paper, we propose a Quantum Cellular Automata clock-phase-based technique for the design of the proposed ALU module. The obtained results using the QCA architecture designer exhibit better performances over the existing designs in terms of size, cell number, cost, latency, and power consumption. We have also used QCAPro software for power consumption estimation. The results show the effectiveness of the design in terms of cost and power. [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]

Misalignment, imbalance, induced vibrations, and noise in rotating machines must be identified early on using condition monitoring and signal processing techniques. If it is not detected early, the machine’s reliability will suffer, potentially resulting in a catastrophic failure of the machine components. In this study, a web application for real-time fault detection is designed and built using a novel approach of edge computing and IoT. Vibration signature analysis are used to determine the severity of faults in machine rotating components and to provide an early warning even when the maintenance crew is located in a remote location. The vibration spectrum analysis results are successfully obtained and verified using the vibration-metre tool VibXpert-II. The purpose of this research is to improve real-time condition monitoring of rotating systems like bearings and induction motors and make it available on an online platform for predictive maintenance. Vibration signatures provide more accurate information regarding the type and location of rotor faults than current signatures. [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]

Logistics 4.0 aims at enabling the sustainable satisfaction of customer demands with optimised costs of services with the use of emerging technologies, such as Internet of Things, streaming analytics, and optimised decision making. The availability of massive sensor data streams over time opens new perspectives for extracting meaningful and timely insights from data-in-motion through streaming analytics. Logistics 4.0 is a relatively new field of research which demands the development of scalable and efficient software solutions and their deployment to successful real-life case studies. In this paper, we propose a software framework for streaming analytics in an edge-cloud computational environment aiming at covering the whole data analytics lifecycle in logistics processes and thus, advancing the evolution and realisation of the Logistics 4.0 concept. The proposed framework takes advantage of edge computing technologies, streaming analytics and proactive decision making in order to monitor, analyse and support decision making in the frame of Logistics 4.0. It is applied and evaluated in a maintenance service logistics use case from the aerospace industry. [ABSTRACT FROM AUTHOR]

The rapid adoption of Cloud, Fog, and Edge computing paradigms, along with Digital Twin (DT) technology, is being observed across numerous domains. Smart Agriculture is one such domain, where diverse requirements exist for applications that range from Augmented Reality, Satellite, and auto harvesters to a multitude of agricultural devices and sensors. While the integration of Cloud, Fog, and Edge computing can address various challenges, it can also pose some difficulties, including resource allocation, resource scheduling, and task scheduling. To address these challenges and enhance the productivity and sustainability of Smart Agriculture, this paper presents a novel architecture based on Cloud, Fog, Edge computing, and Multi-Agent Systems (MAS), incorporating the concept of Digital Twin. This proposed architecture is implemented in Smart Agricultural farms and fields, opening up new contemporary applications in the Agriculture domain. [ABSTRACT FROM AUTHOR]

Cyber physical production system (CPPS) is an essential prerequisite to facilitate online monitoring, visualization, and control in a smart manufacturing system. The goal of this paper is to provide a proof of concept by proposing a CPPS framework, based on complementing cloud, fog and edge computing technologies, to demonstrate the real-time online monitoring, visualization, and control of a conventional 3D printer. The proposed methodology was successfully used for online detection and control of part defects, filament runouts, and smoke generation. The proposed methodology was also used to acquire, monitor, and visualize energy consumption, relative humidity, temperature, volatile organic compounds, particulate matters, and acceleration during 3D printing. The present work utilizes the computing technologies where they do not compete, but instead complement each other to enable intelligent capabilities and online sharing of resources which are scalable, reliable, and efficient. The proposed CPPS framework can be extended to provide low-cost solutions to micro, small and medium enterprises to convert their traditional critical equipment for real-time monitoring, visualization, control, and analytics. [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]

In this work, we construct a data-driven model to address the computing performance problem of the moving particle semi-implicit method by combining the physics intuition of the method with a machine-learning algorithm. A fully connected artificial neural network is implemented to solve the pressure Poisson equation, which is reformulated as a regression problem. We design context-based feature vectors for particle-based on the Poisson equation. The neural network maintains the original particle method's accuracy and stability, while drastically accelerates the pressure calculation. It is very suitable for GPU parallelization, edge computing scenarios and real-time simulations. [ABSTRACT FROM AUTHOR]

The article titled "An Edge Computing Unloading Algorithm for IIoT-based Mobile and Internet of Vehicles (IoV) Applications" has been retracted by the Editor, Institution, and Publisher of the IETE Journal of Research. This retraction is a result of concerns raised about the peer review and decision-making processes for the special issue in which the article was published. An investigation by the Taylor & Francis Publishing Ethics & Integrity team confirmed that the articles in the special issue were not reviewed appropriately. The journal is committed to correcting the scientific record and will cooperate with any institutional investigations. The retracted articles will remain online but will be digitally watermarked as "Retracted." [Extracted from the article]

The machine learning (ML) field has deeply impacted the manufacturing industry in the context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of smart sensors, devices, and machines, to enable smart factories that continuously collect data pertaining to production. ML techniques enable the generation of actionable intelligence by processing the collected data to increase manufacturing efficiency without significantly changing the required resources. Additionally, the ability of ML techniques to provide predictive insights has enabled discerning complex manufacturing patterns and offers a pathway for an intelligent decision support system in a variety of manufacturing tasks such as intelligent and continuous inspection, predictive maintenance, quality improvement, process optimisation, supply chain management, and task scheduling. While different ML techniques have been used in a variety of manufacturing applications in the past, many open questions and challenges remain, from Big data curation, storage, and understanding, data reasoning to enable real-time actionable intelligence to topics such as edge computing and cybersecurity aspects of smart manufacturing. Hence, this special issue is focused on bringing together a wide range of researchers to report the latest efforts in the fundamental theoretical as well as experimental aspects of ML and their applications in manufacturing and productionsystems. [ABSTRACT FROM AUTHOR]

The intelligent treatment of urban garbage is an important component of creating a smart city and also solves several problems associated with urban garbage. Many traditional garbage cans are widely distributed, leading to great consumption of human and material, untimely government. Therefore, in this paper, we propose an intelligent system based on edge computing and the narrow-band Internet of things (NB-IoT) for monitoring smart trash cans (STCs). The experimental results demonstrate that the proposed STC system improves the load capacity of garbage cans, requires few human and material resources, and allows for intelligent management of garbage in smart cities. [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]

In the digital age, businesses collect vast amounts of data, but often lack the knowledge and tools to fully utilize it. This paper analyzes current information management practices across different domains and highlights the need for global standards and cutting-edge technologies such as AI, ML, DL, and cloud/edge computing. By examining trends in published research, the study proposes new research lines and provides a comprehensive overview of the field's development, definitions, and trends. The research contributes significantly to the subject of information management and offers a synthesized description of the field's state. [ABSTRACT FROM AUTHOR]

The development and integration of technologies such as the Internet of Things (IoT) or edge computing devices is contributing to the formation of an increasingly digital, intelligent and connected world. As a result, there is a massive flow of data in different sectors of human activity. One example is intelligent buildings, where thousands of components, devices, systems and suppliers interact. In this context, failures in control and monitoring systems are frequent. To analyze this situation, this paper presents as a case study the problem of fault-tolerant robust adaptive monitoring control with state prediction performance for a class of IoT temperature systems subject to uncertainties of precision states and external disturbances. The authors propose a new control strategy based on consensus game theory and prediction of future precision states to reduce tracking error and improve algorithm efficiency. The authors present the development of a new algorithm that improves the functioning of monitoring and control of parcel networks. This has the purpose of increasing the energy efficiency of the same and ensure the effectiveness of our adaptive temperature control algorithm, compared to existing results. With the simulation presented in this research, it is possible to conclude that a new fault tolerant error tracking algorithm ensures robust monitoring of the reference model. It was shown that the predicted temperature signal is limited by a small range close to the collected temperature data. A case study result is provided to demonstrate the effectiveness of the proposed fault-tolerant adaptive monitoring control algorithm. [ABSTRACT FROM AUTHOR]

In the present literature, various algorithms are available for computing the shortest path between two objects. The maximum number of these algorithms compute the shortest path either between two static objects or one static object and one dynamic object. This article presents an insight to integrated Mobile Edge Computing (MEC) based smart devices for tracking mobile rogue objects based on dynamic source and dynamic destination optimal cost estimation. This device considers any two mobile objects to estimate the shortest path between them. The proposed Ant Colony Optimization (ACO) based algorithm considers the property of dead-end removal and nth path exploration with efficient self-loop removal strategy. To review the performance of the proposed algorithm, experimentations are carried out and compared with several well-established shortest cost estimation techniques available in the literature – Floyd Warshall, Bellman Ford, Dijkstra, A* algorithms and the only dynamic shortest path algorithm. The detailed algorithmic comparisons clearly indicate the superiority of the proposed one over the existing dynamic and present state-of-the-art shortest path estimation methodologies. [ABSTRACT FROM AUTHOR]

Recently, large scale cyber physical systems (LS-CPS) leverage network-cores provided by application providers (APs) to carry out analytics. These CPS-APs uses the automated cloud to gather traffic data streams, thereby reducing infrastructure and maintenance costs. However, network reliability and maintainability considering the arrival rate of user application requests presents a major challenge such as Edge-to-Fog and Fog-to-Cloud resource auto-scaling constraints. QoS dynamic reliability, as well as its flexible management, could offer an efficient network for CPS. In this paper, CloudMesh CPS architecture is presented as a promising solution to support services like Input-Output (IO) data stream, traffic engineering, service function optimization and software defined network monitoring in CPS IPv6 data-center core. Dynamic reliability modeling for QoS parameter scaling based on observable history is presented. Hence, QoS proactive auto-scaling algorithm (PASCQA) embedded with a heuristic predictor is introduced in CloudMesh CPS. IO streams are captured by the predictor which analyzes the QoS history for reliable global performance. Finally, the work realized the critical performance aspects of CPS architecture and analyzed the effects of QoS parameter configuration for real-time deployment. The implementation results show that CloudMesh offers satisfactory QoS metrics compared with Fat-tree-1 and Fat-tree-2 for CPS applications. [ABSTRACT FROM AUTHOR]

As mobile devices have become the preferred tool for communication, work, and entertainment, traffic at the edge of the network is growing more rapidly than ever. To improve user experience, commodity servers are deployed in the edge to form a decentralised network of mini datacenters each serving a localised region. A challenge is how to place these servers geographically to maximise the offloading benefit and be close to the users they respectively serve. We introduce a formulation for this problem to serve applications that involve pairwise communication between mobile devices at different geolocations. We explore several heuristic solutions and compare them in an evaluation using both real-world and synthetic datasets. [ABSTRACT FROM AUTHOR]

We, the Editor, Institution and Publisher of the journal IETE Journal of Research, have retracted the following article, which is part of the Special Issue titled "Federated Learning for Blockchain Systems and Industrial Internet of Things": Wenfan Wang & Junzheng Li (2022) An Edge Computing Unloading Algorithm for IIoT-based Mobile and Internet of Vehicles (IoV) Applications, IETE Journal of Research, DOI: Following publication, concerns were raised about the peer review and decision-making processes for this special issue. After an investigation by the Taylor & Francis Publishing Ethics & Integrity team, in full cooperation with the Editor-in-Chief and the Institution, it was confirmed that the articles included in this special issue were not reviewed appropriately, in line with the Journal's peer review standards and policy. As the stringency of the peer review process is core to the integrity of the publication process, the Editor and Publisher have decided to retract all of the articles within the above-named Special Issue. The journal has not confirmed if the authors were aware of this compromised peer review process. The journal is committed to correcting the scientific record and will fully cooperate with any institutional investigations into this matter. The authors have been informed of this decision. We have been informed in our decision-making by our editorial policies and the COPE guidelines. The retracted articles will remain online to maintain the scholarly record, but they will be digitally watermarked on each page as "Retracted". [ABSTRACT FROM AUTHOR]

Many IoT devices, especially those deployed at the network edge have limited power resources. In this work, we study the effects of a variety of battery draining attacks against edge nodes. Specifically, we implemented hello flooding, packet flooding, selective forwarding, rank attack, and versioning attack in ContikiOS and simulated them in the Cooja simulator. We consider a number of relevant metrics, such as CPU time, low power mode time, TX/RX time, and battery consumption. Besides, we test the stretch attack with three different batteries as an extreme scenario. Our results show that versioning attack is the most severe in terms of draining the power resources of the network, followed by packet flooding and hello flooding attacks. Furthermore, we find that selective forwarding and rank attacks are not able to considerably increase the power resource usage in our scenarios. By quantifying the effects of these attacks, we demonstrate that under specific scenarios, versioning attack can be three to four times as effective as packet flooding and hello flooding attacks in wasting network resources. At the same time, packet flooding is generally comparable to hello flooding in CPU and TX time usage increase but twice as powerful in draining device batteries. [ABSTRACT FROM AUTHOR]

Automatic target recognition and detection in synthetic aperture radar (SAR) images is playing a significant role in military and civilian fields. Traditional methods are weak in migration capabilities and cumbersome in manual design. Existing deep learning-based methods can achieve high recognition and detection accuracy, but most of them have a very large number of parameters and slow recognition and computational efficiency, which makes it difficult for them to migrate to edge computing equipments, and computational efficiency is crucial in rapid maritime rescue and emergency military deployment. Therefore, we propose a lightweight feature extraction block, lightweight split concat block (LSCB), for SAR automatic target recognition and detection. Specifically, LSCB consists of several operators, such as split, depthwise convolution (DC), multiple kernel sizes block (MKSB), and channel-shuffle (CS). MKSB is a novel channel attention mechanism designed by us, which can make feature maps have target features of different scales and adjust their receptive field sizes adaptively. In addition, to assess the performance of LSCB, we use LSCB to build LSCNet-R and LSCNet-D for SAR target recognition and detection, respectively, and conduct extensive experiments on MSTAR, SSDD, and SAR-Ship-Dataset. The experimental results show that our LSCNet-R and LSCNet-D built with LSCB achieve higher recognition and detection accuracy and computational efficiency with few parameters compared with existing excellent methods. This further illustrates that the proposed LSCB can extract fine and stable features with few parameters. [ABSTRACT FROM AUTHOR]

Software-Defined Networking (SDN) and Edge computing are the major elements due to its scalability, flexibility and efficiency. Still, security is one of the significant issues. Distributed Denial of Service (DDoS) is the harmful attack that exhausts many resources. The existing solutions consume large time for detection and present poor mitigation schemes. To address these issues, this paper presents a Protected Cluster-assisted SDWSN for Edge-IoT (ProSD-EdgeIoT) architecture. The ProSD-EdgeIoT follows tetrad architecture that performs clustering in the device layer, first-level DDoS detection in the edge layer,second-level DDoS detection in the control layer and multi-level DDoS mitigation in data forwarding layer. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is a smart technology that has switched the conventional way of living into smart living. As their usage becomes unavoidable, malware attacks in IoT networks have also increased. Many investigations and studies have proposed different methods to detect malware attacks, but these measures have some performance degradation in terms of accuracy, error, and lack of comprehensiveness. The cloud-based IoT infrastructure further creates latency and security problems. The machine learning (ML)-based edge computing can overcome these complications by automating the responses and moving the computation nearer to the network edge, where data is created. In this work, the performance of various prominent ML algorithms, such as logistic regression (LR), naive Bayes (NB), support vector machine (SVM), decision tree (DT), random forest (RF), and k-nearest neighbor (KNN), has been compared to predict malware attack accurately in IoT-edge environment. To enhance the prediction accuracy of the ML algorithms, the unbalanced data is converted into balanced data using the synthetic minority oversampling technique (SMOTE) and optimum features are selected using the Gini impurity-based weighted RF feature selection technique (GIWRF). The investigational results show that among six ML algorithms, RF with GIWRF attained the highest accuracy of 99.39%. [ABSTRACT FROM AUTHOR]

Emergency communication networks play a vital role in disaster monitoring, transmission, and application during disaster emergency response (DER), however, the performance and stability of edge nodes in the emergency communication networks are often weak due to limited communication and computation resources. This weakness directly affects the quality of service (QoS) of the geospatial edge service (GES) chains involved in emergency monitoring. Existing research predominantly addresses service compositions in stable environments, neglecting the aggregation of efficient and robust GES chains in emergency communication networks. This study proposes an evolutionary particle swarm optimization (EPSO)-based emergency monitoring GES chain in an emergency communication network. It includes a GES chain model of emergency environment monitoring for tailing areas, as well as the designs of the particle chromosome encoding method, fitness evaluation model, and particle chromosome swarm update operators of the EPSO-based GES chain. Finally, the study conducts emergency environment monitoring experiments for tailing areas using the proposed method. Experiments results demonstrate that the proposed method significantly enhances the efficiency, stability, and reliability of emergency monitoring GES chains in the emergency communication network. This is crucial to providing fast and reliable services for DER during natural disasters. [ABSTRACT FROM AUTHOR]

Designing a lightweight and robust real-time land cover segmentation algorithm is an important task for land resource applications. In recent years, with the development of edge computing and the increasing resolution of remote sensing images, the huge amount of calculations and parameters have restricted the efficiency of real-time semantic segmentation. Therefore, the emergence of lightweight CNN (convolutional neural network) has accelerated the development of real-time semantic segmentation of land cover. However, nowadays, the time and space span of aerial images is getting larger and larger, resulting in the loss of details and blurred edges of lightweight CNN. Therefore, the existing lightweight CNN model has low segmentation accuracy and poor generalization ability in real-time land cover segmentation tasks. In order to solve the problem of lightweight network in this respect, this paper proposes a Semantics Guided Bottleneck Network (SGBNet) to balance accuracy and reasoning speed. First, a basic unit and the overall network structure are redesigned to increase the overall reasoning efficiency of the model. The model can efficiently extract spatial details and contextual semantic information. Then, the model optimizes the lightweight network and realizes the extraction of details and contextual semantic information. Finally, a lightweight attention mechanism is used to restore high-resolution pixel-level features. The results of comparative experiments show that the method in this paper has a higher segmentation accuracy than existing models while achieving lightweight. [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]

As a natural consequence of Industry 4.0 demands related to industrial processes control digitization, the smart manufacturing (SM) concept was enriched by integrating advanced technologies of digital information processing. This paper presents an original solution for improving the most representative instance of SM, namely, supply chain management (SCM), by using the most recent approach in hierarchical networked control systems (HNCS), that is Edge Computing. The paper presents both the software and hardware architecture of an Edge Gateway device which offers, on one hand, dual communication both on vertical (between the network layers from the local, inferior, represented by IIoT, to the central, superior, represented by the Cloud) and on horizontal (between similar devices located on the same Edge level) and, on the other hand, can locally process data to take fast decisions which facilitate real-time process control. The validity of the proposed solution is supported by a case study performed on a flexible assembly/disassembly line (laboratory model). [ABSTRACT FROM AUTHOR]

We review the latest developments in Fiber-optic Telecommunications and Networks presented at OFC 2022 and compare the progress accomplished since the previous year conference, OFC 2021. In particular, we note this year's emphasis on the application of Neural Networks (NN) as part of Artificial Intelligence (AI) and Machine Learning (ML), as well as continued research in quantum communications and key distribution. We also expect future conferences to include papers about optical variants of quantum computers, a field of great interest that can exploit many quantum systems, as well as papers on continuing developments in High-Speed Communications beyond 800 Gb/s, and increased use of coherent modulation and detection in Data Centers Interconnect, Cloud and Edge computing, the latter in support of 5 G Internet Of Things (IOT). [ABSTRACT FROM AUTHOR]

Despite the extensive deployment of digital instruments in modern times, their stability is challenging to maintain in adverse environmental conditions such as extreme temperatures, pressure, or powerful electromagnetic radiation. Analog meters, owing to their mechanical resilience and electromagnetic impedance, persistently find usage across nuclear power plants, petroleum, and chemical industries. However, under these harsh conditions, manual reading of the instruments may prove to be difficult and dangerous while failing to meet the requirements of real-time monitoring. In recent years, several machine vision-based meter reading systems have been proposed, however, achieving high accuracy through camera-based methods under varying angles and lighting conditions poses significant challenges. Cloud deployment may compromise plant privacy, while edge computing faces limitations in real-time meter reading due to limited computing power. To address these issues, we propose a real-time reading system based on the YolactEdge instance segmentation framework for single-point analog meters. Our system is more accurate than previous studies and is implemented and deployed on the Jetson Xavier NX edge computing device. Our performance evaluation shows that our model outperforms other baselines, with low reference values and relative errors of 0.0237% and 0.0300%, respectively, and an average inference speed of 10.26 FPS with INIT 8 linear acceleration on Nvidia Jetson NX. [ABSTRACT FROM AUTHOR]

As a novel Internet architecture focused on data contents, Named Data Networking (NDN) has been proven to be of great value in supporting the Internet of Things, Edge computing, Blockchain, and other popular topics. They can benefit mainly from NDN's intrinsic properties, such as flexible multicasting, in-network caching, among several others. However, once NDN's forwarding plane suffers Denial of Service (DoS) attacks, the overall system performance would be affected significantly. In NDN data transmission, interest forwarding is the most time-consuming operation and thus opens a possible vector of DoS attacks. It is proposed in this paper a fast name lookup algorithm for NDN interest forwarding, which selects feature prefixes instead of lengths to filter out interest packets in NDN interest forwarding. Due to the excellent filtration with feature prefixes, the algorithm accelerates NDN forwarding processes. Compared with other existing solutions, the proposed algorithm shows more than 70% of time improvement in forwarding malicious interests, while remaining at the same performance level in standard cases. [ABSTRACT FROM AUTHOR]

Mobile edge computing is a new paradigm which provides cloud computing capabilities at the edge of pervasive radio access networks in close proximity to users. The problem of edge server selection in mobile edge environment in terms of user's overhead is investigated in this paper. Due to the limited resources of edge server, we firstly study the task completion probability of edge servers. Secondly, we formally model the problem of edge server selection in terms of time latency and energy consumption. More especially, the computation overhead method for completing the task in cases of both service migration and non-migration is investigated. Then, a new optimized edge server selection algorithm, called combined Genetic algorithm and simulated Annealing algorithm for edge Server Selection (GASS) is designed. Finally, a series of experiments on a real-word data-trace are conducted to evaluate the performance of GASS. The results show that GASS can effectively minimize the overhead of the user and outperform traditional heuristic algorithms. [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]

Abstract The appropriate control of maintenance effort in the edge computing based on open-source software (OSS) relates to stable and reliable operation, because the fault detection phenomenon depends on the effort expenditure. Actually, several software reliability growth models with testing-effort have been proposed in the past. In this paper, we propose cyclic Wiener process models to consider the network environment under the edge OSS operation. Then, we assume that the sawtooth wave and square one as the cyclic noises. Thereby, the proposed models will be able to comprehend two characteristics of OSS and network environment. Then, it will be useful to assess the operation effort expenditures with network environment of edge OSS service. Moreover, actual effort data sets are analyzed to show numerical examples of the proposed model considering the network environment under the edge OSS operation. [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]

Current vehicles are equipped with a number of environmental sensors to improve safety and quality of life for passengers. Many researchers have shown that these sensors can also be exploited for opportunistic crowd-sensing. Useful new services can be developed on top of these data, like urban surveillance of Smart Cities. The spatio-temporal sensing coverage achievable with Vehicular Crowd-Sensing (VCS), however, is an open issue, since vehicles are not uniformly distributed over the road network, undermining the quality of potential services based on VCS data. In this paper, we present an evolution of the standard A ∗ routing algorithm, meant to increase VCS coverage by selecting a route in a random way among all those satisfying a parametric constraint on the total cost of the path. The proposed solution is based on an edge-computing paradigm, not requiring a central coordination but rather leveraging the computational resources available on-board, significantly reducing the back-end infrastructure costs. The proposed solution has been empirically evaluated on two public datasets of 450,000 real taxi trajectories from two cities, San Francisco and Porto, characterized by a very different road network topology. Results show sensible improvements in terms of achievable spatio-temporal sensing coverage of probe vehicles. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of sensing technology and the Internet of Things (IoT), sensors play increasingly important roles in traffic control, medical monitoring, industrial production and etc. They generated high volume of data in a streaming way that often need to be processed in real time. Therefore, streaming data computing technology plays an indispensable role in the real-time processing of sensor data in high throughput but low latency. However, there are two problems in deploying streaming data process ability in cloud computing data centre. Firstly, massive sensor nodes simultaneously upload data to the remote cloud computing data centre, which requires a large number of bandwidth resources supports. The existing network infrastructure cannot provide enough bandwidth at a reasonable price. Secondly, due to the geographical distribution characteristics of the cloud computing data centre, there will inevitably be large transmission delay during the process of data transmission. Such end-to-end delay is intolerable to mobile applications especially for those latency sensitive tasks. In view of the above problems, this paper proposes an autonomous driving oriented edge streaming data processing framework, which migrates the computing and storage capability from the remote cloud data centre to the edge data centre. It focuses on the change of vehicle flow in a specific geographical area, and uses the computing power sunk to edge node to process the massive streaming data generated by autonomous vehicles nearby. The proposed framework is implemented on top of Spark Streaming, which builds up a gray model based traffic flow monitor, a traffic prediction orientated prediction layer and a fuzzy control based Batch Interval dynamic adjustment layer for Spark Streaming. It could forecast the variation of sensors data arrive rate, make streaming Batch Interval adjustment in advance and implement real-time streaming process by edge. Therefore, it can realise the monitor and prediction of the data flow changes of the autonomous driving vehicle sensor data in geographical coverage of edge computing node area, meanwhile minimise the end-to-end latency but satisfy the application throughput requirements. The experiments show that it can predict short-term traffic with no more than 4% relative error in a whole day. By making batch consuming rate close to data generating rate, it can maintain system stability well even when arrival data rate changes rapidly. The Batch Interval can be converged to a suitable value in two minutes when data arrival rate is doubled. Compared with vanilla version Spark Streaming, where there has serious task accumulation and introduces large delay, it can reduce 35% latency by squeezing Batch Interval when data arrival rate is low; it also can significantly improve system throughput by only at most 25% Batch Interval increase when data arrival rate is high. [ABSTRACT FROM AUTHOR]

In the context of the Industrial Internet of things (IIoT), large-scale IIoT data is generated, which can be effectively mined to provide valuable information for condition monitoring (CM). However, traditional CM methods cannot meet unprecedented challenges concerning large-scale IIoT data transmission, storage and analysis. Therefore, manufacturers have begun to shift from the traditional manufacturing paradigm to smart manufacturing, which integrates the encapsulated manufacturing services and the enabling cloud-edge computing technology to handle large-scale IIoT data. To enhance the agility, scalability and portability of traditional manufacturing services, a microservices-based cloud-edge collaborative CM platform for smart manufacturing systems is proposed. First, leveraging the microservices management system, the lightweight edge and cloud services are constructed from the microservices level, which enables flexible deployment and upgrade of services. Next, the proposed platform architecture effectively integrates the computing and storage capabilities of the cloud layer and the real-time nature of the edge layer, where the cloud-edge collaborative mechanism is introduced to achieve real-time diagnosis and enhance prognosis accuracy. Finally, based on the proposed system, the diagnosis and prognosis tasks are implemented on a manufacturing line, and the results show that the diagnostic accuracy is 90% and the prediction error is 50%. [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]

The purpose of this study is to improve healthcare system performance by utilizing cutting-edge computing technologies like blockchain and the Internet of Things. Blockchain-based data transfer, Association Rule hiding, and ideal key generation are the three primary aspects of the proposed work. Initially, data are altered using blockchain, then the data enter the Proposed Association Rule concealing stage. In this research a novel association rule concealment phase is implemented, which has three crucial processes: (1) data pattern mining using the improved apiori algorithm, (2) detection of sensitive data based on the improved apiori algorithm, and (3) a method for cleaning and restoring data. Using the generated optimal key, the sanitized sensitive data are recovered. Keys are critical to both the data sanitization and restoration procedures. Hence, a multi-objective hybrid optimization model is known as the Rock Hyraxes Updated Marriage in Honey Bee Optimization (RHUMBO) is employed. Then, the confidentiality of the suggested model's performance has been validated. From the experimental analysis the proposed model achieved 97% for Cleveland dataset at 90th learning percentage which is the best score. And the cost function of the suggested model is minimum (∼0.08 at 100th iteration). [ABSTRACT FROM AUTHOR]

The exponential growth of networking technology has resulted in a massively expanded computer ecosystem. With implications in the Industrial Internet of Things (IIoT) and virtual reality, mobile networks operate and provide a multi-aspect strategy for multiple resource allocation paradigms and service-oriented possibilities in the computing sectors. The Mobile Edge Computing (MEC) model combines a virtual source with edge communication among execution. Thus, this study is to develop and implement a revolutionary resource allocation technique in the IIoT by combining optimal Reinforcement Learning (RL) with a hybrid meta-heuristic algorithm. The three basic levels in the proposed paradigm are "Data input layer," "Data management layer," and "Data analytics layer". The data management layer is responsible for the data collected from edge devices and external devices. The proposed model's goal at the data management layer is to provide an intelligent work allocation mechanism. The Harris Hawks-Spider Monkey Optimization (HH-SMO) method combines Harris Hawks Optimization (HHO) and Spider Monkey Optimization (SMO) to find the best job allocation. From the statistical analysis, the mean of HH-SMO-RL is 15.88%, 14.91%, 14.79%, and 5.99% superior to SMO-RL, HHO-RL, JA-RL, and DHOA-RL respectively, which has shown the resource allocation in IIoT using optimized RL respectively. [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]

Rapid advances in new generation information technologies, such as big data analytics, internet of things (IoT), edge computing and artificial intelligence, have nowadays driven traditional manufacturing all the way to intelligent manufacturing. Intelligent manufacturing is characterised by autonomy and self-optimisation, which proposes new demands such as learning and cognitive capacities for manufacturing cell, known as the minimum implementation unit for intelligent manufacturing. Consequently, this paper proposes a general framework for knowledge-driven digital twin manufacturing cell (KDTMC) towards intelligent manufacturing, which could support autonomous manufacturing by an intelligent perceiving, simulating, understanding, predicting, optimising and controlling strategy. Three key enabling technologies including digital twin model, dynamic knowledge bases and knowledge-based intelligent skills for supporting the above strategy are analysed, which equip KDTMC with the capacities of self-thinking, self-decision-making, self-execution and self-improving. The implementing methods of KDTMC are also introduced by a thus constructed test bed. Three application examples about intelligent process planning, intelligent production scheduling and production process analysis and dynamic regulation demonstrate the feasibility of KDTMC, which provides a practical insight into the intelligent manufacturing paradigm. [ABSTRACT FROM AUTHOR]