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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]

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]

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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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 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]

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]

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]

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]

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]

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]

As the technology of autonomous vehicle develops, online hailing autonomous taxi system is regarded as one of the most popular public transportation services in the future. Studies related to demand forecasting, ride matching, path planning, relocation, and pricing strategy for shared online hailing and autonomous taxi services have emerged in recent years. In this study, we conducted a survey based on 141 representative literatures from 1995 to 2022 to understand the state-of-the-art of the key problems of operating autonomous taxi service. First, a comprehensive review of the components of the shared autonomous taxi modelling is presented. Then, how the emerging technologies such as internet of vehicles, big data, cloud and edge computing, and blockchain can be used to enhance the autonomous taxi service is discussed. Last, the current research challenges and the concern or hurdle in public's adoption of autonomous taxi services are identified. [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]

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]

Traditional health systems mostly rely on rules created by experts to offer adaptive interventions to patients. However, with recent advances in artificial intelligence (AI) and machine learning (ML) techniques, health-related systems are becoming more sophisticated with higher accuracy in providing more personalized interventions or treatments to individual patients. In this paper, we present an extensive literature review to explore the current trends in ML-based adaptive systems for health and well-being. We conduct a systematic search for articles published between January 2011 and April 2022 and selected 87 articles that met our inclusion criteria for review. The selected articles target 18 health and wellness domains including disease management, assistive healthcare, medical diagnosis, mental health, physical activity, dietary management, health monitoring, substance use, smoking cessation, homeopathy remedy finding, patient privacy, mobile health (mHealth) apps finder, clinician knowledge representation for neonatal emergency care, dental and oral health, medication management, disease surveillance, medical specialty recommendation, and health awareness. Our review focuses on five key areas across the target domains: data collection strategies, model development process, ML techniques utilized, model evaluation techniques, as well as adaptive or personalization strategies for health and wellness interventions. We also identified various technical and methodological challenges including data volume constraints, data quality issues, data diversity or variability issues, infrastructure-related issues, and suitability of interventions which offer directions for future work in this area. Finally, we offer recommendations for tackling these challenges, leveraging on technological advances such as multimodality, Cloud technology, online learning, edge computing, automatic re-calibration, Bluetooth auto-reconnection, feedback pipeline, federated learning, explainable AI, and co-creation of health and wellness interventions. [ABSTRACT FROM AUTHOR]

The article deals with the creation of an intelligent architecture of the Internet of Things transport environment based on software-defined network (SDN) and blockchain for detecting threats and attacks. The transport environment is created for the monitoring system of critical events in the road transport infrastructure. Blockchain technology is used to authenticate network nodes, store sensor data in a distributed ledger. The network packet clustering method based on a fuzzy neural network is used to detect packets with possible malicious content. The intelligent SDN architecture is a hierarchy of four layers with six levels and includes: a) edge computing layer (sensor nodes and routers level, SDN switches data level), b) fog computing layer (zone server level, control level in SDN controllers), c) a cloud computing layer with data center servers, d) a layer for presenting monitoring results on user devices and applications. Detection of threats and attacks is implemented by validating network nodes and analyzing header fields of IP packets and TCP segments. The intrusion detection system includes a parser and analyzer of data packets, a module for filtering traffic by type, port numbers and other characteristics of packets, a module for synthesizing digital signatures of trusted nodes and their validation, a module for analyzing and clustering packets based on fuzzy logic and a neural network, modules for logging procedures. The probability function of packets belonging to clusters is tuned through deep learning of a five-layer neural network. The conclusion about belonging and degree of similarity with malicious packages is formed using the fuzzy logic apparatus. To train the neural network, the previously synthesized rules of the flow tables and the identified signs of atypical data packets are used. The functionality and effectiveness of the SDN architecture with an intrusion detection system is validated by simulating procedures in the NS3 Simulator system, evaluating authenticity, latency, throughput, response time, and accuracy in detecting atypical data packets. [ABSTRACT FROM AUTHOR]

A Wavelength Division Multiplexing (WDM)-enabled analog mobile fronthaul architecture is presented supporting enhanced flexibility by exploiting the fine filtering properties of a fully reconfigurable, silicon photonics-based filtering element. Numerical studies are presented emphasizing on the flexibility, low-energy consumption, and the compatibility of our proposed chip-scale implementations with the analog Radio over Fiber (RoF)-based mobile transport. Proof-of-concept analog photonic processing of 1 Gbaud Quadrature Phase Shift Keying (QPSK)-modulated radio is successfully demonstrated through RoF transmission experiments. The integration of the analog mobile fronthauling segment in a WDM-enabled, Reconfigurable Optical Add/Drop Multiplexers (ROADM)-assisted fronthaul/midhaul infrastructure supporting low-latency edge computing is also discussed. [ABSTRACT FROM AUTHOR]

Linear response characteristics of sensors are affected by ageing and changes in material properties. However, existing methods for improving these characteristics are disadvantaged by recurrent full-scale errors and insufficient speed. Accordingly, this study aims to reduce temperature sensor non-linearity through implementation of evolutionary optimised non-linear functions via a translinear-based application-specific integrated circuit. Moreover, the translinear-based analogue computation method is compared to a proposed field programmable gate array (FPGA)-based digital computation methodology to develop an alternative method for linearisation at sensor nodes (edge computing). The optimal values of the linearisation parameters for selected thermocouples are determined using the covariance matrix adaptation evolutionary strategy. Specifically, these optimal values are input in order to realise the non-linear functions. Experimental results reveal that FPGA offers a lower full-scale error than that of the translinear-based computation, although the translinear method can accomplish the required linearisation with fewer components (e.g. Data Converters), less delay and less power. Further, analogue computation through the proposed single chip-based method can utilise the latest processes, e.g. gallium nitride or pseudomorphic high electron mobility, for an enhanced error reduction. The comparative result shows that the translinear method with recommended processes can correct the non-linearity of sensors in all aspects. [ABSTRACT FROM AUTHOR]

Building energy consumption accounts for a large fraction of the total global energy usage, and considerable energy savings are expected to be achieved in this respect through residential electrical load monitoring. Due to the limitations on the practical implementation of in-depth and expensive monitoring systems, non-intrusive load monitoring (NILM) is becoming a hot topic. In this paper, an overview of the state of the art residential electrical load monitoring is presented. Different from previous reviews, the applications of load monitoring are particularly addressed, based on which, technical challenges of load monitoring techniques, including NILM, are identified and thoroughly discussed, together with possible developments and trends predicted from the authors' perspective. [ABSTRACT FROM AUTHOR]