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Designing high-speed and energy-efficient blocks for image and digital signal processing (DSP) architecture is an evolving research field. This work designs a high-speed and energy-efficient multiply-accumulate (MAC) unit to augment the performance of field-programmable gate array (FPGA)-based accelerators and softcore processors. In this work, three discrete 32-bit fixed-point signed MAC architectures were designed in Verilog and synthesized for the Zynq 7000 ZedBoard to obtain efficient MAC architecture. The ultimate goal of this work is to design a fast and energy-efficient MAC unit that can achieve speed up to the DSP48 block to reduce the latency of IoT edge computing. Energy efficiency was achieved in PPG and partial product addition (PPA) for the proposed Booth radix-4 Dadda (BR4D)-based MAC. At PPG, the width of the partial product (PP) terms was optimized with Bewick's signed extension to reduce the power consumption. At PPA, the number of PP rows reduces the critical path delay (CPD) with Dadda-based PPA. The proposed BR4D MAC unit offers a reduction in dynamic power, CPD, power-delay product (PDP) and energy-delay product (EDP) by 22%, 9%, 29% and 36%, respectively, compared to standard Booth radix-4 Wallace tree (BR4WT) based MAC. Furthermore, hybrid MACs (BR4WT and BR4D) were compared with the current state-of-the-art (SoA) designs, and it was found that the proposed BR4D MAC is 47% faster compared to the same design in SoA. The proposed BR4D was tested for frequency scaling technique by reducing the frequency in steps of 10 MHz from a maximum usable frequency (MUF) of 64 MHz to 10 MHz to evaluate the performance for low-power applications. Reducing clock frequency by 84% will reduce the power consumption at the same proportion and speed by 38%. Additionally, the proposed design helps to improve the battery life of IoT end nodes with a reduction in energy consumption and EDP by 76% and 61%, respectively. [ABSTRACT FROM AUTHOR]

Summary: Due to geographical factors, it is impossible to build large‐scale communication network infrastructure in remote areas, which leads to poor network communication quality in these areas and a series of delay‐sensitive tasks cannot be timely processed and responded. Aiming at the problem of limited coverage in remote areas, the space‐air‐ground integrated networks (SAGIN) combined with mobile edge computing (MEC) can provide low latency and high reliability transmission for offloading delay‐sensitive tasks for users in remote areas. Considering the strong limitation of satellite resources in the space‐ground integrated network and insufficient energy of local user equipment, firstly, a satellite‐UAV cluster‐ground three‐layer edge computing network architecture is proposed in this paper. Under the condition that the delay requirements of various ground tasks are met, the task offloading problem is transformed into a Stackelberg game between ground user equipment and edge servers. In addition, it is proved that the existence of Nash equilibrium in non‐cooperative game between ground user equipment by using potential game. Finally, a Nash equilibrium iterative offloading algorithm based on Stackelberg game (NEIO‐SG) is proposed to find the optimal offloading strategy for tasks to minimize the system offloading cost and the optimal forwarding percentage strategy for offloading tasks to maximize the utility function of the edge server. Simulation results show that compared to other baseline algorithms, NEIO‐SG reduces the total system latency during task offloading by about 13%$$ \% $$ and the energy consumption of the edge server by about 35%$$ \% $$. [ABSTRACT FROM AUTHOR]

The remarkable miniaturization of Internet of Things (IoT)-based systems and the rise of distributed intelligence are promising research paradigms in the design of smart cities. IoT and distributed intelligence are conjoined. On the one hand, IoT provides a digital connection to everyday physical devices, and on the other hand, distributed intelligence allows these connected devices to work together in a distributed environment to solve complex problems through learning and reasoning. In this article, we investigate and report the recent premier advances in distributed intelligence for IoT-based smart cities. We use bibliometric techniques which provide essential insights into many studies to evaluate the literature including publication year, type, and citation status on distributed intelligence and its variations published between 2013 and 2023. We categorize and classify literature by devising a taxonomy based on essential parameters, technologies, approaches, applications, and implications with respect to distributed intelligence. Moreover, we provide recent ongoing advancements in distributed intelligence and IoT such as sustainability, blockchain, 5G technologies, AI, edge computing expansion and IoT expansion. We also highlight some of the use cases which include traffic management, energy management, waste management, public safety, and healthcare. We provide core layers and services for implementing distributed intelligence in IoT-based smart cities and present several research challenges and issues. [ABSTRACT FROM AUTHOR]

Reducing network energy consumption and balancing workload are two key optimization goals for data transmission in edge computing field. However, these two goals are likely to be conflicting in some cases and fail to achieve the optimum simultaneously. In this paper, we design a new data transmission optimization algorithm using multi-objective reinforcement learning. We design the vector of rewards for the two objectives, and update Pareto approximate set by multiple state steps to approach the optimal solution. In every step, we classify the candidate links into four different levels for path selection. We aggregate network traffic to construct minimum topology subset, minimizing the number of occupied device to reduce energy consumption. We optimize the load distribution on those selected links, minimizing maximum congestion factor to balance workload. For action selection, we leverage roulette-based Chebyshev scalarization function to solve the weight selection problem for multi-objectives and enforce exploration to avoid falling into local optimum. To improve the convergence rate, we design heuristic factor to control the search of solution space and enhance the guiding effect of the existing optimal solution. Simulation result shows that the proposed algorithm achieves good performance in energy-saving and load balance at the same time. [ABSTRACT FROM AUTHOR]

The development of the Internet of Things has led to a surge in edge devices. The image detection algorithm, one of the commonly used algorithms in edge computing, is affected by environments such as weather, light, air humidity, smoke, and dust, so an image restoration algorithm is needed to preprocess the image in practice. Most currently proposed deep learning image restoration algorithms are based on general-purpose servers with high computational overhead to minimize the environmental effects. Edge devices are limited in size, power consumption, and computing performance, making the performance of deep learning-based image restoration algorithms on edge devices poor. In this work, we propose an image restoration algorithm that combines wavelet transform and transformer, named MWformer, to reduce computational overhead, optimize the feature map size, network structure, and network depth, and introduce the wavelet transformation to reduce the super-parameters. Experimental tests on multiple public datasets for various image restoration tasks show that the proposed MWformer ensures high performance in numerous image restoration tasks, and the computational overhead is 10% of the state-of-the-art algorithm, on average. [ABSTRACT FROM AUTHOR]

With the development of mobile edge computing and neural network Deep Learning (DL), more and more scholars are studying the combination of the two. This paper mainly studies the application of mobile edge computing and neural network DL in IoT computing collaboration and data-aware routing algorithms. Therefore, this paper proposes the deployment options of MEC technology and ETSIMEC in mobile edge computing, combining mobile edge computing with DL, designs an optimization algorithm based on Markov decision process and feature expression learning, and then analyzes and optimizes IoT computing and VANET routing algorithm. In order to have a clearer direction for the optimization algorithm, this paper also designs the edge computing model training and experiment comparison, the DL algorithm comparison experiment, and the routing algorithm simulation experiment and performance analysis. Combined with the experimental results, it is optimized and compared with traditional IoT computing and routing algorithms. Finally, it is concluded that the computing efficiency of IoT computing based on edge computing and DL designed in this paper is 21.33% higher than that of traditional IoT computing. The efficiency of the routing algorithm based on edge computing and DL designed in this paper is 9.29% higher than that of the traditional routing algorithm. [ABSTRACT FROM AUTHOR]

Implementing algorithms purely on digital computing platforms dramatically halts the performance of conventional computing systems. Revolutionary computing systems with extreme energy efficiency and high accuracy are demanded to handle the growing computing tasks. Here, the research on hybrid analog–digital computing platforms enabled by memristor‐based optimization solvers for achieving ultrafast computations is presented. By utilizing tunable memristors as parameters to solve linear programming (LP) and quadratic programming (QP) problems, a real‐time control algorithm for micro air vehicles (MAVs) and a support vector machine (SVM) algorithm for cancer diagnosis are implemented. These experiments demonstrate over 2000x speed‐up compared to conventional digital platforms, with negligible energy consumption, using a memristor‐based system consisting of six memristors. These findings underscore the vast potential of memristor‐based optimization solvers not only in hybrid analog–digital computing platforms but also as a transformative solution for a wide range of modern computing challenges. This approach promises significant advancements in energy efficiency and ultrafast speed, positioning it as a leading contender for next‐generation computing paradigms. [ABSTRACT FROM AUTHOR]

Energy efficiency and physical-layer security are crucial considerations in the advancement of mobile edge computing systems. This paper addresses the trade-off between secure-reliability and energy consumption in finite blocklength (FBL) communications. Specifically, we examine a three-node scenario involving a user, a legitimate edge computing server, and an eavesdropper, where the user offloads sensitive data to the edge server while facing potential eavesdropping threats. We propose an optimization framework aimed at minimizing energy consumption while ensuring secure-reliability by decomposing the problem into manageable subproblems. By demonstrating the convexity of the objective function concerning the variables, we establish the existence of an optimal parameter selection for the problem. This implies that practical optimization of parameters can significantly enhance system performance. Our numerical results demonstrate that the application of FBL regime and retransmission mechanism can effectively reduce the energy consumption of the system while ensuring secure-reliability. For the quantitative analyses, the retransmission mechanism is 33.1% better than no retransmission, and the FBL regime is 13.1% better than infinite blocklength (IBL) coding. [ABSTRACT FROM AUTHOR]

To achieve cost‐effectiveness, researchers are exploring various memristors for their adaptation in neuromorphic computing. Recent studies have focused on developing versatile functioning singular memristors, such as those involved in on‐receptor computing, which integrates sensory functions into current neuromorphic computing paradigms. Additionally, adaptations like reservoir computing are being investigated for computing systems. In this study, a memristor composed of a stack of Ti/NbOx/Pt layers is fabricated to explore multifunctional behaviors within a single memristor. By applying bias toward the top Ti electrode, gradual current changes with volatile features are demonstrated, revealing an ion‐migration‐based nonfilamentary switching memristor. Leveraging this volatile functionality, an artificial nociceptor is first implemented, demonstrating key functions of biological nociceptors including thresholding, relaxation, no‐adaptation, and sensitization. Subsequently, synapse emulation akin to the biological brain is achieved through easy conductance potentiation and depression with diverse synapse functions, enabling the memristor to mimic learning activities with spike firing. Lastly, computational applications are explored by adapting edge computing and multi‐bit reservoir computing, expanding the memristor's applications across diverse fields with versatile behaviors. [ABSTRACT FROM AUTHOR]

Edge analytics receives an ever-increasing interest since processing streaming data closer to where they are produced, rather than transferring them to the cloud, ensures lower latency while also addresses data privacy issues. In this work, we deal with the placement of analytic tasks to heterogeneous geo-distributed edge devices while targeting three objectives, namely latency, quality of results, and resource utilization. In addition, we investigate this multi-objective problem in a multi-query setting, where we jointly optimize multiple analytic jobs while dynamically adjusting task placement decisions. We explore multiple solutions that we thoroughly evaluate; interestingly, in a multi-query setting, all three objectives can be improved simultaneously by our proposals in many cases. Furthermore, we develop a proof-of-concept prototype using Apache Storm. Our solutions are thoroughly evaluated and shown to yield improvements by more than 50% compared to advanced baselines targeting only latency. Moreover, our software prototype managed to achieve speedups of up to 6 × over the Resource Aware Apache Storm scheduler, with an average speedup of 2.76 × , when deployed over a small-scale infrastructure. [ABSTRACT FROM AUTHOR]

The cloud service provider market has recently expanded its offerings by providing edge as a service. This involves offering resources equivalent to those already available in the cloud, but through data centers located closer to the end user, with the goal of improving service latencies. Application providers face the challenge of selecting appropriate resources, both from the edge and cloud, to deploy their applications in a way that minimizes deployment costs while satisfying latency requirements. This paper presents Edarop (EDge ARchitecture OPtimization), an innovative orchestration mechanism for the optimal allocation of virtual machines in geographically distributed edge and cloud infrastructures. Edarop is capable of handling different edge and cloud vendors, each offering various types of VMs in different regions, with different prices, and network latencies. It also supports multiple simultaneous applications with different latency requirements and load profiles. Edarop employs Integer Linear Programming (ILP) to ensure the globally optimal solution within a reasonable time frame for the considered use cases. Several variants of the mechanism are provided, depending on whether the objective is to minimize cost, response times, or both. These variants are compared to each other and to alternative approaches, with the results showing that, unlike other methods, Edarop consistently respects latency constraints while minimizing the proposed objectives. [ABSTRACT FROM AUTHOR]

Both academics and the IT industry are now researching the Internet of Things and software-defined networks. They have received a number of criticisms in the SD-IoT due to their novelty. One of the 5 G technologies that makes it possible to construct complex, controllable, economical, and adaptive networks is software-defined networking (SDN). In contrast, edge computing (EC) uses data from sensors, network switches, or other devices to automatically do analytical computing rather than waiting for the data to be sent back to a centralised data repository. This article offers a study on feature selection using an ensemble of filter methods to create a lightweight IDS for SD-IoT edge devices that support OpenFlow in order to defend against such attacks. To create the ensemble of filter methods, three filter-based methods, namely Pearson's correlation coefficient (PCC), mutual information (MI), and Fisher's score, have been used. The features selected by this ensemble is sent to the ensemble of classifiers called stack of the classifiers that comprises of support vector machine (SVM) and K-nearest neighbour (KNN) at level '0' and logistic regression (LR) at level '1'. To check the effectiveness of the selected features, stack of the classifiers and individual classifiers are trained and tested with 'All' and 'Selected' features, and then their performances are compared. Two datasets, the BoT-IoT dataset and the TON-IoT dataset, were utilised to complete this work. The performance is compared under some performance measuring metrics, namely recall, accuracy, FAR, F1, precision, CKC, and prediction time. It has been discovered that classifiers perform better when trained with selected features rather than all the features. Also, it is discovered that stack of the classifiers with chosen features outperforms all individual classifiers, hence it is chosen for deployment in OpenFlow enabled edge devices of the SD-IoT data plane where it can identify and counteract threats in real-world settings. This offers the SD-IoT distributed attack detection approach. [ABSTRACT FROM AUTHOR]

Vehicular fog computing (VFC) is a technology that enhances vehicular applications by offloading the task of the resource-restricted vehicle to the resourceful fog node (vehicle or static fog node). The mobility of vehicles leads to various challenges in task offloading, i.e., low successful offloading ratio, high response time, etc. Apart from this, the available idle computation resource at the fog node is different at the time of advertisement of computation capability and at the time of task allocation. This change results in less efficient resource utilization and unequal load balance at the fog node. Hence to overcome this, we have proposed an approach based on future mobility and imminent computation awareness (FMICA). The future mobility prediction allows us to offload the task to the fog node, which will be near during task retrieval. Additionally, imminent computation awareness of the available resources (at the imminent time τ ) allows us to efficiently utilize the idle resources through offloading higher deadline tasks at the τ time. FMICA optimizes resources according to various criteria such as offloading delay, load variance, within-range ratio, and successful offloading ratio using a Strength Pareto Evolutionary Algorithm 2 (SPEA2). An order preference by similarity to the ideal solution (TOPSIS) and multiple criteria decision-making (MCDM) technique is used to select the best solution. Through the experimental analysis, we have found that the FMICA has an improvement of 37.64%, 25.57%, 9.96%, and 12.62% in metrics average response time, successful offloading ratio, resource utilization, and average load variance against available approach, respectively. [ABSTRACT FROM AUTHOR]

Machine failures during the manufacturing process can have severe consequences, causing extensive downtime and financial losses. Hence, predictive maintenance (PdM) plays a crucial role within the Industrial Internet of Things (IIoT) by estimating the remaining useful life (RUL) of machines so that proactive maintenance measures can be taken to mitigate potential failures and minimize disruptions. RUL estimation is enabled by gathering and processing the data sensed by sensors mounted on and around the machines at the central server (base station or cloud) after analyzing the failure patterns. However, this approach imposes a significant load on network bandwidth and leads to poor response time for the monitoring system because a large volume of sensed data has to be transmitted to the central server for processing. Moreover, due to the singularity of the computing resource, many problems, such as inefficient resource utilization, frequent offloading, single-point failure, etc., have become major challenges. To address these issues, this article proposes an edge computing-enabled predictive maintenance framework called "Collaborative Predictive Maintenance Framework" (CollabRULe), which first identifies the optimal locations for edge server placement in the deployment region by considering the QoS parameters, such as energy, delay and connectivity. Then, it uses federated learning for predictive maintenance by estimating the RUL of the machines. Simulation results show that the proposed mechanism effectively minimizes the overall network energy consumption and end-to-end delay by ≈ 60 % and ≈ 35 % , respectively, as compared to state-of-the-art approaches. Furthermore, it shows significant improvement in the accuracy of RUL prediction as compared to its counterparts. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging paradigm that enables low-latency and high-performance computing at the network edge. However, effectively scheduling complex and interdependent tasks on heterogeneous and dynamic edge computing nodes presents a significant challenge in meeting users' real-time response requirements. To solve this problem, a DGCQN scheduling network that leverages reinforcement learning and graph convolutional neural networks to learn an optimal scheduling strategy is proposed in this paper. The proposed method embeds the graph structure of Directed Acyclic Graph (DAG) tasks and node information of Kubernetes (K8s) clusters into a Q value function, guiding the DQN network in selecting the best action at each step. The method is evaluated across various DAG tasks and edge computing scenarios. Compared with HEFT, DQN, and GOSU, the task completion time of the proposed method is reduced by about 20, 10, and 1.5%, respectively. The results demonstrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

In edge computing, repetitive computations are a common occurrence. However, the traditional TCP/IP architecture used in edge computing fails to identify these repetitions, resulting in redundant computations being recomputed by edge resources. To address this issue and enhance the efficiency of edge computing, Information-Centric Networking (ICN)-based edge computing is employed. The ICN architecture leverages its forwarding and naming convention features to recognize repetitive computations and direct them to the appropriate edge resources, thereby promoting "computation reuse". This approach significantly improves the overall effectiveness of edge computing. In the realm of edge computing, dynamically generated computations often experience prolonged response times. To establish and track connections between input requests and the edge, naming conventions become crucial. By incorporating unique IDs within these naming conventions, each computing request with identical input data is treated as distinct, rendering ICN's aggregation feature unusable. In this study, we propose a novel approach that modifies the Content Store (CS) table, treating computing requests with the same input data and unique IDs, resulting in identical outcomes, as equivalent. The benefits of this approach include reducing distance and completion time, and increasing hit ratio, as duplicate computations are no longer routed to edge resources or utilized cache. Through simulations, we demonstrate that our method significantly enhances cache reuse compared to the default method with no reuse, achieving an average improvement of over 57%. Furthermore, the speed up ratio of enhancement amounts to 15%. Notably, our method surpasses previous approaches by exhibiting the lowest average completion time, particularly when dealing with lower request frequencies. These findings highlight the efficacy and potential of our proposed method in optimizing edge computing performance. [ABSTRACT FROM AUTHOR]

Network reliability, named multistate stochastic cloud/edge-based network (MCEN) reliability afterwards, is defined as the probability that demands can be satisfied for an MCEN. It can be regarded as a performance indicator of the MCEN to measure the service capability. The concept of existing algorithms is to produce all of minimal system-state vectors for calculating MCEN reliability. However, such concept cannot response MCEN reliability in time when the MCEN scale becomes complicated in the Industry 4.0 environment. For providing MCEN reliability for decision making immediately, an architecture of a deep neural network (DNN) is developed to propose a prediction model for MCEN reliability such that MCEN capability with varied data can be learned promptly. To train the reliability prediction model, MCEN information is transformed to the suitable format, and the related information for DNN setting, including the determination of related functions, are defined with appropriate hyperparameters by using Bayesian Optimization. An illustrative case and a practical case of Amazon Web Service are provided to demonstrate the prediction model for MCEN reliability to show the availability and the efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces a new network model - the Image Guidance Encoder-Decoder Model (IG-ED), designed to enhance the efficiency of image captioning and improve predictive accuracy. IG-ED, a fusion of the convolutional network VGGNet-16 and the long short-term memory network (LSTM), is designed based on the encoder-decoder structure. The image captioning performance sees significant enhancements when leveraging the IG-ED network model. The network training process unfolds in a series of steps. Initially, the input image undergoes convolution via the VGGNet-16 network, producing a 512-dimensional vector. Concurrently, each word in the image's caption is encoded to generate a corresponding 512-dimensional vector consistent with the image feature dimension. These two vectors form the input for the decoding process. Subsequently, the vectors are fed into the redesigned fusion LSTM (F-LSTM) network at different time steps to gradually train the parameters of the IG-ED framework. The training process is completed by utilizing a loss function for determining convergence. Evaluation of the IG-ED model's performance is conducted using CIDEr and seven other evaluation metrics on the MSCOCO 2014 dataset. The results exhibit substantial improvements over the "Adaptive Attention Mode" network and "Neural Talk" network. Additionally, the parameter count of the IG-ED architecture is significantly reduced compared to the "Adaptive Attention Mode" network, leading to decreased computational resource requirements and enabling edge computing on the neural network. [ABSTRACT FROM AUTHOR]

Currently, web service-based edge computing networks are across-the-board, and their users are increasing dramatically. The network users request various services with specific Quality-of-Service (QoS) values. The QoS-aware Web Service Composition (WSC) methods assign available services to users’ tasks and significantly affect their satisfaction. Various methods have been provided to solve the QoS-aware WSC problem; However, this field is still one of the popular research fields since the dimensions of these networks, the number of their users, and the variety of provided services are growing outstandingly. Consequently, this study presents an enhanced Fox Optimizer (FOX)-based framework named EQOLFOX to solve QoS-aware web service composition problems in edge computing environments. In this regard, the Quasi-Oppositional Learning is utilized in the EQOLFOX to diminish the zero-orientation nature of the FOX algorithm. Likewise, a reinitialization strategy is included to enhance EQOLFOX's exploration capability. Besides, a new phase with two new movement strategies is introduced to improve searching abilities. Also, a multi-best strategy is recruited to depart local optimums and lead the population more optimally. Eventually, the greedy selection approach is employed to augment the convergence rate and exploitation capability. The EQOLFOX is applied to ten real-life and artificial web-service-based edge computing environments, each with four different task counts to evaluate its proficiency. The obtained results are compared with the DO, FOX, JS, MVO, RSA, SCA, SMA, and TSA algorithms numerically and visually. The experimental results indicated the contributions' effectiveness and the EQOLFOX's competency. [ABSTRACT FROM AUTHOR]

As virtual and actual universes merge inside the creating metaverse, requests have pointedly ascended for continuous, intuitive, and intense encounters. The ability of the metaverse to effectively analyze and render complicated links and information supplied by clients is critical for realizing that goal. These demanding computational demands are starting to be supported by parallel processing, and high-performance computing (HPC) is beyond uncertainty key to this domain. The integrative framework presented in this paper addresses the core challenges of inertness, flexibility, and ease of use while integrating equal registration into the metaverse. The system enables prompt handling of client actions and quick response times by distributing calculations over multiple processors, which is essential for the seamless client experience. It also manages the vast amount of metaverse material and interactions as well as the various data processing needs. The paper looks at intrinsic equal processing difficulties in this unique climate, including creating versatile and energy-effective equal calculations that consider load adjusting and asset designation. It features the need to democratize equal figuring assets to produce metaverse extension while accentuating the significance of information protection and security conventions in multiclient settings. The cooperative energy between metaverse development and equal registering progressions vows to push limits, empowering remarkable degrees of virtual submersion and collaboration. [ABSTRACT FROM AUTHOR]

Modeling IoT applications in distributed computing systems as workflows enables automating their procedure. There are different types of workflow-based applications in the literature. Executing IoT applications using device-to-device (D2D) communications in distributed computing systems especially edge paradigms requiring direct communication between devices in a network with a graph topology. This paper introduces a toolkit for simulating resource management of scientific workflows with different structures in distributed environments with graph topology called WIDESim. The proposed simulator enables dynamic resource management and scheduling. We have validated the performance of WIDESim in comparison to standard simulators, also evaluated its performance in real-world scenarios of distributed computing. The results indicate that WIDESim’s performance is close to existing standard simulators besides its improvements. Additionally, the findings demonstrate the satisfactory performance of the extended features incorporated within WIDESim. [ABSTRACT FROM AUTHOR]

Edge computing (EC) serves as an effective technology, empowering end‐users to attain high bandwidth and low latency by offloading tasks with high computational demands from mobile devices to edge servers. However, a major challenge arises when the processing load fluctuates continuously, leading to a performance bottleneck due to the inability to rescale edge node (EN) resources. To address this problem, the approach of task migration is introduced, and EN load prediction model, the resource constrained model, optimal communication overhead model, optimal task migration model, and energy consumption model are built to form a theoretical foundation from which to propose a task migration based resilient framework construction method in EC. With the aid of the domino effect and the combined effect of task migration, a dynamic node‐growing algorithm (DNGA) and a dynamic node‐shrinking algorithm (DNSA), both based on the task migration strategy, are proposed. Specifically, the DNGA smoothly expands the EN scale when the processing load increases, while the DNSA shrinks the EN scale when the processing load decreases. The experimental results show that for standard benchmarks deployed on an elastic framework, the proposed method realizes a smooth scaling mechanism in the EC, which reduces the latency and improves the reliability of data processing. [ABSTRACT FROM AUTHOR]

The advent of federated learning (FL) has presented a viable solution for distributed training in edge environment, while simultaneously ensuring the preservation of privacy. In real‐world scenarios, edge devices may be subject to label noise caused by environmental differences, automated weakly supervised annotation, malicious tampering, or even human error. However, the potential of the noisy samples have not been fully leveraged by prior studies on FL aimed at addressing label noise. Rather, they have primarily focused on conventional filtering or correction techniques to alleviate the impact of noisy labels. To tackle this challenge, a method, named DETECTION, is proposed in this article. It aims at effectively detecting noisy clients and mitigating the adverse impact of label noise while preserving data privacy. Specially, a confidence scoring mechanism based on local intrinsic dimensionality (LID) is investigated for distinguishing noisy clients from clean clients. Then, a loss function based on prototype contrastive learning is designed to optimize the local model. To address the varying levels of noise across clients, a LID weighted aggregation strategy (LA) is introduced. Experimental results on three datasets demonstrate the effectiveness of DETECTION in addressing the issue of label noise in FL while maintaining data privacy. [ABSTRACT FROM AUTHOR]

The integration of remote sensing object counting in the Mobile Edge Computing (MEC) environment is of crucial significance and practical value. However, the presence of significant background interference in remote sensing images poses a challenge to accurate object counting, as the results are easily affected by background noise. Additionally, scale variation within remote sensing images presents a further difficulty, as traditional counting methods face challenges in adapting to objects of different scales. To address these challenges, we propose a selective spatial‐frequency pyramid network (SSFPNet). Specifically, the SSFPNet consists of two core modules, namely the pyramid attention (PA) module and the hybrid feature pyramid (HFP) module. The PA module accurately extracts target regions and eliminates background interference by operating on four parallel branches. This enables more precise object counting. The HFP module is introduced to fuse spatial and frequency domain information, leveraging scale information from different domains for object counting, so as to improve the accuracy and robustness of counting. Experimental results on RSOC, CARPK, and PUCPR+ benchmark datasets demonstrate that the SSFPNet achieves state‐of‐the‐art performance in terms of accuracy and robustness. [ABSTRACT FROM AUTHOR]

The study focuses on utilizing the computational resources present in vehicles to enhance the performance of multi-access edge computing (MEC) systems. While vehicles are typically equipped with computational services for vehicle-centric Internet of Vehicles (IoV) applications, their resources can also be leveraged to reduce the workload on edge servers and improve task processing speed in MEC scenarios. Previous research efforts have overlooked the potential resource utilization of passing vehicles, which can be a valuable addition to MEC systems alongside parked cars. This study introduces an assisted MEC scenario where a base station (BS) with an edge server serves various devices, parked cars, and vehicular traffic. A cooperative approach using the Deep Deterministic Policy Gradient (DDPG) based Federated Learning method is proposed to optimize resource allocation and job offloading. This method enables the transfer of device operations from devices to the BS or from the BS to vehicles based on specific requirements. The proposed system also considers the duration for which a vehicle can provide job offloading services within the range of the BS before leaving. The objective of the DDPG-FL method is to minimize the overall priority-weighted task computation time. Through simulation results and a comparison with three other schemes, the study demonstrates the superiority of their proposed method in seven different scenarios. The findings highlight the potential of incorporating vehicular resources in MEC systems, showcasing improved task processing efficiency and overall system performance. [ABSTRACT FROM AUTHOR]

Summary: Mobile edge computing (MEC) promotes the development and popularity of Internet of Things (IoT) devices with higher connectivity and ultra‐low latency through network topology optimization and real‐time data analysis. With the exponential growth of data traffic generated by IoT devices, it is essential to prevent edge servers (ESs) from overloading through efficient task scheduling to ensure the quality of service (QoS). However, the lack of trust and incentive mechanisms between ESs deployed and managed by different infrastructure providers makes it challenging to perform peer‐offloaded tasks. Moreover, to meet the low‐latency requirement of many IoT applications, the execution efficiency of the task scheduling scheme in MEC needs to be improved. To address the above challenges systematically, a QoS‐driven task scheduling scheme with blockchain in MEC named SharpEdge is proposed. In SharpEdge, ESs publish tasks with rewards and then select the most reliable executors through a historical performance‐based reputation mechanism to perform peer offloading. The performance of the executor will be recorded into the blockchain after the task is completed. In addition, a concurrent consensus mechanism using sharding technology is designed, improving task scheduling efficiency. We implement SharpEdge based on Hyperledger Fabric and verify its performance in a simulated MEC environment. [ABSTRACT FROM AUTHOR]

In the last few years, the rapid advancement of the Internet of Things (IoT) and the widespread adoption of smart cities have posed new challenges to computing services. Traditional cloud computing models fail to fulfil the rapid response requirement of latency‐sensitive applications, while mobile edge computing (MEC) improves service efficiency and customer experience by transferring computing tasks to servers located at the network edge. However, designing an effective computing offloading strategy in complex scenarios involving multiple computing tasks, nodes, and services remains a pressing issue. In this paper, a computing offloading approach based on Deep Reinforcement Learning (DRL) is proposed for large‐scale heterogeneous computing tasks. First, Markov Decision Processes (MDPs) is used to formulate computing offloading decision and resource allocation problems in large‐scale heterogeneous MEC systems. Subsequently, a comprehensive framework comprising the "end‐edge‐cloud" along with the corresponding time‐overhead and resource allocation models is constructed. Finally, through extensive experiments on real datasets, the proposed approach is demonstrated to outperform existing methods in enhancing service response speed, reducing latency, balancing server loads, and saving energy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) necessitates secure communication and high availability among objects at the network edge to ensure reliable object-to-object transactions. In the IoT networks, despite resource limitations, especially at the edge of the network, the potential for error is high. Therefore, a mechanism to increase the reliability, lifetime, and stability of the network is necessary. In this paper, we introduce a trust evaluation framework based on a reliability-based friendly relationship method in IoT networks. We present a conceptual trust model that captures the overall performance of the IoT social network based on parameters such as nodes’ communication history experiences. Trust in the IoT network is built upon a harmonious communication environment that aligns with the trustworthiness of each object and its ability to maintain continuous interactions. We propose an empirical Trust Indicator (TI) that captures individual agents’ experiences in IoT groups, considering the results of system executions, current experience values, and timestamps of interactions. Mathematical models are developed to analyze the dynamics of trust, including trust increase through increased reliability and collaborative interactions and trust decay due to non-cooperative interactions and lack of communication. The model parameters in IoT groups through simulation show that in this system based on the level of reliability and its increase or decrease, its direct effect can be evaluated by quantitative measurement of mean time to failure (MTTF), which is a measure of devices trust and the network itself. [ABSTRACT FROM AUTHOR]

Image super‐resolution (SR) reconstruction is a key technique for improving image quality and details. Conventional methods are frequently limited by interpolation, filtering, or statistical approaches; thus, they are unable to reconstruct high‐quality continuously enlarged images with detailed information. This study proposes an image SR reconstruction network model, called LALNet, based on implicit image functions and residual multilayered perceptron (RAMLP) with an attention mechanism. Through the implicit image function and RAMLP + attention, high‐quality SR reconstruction with continuous scale factors is achieved, and LALNets can run on embedded edge computing platforms. This method exhibits the following advantages: lightweight network structure reduces computing requirements, introduction of implicit image functions and RAMLP improves reconstruction quality, and attention mechanism suppresses artefacts and distortions. Experimental results show that LALNet outperforms traditional and other deep learning methods in terms of reconstruction performance and computational efficiency. This research provides new ideas and methods for the further development of the field of image SR reconstruction. [ABSTRACT FROM AUTHOR]

To meet the grand challenges of agricultural production including climate change impacts on crop production, a tight integration of social science, technology and agriculture experts including farmers are needed. Rapid advances in information and communication technology, precision agriculture and data analytics, are creating a perfect opportunity for the creation of smart connected farms (SCFs) and networked farmers. A network and coordinated farmer network provides unique advantages to farmers to enhance farm production and profitability, while tackling adverse climate events. The aim of this article is to provide a comprehensive overview of the state of the art in SCF including the advances in engineering, computer sciences, data sciences, social sciences and economics including data privacy, sharing and technology adoption. More specifically, we provide a comprehensive review of key components of SCFs and crucial elements necessary for its success. It includes, high-speed connections, sensors for data collection, and edge, fog and cloud computing along with innovative wireless technologies to enable cyber agricultural system. We also cover the topic of adoption of these technologies that involves important considerations around data analysis, privacy, and the sharing of data on platforms. From a social science and economics perspective, we examine the net-benefits and potential barriers to data-sharing within agricultural communities, and the behavioral factors influencing the adoption of SCF technologies. The focus of this review is to cover the state-of-the-art in smart connected farms with sufficient technological infrastructure; however, the information included herein can be utilized in geographies and farming systems that are witnessing digital technologies and want to develop SCF. Overall, taking a holistic view that spans technical, social and economic dimensions is key to understanding the impacts and future trajectory of Smart and Connected Farms. [ABSTRACT FROM AUTHOR]

The detection of the vertical profiles of atmospheric variables or aerosols is a vital component in the accurate calculation of the atmospheric boundary layer height (ABLH). In many existing techniques, the precise ABLH detection has poor resource consumption, heterogeneous IoT conditions, lack of interoperability, and online and offline analyzers overdue are impacted by the processing of high streaming data from the ceilometer. Hence, a novel Edge AI cosmos blockchain distributed network is proposed to eliminate the efficient lowering parameters to provide a user-scalable networking based on a deep learning algorithm, in which the cosmos ledger blockchain method gets the data without affecting its precision or specificity, eliminates interoperability, increases usability. Moreover, the existing techniques has no real-time adjustment when processing the high streaming data. Hence, a model Real-time batch scheduling technique has been introduced to reduce a significant amount of computing power. In existing techniques, the scheduling process is very complicated as the ABLH layer attribution, precipitation, lofted aerosol layers, low aerosol conditions, and clouds. To overcome these issues, a gubernatorial algorithm is used to detect the ABLH value hourly which is gathered and stored data in a very optimized and efficient manner. Overall, the proposed technique compares effective data management and produces a computer environment which is optimal for ABLH. [ABSTRACT FROM AUTHOR]

Various deep learning techniques, including blockchain-based approaches, have been explored to unlock the potential of edge data processing and resultant intelligence. However, existing studies often overlook the resource requirements of blockchain consensus processing in typical Internet of Things (IoT) edge network settings. This paper presents our FLCoin approach. Specifically, we propose a novel committee-based method for consensus processing in which committee members are elected via the FL process. Additionally, we employed a two-layer blockchain architecture for federated learning (FL) processing to facilitate the seamless integration of blockchain and FL techniques. Our analysis reveals that the communication overhead remains stable as the network size increases, ensuring the scalability of our blockchain-based FL system. To assess the performance of the proposed method, experiments were conducted using the MNIST dataset to train a standard five-layer CNN model. Our evaluation demonstrated the efficiency of FLCoin. With an increasing number of nodes participating in the model training, the consensus latency remained below 3 s, resulting in a low total training time. Notably, compared with a blockchain-based FL system utilizing PBFT as the consensus protocol, our approach achieved a 90% improvement in communication overhead and a 35% reduction in training time cost. Our approach ensures an efficient and scalable solution, enabling the integration of blockchain and FL into IoT edge networks. The proposed architecture provides a solid foundation for building intelligent IoT services. [ABSTRACT FROM AUTHOR]

This paper presents a new edge detection process implemented in an embedded IoT device called Bee Smart Detection node to detect catastrophic apiary events. Such events include swarming, queen loss, and the detection of Colony Collapse Disorder (CCD) conditions. Two deep learning sub-processes are used for this purpose. The first uses a fuzzy multi-layered neural network of variable depths called fuzzy-stranded-NN to detect CCD conditions based on temperature and humidity measurements inside the beehive. The second utilizes a deep learning CNN model to detect swarming and queen loss cases based on sound recordings. The proposed processes have been implemented into autonomous Bee Smart Detection IoT devices that transmit their measurements and the detection results to the cloud over Wi-Fi. The BeeSD devices have been tested for easy-to-use functionality, autonomous operation, deep learning model inference accuracy, and inference execution speeds. The author presents the experimental results of the fuzzy-stranded-NN model for detecting critical conditions and deep learning CNN models for detecting swarming and queen loss. From the presented experimental results, the stranded-NN achieved accuracy results up to 95%, while the ResNet-50 model presented accuracy results up to 99% for detecting swarming or queen loss events. The ResNet-18 model is also the fastest inference speed replacement of the ResNet-50 model, achieving up to 93% accuracy results. Finally, cross-comparison of the deep learning models with machine learning ones shows that deep learning models can provide at least 3–5% better accuracy results. [ABSTRACT FROM AUTHOR]

In the era of ubiquitous computing, the challenges imposed by the increasing demand for real-time data processing, security, and energy efficiency call for innovative solutions. The emergence of fog computing has provided a promising paradigm to address these challenges by bringing computational resources closer to data sources. Despite its advantages, the fog computing characteristics pose challenges in heterogeneous environments in terms of resource allocation and management, provisioning, security, and connectivity, among others. This paper introduces COGNIFOG, a novel cognitive fog framework currently under development, which was designed to leverage intelligent, decentralized decision-making processes, machine learning algorithms, and distributed computing principles to enable the autonomous operation, adaptability, and scalability across the IoT–edge–cloud continuum. By integrating cognitive capabilities, COGNIFOG is expected to increase the efficiency and reliability of next-generation computing environments, potentially providing a seamless bridge between the physical and digital worlds. Preliminary experimental results with a limited set of connectivity-related COGNIFOG building blocks show promising improvements in network resource utilization in a real-world-based IoT scenario. Overall, this work paves the way for further developments on the framework, which are aimed at making it more intelligent, resilient, and aligned with the ever-evolving demands of next-generation computing environments. [ABSTRACT FROM AUTHOR]

Intelligent mobile image sensing powered by deep learning analyzes images captured by cameras from mobile devices, such as smartphones or smartwatches. It supports numerous mobile applications, such as image classification, face recognition, and camera scene detection. Unfortunately, mobile devices often lack the resources necessary for deep learning, leading to increased inference latency and rapid battery consumption. Moreover, the inference accuracy may decline over time due to potential data drift. To address these issues, we introduce a new cost-efficient framework, called Corun, designed to simultaneously handle multiple inference queries and continual model retraining/fine-tuning of a pre-trained model on a single commodity GPU in an edge server to significantly improve the inference throughput, upholding the inference accuracy. The scheduling method of Corun undertakes offline profiling to find the maximum number of concurrent inferences that can be executed along with a retraining job on a single GPU without incurring an out-of-memory error or significantly increasing the latency. Our evaluation verifies the cost-effectiveness of Corun. The inference throughput provided by Corun scales with the number of concurrent inference queries. However, the latency of inference queries and the length of a retraining epoch increase at substantially lower rates. By concurrently processing multiple inference and retraining tasks on one GPU instead of using a separate GPU for each task, Corun could reduce the number of GPUs and cost required to deploy mobile image sensing applications based on deep learning at the edge. [ABSTRACT FROM AUTHOR]

With the rapid development of the Industrial Internet of Things in rotating machinery, the amount of data sampled by mechanical vibration wireless sensor networks (MvWSNs) has increased significantly, straining bandwidth capacity. Concurrently, the safety requirements for rotating machinery have escalated, necessitating enhanced real-time data processing capabilities. Conventional methods, reliant on experiential approaches, have proven inefficient in meeting these evolving challenges. To this end, a fault detection method for rotating machinery based on mobileNet in MvWSNs is proposed to address these intractable issues. The small and light deep learning model is helpful to realize nearly real-time sensing and fault detection, lightening the communication pressure of MvWSNs. The well-trained deep learning is implanted on the MvWSNs sensor node, an edge computing platform developed via embedded STM32 microcontrollers (STMicroelectronics International NV, Geneva, Switzerland). Data acquisition, data processing, and data classification are all executed on the computing- and energy-constrained sensor node. The experimental results demonstrate that the proposed fault detection method can achieve about 0.99 for the DDS dataset and an accuracy of 0.98 in the MvWSNs sensor node. Furthermore, the final transmission data size is only 0.1% compared to the original data size. It is also a time-saving method that can be accomplished within 135 ms while the raw data will take about 1000 ms to transmit to the monitoring center when there are four sensor nodes in the network. Thus, the proposed edge computing method shows good application prospects in fault detection and control of rotating machinery with high time sensitivity. [ABSTRACT FROM AUTHOR]

Cotton aphids (Aphis gossypii Glover) pose a significant threat to cotton growth, exerting detrimental effects on both yield and quality. Conventional methods for pest and disease surveillance in agricultural settings suffer from a lack of real-time capability. The use of edge computing devices for real-time processing of cotton aphid-damaged leaves captured by field cameras holds significant practical research value for large-scale disease and pest control measures. The mainstream detection models are generally large in size, making it challenging to achieve real-time detection on edge computing devices with limited resources. In response to these challenges, we propose GVC-YOLO, a real-time detection method for cotton aphid-damaged leaves based on edge computing. Building upon YOLOv8n, lightweight GSConv and VoVGSCSP modules are employed to reconstruct the neck and backbone networks, thereby reducing model complexity while enhancing multiscale feature fusion. In the backbone network, we integrate the coordinate attention (CA) mechanism and the SimSPPF network to increase the model's ability to extract features of cotton aphid-damaged leaves, balancing the accuracy loss of the model after becoming lightweight. The experimental results demonstrate that the size of the GVC-YOLO model is only 5.4 MB, a decrease of 14.3% compared with the baseline network, with a reduction of 16.7% in the number of parameters and 17.1% in floating-point operations (FLOPs). The mAP@0.5 and mAP@0.5:0.95 reach 97.9% and 90.3%, respectively. The GVC-YOLO model is optimized and accelerated by TensorRT and then deployed onto the embedded edge computing device Jetson Xavier NX for detecting cotton aphid damage video captured from the camera. Under FP16 quantization, the detection speed reaches 48 frames per second (FPS). In summary, the proposed GVC-YOLO model demonstrates good detection accuracy and speed, and its performance in detecting cotton aphid damage in edge computing scenarios meets practical application needs. This research provides a convenient and effective intelligent method for the large-scale detection and precise control of pests in cotton fields. [ABSTRACT FROM AUTHOR]

With the development of edge computing and deep learning, intelligent human behavior recognition has spawned extensive applications in smart worlds. However, current edge computing technology faces performance bottlenecks due to limited computing resources at the edge, which prevent deploying advanced deep neural networks. In addition, there is a risk of privacy leakage during interactions between the edge and the server. To tackle these problems, we propose an effective, privacy-preserving edge–cloud collaborative interaction scheme based on WiFi, named P-CA, for human behavior sensing. In our scheme, a convolutional autoencoder neural network is split into two parts. The shallow layers are deployed on the edge side for inference and privacy-preserving processing, while the deep layers are deployed on the server side to leverage its computing resources. Experimental results based on datasets collected from real testbeds demonstrate the effectiveness and considerable performance of the P-CA. The recognition accuracy can maintain 88%, although it could achieve about 94.8% without the mixing operation. In addition, the proposed P-CA achieves better recognition accuracy than two state-of-the-art methods, i.e., FedLoc and PPDFL, by 2.7% and 2.1%, respectively, while maintaining privacy. [ABSTRACT FROM AUTHOR]

Industrial Internet of Things (IIoT) is a pivotal driving force behind the intelligent transformation of the global industrial system, bringing about a gradual shift in production methods. However, due to users completing remote access and data transmission through open channels, ensuring user legitimacy and data security has become an important factor in IIoT. In addition, resource limited users or devices need to collect and publish corresponding messages while also timely processing instant messages obtained from the network. Leveraging edge computing technology effectively mitigates the transmission time of messages, addressing the challenges of high computing pressure and prolonged response time associated with cloud server computing. Therefore, investigating authentication key agreement based on edge computing in the IIoT environment holds substantial theoretical significance and practical importance. This article introduces an IIoT authentication that ensures both security and efficiency by integrating elliptic curve cryptography and three-factor authentication. Within this scheme, the user and the edge server accomplish security authentication. Through security analysis, the proposed scheme is proven to meet the essential security requirements. Performance analysis shows that it achieves higher security, supports more functions, and achieves lower computational and communication costs compared to related schemes. [ABSTRACT FROM AUTHOR]

Edge computing enables efficient data aggregation for services like data sharing and analysis in distributed IoT applications. However, uploading dynamic high-dimensional data to an edge server for efficient aggregation is challenging. Additionally, there is the significant risk of privacy leakage associated with direct such data uploading. Therefore, we propose an edge-based differential privacy data aggregation method leveraging progressive UMAP with a dynamic time window based on LSTM (EDP-PUDL). Firstly, a model of the dynamic time window based on a long short-term memory (LSTM) network was developed to divide dynamic data. Then, progressive uniform manifold approximation and projection (UMAP) with differential privacy was performed to reduce the dimension of the window data while preserving privacy. The privacy budget was determined by the data volume and the attribute's Shapley value, adding DP noise. Finally, the privacy analysis and experimental comparisons demonstrated that EDP-PUDL ensures user privacy while achieving superior aggregation efficiency and availability compared to other algorithms used for dynamic high-dimensional data aggregation. [ABSTRACT FROM AUTHOR]

In Internet of Things (IoT) applications, user behavior is influenced by factors such as network structure, user activity, and location. Extracting valuable patterns from user activity traces can lead to the development of smarter, more personalized IoT applications and improved user experience. This paper proposes a LIME-based user behavior preference mining algorithm that leverages Explainable AI (XAI) techniques to interpret user behavior data and extract user preferences. By training a black-box neural network model to predict user behavior using LIME and approximating predictions with a local linear model, we identify key features influencing user behavior. This analysis reveals user behavioral patterns and preferences, such as habits at specific times, locations, and device states. Incorporating user behavioral information into the resource scheduling process, combined with a feedback mechanism, establishes an active discovery network of user demand. Our approach, utilizing edge computing capabilities, continuously fine-tunes and optimizes resource scheduling, actively adapting to user perceptions. Experimental results demonstrate the effectiveness of feedback control in satisfying diverse user resource requests, enhancing user satisfaction, and improving system resource utilization. [ABSTRACT FROM AUTHOR]

The integration of edge computing with IoT (EC-IoT) systems provides significant improvements in addressing security and privacy challenges in IoT networks. This paper examines the combination of EC-IoT and artificial intelligence (AI), highlighting practical strategies to improve data and network security. The published literature has suggested decentralized and reliable trust measurement mechanisms and security frameworks designed explicitly for IoT-enabled systems. Therefore, this paper reviews the latest attack models threatening EC-IoT systems and their impacts on IoT networks. It also examines AI-based methods to counter these security threats and evaluates their effectiveness in real-world scenarios. Finally, this survey aims to guide future research by stressing the need for scalable, adaptable, and robust security solutions to address evolving threats in EC-IoT environments, focusing on the integration of AI to enhance the privacy, security, and efficiency of IoT systems while tackling the challenges of scalability and resource limitations. [ABSTRACT FROM AUTHOR]

In the rapidly evolving domain of edge computing, efficient task scheduling emerges as a pivotal challenge due to the increasing complexity and volume of tasks. This study introduces a sophisticated dual-layer hybrid scheduling model that harnesses the strengths of Graph Neural Networks and Deep Reinforcement Learning to enhance the scheduling process. By simplifying task dependencies with Graph Neural Network at the upper layer and integrating Deep Reinforcement Learning with heuristic algorithms at the lower layer, this model optimally allocates tasks, significantly improving scheduling efficiency and reducing response times, particularly beneficial for logistics cloud robots operating in edge computing contexts. We validated the effectiveness of this innovative model through rigorous simulation experiments on the EdgeCloudSim platform, comparing its performance against traditional heuristic methods such as Shortest Job First, First Come First Serve and Heterogeneous Earliest Finish Time. The results confirm that our model consistently achieves superior task scheduling performance across various task volumes, effectively meeting the scheduling demands. This study demonstrates the effectiveness of integrating advanced machine learning techniques with heuristic algorithms to enhance task scheduling processes, making it particularly suitable for scenarios with high demands on response times. This approach not only facilitates more efficient task management but also aligns with the needs of modern edge computing applications, streamlining operations and boosting overall system performance. [ABSTRACT FROM AUTHOR]

Summary: The metaverse, a future digital world for living, working, learning, and interacting, is rapidly gaining significance, particularly in the domain of remote work and collaboration. This emerging digital landscape demands high‐performance, low‐latency, and scalable services to provide an immersive user experience. The current technology used in metaverse systems has some limitations, which emphasize the importance of adopting emerging technologies like Edge Computing (EC). However, as the number of users and data volume increases, it can impact both system performance and scalability of the edge‐assisted metaverse system. Additionally, the uneven distribution of edge servers can cause inconsistencies and result in high latency. To overcome these challenges, this paper proposes a dynamic edge clustering and task scheduling approach for edge‐assisted metaverse systems (DTAM) in the field of remote work and collaboration. The proposed approach addresses the challenges of high user volume and uneven resource distribution by incorporating dynamic clustering and edge server assistance to improve clustering performance. Furthermore, a Prioritized Experience Replay‐based Deep Q‐learning algorithm with state augmentation (PERDQSA) for task scheduling is introduced to improve sample efficiency and performance. The performance of the proposed DTAM is evaluated against existing techniques, and experimental results demonstrate its significant superiority in terms of specific metrics such as bandwidth, task response time, energy efficiency, and latency. The experiments demonstrated that DTAM outperforms Transformation‐based Edge Computing Deep Q‐Learning (TransEC‐DQL) in several key metrics. Specifically, DTAM achieves 28.5% reduction in latency, 13.7% reduction in response time, and 6.4% improvement in bandwidth compared to TransEC‐DQL. These results signify that DTAM can deliver a significantly enhanced user experience in the metaverse, particularly in the context of remote work and collaboration. [ABSTRACT FROM AUTHOR]

Summary: Mobile edge computing has become popular in the past few years as a means of creating computing resources close to end‐user nodes at the network edge. Nodes—end users—demand work offloading to improve service utilization. Furthermore, when the number of users in mobile edge computing increases, the minimal resources deployed at the edge become a problem. Develop the idea of reinforcement learning using a metaheuristic technique intended to achieve effective resource allocation and resolve offloading issues to handle this issue. The ideal way to manage the implementation of mobile edge computing with a cognitive agent's help is to request compensation for all client necessities. To complete the infrastructure type for the Internet of Things (IoT), the operator information is combined with its distinctive methodology. Neural caching during task execution is provided by reinforcement learning based on snake swarm optimization (SSO). Neural caching during task execution is provided by reinforcement learning based on SSO. In the process of creating the cost mapping table and incentive factor‐based optimal resource allocation, this suggested method is applied to a contract with effective resource allocation among the end manipulators. Using performance metrics like delivery ratio, energy consumption, throughput, and delay, the suggested approach is put into practice and examined. It is also contrasted with traditional methods like Gray Wolf Optimization (GWO) ant colony optimization (ACO) and genetic algorithms (GA). [ABSTRACT FROM AUTHOR]

This paper aims to increase the Unmanned Aerial Vehicle's (UAV) capacity for target tracking. First, a control model based on fuzzy logic is created, which modifies the UAV's flight attitude in response to the target's motion status and changes in the surrounding environment. Then, an edge computing-based target tracking framework is created. By deploying edge devices around the UAV, the calculation of target recognition and position prediction is transferred from the central processing unit to the edge nodes. Finally, the latest Vision Transformer model is adopted for target recognition, the image is divided into uniform blocks, and then the attention mechanism is used to capture the relationship between different blocks to realize real-time image analysis. To anticipate the position, the particle filter algorithm is used with historical data and sensor inputs to produce a high-precision estimate of the target position. The experimental results in different scenes show that the average target capture time of the algorithm based on fuzzy logic control is shortened by 20% compared with the traditional proportional-integral-derivative (PID) method, from 5.2 s of the traditional PID to 4.2 s. The average tracking error is reduced by 15%, from 0.8 m of traditional PID to 0.68 m. Meanwhile, in the case of environmental change and target motion change, this algorithm shows better robustness, and the fluctuation range of tracking error is only half of that of traditional PID. This shows that the fuzzy logic control theory is successfully applied to the UAV target tracking field, which proves the effectiveness of this method in improving the target tracking performance. [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]