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While cloud computing is crucial in processing data from devices with low computational power, the latency introduced by the Internet backhaul limits real-time applications. By situating computing resources at the network's edge, edge computing offers low-latency services by offloading computations from high-performance computing (HPC) data centers to the edge servers, reducing wide Area network (WAN) strain. As a result, edge computing has unlocked opportunities for innovative applications that were previously unfeasible, such as connected vehicles or medical robotics. Nonetheless, deploying the infrastructure required to support edge computing services raises sustainability and energy consumption concerns. Consequently, the development of tools enabling researchers to explore innovative approaches to reducing the energy impact of edge computing is crucial. In this work, we present MintEDGE, a network simulator focused on the energy consumption of edge computing. Our simulator allows testing energy-saving approaches and task placement algorithms in realistic large-scale scenarios encompassing entire regions. [ABSTRACT FROM AUTHOR]

In the context of small unmanned aerial vehicles (UAVs), infrared imaging faces challenges such as low quality, difficulty in detecting small targets, high false alarm rates, and computational resource constraints. To address these issues, we introduce AIMED-Net, an enhancing infrared small target detection net in UAVs with multi-layer feature enhancement for edge computing. Initially, the network encompasses a multi-layer feature enhancement architecture for infrared small targets, including a generative adversarial-based shallow-feature enhancement network and a detection-oriented deep-feature enhancement network. Specifically, an infrared image-feature enhancement method is proposed for the shallow-feature enhancement network, employing multi-scale enhancement to bolster target detection performance. Furthermore, within the YOLOv7 framework, we have developed an improved object detection network integrating multiple feature enhancement techniques, optimized for infrared targets and edge computing conditions. This design not only reduces the model's complexity but also enhances the network's robustness and accuracy in identifying small targets. Experimental results obtained from the HIT-UAV public dataset indicate that, compared to YOLOv7s, our method achieves a 2.5% increase in F1 score, a 6.1% rise in AP for detecting OtherVehicle targets, and a 2.6% improvement in mAP across all categories, alongside a 15.2% reduction in inference time on edge devices. Compared to existing state-of-the-art approaches, our method strikes a balance between detection efficiency and accuracy, presenting a practical solution for deployment in aerial edge computing scenarios. [ABSTRACT FROM AUTHOR]

The intermittent and uncertainty of new energy in the grid connection process affects the overall quality of the grid. To resolve the scattered geographical locations, small individual capacities and poor controllability of distributed energy storage (DES) devices, edge computing is applied in conjunction with aggregate service providers to build an edge computing‐based aggregate model of DES. Regarding its characteristics, the schedulable potential of the energy storage device is analysed through modelling. A multi‐objective scheduling model with multiple parties participating in the subject's behaviour is established using the Internet of Things (IoT) optimization strategy. The simulation example of concrete data further verifies the validity of the model. Results demonstrate that the edge computing‐based aggregate model of DES effectively reduces the calculation load's peak‐valley load and reduces the wind power abandonment. In the meantime, the multiple starts and stops of the thermal power unit reduce the operation cost of the unit. The multi‐objective dispatch model can reduce the opportunity cost and payment of DES effectively. This model achieves load peak reduction and valley filling and reduces the peak dispatch cost of the power grid. The research results can provide some ideas for storing and utilizing the new energy. [ABSTRACT FROM AUTHOR]

Mobile edge computing has been incorporated with Internet of Things (IoHT) with the development of 5G technology, which can enhance the network performance efficiently. The existing approaches for allocating computing resources in cloud edges have several issues, like lower security, high utilization of resources, and high delay. In this manuscript, a Hybrid GarraRufa Fish optimization and Nomadic People Optimizer model is proposed for computing resource allocation of massive Internet of Health Things (IoHT) devices (RA-Hyb-GRF-NPO-IoHT) contemplating privacy protection in cloud edge computing (CEC) environment". Initially, CEC network of massive IoHT devices developed and allocate computing needs of devices to the edge computing based on the network status. After that, the resource allocation, computing delay and communication of the servers are computed, which are considered as the optimization goal. Thus, the Hybrid GarraRufa Fish optimization and Nomadic People Optimizer model is proposed for optimizing the computing resource allocation in the network. Finally, a security mode instant messaging privacy data is created taking into account the possibility of extensive privacy data leakage in IoHT. Thus, the proposed RA-Hyb-GRF-NPO-IoHT approach is implemented in MATLAB and performance metrics like delay, computational cost, edge computing server's CPU utilization, computational time, overhead, resource utilization and throughput are measured. The proposed RA-Hyb-GRF-NPO-IoHT approach has attained 24.7, 29.8, and 17.96% lower delay, 17.8, 11.85, and 15.76% lower CPU utilization, 23.54, 20.98, and 32.76% lower energy consumption, and 12.87, 10.9, and 8.07% better throughput than the existing methods, like Resource Allocation Strategy by Priority based optimization of IoHT (RA-PO-IoHT), Resource Allocation using Elman neural network classifier with cloud supported internet of things structure for health monitoring system (RA-ENN-IoHT) and Markov Learning utilization mode for resource allocation in cloud of health thing network methods (RA-MLUM-IoHT) respectively. [ABSTRACT FROM AUTHOR]

Machine‐learning‐enhanced nanosensors are rapidly emerging as a promising solution in the field of sensor technology, as traditional sensors encounter limitations of data analysis in their development. Since the inception of machine‐learning algorithms being applied to enhance nanosensors, they have gained significant attention due to their adaptive and predictive capabilities, which promise to dramatically improve efficiency in data collection and processing applications. Herein, a comprehensive overview of technological innovation is provided by reviewing the latest developments in cloud computing, edge computing, and the burgeoning realm of neuromorphic computing. Cloud computing has emerged as a powerhouse, harnessing formidable computational capabilities to process vast volumes of high‐dimensional data. Then, the research directions for various applications of these cloud artificial intelligence (AI)‐enhanced nanosensors are outlined. Moreover, the integration of AI and nanosensor technology into chip‐level edge computing, although promising, still faces challenges such as energy‐efficient hardware development, algorithm optimization, and scalability for mass production. Finally, a forward‐looking perspective on the future of machine‐learning‐enhanced nanosensors is provided, delineating the challenges and opportunities for further research and innovation in this exciting field. [ABSTRACT FROM AUTHOR]

Edge computing (EC) aided Internet of Things (IoT) based applications require real-time processing as well as high-volume data-intensive services as 5G networks evolve. It is difficult to fit IoT services into available edge nodes (ENs) while maintaining performance measures on quality of service (QoS) because of the heterogeneity, restricted resources, and changing resource demand of IoT applications. This study aims to examine a quantum healthcare model that is built on mobile edge computing networks linked with smart IoT. Serverless computing with edge computing may handle quick or small-scale tasks effectively at edge devices, lowering latency. Moreover, serverless edge computing now faces significant hurdles from security and processing performance. In order to provide trustworthy and secure edge serverless services, it is possible to use blockchain technology to boost processing speed and quantum computing to enhance security. In terms of decreasing dependency on IoT cloud analytics or storage facilities, the test results were promising. Lastly, we cover the many aspects that impact the viability of integrating an edge-IoT ecosystem, such as design, capability requirements, functional challenges, and selection criteria. [ABSTRACT FROM AUTHOR]

With the number of attacks and cyberincidents affecting Internet of Things (IoT) devices on the rise, the need for carrying out forensic investigations to determine what has happened has grown at the same pace. However, due to the characteristics and requirements of this environment, the solutions used until now in the field are not suitable to be followed, as they are not able to guarantee the effective retrieval and study of the pieces of evidence. Under these circumstances, new ways of interacting with IoT units are needed, and the edge computing paradigm has emerged as an interesting asset to complement IoT networks. Attack, anomaly and intrusion detection, or data encryption are some of the fields in which this approach has been successfully applied. Following this concept, this article presents an IoT forensic methodology that integrates the edge computing technology in order to assist in the investigation process, trying to address some of the issues that are hindering the effectiveness of examinations in this scenario. In addition, this proposal is compared with the existing ones in the research community, and evaluated by testing it in two case studies representing real-life scenarios, ultimately demonstrating suitability to be used for IoT forensic examinations. [ABSTRACT FROM AUTHOR]

This paper proposes a parallel algorithm exploiting heterogeneous computing and edge computing for anomaly detection (AD) in remotely sensed multispectral images. These images present high spatial resolution and are captured onboard unmanned aerial vehicles. AD is applied to identify patterns within an image that do not conform to the expected behavior. In this paper, the anomalies correspond to human-made constructions that trigger alarms related to the integrity of fluvial ecosystems. An algorithm based on extracting spatial information by using extinction profiles (EPs) and detecting anomalies by using the Reed–Xiaoli (RX) technique is proposed. The parallel algorithm presented in this paper is designed to be executed on multi-node heterogeneous computing platforms that include nodes with multi-core central processing units (CPUs) and graphics processing units (GPUs) and on a mobile embedded system consisting of a multi-core CPU and a GPU. The experiments are carried out on nodes of the FinisTerrae III supercomputer and, with the objective of analyzing its efficiency under different energy consumption scenarios, on a Jetson AGX Orin. [ABSTRACT FROM AUTHOR]

Edge computing provides higher computational power and lower transmission latency by offloading tasks to nearby edge nodes with available computational resources to meet the requirements of time-sensitive tasks and computationally complex tasks. Resource allocation schemes are essential to this process. To allocate resources effectively, it is necessary to attach metadata to a task to indicate what kind of resources are needed and how many computation resources are required. However, these metadata are sensitive and can be exposed to eavesdroppers, which can lead to privacy breaches. In addition, edge nodes are vulnerable to corruption because of their limited cybersecurity defenses. Attackers can easily obtain end-device privacy through unprotected metadata or corrupted edge nodes. To address this problem, we propose a metadata privacy resource allocation scheme that uses searchable encryption to protect metadata privacy and zero-knowledge proofs to resist semi-malicious edge nodes. We have formally proven that our proposed scheme satisfies the required security concepts and experimentally demonstrated the effectiveness of the scheme. [ABSTRACT FROM AUTHOR]

Recently, unmanned aerial vehicles (UAVs) have been widely used in mobile edge computing (MEC) scenarios due to their flexibility, rapid deployment, and ability to expand communication coverage. This paper explores how UAVs can compute offloading more effectively when serving as mobile edge servers in the scenario of UAVs-assisted MEC. We aim to maximize the utility of the UAV operator by joint flight trajectory of UAVs, users' offloading decisions, and collaborative strategy among UAVs while considering the latency and energy consumption. To this end, we first propose a dynamic latency-aware collaborative framework based on deep reinforcement learning. This framework utilizes a queuing model to implement the dynamic task backlog and models the reward of latency-sensitive tasks using a service level agreement. Then, considering the dynamics of the environment and the complexity of the problem, we describe the problem as a markov decision process and propose a proximal policy optimization-based computation offloading algorithm to solve the problem. The experimental results finally demonstrate that the proposed offloading algorithm converges rapidly and exhibits better performance compared to benchmark algorithms. [ABSTRACT FROM AUTHOR]

This paper focuses on the use of smart manufacturing in lathe-cutting tool machines, which can experience thermal deformation during long-term processing, leading to displacement errors in the cutting head and damage to the final product. This study uses time-series thermal compensation to develop a predictive system for thermal displacement in machine tools, which is applicable in the industry using edge computing technology. Two experiments were carried out to optimize the temperature prediction models and predict the displacement of five axes at the temperature points. First, an examination is conducted to determine possible variances in time-series data. This analysis is based on the data obtained for the changes in time, speed, torque, and temperature at various locations of the machine tool. Using the viable machine-learning models determined, the study then examines various cutting settings, temperature points, and machine speeds to forecast the future five-axis displacement. Second, to verify the precision of the models created in the initial phase, other time-series models are examined and trained in the subsequent phase, and their effectiveness is compared to the models acquired in the first phase. This work also included training seven models of WNN, LSTNet, TPA-LSTM, XGBoost, BiLSTM, CNN, and GA-LSTM. The study found that the GA-LSTM model outperforms the other three best models of the LSTM, GRU, and XGBoost models with an average precision greater than 90%. Based on the analysis of training time and model precision, the study concluded that a system using LSTM, GRU, and XGBoost should be designed and applied for thermal compensation using edge devices such as the Raspberry Pi. [ABSTRACT FROM AUTHOR]

Aiming at the problems of poor real-time processing and low security performance caused by the massive information of the Internet of things, a security early warning model of the Internet of things based on deep learning in edge computing environment is proposed. Firstly, the system architecture of the Internet of things is designed by using the edge computing technology, in which the intelligent router is used to obtain the network stub and send it to the nearest edge computing node for anomaly detection. Then, the attention mechanism is used to improve the long short-term memory network (LSTM), and the multidimensional LSTM model is constructed. At the same time, it is used to analyze the combined network data. Finally, according to the set threshold value, judge whether there is abnormal behavior in the network, and give early warning in time to take security defense measures. The experimental analysis of the proposed model based on NS2 simulation platform shows that its early warning success rate and time are 95.2% and 20.6 ms, respectively, and it can detect and defend various network attacks well, with high security performance. [ABSTRACT FROM AUTHOR]

Blockchain, a promising technology, has been extensively applied in numerous fields, such as network security, finance, and medical care. However, due to the low power consumption and weak computing power of the mobile environment, the application of blockchain in this environment still faces many challenges. Therefore, edge computing has been introduced to improve the computing power of mobile devices and encourage more mobile edge devices to join the blockchain network. In this paper, we propose a double auction model to address the issue of edge computing resource allocation in blockchain networks. Based on this auction model, we first propose a truthful double auction mechanism based on breakeven (TDAMB) to determine matched pairs of edge computing service providers (ECSPs) and miners. Furthermore, to improve the system efficiency, we propose a double auction mechanism based on a critical value (DAMCV). We also theoretically analyze the individual rationality, budget balance and truthfulness of the proposed mechanisms. Extensive experiments show that TDAMB and DAMCV have good effects on edge computing resource allocation in blockchain networks. [ABSTRACT FROM AUTHOR]

The convergence of edge computing systems with Field-Programmable Gate Array (FPGA) technology has shown considerable promise in enhancing real-time applications across various domains. This paper presents an innovative edge computing system design specifically tailored for pavement defect detection within the Advanced Driver-Assistance Systems (ADASs) domain. The system seamlessly integrates the AMD Xilinx AI platform into a customized circuit configuration, capitalizing on its capabilities. Utilizing cameras as input sensors to capture road scenes, the system employs a Deep Learning Processing Unit (DPU) to execute the YOLOv3 model, enabling the identification of three distinct types of pavement defects with high accuracy and efficiency. Following defect detection, the system efficiently transmits detailed information about the type and location of detected defects via the Controller Area Network (CAN) interface. This integration of FPGA-based edge computing not only enhances the speed and accuracy of defect detection, but also facilitates real-time communication between the vehicle's onboard controller and external systems. Moreover, the successful integration of the proposed system transforms ADAS into a sophisticated edge computing device, empowering the vehicle's onboard controller to make informed decisions in real time. These decisions are aimed at enhancing the overall driving experience by improving safety and performance metrics. The synergy between edge computing and FPGA technology not only advances ADAS capabilities, but also paves the way for future innovations in automotive safety and assistance systems. [ABSTRACT FROM AUTHOR]

Recently, the unmanned aerial vehicle (UAV)-assisted edge computing is proposed to improve the quality of service in some scenarios within sparse or unavailable base stations (BSs). Meanwhile, the caching technology is adopted to reduce the wireless traffic load and the data transmission delay. However, due to the limited storage capacity of edge nodes, the edge nodes cannot store all of the contents required by user equipment (UE). So, how to select the reasonable contents for caching on edge nodes to reduce the content delivery delay becomes a challenge in the UAV-assisted edge computing environment. In this paper, the Dijkstra algorithm based cooperative caching strategy for UAV-assisted edge computing system is proposed. Specially, the content transmission delay between two nodes is computed. Then, for each requested content, the weighted edge-undirected graph (WEUG), in which one vertex represents one node, is built. Furthermore, Dijkstra algorithm is adopted to achieve the minimal content transmission delay from the edge node caching the requested content to UE. Finally, the optimization problem of content caching is built, and the corresponding cache strategy is achieved by solving the optimization problem. The experimental results imply that the proposed cooperative caching algorithm can achieve better performance on the average content transmission delay, the average cache hit rate, and the total of hops, respectively, comparing with the benchmark algorithms. [ABSTRACT FROM AUTHOR]

Federated learning (FL) in mobile edge computing has emerged as a promising machine-learning paradigm in the Internet of Things, enabling distributed training without exposing private data. It allows multiple mobile devices (MDs) to collaboratively create a global model. FL not only addresses the issue of private data exposure but also alleviates the burden on a centralized server, which is common in conventional centralized learning. However, a critical issue in FL is the imposed computing for local training on multiple MDs, which often have limited computing capabilities. This limitation poses a challenge for MDs to actively contribute to the training process. To tackle this problem, this paper proposes an adaptive dataset management (ADM) scheme, aiming to reduce the burden of local training on MDs. Through an empirical study on the influence of dataset size on accuracy improvement over communication rounds, we confirm that the amount of dataset has a reduced impact on accuracy gain. Based on this finding, we introduce a discount factor that represents the reduced impact of the size of the dataset on the accuracy gain over communication rounds. To address the ADM problem, which involves determining how much the dataset should be reduced over classes while considering both the proposed discounting factor and Kullback–Leibler divergence (KLD), a theoretical framework is presented. The ADM problem is a non-convex optimization problem. To solve it, we propose a greedy-based heuristic algorithm that determines a suboptimal solution with low complexity. Simulation results demonstrate that our proposed scheme effectively alleviates the training burden on MDs while maintaining acceptable training accuracy. [ABSTRACT FROM AUTHOR]

New‐type professional farmers are new‐type rural talents with scientific and cultural literacy and professional knowledge of agriculture under the background of rural revitalization. The new type of professional farmers is of great significance for accelerating agricultural modernization and promoting the construction of a powerful modern socialist country. In the process of cultivating new types of professional farmers in rural areas, it is necessary to break through the previous informatization constraints of poor information flow, and give full play to the role of advanced information technology to promote the sustainable economic development. This paper proposes a new type of professional farmer cultivation platform based on edge computing architecture to improve the cultivation efficiency of new type of professional farmers, so as to better promote the development of rural informatization. Aiming at the challenges of energy consumption under the edge platform architecture, this paper proposes a dynamic scheduling method for semi‐online tasks for edge computing platforms. In order to evaluate the effectiveness of the algorithm, the method in this paper is compared with the classic scheduling algorithm, and simulated and verified on the CloudSim platform. Experimental results show that the proposed method outperforms other algorithms in the task completion time metric. With the expansion of task scale, more energy consumption can be saved by using the algorithm proposed in this paper. [ABSTRACT FROM AUTHOR]

Edge computing is a scalable, modern, and distributed computing architecture that brings computational workloads closer to smart gateways or Edge devices. This computing model delivers IoT (Internet of Things) computations and processes the IoT requests from the Edge of the network. In a diverse and independent environment like Fog-Edge, resource management is a critical issue. Hence, scheduling is a vital process to enhance efficiency and allocation of resources properly to the tasks. The manuscript proposes an Artificial Neural Network (ANN) inspired Antlion algorithm for task orchestration Edge environments. Its aim is to enhance resource utilization and reduce energy consumption. Comparative analysis with different algorithms shows that the proposed algorithm balances the load on the Edge layer, which results in lower load on the cloud, improves power consumption, CPU utilization, network utilization, and reduces average waiting time for requests. The proposed model is tested for healthcare application in Edge computing environment. The evaluation shows that the proposed algorithm outperforms existing fuzzy logic algorithms. The performance of the ANN inspired Antlion based orchestration approach is evaluated using performance metrics, power consumption, CPU utilization, network utilization, and average waiting time for requests respectively. It outperforms the existing fuzzy logic, round robin algorithm. The proposed technique achieves an average cloud energy consumption improvement of 95.94%, and average Edge energy consumption improvement of 16.79%, 19.85% in average CPU utilization in Edge computing environment, 10.64% in average CPU utilization in cloud environment, and 23.33% in average network utilization, and the average waiting time decreases by 96% compared to fuzzy logic and 1.4% compared to round-robin respectively. [ABSTRACT FROM AUTHOR]

Timely status updating in mobile edge computing (MEC) systems has recently gained the utmost interest in internet of things (IoT) networks, where status updates may need higher computations to be interpreted. Moreover, in real-life situations, the status update streams may also be of different priority classes according to their importance and timeliness constraints. The classical disciplines used for priority service differentiation, preemptive and non-preemptive disciplines, pose a dilemma of information freshness dissatisfaction for the whole priority network. This work proposes a hybrid preemptive/non-preemptive discipline under an M/M/1/2 priority queueing model to regulate the priority-based contention of the status update streams in MEC systems. For this hybrid discipline, a probabilistic discretionary rule for preemption is deployed to govern the server and buffer access independently, introducing distinct probability parameters to control the system performance. The stochastic hybrid system approach is utilized to analyze the average age of information (AoI) along with its higher moments for any number of classes. Then, a numerical study on a three-class network is conducted by evaluating the average AoI performance and the corresponding dispersion. The numerical observations underpin the significance of the hybrid-discipline parameters in ensuring the reliability of the whole priority network. Hence, four different approaches are introduced to demonstrate the setting of these parameters. Under these approaches, some outstanding features are manifested: exploiting the buffering resources efficiently, conserving the aggregate sensing power, and optimizing the whole network satisfaction. For this last feature, a near-optimal low-complex heuristic method is proposed. [ABSTRACT FROM AUTHOR]

Summary: With increasing challenges and research in edge‐assisted IoT models, an improved resource scheduling approach exploiting deep learning concepts is proposed in this research work. Improvement in performance in the proposed work is achieved primarily by addressing the response time and waiting time. This could be achieved if the optimal resources are scheduled without any delay. The presented concatenated deep learning technique considers the time series IoT network source requirements and allocates optimal resources from the resource pool, considering resource availability, workload, and computation time. Two deep learning techniques, namely, CNN and GRU, are utilized for the concatenation process, while resource characteristics are considered as features that are extracted and classified to schedule optimal resources. Novelty in the proposed work is exhibited in the form of the concatenation process proposed. The proposed resource scheduling performance metrics are compared with the performance of the existing scheduling model through simulation analysis for better validation. The proposed model selects the optimal resources from the resource pool using concatenated features and schedules for respective requests with minimum delay and waiting time, which increases the overall efficiency of the edge computing IoT networks. [ABSTRACT FROM AUTHOR]

LoRaWAN has become the technology of choice for increasing Internet of Things applications owing to its long range and low power consumption characteristics. However, in the uplink confirmed messaging cases, the entire retransmission could take several seconds, so it cannot be used in scenarios that require rapid confirmed messaging, such as emergency alerting and real-time controlling applications. Nevertheless, there has been limited work targeting this issue. This study presents a novel LoRaWAN gateway using edge computing to expedite the confirmed messaging process by generating the acknowledgment (ACK) locally, so that the confirmed messaging time can be significantly reduced. Additionally, the resource utilization of the network server can also be decreased due to the use of edge computing. We verified the effectiveness of our solution through extensive simulations and experiments. The confirmed messaging time between the end nodes and the gateway averaged 43 ms for a maximum of 2 retransmissions. With the adoption of edge computing on the gateway, the network server's central processing unit (CPU), memory, and bandwidth peak utilization decrease from 53.51 to 39.46, 73.88 to 72.11%, and 4422.68 kbps to 3271.27 kbps, respectively. In addition, the network server's system load decreases from 2.15 to 1.69, while the gateway cost is reduced by almost $ 38 compared to the benchmark products. [ABSTRACT FROM AUTHOR]

In the mobile edge computing (MEC) environment, the edge caching can provide the timely data response service for the intelligent scenarios. However, due to the limited storage capacity of edge nodes and the malicious node behavior, the question of how to select the cached contents and realize the decentralized security data caching faces challenges. In this paper, a blockchain-based decentralized and proactive caching strategy is proposed in an MEC environment to address this problem. The novelty is that the blockchain was adopted in an MEC environment with a proactive caching strategy based on node utility, and the corresponding optimization problem was built. The blockchain was adopted to build a secure and reliable service environment. The employed methodology is that the optimal caching strategy was achieved based on the linear relaxation technology and the interior point method. Additionally, in a content caching system, there is a trade-off between cache space and node utility, and the caching strategy was proposed to solve this problem. There was also a trade-off between the consensus process delay of blockchain and the caching latency of content. An offline consensus authentication method was adopted to reduce the influence of the consensus process delay on the content caching. The key finding was that the proposed algorithm can reduce latency and can ensure the security data caching in an IoT environment. Finally, the simulation experiment showed that the proposed algorithm can achieve up to 49.32%, 43.11%, and 34.85% improvements on the cache hit rate, the average content response latency, and the average system utility, respectively, compared to the random content caching algorithm, and it achieved up to 9.67%, 8.11%, and 5.95% increases, successively, compared to the greedy content caching algorithm. [ABSTRACT FROM AUTHOR]

Summary: In the era of big data, massive amounts of data hold great value. However, much data exists as isolated islands, and the maximum value of the data cannot be fully utilized. Federated learning allows each client to train local data and then share the training model parameters securely, which can address the isolated data island problem and exploit data value while ensuring data privacy and security. Accordingly, in order to securely complete the electric power load forecasting using existing data, this paper constructs a federated learning‐based privacy‐preserving scheme to support electricity load forecasting in edge computing scenarios. To address the problems of the data‐isolated islands and data privacy in electric power systems, this paper proposes a decentralized distributed solution based on the federated learning technique. Our scheme achieves electricity load forecasting for power systems through the federated learning‐based framework and uses edge computing architecture to improve real‐time data capability and reduce network latency. For the hierarchical scheduling structure in power systems, we divide the system into a cloud‐side‐device three‐layer architecture, which achieves structural coordination and balance, and each layer collects information according to the scheduling control tasks, promoting scheduling effectiveness. Finally, different privacy protection methods are used on the cloud‐edge and edge‐device sides to significantly enhance data security. Moreover, We have conducted extensive experimental simulations for our proposed scheme. The experimental results show that the relative error of electricity load forecasting is around 1.580%. Meanwhile, our scheme achieves high accuracy and low memory usage. The security analysis proves the feasibility and security of our scheme. [ABSTRACT FROM AUTHOR]

Time-Sensitive Networking (TSN) and edge computing are promising networking technologies for the future of the Industrial Internet. TSN provides a reliable and deterministic low-latency communication service for edge computing. The Frame Replication and Elimination for Reliability (FRER) mechanism is important for improving the network reliability of TSN. It achieves high reliability by transmitting identical frames in parallel on two disjoint paths, while eliminating duplicated frames at the destination node. However, there are two problems with the FRER mechanism. One problem is that it does not consider the path reliability, and the other one is that it is difficult to find two completely disjoint path pairs in some cases. To solve the above problems, this paper proposes a method to find edge-disjoint path pairs considering path reliability for FRER in TSN. The method includes two parts: one is building a reliability model for paths, and the other one is computing a working path and a redundant path with the Edge-Disjoint Path Pairs Selection (EDPPS) algorithm. Theoretical and simulation results show that the proposed method effectively improves path reliability while reducing the delay jitter of frames. Compared with the traditional FRER mechanism, the proposed method reduces delay jitter by 15.6% when the network load is 0.9. [ABSTRACT FROM AUTHOR]

Facial expression recognition (FER) is a technology that recognizes human emotions based on biometric markers. Over the past decade, FER has been a popular research area, particularly in the computer vision community. With deep learning (DL) development, FER can achieve impressive recognition accuracy. In addition, favorable Internet-of-Things (IoT) advancements generate massive amounts of visual data needed to enable reliable DL-based emotion analysis. However, training DL models can suffer from significant memory consumption and computational costs, complicating many vision tasks. Additionally, the direct use of RGB images during the training and inference stages might raise privacy concerns in various FER applications. On the other hand, adopting large deep networks hampers quick and accurate recognition on resource-constrained end devices such as smartphones. As a viable solution, edge computing can be employed to bring data storage and computation closer to end devices rather than relying on a central cloud server. As a result, it can potentially facilitate the deployment of real-time FER applications since the latency and efficiency problems are well addressed by utilizing the computing resources at the edge. In this paper, we develop an efficient FER framework that integrates DL with edge computing. Our framework relies on facial landmarks to enable privacy-preserving and low-latency FER. Accordingly, various landmark detection models and feature types are studied empirically to investigate their capabilities in capturing the dynamic information of facial expressions in videos. Then, using the extracted landmark-based features, we design lightweight DL models to classify human emotions on IoT devices. Extensive experiments performed on benchmark datasets further validate the efficiency and robustness of our framework. [ABSTRACT FROM AUTHOR]

To solve the problem of multi-SDN controller load balancing, a low-latency controller load balancing switch migration algorithm is proposed, and a load balancing framework consisting of three modules of load monitoring, decision-making, and switch migration is designed. Migrate the switch with the highest request rate to the controller with stronger processing capacity and closer distance, and achieve load balancing through multiple iterations, effectively solving the problem that the static controller deployment scheme cannot cope with the dynamic network environment. Since the current research on computing offloading does not consider the stability of the MEC system, an offloading decision-generation algorithm based on Lyapunov optimization is proposed. This algorithm designs a task queue scheduling model to transform the system stability problem into a queue backlog problem, considering the profit of the edge server and the delay of task processing, and establishing a resource optimization model to maximize the profit of the MEC system under the premise of meeting the stability and delay requirements of the MEC system. The experimental results show that the proposed controller load-balancing algorithm can speed up the load-balancing process and reduce the average response delay of the system by about 22.1% while maintaining high throughput. The proposed computing offload algorithm can reduce the average delay of the system by 52%, better allocate computing tasks, and make the edge server obtain higher profits. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study the task offloading optimization problem in satellite edge computing environments to reduce the whole communication latency and energy consumption so as to enhance the offloading success rate. A three‐tier machine learning framework consisting of collaborative edge devices, edge data centers, and cloud data centers has been proposed to ensure an efficient task execution. To accomplish this goal, we also propose a Q‐learning‐based reinforcement learning offloading strategy in which both the time‐sensitive constraints and data requirements of the computation‐intensive tasks are taken into account. It enables various types of tasks to select the most suitable satellite nodes for the computing deployment. Simulation results show that our algorithm outperforms other baseline algorithms in terms of latency, energy consumption, and successful execution efficiency. [ABSTRACT FROM AUTHOR]

Edge computing has been proved an efficient approach to provisioning computation offloading service to vehicles on road through Road‐Side Units (RSUs). However, the traffic volume on road is highly dynamic, while RSU‐based edge servers are static in terms of geographical location and computation capacity. To address this problem, this paper proposes a mobile edge server placement strategy using cruising UAVs along the roads based on the genetic algorithm. A mathematical model is first built to characterize the deployment cost of these UAV‐mounted servers and their routes. Next, a heuristic UAV‐mounted edge server deployment scheme based on K‐medoid clustering and genetic algorithms is designed. Experimental results verify that the proposed UAV deployment scheme satisfies the offloading demand of IoV nodes while reducing the total deployment cost by 17.05–48.94% compared with existing popular approaches. [ABSTRACT FROM AUTHOR]

At present, most of the resource allocation methods in mobile edge computing allocate computing resources according to the time order in which task requests are calculated and unloaded, without considering the priority of tasks in practical applications. According to the computing requirements in such cases, a priority task‐oriented resource allocation method is proposed. According to the average processing time of the task execution, the corresponding priority for task is given. The tasks with different priorities are weighted to allocate computing resources, which not only ensures that the high‐priority tasks obtain sufficient computing resources, but also reduces the total time and energy consumption to complete the calculation of all tasks, thus improving the quality of service. The experimental results show that the proposed method can achieve better performance. [ABSTRACT FROM AUTHOR]

With the development of collaborative edge computing (CEC), the manufacturing market is gradually moving toward large-scale, multi-scenario, and dynamic directions. The existing scheduling strategies based on machine learning or deep learning are only applicable to specific scenarios, which is difficult to meet the requirements of dynamic real-time scheduling in multiple scenarios. The proposed digital twin technology provides a new solution for real-time scheduling of multiple scenarios. In this paper, a digital twin-oriented multi-scene real-time scheduler (GNN-RL) is proposed. This scheduler converts task sequences into node trees and sets up two learning layers. The first layer is an online learning representation layer, which uses GNN to learn node features of embedded structures in real time to boost large instances without additional training. The second layer is the online learning policy layer, which introduces imitation learning mappings into optimal scheduling behavior policies adapted to multiple scenarios. Finally, our approach is validated in several scenarios in 3D digital twin factories, such as computationally intensive, delay-sensitive, and task-urgent scenarios. Since the scheduler proposed in this paper learns general features of the embedding graph rather than instance-specific features, it has good generality and scalability, with good generalization and scalability, outperforming other scheduling rules and schedulers on various benchmarks. [ABSTRACT FROM AUTHOR]

With the development of technology and its integration with scientific realities, computer systems continue to evolve as infrastructure. One of the most important obstacles in front of quantum computers with high‐speed processing is that its existing systems cause security vulnerabilities. Therefore, in order to take advantage of quantum systems, existing systems that are already secure must also be secure in the post‐quantum scenario. One of these systems is edge computing. There are challenges in terms of computational power for the implementation of pre‐ and post‐quantum methods in structures with resource‐constrained devices. This article reviews the post‐quantum security threats of edge devices and systems and the secure methods developed for them. Although there is relatively little research in this field, it remains relevant. In the studies reviewed, lattice‐based approaches are often highlighted for making edge systems quantum‐resistant. Additionally, these studies indicate that there has been an increasing trend in this field in recent years. This article is categorized under:Applications of Computational Statistics > Defense and National SecurityAlgorithms and Computational Methods > Networks and Security [ABSTRACT FROM AUTHOR]

Many modern day cloud hosted applications such as virtual reality, real time games require low latency data access and computation to improve response time. So it is essential to bring the computation and data storage edge servers closer to the user's geographical location to improve response times and save bandwidth. In particulars, in online gaming and on demand video services, the required application data present at cloud servers need to be placed on the edge servers to provide low latency app-functionalities. The transfer of huge amount of data from cloud server to edge server incurs high cost and time penalties. Thus, we need an efficient way to solve edge data distribution (EDD) problem which distribute the application data to the edge servers that minimizes transfer cost. In this work, we provide a refined formulation of an optimal approach to solve the EDD problem using integer linear programming (ILP) technique. Due to the time complexity limitation of the ILP approach, we propose an O(k) approximation algorithm based on network Steiner tree estimation (EDD-NSTE) for estimating solutions to dense large-scale EDD problem. The proposed approach is analyzed to be 11/6 approximation which is better than the state-of-the-art 2 approximation EDD-A approach. The experimental evaluation through simulation using real world EUA data set demonstrate that the EDD-NSTE outperform state-of-the-art approach and other representative approaches. [ABSTRACT FROM AUTHOR]

Artificial intelligence (AI) edge devices prefer employing high-capacity nonvolatile compute-in-memory (CIM) to achieve high energy efficiency and rapid wakeup-to-response with sufficient accuracy. Most previous works are based on either memristor-based CIMs, which suffer from accuracy loss and do not support training as a result of limited endurance, or digital static random-access memory (SRAM)-based CIMs, which suffer from large area requirements and volatile storage. We report an AI edge processor that uses a memristor-SRAM CIM-fusion scheme to simultaneously exploit the high accuracy of the digital SRAM CIM and the high energy-efficiency and storage density of the resistive random-access memory memristor CIM. This also enables adaptive local training to accommodate personalized characterization and user environment. The fusion processor achieved high CIM capacity, short wakeup-to-response latency (392 microseconds), high peak energy efficiency (77.64 teraoperations per second per watt), and robust accuracy (>0.5% accuracy loss). This work demonstrates that memristor technology has moved beyond in-lab development stages and now has manufacturability for AI edge processors. [ABSTRACT FROM AUTHOR]

With the development of tourism, it is necessary to understand and evaluate the status of tourist resources in forest parks in order to improve visitor experience and manage forest parks. This paper presents an embedded edge computing method, which uses optical imaging technology to monitor and evaluate tourism resources in forest parks. Select the appropriate optical imaging equipment and embed it into the forest park monitoring system. Then the optical image data of each corner of the forest park is collected by the equipment, and these data are preprocessed and analyzed. A computer vision algorithm is used to detect, extract and recognize tourist resources in optical images, such as scenic spots, plants and animals. By combining embedded edge computing technology with optical imaging, accurate and detailed evaluation results of forest park tourism resources are obtained. These results can provide valuable information to forest park managers to help them understand the utilization of tourism resources, hot spots and room for improvement, so as to enhance the tourism experience and sustainable development of forest parks. [ABSTRACT FROM AUTHOR]

Driven by the demand for ubiquitous connection in the Internet of everything era, this paper introduces a new low earth orbit (LEO) satellite-based multi-access edge computing fusion architecture. The architecture regards LEO satellites deployed with high-performance edge modules as superior nodes, which can provide onboard processing mode edge task offloading service for users in complex regions. At the same time, the superior node also has the offloading decision and can offload some edge user tasks to the edge service center in the ground network. Based on differential game theory, we propose a two-stage computing resource purchase strategy and task-offloading resource pricing strategy to ensure the minimum cost of edge computing services for edge users and the maximum benefit for edge service providers and prove the existence of a unique Nash equilibrium solution using Piccard theorem. An algorithm based on the Runge–Kutta method is designed to solve Nash equilibrium, and the simulation results show the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

As the performance of Radio Frequency Identification (RFID) devices is susceptible to the influence of the surrounding environment, making the original data collected by RFID devices with uncertain, of which missed data and redundant data are the main source of uncertainty data, these uncertainty data will seriously affect the quality of RFID upper layer applications. Therefore, to solve the uncertainty in RFID data, the original data must be cleaned. For the shortcomings of tag dynamics detection in the traditional data cleaning algorithm named Statistical Smoothing for Unreliable RFID data (SMURF), an adaptive sliding window-based data cleaning algorithm for RFID single-tag is proposed. The method takes into account the influence of tag speed on tag integrity judgment, and divides sliding sub-windows to accurately detect tag changes and then reasonably adjusts the sliding window size. In addition, considering that the average reading rate of tags is affected by the collision between multiple tags, an RFID multi-tag cleaning method based on twice-tag number estimation is proposed. The method accurately estimates the number of tags by the twice-tag estimation method, controls the read period by the Markov chain, and reduces the occurrence of multiple tag collisions by using an unequal time slot optimization method. Experimental results show that the proposed method in this paper can form a complete set of RFID data stream cleaning algorithms, which effectively reduces the uncertain data and improves the data accuracy. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) edge is an emerging technology of sensors and devices that communicate real-time data to a network. IoT edge computing was introduced to handle the latency concerns related to cloud computing data management, as the data are processed closer to their point of origin. Clustering and scheduling tasks on IoT edge computing are considered a challenging problem due to the diverse nature of task and resource characteristics. Metaheuristics and optimization methods are widely used in IoT edge task clustering and scheduling. This paper introduced a new task clustering and scheduling mechanism using differential evolution optimization on IoT edge computing. The proposed mechanism aims to optimize task clustering and scheduling to find optimal execution times for submitted tasks. The proposed mechanism for task clustering is based on the degree of similarity of task characteristics. The proposed mechanisms use an evolutionary mechanism to distribute system tasks across suitable IoT edge resources. The clustering tasks process categorizes tasks with similar requirements and then maps them to appropriate resources. To evaluate the proposed differential evolution mechanism for IoT edge task clustering and scheduling, this study conducted several simulation experiments against two established mechanisms: the Firefly Algorithm (FA) and Particle Swarm Optimization (PSO). The simulation configuration was carefully created to mimic real-world IoT edge computing settings to ensure the proposed mechanism's applicability and the simulation results' relevance. In the heavyweight workload scenario, the proposed DE mechanism started with an execution time of 916.61 milliseconds, compared to FA's 1092 milliseconds and PSO's 1026.09 milliseconds. By the 50th iteration, the proposed DE mechanism had reduced its execution time significantly to around 821.27 milliseconds, whereas FA and PSO showed lesser improvements, with FA at approximately 1053.06 milliseconds and PSO stabilizing at 956.12 milliseconds. The simulation results revealed that the proposed differential evolution mechanism for edge task clustering and scheduling outperforms FA and PSO regarding system efficiency and stability, significantly reducing execution time and having minimal variation across simulation iterations. [ABSTRACT FROM AUTHOR]

With the development of science and technology, vehicles have gradually become smart network vehicles. However, applications on vehicles require great network and computing performance. It poses a great challenge to the existing Internet of Vehicles (IoV). Cloud computing can relieve the pressure of data processing, but the response time could take a long time. In the edge computing, edge server processing requests can effectively reduce response time. For example, during rush hour, in order to respond quickly, a large number of requests can be sent from the vehicles layer to the edge server. However, due to the limited bandwidth resources of edge servers, it is necessary to develop an effective bandwidth allocation model. The research in this paper consists of two main parts. First, this paper proposes a multi-objective bandwidth allocation optimization model for addressing this issue. The goal is to minimize average response time and maximize bandwidth utilization. Second, a multi-objective sparrow search algorithm (KMSSA) based on K-means algorithm is proposed to solve multi-objective bandwidth allocation. This paper compares KMSSA with particle swarm optimization (PSO) and sparrow search algorithm (SSA) on iFogSim platform. The results show that KMSSA has good optimization performance. The algorithm can be successfully applied to solve global multi-objective optimization problems. To compare with PSO algorithm, the performance of KMSSA is improved by 10%. To compare with SSA, the performance of KMSSA is improved by 5%. Also, KMSSA has superiority in solving practical problems. Its global optimal solution has superior performance in terms of average response time, bandwidth utilization, energy consumption, and cost. [ABSTRACT FROM AUTHOR]

Mobile edge computing is an energy-efficient paradigm which has great support for resource-limited user devices performing compute-intensive programs and applications. However, limited edge resources, mobility of user equipment, growth of service requirements, and dynamic nature of service types make it a challenging task to configure computing and storage resources for executing various services on edge servers. Therefore, an adaptive joint service placement framework in the edge system with various devices of user and mobile edge servers is proposed in this paper. The proposed framework takes into account the mobility of user devices, the dynamic changes in various types of services, the cost of service placement and service usage, and optimizes the service placement scheme from the perspective of different target groups. Simultaneously, we design a deep deterministic policy gradient based service placement tuning approach in which centralized critic networks and actor networks are jointly used to improve the service placement performance. The relative evaluation results validate the effectiveness of the proposed framework and approach in improving the performance of the edge system. [ABSTRACT FROM AUTHOR]

With the development of the Internet of Things (IoT) technology, massive amounts of sensor data in applications such as fire monitoring need to be transmitted to edge servers for timely processing. However, there is an energy-hole phenomenon in transmitting data only through terrestrial multi-hop networks. In this study, we focus on the data collection task in an unmanned aerial vehicle (UAV)-assisted mobile edge computing (MEC) network, where a UAV is deployed as the mobile data collector for the ground sensor nodes (SNs) to ensure high information freshness. Meanwhile, the UAV is equipped with an edge server for data caching. We first establish a rigorous mathematical model in which the age of information (AoI) is used as a measure of information freshness, related to both the data collection time and the UAV's flight time. Then a mixed-integer non-convex optimization problem is formulated to minimize the peak AoI of the collected data. To solve the problem efficiently, we propose an iterative two-step algorithm named the AoI-minimized association and trajectory planning (AoI-MATP) algorithm. In each iteration, the optimal SN–collection point (CP) associations and CP locations for the parameter ε are first obtained by the affinity propagation clustering algorithm. The optimal UAV trajectory is found using an improved elite genetic algorithm. Simulation results show that based on the optimized ε , the AoI-MATP algorithm can achieve a balance between data collection time and flight time, reducing the peak AoI of the collected data. [ABSTRACT FROM AUTHOR]

Wireless Powered Mobile Edge Computing (WPMEC) is an integration of Mobile Edge Computing (MEC) and Wireless Power Transfer (WPT) technologies, to both improve computing capabilities of mobile devices and energy compensation for their limited battery capabilities. Generally, energy transmitters, mobile devices, and edge servers form a WPMEC system that realizes a closed loop of sending and collecting energy as well as offloading and receiving task data. Due to constraints of time-varying network environments, time-coupled battery levels, and the half-duplex character of mobile devices, the joint design of computation offloading and resource allocation solutions in WPMEC systems has become extremely challenging, and a great number of studies have been devoted to it in recent years. In this article, we first introduce the basic model of the WPMEC system. Then, we present key issues and techniques related to WPMEC. In addition, we summarize solutions for computation offloading and resource allocation to solve critical issues in WPMEC networks. Finally, we discuss some research challenges and open issues. [ABSTRACT FROM AUTHOR]

Offloading strategies in mobile edge computing are hot research, whereas, existing offloading strategies at the edge hard handle the issues of multi-user intensive task scheduling, resulting in the poor utilization of network resource. Therefore, this makes the quality of experience for end users far from satisfactory. To address this, this paper proposes a novel joint offloading strategy consisting of the back propagation neural network and the genetic algorithm. Firstly, using the genetic algorithm optimizes the learning error of the back propagation neural network, and then energy consumption in the system and response delay are jointly optimized by the back propagation neural network. Under long-term total overhead-cost constraints, the joint strategy can achieve the search of the optimal solutions to generate superior calculated offloading results. Unlike those approaches devoting into reducing response delay only for end users, this work takes account into the total overhead-cost in the system thereby affording more efficient for application service providers. Multiple simulation results indicate that the proposed strategy can not only reduce the average response delay of the mobile edge computing system, but also remain a low average energy consumption. [ABSTRACT FROM AUTHOR]

In recent years, more and more devices are connected to the network, generating an overwhelming amount of data. This term that is booming today is known as the Internet of Things. In order to deal with these data close to the source, the term Edge Computing arises. The main objective is to address the limitations of cloud processing and satisfy the growing demand for applications and services that require low latency, greater efficiency and real-time response capabilities. Furthermore, it is essential to underscore the intrinsic connection between artificial intelligence and edge computing within the context of our study. This integral relationship not only addresses the challenges posed by data proliferation but also propels a transformative wave of innovation, shaping a new era of data processing capabilities at the network's edge. Edge devices can perform real-time data analysis and make autonomous decisions without relying on constant connectivity to the cloud. This article aims at analysing and comparing Edge Computing devices when artificial intelligence algorithms are deployed on them. To this end, a detailed experiment involving various edge devices, models and metrics is conducted. In addition, we will observe how artificial intelligence accelerators such as Tensor Processing Unit behave. This analysis seeks to respond to the choice of a device that best suits the necessary AI requirements. As a summary, in general terms, the Jetson Nano provides the best performance when only CPU is used. Nevertheless the utilisation of a TPU drastically enhances the results. [ABSTRACT FROM AUTHOR]

Traffic flow analysis is essential to develop smart urban mobility solutions. Although numerous tools have been proposed, they employ only a small number of parameters. To overcome this limitation, an edge computing solution is proposed based on nine traffic parameters, namely, vehicle count, direction, speed, and type, flow, peak hour factor, density, time headway, and distance headway. The proposed low-cost solution is easy to deploy and maintain. The sensor node is comprised of a Raspberry Pi 4, Pi camera, Intel Movidius Neural Compute Stick 2, Xiaomi MI Power Bank, and Zong 4G Bolt+. Pre-trained models from the OpenVINO Toolkit are employed for vehicle detection and classification, and a centroid tracking algorithm is used to estimate vehicle speed. The measured traffic parameters are transmitted to the ThingSpeak cloud platform via 4G. The proposed solution was field-tested for one week (7 h/day), with approximately 10,000 vehicles per day. The count, classification, and speed accuracies obtained were 79.8%, 93.2%, and 82.9%, respectively. The sensor node can operate for approximately 8 h with a 10,000 mAh power bank and the required data bandwidth is 1.5 MB/h. The proposed edge computing solution overcomes the limitations of existing traffic monitoring systems and can work in hostile environments. [ABSTRACT FROM AUTHOR]

Real-time performance and reliability are two critical indicators in cyber–physical production systems (CPPS). To meet strict requirements in terms of these indicators, it is necessary to solve complex job-shop scheduling problems (JSPs) and reserve considerable redundant resources for unexpected jobs before production. However, traditional job-shop methods are difficult to apply under dynamic conditions due to the uncertain time cost of transmission and computation. Edge computing offers an efficient solution to this issue. By deploying edge servers around the equipment, smart factories can achieve localized decisions based on computational intelligence (CI) methods offloaded from the cloud. Most works on edge computing have studied task offloading and dispatching scheduling based on CI. However, few of the existing methods can be used for behavior-level control due to the corresponding requirements for ultralow latency (10 ms) and ultrahigh reliability (99.9999% in wireless transmission), especially when unexpected computing jobs arise. Therefore, this paper proposes a dynamic resource prediction scheduling (DRPS) method based on CI to achieve real-time localized behavior-level control. The proposed DRPS method primarily focuses on the schedulability of unexpected computing jobs, and its core ideas are (1) to predict job arrival times based on a backpropagation neural network and (2) to perform real-time migration in the form of human–computer interaction based on the results of resource analysis. An experimental comparison with existing schemes shows that our DRPS method improves the acceptance ratio by 25.9 % compared to the earliest deadline first scheme. [ABSTRACT FROM AUTHOR]

In the field of Internet of Things, mobile communication will be deeply integrated with industrial construction, biomedicine, agricultural production, transportation and other industries to fully realize the "Internet of Everything" and realize smart city and sustainable development. This paper intends to solve problems in increasing latency and consumption of energy and security after deploying Mobile Edge Computing (MEC) servers in single cell multi-user network model, and has advocated a strategy of allocating the resources of 5G communication in regards to the protection mechanism for privacy in the environment of edge computing. First of all, solution model for the problem is characterized as a model with time delay and energy consumption. Second, privacy is used to measure the uncertainty of data in order to quantify privacy data while computing the allocation of tasks and facilitate the design of objective function. Finally, the task migration problem of minimizing energy consumption under the limitation of completion time is constructed as a nonlinear 0–1 programming problem. Besides, we design the optimal resource allocation strategy of discrete algorithm of binary particle swarm optimization (BPSO) for problem solution. Results indicate that while the quantity of users is 100, the total user expenditure of proposed method is 18.3J, which is lower than 20.0J and 30.7J of the compared methods. Moreover, the proposed method has lower latency and energy consumption, which can well balance the load of MEC servers. [ABSTRACT FROM AUTHOR]

The existing algorithms for identifying and tracking pigs in barns generally have a large number of parameters, relatively complex networks and a high demand for computational resources, which are not suitable for deployment in embedded-edge nodes on farms. A lightweight multi-objective identification and tracking algorithm based on improved YOLOv5s and DeepSort was developed for group-housed pigs in this study. The identification algorithm was optimized by: (i) using a dilated convolution in the YOLOv5s backbone network to reduce the number of model parameters and computational power requirements; (ii) adding a coordinate attention mechanism to improve the model precision; and (iii) pruning the BN layers to reduce the computational requirements. The optimized identification model was combined with DeepSort to form the final Tracking by Detecting algorithm and ported to a Jetson AGX Xavier edge computing node. The algorithm reduced the model size by 65.3% compared to the original YOLOv5s. The algorithm achieved a recognition precision of 96.6%; a tracking time of 46 ms; and a tracking frame rate of 21.7 FPS, and the precision of the tracking statistics was greater than 90%. The model size and performance met the requirements for stable real-time operation in embedded-edge computing nodes for monitoring group-housed pigs. [ABSTRACT FROM AUTHOR]

Multi-Access Edge Computing (MEC) is known as a promising communication paradigm that enables IoT and 5G scenarios by using edge servers located in the proximity of end users. As an integral part of MEC, edge servers provide virtualized resources and host different MEC applications. Therefore, user equipment and IoT devices can offload tasks to edge servers instead of remote cloud data centers. A proper edge server placement strategy can significantly improve the performance of mobile applications. The optimum number of edge servers and their placement is a problem called edge server placement. This optimization problem is NP-hard. In recent years, the research community has made great efforts to solve this problem. To achieve Quality of Service (QoS), various metrics are considered in different research papers. However, a comprehensive overview of the different aspects of the Edge Server Placement Problem (ESPP) is still missing. This paper first highlights the edge server's importance and gives its applications. Then, it provides a comprehensive summary and taxonomies based on the objectives, applications, datasets, frameworks, and strategies of the different research on the placement of edge servers. Also, this paper summarizes the ESPP and other optimization problems raised as joint optimization problems. Finally, considering the capabilities and features of the MEC environment, some open issues are presented. [ABSTRACT FROM AUTHOR]

Summary: The unmanned aerial vehicle (UAV) coalition networks have been widely used in emergency mission scenarios. The introduction of the mobile edge computing (MEC) paradigm into multi‐coalition UAV networks further improves the mission processing performance of UAV coalitions. In this paper, we investigate the problem of minimizing total task processing delay of UAV members in MEC‐enabled coalition‐based UAV networks. First, we propose a hierarchical offloading model in which multiple UAV heads decide its position selection strategy and multiple UAV members decide its offloading strategy when offloading tasks to UAV heads. Considering data arrival from multiple UAV member nodes at each UAV head, the first come first served (FCFS) queuing model is introduced when the UAV head processes tasks from members. Second, the hierarchical offloading delay minimization problem is formulated as a multi‐leader multi‐follower Stackelberg game. The existence of a Stackelberg equilibrium (SE) is proved by showing that multi‐leader subgame and multi‐follower subgame are exact potential games (EPGs) with Nash equilibrium (NE). We design a best response‐based hierarchical iterative offloading algorithm to solve SE. Finally, the simulation results show that the performance of the proposed scheme is better than that of other benchmark methods and the proposed scheme can effectively reduce the total delay for all UAV members. [ABSTRACT FROM AUTHOR]

Mobility is a fundamental feature of mobile edge computing. Due to the mobility of users, the contextual attributes of cloudlets such as server resources and network state will dynamically change with time during offloading, showing time-varying and fuzzy characteristics. To this end, how to make efficient offloading decision to provide low-latency, low-power and highly reliable services in MEC has become a critical issue. In this paper, we propose a time-varying context-aware cloudlet decision algorithm based on neutrosophic set, TConNS (The Code of TConNS is available at https://github.com/zhengLabs/NSO) . Firstly, we establish a representation model of the multi-dimensional time-varying context of candidate cloudlets, including the mobile residence time. Secondly, we adopt the backward generator of cloud model theory to transform the contextual raw data into a single-valued neutrosophic set with the expression ability for fuzzy information. Finally, we use a series of appropriate operations under the own unique computing system of neutrosophic set to obtain the best cloudlet. Extensive experiments show that TConNS reduces the average response time by about 49% and the average energy consumption by about 46%, and also reduces the number of task failures. [ABSTRACT FROM AUTHOR]