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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]

Integrating cloud, fog, and edge computing with future Internet-of-Things (IoT) devices and their applications in 5G/6G networks will become more feasible soon. This research analyzes the effectiveness of the Hybrid-MiGrror and hybrid-copy migration methods in scenarios involving heterogeneous multiple VMs/containers. This research introduces mathematical models for the Hybrid-MiGrror approach and offers suggestions and comparisons for migrating services to be deployed as multiple services. The model's notable feature is its ability to use both average and non-average values for various parameters during migration, resulting in enhanced and more precise outcomes. In contrast, previous hybrid migration studies mostly rely only on average values. This study demonstrates that relying solely on average parameter values in hybrid migration can result in imprecise outcomes. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has been growing quickly since the 5 G era arrived, which has boosted the quantity of data stream and computational demands. Ultra-dense Edge Computing (UDEC) is a result of the growing popularity of Mobile Edge Computing (MEC) and Ultra-dense Network (UDN), two vital technologies with bright futures. Task offloading is viewed in the UDEC network as a successful strategy to offer mobile users low-latency and flexible computing capabilities. However, the edge cloud's constrained computing capacity and mobile users' dynamically shifting computing requirements present difficulties for the reasonable scheduling of computing requests to the edge cloud. The Power Allocation (PA) problem for mobile users to address this issue. This problem attempts to use quasiconvex technique to minimize energy consumption. Next, the Joint Request Offloading and Resource Scheduling (JRORS) problem is treated as a mixed-integer nonlinear programming problem to minimize request delay and improve welfare. The request offloading problem and the computational resource scheduling challenge are two subproblems of the JRORS problem that can be further broken down, the JRORS issue is therefore viewed as a dual decision-making problem. A Ratio of Power Consumption to Welfare (RCW) problem through mathematical modeling. An improved snake optimizer (SO) is therefore proposed, which was added with oscillation factor to optimize the location of the weight parameter in the location update and the variation of food production. It can enhance the global search capability and local search ability. By conducting simulation experiments, divide the mobile users into nine groups for testing, select energy consumption, welfare, and RCW as the objective functions, respectively, and compare them with other swarm optimization algorithms, such as Differential Evolution (DE), Particle Swarm Optimization (PSO), Ant-Lion Optimizer (ALO), Butterfly Optimization Algorithm (BOA), and Gray Wolf Optimizer (GWO). The improved algorithm achieved minimal energy consumption and optimal welfare in nearly every test case, contributing to the enhanced effectiveness and cost-efficiency of edge systems, thereby achieving the optimization of task offloading and scheduling. [ABSTRACT FROM AUTHOR]

In the rapidly changing world of new technologies, AI and ML have been delivered to the network's edge, allowing edge devices to compute simple models that can be applied later in practical applications. Edge computing allows the deployment of cloudlike services to the network edge. Moreover, it allows network devices to contribute to Federated Learning (FL) by using their processing power. Federated learning was developed when privacy and security concerns emerged among people. Here, multiple clients work together to solve machine-learning problems. Instead of collecting users' raw data, this technique protects user privacy by aggregating model parameters from each client to a central server. Even though FL techniques have several benefits, such as scalability and data privacy, when used with heterogeneous devices, they can introduce significant problems in processing complexity and speed. This paper thoroughly surveyed the important and efficient technological advancements based on edge federated learning. We review the widely used FL frameworks that effectively facilitate communication between clients and servers. We also present a case study on handwritten digit recognition using the MNIST dataset to state the effectiveness of federated learning. On applying this approach, we were able to achieve 94.56% accuracy. [ABSTRACT FROM AUTHOR]

Now a days, the revolution or advancements in Information Communication Technology (ICT) are changing the way of handling the situations in the present environment. In our society, several people are present who live with physical difficulties and can not complete their basic household tasks for their own. The disabled person is all alone at home and caring the house where intermediator may gain the benefit of the opportunity. Though the modern era is revolutionizing the whole communication and interaction process among environment and human. The disabled person who cannot walk, and is deaf completely unable to hear, run or handle the current situation, the automatic systems or alarm may be compromised by the intruder and can access the house with the mean of steal. The aim of this paper is to propose a secure and competent communication mechanism for AI and edge-computing based home automation in case of disabled persons handling the situation. The proposed mechanism integrates the distrust-based recommendation system along with Social Contact Similarity mechanism that improves the efficiency and quick action decision corresponding to the altered device that may cause severe harm at the edge-level or to the person. The proposed mechanism is simulated and substantiated against various existing scheme over several performance metrics such as delay, alteration rate, accuracy and response time. [ABSTRACT FROM AUTHOR]

In this day and age, the Industrial Internet of Things (IIoT) has been considered to revolutionize industrial manufacturing by capturing and accessing massive data sources with incredible speed and efficiency than before. Combined with it, Mobile Edge Computing (MEC) is a comprehensive Digital Transformation tendency to solve the problems Cloud computing faces. However, the fundamental challenges of energy and latency make deploying IIoT MEC networks difficult. Accordingly, this paper considers the efficient design of time allocation for successful computation probability (SCP) maximization for multiple energy-constrained mobile devices (MD) and multiple antennas access point (AP) in uplink radio frequency energy harvesting (RF EH) non-orthogonal multiple access (NOMA) IIoT network. Specifically, multiple MDs need to receive the energy and compute support of a MEC server placed in a multiple antenna wireless AP to complete the task immediately. Accordingly, a four-phase communication protocol is proposed to ensure system performance. The system follows the cluster head (CH) scheme based on the channel state information (CSI) to harvest RF energy from the AP. To ensure the highest system performance, we propose two algorithms for determining the optimal EH time for two CHs: SCPM-GSS and SCPM-GA. In addition, we derive the closed-form expressions for the SCP of the system and each CH. Monte Carlo simulations are used to verify the results of the analysis. The numerical results demonstrate the effects of crucial system parameters of our proposed NOMA scheme with those of conventional orthogonal multiple access (OMA) schemes. Furthermore, the proposed optimization algorithms allow the system to avoid outages like the random parameters setting approach and improve the SCP by 3 to 30% compared to the fixed parameters set when the transmit power is low and medium. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is an essential technique in next-generation networks to serve ultra-low latency and computation-intensity applications. At the same time, nonorthogonal multiple access (NOMA) is a technique to help multi-user service, saving energy and increasing spectrum efficiency. In this study, we investigate the NOMA MEC-based wireless Tactile Internet of Things (IoT) network and propose the optimization algorithms for system and users performance: We propose a network model consisting of a MEC server at the access point that supports computation for two sensor clusters in the Tactile IoT environment. We analyze the performance of the system and cluster heads (CHs) using the successful computation probability (SCP). Asymptotic SCP at high SNRs was analyzed and compared by us to give a better view of the system's behavior. Then, we maximize the SCP of the proposed system and simultaneously maximize the SCP of the CHs to clarify the performance trade-off problem in the NOMA MEC network by proposing low-complexity meta-heuristic algorithms. Monte-Carlo simulation results show that our proposed approach can significantly improve system performance by up to 30% compared to OMA traditional methods. [ABSTRACT FROM AUTHOR]

With the rapid proliferation of edge computing-based solutions, many edge computing applications use the cloud for data processing and analysis. However, latency-sensitive applications have low latency requirements and can be very bandwidth hungry, so processing their collected data through cloud servers is not efficient and cost-effective. For example, traffic management and health condition monitoring applications, require real-time data processing at the network edge to respond immediately to unexpected events. These applications are typically executed as workflows with dependent tasks that require careful scheduling to allocate the appropriate resources to the tasks so that the execution is done in a way that satisfies the users' target functions. In this work, we evaluate some traditional scheduling heuristics for the execution of workflow tasks in an edge-computing scenario. Our goal is to compare their performance in terms of execution time and cost and show that these heuristics, previously used in scheduling in the context of cloud environments, can also be used in edge computing scenarios. The results show that the MinMin and PSO scheduling algorithms offer the best results with regard to execution time and cost. [ABSTRACT FROM AUTHOR]

With the operation of wireless sensor networks (WSNs), energy-constrained sensor nodes and inadequate energy constraint methods are becoming obsolete, as they reduce the efficiency of data transmission. The appearance of edge computing (EC) and causality graph provide a new opportunity for energy fault analysis and assessment in WSNs. As a result, by selecting a reliable data transmission path and averaging the energy consumption, we present an energy fault and consumption optimization (EFCO) algorithm for solving the energy hole and consumption balance (EHCB) problem in wireless sensor networks with edge computing (ECWSNs). Specifically, we first build a novel four-layer network architecture by using edge computing technology and causality graph theory. Then, the energy fault cost (EFC) in ECWSNs is formulated as an optimization problem that is constrained by the energy allocation of relay nodes. Furthermore, we propose a causal reasoning algorithm to deduce the single-value fault status probability of relay nodes. Finally, we utilize version 2 of a network simulator (NS-2) to evaluate the fault derivation and energy allocation efficiency of the EFCO algorithm in ECWSNs. [ABSTRACT FROM AUTHOR]

New and innovative wearable IoT devices for health monitoring systems (HMS) have been invented one after another. However, most of these devices are resource-constrained with restricted energy and computation power. The HMS data need to be processed via mobile edge computing (MEC) to improve the response time to fulfill the latency-sensitive and computation-intensive applications and to reduce bandwidth consumption. This paper presents an efficient task scheduling and resource allocation mechanism in MEC to meet these demands in contemplating emergency conditions under HMS. We propose a priority-based task-scheduling and resource-allocation (PTS-RA) mechanism that can assign different priorities to different tasks by considering the tasks' emergency levels computed with respect to the data aggregated from a patient's smart wearable devices. The mechanism can optimally determine whether a task should be processed locally at the hospital workstations (HW) or in the cloud. This is aimed to reduce the total task processing time and the bandwidth cost as much as possible. The proposed approach is to ensure that tasks related to the emergency are given higher priorities and to run first. After the tasks' computations, results are sent to the doctor to response promptly with quick decisions. The proposed PTS-RA was benchmarked against state-of-the-art algorithms concerning average latency, task scheduling efficiency, task execution time, network usage, CPU utilization, and energy consumption. The benchmarking results are promising as PTS-RA is capable to manage the emergency conditions and is meeting the latency-sensitive tasks' requirements with reduced bandwidth cost. [ABSTRACT FROM AUTHOR]

This paper proposes a multi-unmanned aerial vehicle (UAV)-enabled autonomous mobile edge computing (MEC) system, in which several UAVs are deployed to provide services to user devices (UDs). The aim is to reduce/minimize the overall energy consumption of the autonomous system via designing the optimal trajectories of multiple UAVs. The problem is very complicated to be solved by traditional methods, as one has to take into account the deployment updation of stop points (SPs), the association of SPs with UDs and UAVs, and the optimal trajectories designing of UAVs. To tackle this problem, we propose a variable-length trajectory planning algorithm (VLTPA) consisting of three phases. In the first phase, the deployment of SPs is updated via presenting a genetic algorithm (GA) having variable-length individuals. Accordingly, the association between UDs and SPs is addressed by using a close rule. Finally, a multi-chrome GA is proposed to jointly handle the association of SPs with UAVs and their order for UAVs. The proposed VLTPA is tested via performing extensive experiments on eight instances ranging from 60 to 200 UDs, which reveal that the proposed VLTPA outperforms other compared state-of-the-art algorithms. [ABSTRACT FROM AUTHOR]

With the massive access to sensing, measuring, and user-side intelligent terminal devices, the functionalities and applications at the edge-side of digital distribution networks have become significantly enriched. However, the limited computation resources of edge computing devices must be utilized efficiently to achieve various functions, presenting challenges to resource scheduling within digital distribution networks. To tackle these challenges, this paper proposes an optimal resource scheduling method for multi-functional edge computing devices. The collaborative processing relationships of multi-functional applications for edge computing devices in digital distribution networks are analyzed to achieve various functions. These applications are further abstracted into computational task models with different characteristics. On this basis, constraints for resource scheduling are established, including the logical relationships between tasks, the multi-core configuration, and the resource limitation of devices. With the proposed scheduling method, computation resources of the edge computing device can be optimally allocated to different tasks, achieving multiple objectives such as reducing the process latency, avoiding task abandonment, and maximizing resource backup. The results of the case study indicate that using the proposed method, the overall task completion time is reduced by 20%, the task processing success rate increases to 95%, and the adequate resource reservation ratio improves to 40%. [ABSTRACT FROM AUTHOR]

• Chiming in at the advent of 6 G, communication security is increasingly taken into account. • Use efficient iterative method to optimize the variables that affect the cost. • In order to cut system costs down, we propose an interference power price control algorithm based on simulated annealing algorithm, which prioritizes ground users based on this mechanism. • We have innovatively utilized idle roadside units by collaborating with ground users to interfere with eavesdroppers eavesdropping on data, thereby improving the security of the data transmission link. • Ensure that safety performance is maximized in the case of the highest group interest. For the past few years, the security of data transmission links has been increasingly valued. However, with the increasing requirements for the security performance of data transmission links, how to cut the cost of upgrading while improving security has become a serious challenge. In response to the issue of ensuring low-cost and secure data transmission in wireless communication environments with eavesdroppers, this article innovatively starts with idle roadside units and studies how to incentivize roadside units to transmit interference power to interfere with eavesdroppers, thereby efficiently and economically improving the security performance of mobile user data transmission. Firstly, the impact of roadside unit transmission interference power on the security performance of data transmission links was analyzed by establishing a theoretical model; Then, this paper proposes a three-layer model consisting of ground user groups, eavesdropping users, roadside units, and UAVs equipped with mobile edge computing servers. In this model, by encouraging idle roadside units to send interference power, ground users are assisted to safely unload their own data to mobile edge computing servers. Meanwhile, considering the selfishness of idle roadside units, an improved algorithm based on simulated annealing algorithm is proposed to achieve the optimal mapping between ground users and idle roadside units. Finally, through the analysis of simulation results, this scheme can achieve the goal of reducing system costs while improving the security of data transmission. [ABSTRACT FROM AUTHOR]

In this era of extensive big-data, efficient processing of this data has become a critical aspect of computing. Resistive random access memory devices have exemplary non-volatile memory properties and have received significant research attention as next-generation devices for emulating neuromorphic computing. In this study, we present a synaptic memristor incorporating a thin Al-doped hafnium oxide (HfAlO) film layer sandwiched between two electrodes, which is designed to emulate various functions of the biological brain. Investigations into endurance and retention revealed the non-volatile nature of the memristor, with consistent resistive switching observed during continuous operation and a low operating voltage requirement of less than 1.7 V. Additionally, by applying different conditional stimuli, we successfully implemented Pavlovian associative learning. Furthermore, using sequential pulses with varying sequences resulted in the creation of 4-bit edge computing, demonstrating capabilities for energy- and time-efficient data processing. The synaptic and computing properties exhibited by the Mo/HfAlO/TiN device highlighted its valuable features, positioning the device as a promising candidate for energy-efficient neuromorphic computing hardware in the field of artificial intelligence. • CMOS compatible memristor is designed with material analysis. • Highly controllable memristive switching is achieved in HAO memristor. • Artificial synapse behaviors are emulated by pulse modulation. • System level neurormorphic simulation evaluates the performance of each operation. • Edge-computing function is conducted. [ABSTRACT FROM AUTHOR]

• Multi-modal fusing algorithm with rice disease image and non-imaging spectral signals. • Image feature extraction with a new neural network root in MobilenetV2. • Linear SVM method with fused features obtained by effective fusion strategy for crop disease classification. Frequent occurrences of pests and diseases in farmland pose a severe threat to crop yield and quality. Various techniques utilized for the automatic detection of diseases and pests are mostly based on a single modality with limited diagnostic information. In this study, a multi-modal classification algorithm, ISMSFuse, was proposed to fuse the image and spectral information. This algorithm combines a MobilenNetV2 lightweight neural network with a traditional low-computational linear SVM method to adapt to edge computing terminals. The spectral information was extracted with non-imaging spectral signals to reduce the acquisition costs and hardware requirements. A proprietary multi-mode rice bacterial blight dataset, denoted as ImgS-RBB2022, was curated specifically for evaluating the algorithm. The effectiveness of the image features extracted by the algorithm model was evaluated by the t-SNE method and the Fisher scores. Finally, comparing the results with only using imaging data, the multi-mode fusion algorithm had a superior recognition accuracy of 98.14%. Through the SHAP method, it was shown that the added spectral features became the most critical feature affecting the output of the algorithm model. To further explore the application potential of the proposed algorithm, we deployed it on a board-based microcomputer Raspberry Pi to test its feasibility in an edge computing circumstance. The proposed method can be applied to various agricultural diseases and pest detection tasks, particularly in the case of foliar-type diseases and pests. [ABSTRACT FROM AUTHOR]

With the rise of smart city applications, the accessibility of users' location data by smart devices has increased significantly. However, this poses a privacy concern as attackers can deduce personal information from the raw location data. In this paper, we propose a framework to collect user location data while ensuring local differential privacy (LDP) in the last-mile delivery system of Unmanned Aerial Vehicles (UAVs) within an edge computing environment. Firstly, we obtain the user location distribution Quad-tree by employing a region partitioning method based on Quad-tree retrieval in the specified data collection area. Next, the user location matrix is retrieved from the obtained Quad-tree, and we perturb the user location data using an LDP perturbation scheme on the location matrix. Finally, the collected data is aggregated using blockchain to evaluate the utility of the dataset from various regions. Furthermore, to validate the effectiveness of our framework in a real-world scenario, we conduct extensive simulations using datasets from multiple cities with varying urban densities and mobility patterns. These simulations not only demonstrate the scalability of our approach but also showcase its adaptability to different urban environments and delivery demands. Finally, our research opens new avenues for future work, including the exploration of more sophisticated LDP mechanisms that can offer higher levels of privacy without significantly compromising the quality of service. Additionally, the integration of emerging technologies such as 5G and beyond in the edge computing environment could further enhance the efficiency and reliability of UAV-based delivery systems, while also offering new challenges and opportunities for privacy-preserving data collection and analysis. [ABSTRACT FROM AUTHOR]

Due to the wide range of intensive computational applications and ubiquitous connectivity of the Internet of Things (IoT) paradigms, it has become crucial to develop a new platform that can achieve low delay, high network throughput, and enhanced quality of service (QoS). This paper proposes a joint user association, service caching, and task offloading strategy to reduce delay and enhance users' QoS in multi-tier communication and multi-tier edge computing heterogeneous networks (HetNets). The considered system model consists of multi-users with different tasks and service data sizes communicating in a heterogeneous network of one massive multiple-input multiple-output (M-MIMO) macro base station and some small base stations. The proposed work investigates user association, power allocation, optimum service data caching, and task offloading strategies at the computing network edges. Thereby, the objectives of this work are to propose an efficient framework to reduce the system delay, increase the network throughput, and meet the user requirements in multi-tier communication and multi-tier edge computing heterogeneous networks. The simulation results show that the proposed algorithm outperforms the state-of-the-art with a 49.48% decrease in system delay, 80% reduction in cost and hardware complexity in terms of the reduced number of installed antennas, and 48.58% enhancement in the network throughput. [ABSTRACT FROM AUTHOR]

With mobile edge computing, vehicles can obtain nearby network resources and computability and meet the growing demand for vehicular service at large scales. However, as a result of vehicle mobility and offloading of an extensive number of tasks, the congestion in wireless networks and the insufficient computing resources of edge servers make it difficult to maintain good service quality for users. Moreover, network access point selection is not often considered a factor in task execution latency. In this paper, we propose a smart metaheuristic optimization model to address the problem of low service quality due to vehicle movements and limited edge coverage. Then, the proposed model is used to characterize the overall latency of vehicle task offloading by considering resource utilization, workload at edge servers and vehicle movement characteristics. There are two advantages of our proposed framework. First, the design of the optimization model offers an adaptive task offloading strategy by automatically providing preallocation decisions with respect to the edge server workload state. Second, the proposed metaheuristic approach benefits from recent advances in graphics processing unit (GPU) architectures. In fact, the design of the PSO on a GPU shifts the optimization of the task offloading process to a promising area in terms of time and precision. Extensive experimental results are presented to demonstrate the effectiveness of the proposed framework. [ABSTRACT FROM AUTHOR]

The ever-increasing number of recording sites of silicon-based probes imposes a great challenge for detecting and evaluating single-unit activities in an accurate and efficient manner. Currently separate solutions are available for high precision offline evaluation and separate solutions for embedded systems where computational resources are more limited. We propose a deep learning-based spike sorting system, that utilizes both unsupervised and supervised paradigms to learn a general feature embedding space and detect neural activity in raw data as well as predict the feature vectors for sorting. The unsupervised component uses contrastive learning to extract features from individual waveforms, while the supervised component is based on the MobileNetV2 architecture. One of the key advantages of our system is that it can be trained on multiple, diverse datasets simultaneously, resulting in greater generalizability than previous deep learning-based models. We demonstrate that the proposed model does not only reaches the accuracy of current state-of-art offline spike sorting methods but has the unique potential to run on edge Tensor Processing Units (TPUs), specialized chips designed for artificial intelligence and edge computing. We compare our model performance with state of art solutions on paired datasets as well as on hybrid recordings as well. The herein demonstrated system paves the way to the integration of deep learning-based spike sorting algorithms into wearable electronic devices, which will be a crucial element of high-end brain–computer interfaces. • Deep learning methods can generalize on spike sorting data. • Contrastive learning delivers high-quality feature space for spike waveforms. • MobileNet architecture detects spikes at a state-of-art performance. • Edge TPU can be used to perform fast spike sorting on devices with limited resources. [ABSTRACT FROM AUTHOR]

There are several important factors to consider in edge computing systems including latency, reliability, power consumption, and queue load. Task replication requires additional energy costs in mobile edge offloading scenarios based on master-slave replication for fault tolerance. Excessive task offloading may lead to a sharp increase in the total energy consumption of the system including replication costs. Conversely, new tasks cannot enter the waiting queue and are lost, resulting in reliability issues. This paper proposes an adaptive task offloading strategy for balancing the edge node queue load and offloading cost (Lyapunov and Differential Evolution based Offloading schedule strategy, LDEO). The LDEO strategy innovatively customizes the Lyapunov drift-plus-penalty function by incorporating replication redundancy offloading costs to establish a balance model between the queue load and offloading cost. The LDEO strategy computes the optimal offloading decisions with dynamic adjustment characteristics by integrating a low-complexity differential evolution method, aiming to find the optimal balance point that minimizes the offloading cost while maintaining reliability performance. The experimental results show that compared with the existing strategies, LDEO strategy effectively reduces the redundancy of fault tolerance cost and the waiting time under the condition of ensuring that the task will not be discarded over time. It stabilizes the queue length in a reasonable range, controls the waiting time and loss rate of tasks, reduces the extra energy consumption paid by replication redundancy, and effectively realizes the optimal balance under multiple conditions. [ABSTRACT FROM AUTHOR]

Quantum Key Distribution (QKD) holds the promise of a secure exchange of cryptographic material between applications that have access to the same network of QKD nodes, interconnected through fiber optic or satellite links. Worldwide several such networks are being deployed at a metropolitan level, where edge computing is already offered by the telco operators to customers as a viable alternative to both cloud and on-premise hosting of computational resources. In this paper, we investigate the implications of enabling QKD for edge-native applications from a practical perspective of resource allocation in the QKD network and the edge infrastructure. Specifically, we consider the dichotomy between aggregating all the applications on the same source–destination path vs. adopting a more flexible micro-flow approach, inspired from Software Defined Networking (SDN) concepts. Our simulation results show that there is a fundamental trade-off between the efficient use of resources and the signaling overhead, which we managed to diminish with the use of suitable hybrid solutions. [ABSTRACT FROM AUTHOR]

With the development of 5 G mobile users are increasing massively. Some mobile applications like healthcare are latency-critical and requires real-time data processing. A preference-based task offloading framework in mobile edge computing with a device-to-device offloading (MECD2D) system has been proposed to fulfill the latency demands of such applications for minimum energy consumption ensuring resiliency. The problem is formulated as a constraint-based non-linear optimization problem which is complex. The resources are allocated in two steps. In the first step, resources are allocated based on latency demand to ensure resiliency. In the second step, allocated resources are optimized using a non-cooperative mean field game for dynamic system. To ensure the performance of the system for dynamic network, the results are executed on a real-time Shanghai dataset. The computational results indicate that the proposed algorithm performs better in terms of energy consumption. Other parameters such as throughput, network utilization and task computation are also analysed. The results are verified by performing the proposed algorithm with existing Q learning and mean-field game algorithms. The results performed on the dataset indicate an improvement in energy consumption by 5–10 %, and 10–50 % as compared to Q learning and mean-field game respectively. • Energy consumption optimization ensuring resiliency. • Preference base resource allocation according to user demand. • Optimal resource allocation for a dynamic system. • Improved throughput, network utilization and amount of task to be computed. [ABSTRACT FROM AUTHOR]

With the rapid advance of the Internet of Things (IoT) and the surging user-centric smart devices, video services such as short videos, real-time video streaming, and Virtual Reality (VR) gaming are emerging. Traditional cloud-centric caching is no longer able to satisfy the low-latency needs of mobile user groups. Predicting the most popular content in advance and caching it on edge devices such as small base stations (SBS) is a promising solution to alleviate network congestion and supply better Quality of Service (QoS) for mobile users. However, frequently changing popular content, limited edge caching resources, privacy of user data, and user mobility pose significant challenges to edge video caching. To tackle these challenges, we propose an efficient asynchronous federated learning (AFL)-based mobility-aware proactive video caching scheme (AF-MPVC), which significantly improves the overall network performance while preventing the risk of user data leakage. First, we model the proactive video caching problem in mobile edge computing systems and consider the impact of user mobility on AFL, representing the problem as an autoencoder (AE) model loss minimization problem. Then, a proactive video caching algorithm is proposed based on a filtering model, which uses the hidden features obtained from the trained AE model to compute the user similarity to find the popular videos that are most likely to be accessed by users and combine them with the most frequently requested videos. Meanwhile, we take into account the short duration of high mobility users staying on the current SBS and cache some videos to neighboring edge servers to have more redundant space to store more less popular videos. Finally, experiments on three real-world datasets demonstrate that the presented caching scheme outperforms other baseline caching schemes with respect to cache hit rate and latency reduction. • Efficient proactive video caching scheme based on asynchronous federated learning. • Predicts and caches popular video content while preserving user privacy. • Considers user mobility and caches videos to edge servers. • AF-MPVC outperforms baseline schemes in cache hit rate, user waiting delay, and backhaul traffic. [ABSTRACT FROM AUTHOR]

The utilization of unmanned aerial vehicles (UAVs) as aerial mobile edge computing (MEC) servers presents an effective approach for mitigating the computational limitations of intelligent mobile devices. However, the limited battery capacity of UAVs poses a significant challenge in ensuring sustained service provision. To counter this obstacle and enhance energy efficiency, we introduce a partial offloading MEC scheme assisted by layered UAVs (POAL). Our objective is to minimize the total energy consumption of UAVs by jointly optimizing several factors, including wireless channels, transmit power, task partition, computing power, and UAV trajectories. Due to the non-convexity of the problem, we decompose it into three sub-problems and address them iteratively. Firstly, we employ the CVX optimization tool to allocate wireless channels and utilize Lagrangian duality to determine the optimal transmit power. Secondly, leveraging the monotonicity of the optimization objective, we derive the optimal computing power. Lastly, we employ the successive convex approximation (SCA) technique to tackle the trajectory planning sub-problem. Numerical results demonstrate that the proposed algorithm can effectively position UAVs within 15 iterations. Furthermore, our layered scheme significantly reduces overall energy consumption. [ABSTRACT FROM AUTHOR]

With the advancement in the Internet of Things (IoT) and Wireless Sensor Node (WSN) applications computation on edge is preferred over computation on the cloud. However, due to limited resource availability in the edge environment, energy efficiency can be seen as the primary barrier in the growth of edge computing devices. In edge computing applications like wearable devices, forest monitoring systems, and other such applications, power is of utmost importance. The use of a complex, pipelined, general-purpose processor is redundant for such low-end applications and it leads to high power consumption. Hence, edge computing systems require low-power solutions while still maintaining a minimum performance level. In this work, an energy-efficient single-cycle RISC-V instruction set architecture (ISA) based RV32I processor is designed for low-end edge computing applications. The design incorporates base integer implementation of RISC-V ISA. The proposed computing system is designed using Verilog HDL and synthesized on Semi-conductor Laboratory (SCL) 180 nm CMOS process technology node. The synthesis tool reports power consumption of 14.7 mW at 50 MHz frequency with an area of 0.24 mm2. Though synthesis of this design reports area of 0.24 mm2, the design is taped out as a part of an available shuttle project where die size is fixed to 5 mm × 5 mm. The PNR (Place and Route) tool reports power consumption of 11.8 mW at an operating frequency of 40 MHz. A basic functional testing of proposed fabricated RV32I chip is carried out in laboratory experimental setup and results are presented. The comparison result shows that the proposed RV32I processor consumes 0.29 mW/MHz power which is less than state-of-the-art low-end processors. Hence, the proposed design is suitable for low-end edge computing applications. • For low-end edge computing applications, the use of a complex, pipelined, general-purpose processor is redundant, and it leads to high power consumption. • In this work, an energy-efficient single-cycle RISC-V ISA-based RV32I processor is designed for low-end edge computing applications. • The proposed processor architecture is implemented using Verilog HDL and prototyped on FPGA. • Further, the proposed design is tapedout for ASIC implementation on 180 nm CMOS process technology node. • The fabricated RV32I chip is validated in laboratory experimental setup. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of UAV technology has greatly improved the efficiency of the detection of concrete bridge cracks. With the increase in the number of bridge inspection UAVs, the number of tasks handled by cloud services has increased linearly, resulting in increased computational pressure on cloud services. In order to reduce the computational load of cloud servers, we proposed a crack segmentation network based on UAV-enabled edge computing. However, due to the limitation of computational capability of edge computing and the strength inhomogeneity and background complexity of cracks, crack detection is still a challenging task. Thus, we proposed an effective concrete crack segmentation network based on UAV-enabled edge computing, the network used feature map fusion to fuse different levels of feature map information into lower-level features for crack detection. The atrous spatial pyramid pooling network was used to increase the low-resolution feature map receptive field information for cracks and to enhance the detection accuracy for cracks of different scales. In addition, loss functions for crack datasets were proposed to solve the problem of imbalance due to positive and negative samples in the concrete crack images. Experiments demonstrated that the proposed methods are better than the state-of-the-art edge detection and semantic segmentation methods in terms of accuracy and generality. [ABSTRACT FROM AUTHOR]

Edge computing is an emerging computing paradigm, which offers a great opportunity to implement data mining-based services and applications for a large number of devices and sensors in Internet of Things. However, the new paradigm is faced with security and privacy challenges due to the diversity and the limited capability of edge components. In particular, data privacy is one of the most concerned problems for all the participants. In this paper, we propose a framework of privacy-preserving data mining based on private random decision trees in edge computing, which not only gives the strong privacy guarantee, but also provides a certain amount of data utility. Firstly, we design a preservation framework to implement private random decision trees satisfying local differential privacy. Secondly, we present the concrete implementations of algorithms and the corresponding task that each participant needs to undertake. Thirdly, we analyze the key factors to influence privacy and utility, including the allocation of data and privacy budget. Fourthly, we give the improved algorithms to further increase the utility with strong privacy preservation. Finally, extensive experiments demonstrate the good performance of our designed framework. [ABSTRACT FROM AUTHOR]

Soon after the proliferation of User-Equipments (UEs) that work with cloud computing, there was an explosion in the volume of generated data. Furthermore, emerging IoT applications, e.g. virtual reality, augmented reality, video streaming, intelligent transportation, and healthcare, require low latency and real-time data, processing and communication. Fog/Edge computing is a novel criterion in which distributed fog/edge nodes provide resources in close proximity to end devices due to their limited resources. Instead of sending large amounts of data to cloud servers, fog/edge nodes could use local resources to analyze, filter, and process data. Mobile Edge Computing (MEC) can significantly reduce processing delays and network traffic between UEs and servers, especially when user mobility is taken into account. This paper proposed a new approach to migration in fog/edge computing to reduce downtime. Our simulations demonstrate less downtime, packet loss, migration time, and latency compared to current techniques. [ABSTRACT FROM AUTHOR]

In the edge computing network environment, our applications can be deployed on edge servers. The request to execute the application can be produced on the edge device and transmitted to the edge server for calculation. A complex request may be divided into multiple computing tasks and transmitted to different servers and then parallel calculated before obtaining the final result. How to schedule computing tasks in multiple requests so that all requests can be completed faster is a difficult problem, especially in the edge computing environment where we should consider the communicating work and the calculating work simultaneously. In this paper, we first build a task dependency model of the computing tasks included in the request based on the idea of dividing the method components of the application. The dependencies include sequence, selection and parallel. Then we propose an online scheduling algorithm MCOS based on optimizing the task with the maximum amount of calculation to solve the problem of the minimum sum of the completion time of all requests. In simulations, we show the algorithm MCOS has a better completion time. [ABSTRACT FROM AUTHOR]

Blockchain empowered agricultural knowledge discovery system provides the secured environment for the people to store and exchange agricultural data. The integration of the Internet of Things (IoT), blockchain technology, and edge computing supports the agricultural fields to increase crop productivity and reduce the usage of natural resources. The smart decision support system provides efficient data management to the farmer to make the best decision to increase crop productivity and profit. In the agriculture field, different sensors and devices collect different data about the land then it can be sent to the server in the cloud environment. These data are highly sensitive and need to be securely stored and protected against unauthorized access. The farmers can communicate with the intelligent decision support system using the mobile app and web applications to get information about the land from domain experts. The proposed system used blockchain technology to provide privacy, security, easy accessibility, and availability of information to the people to use natural resources effectively and reduce the effect of soil and water pollution by using pesticides and fertilizer based on data stored in the knowledge base. We have implemented a knowledge discovery system using the platform hyperledger for efficient storage and sharing of sensor data which provides security and data privacy. Precision agriculture with IoT and Edge computing allows people and devices to be connected anywhere, anytime in a smart cloud environment to provide intelligent service to the farmers. [ABSTRACT FROM AUTHOR]

Eye-tracking is a technique used for determining where users are looking and how long they keep their gaze fixed on a particular location. Developments in mobile technology have made mobile applications pervasive; however, eye tracking on mobile devices is still uncommon. This paper proposes a mobile edge computing architecture for eye tracking. We evaluate four lightweight CNN models (LeNet-5, AlexNet, MobileNet, and ShuffleNet) for gaze estimation on mobile devices using a publicly available dataset called GazeCapture. In order to analyse the feasibility of different inference modes such as on-device, edge-based and cloud-based, we conduct an empirical measurement study to quantify inference time, communication time, and resource consumption in these inference modes. Our analysis indicates that while cloud-based inference provides faster predictions, the communication time between the mobile device and the cloud introduces significant latency into the application. This effectively eliminates the ability to perform real-time eye tracking via cloud inference. Furthermore, our findings show that on-device inference performance is limited by energy and memory consumption, making it unsuitable to provide a high-quality user experience. Additionally, we demonstrated that edge-based inference results in a reasonable response time, memory usage, and energy consumption for eye-tracking applications on mobile devices. [ABSTRACT FROM AUTHOR]

With the rapid development of cloud computing, Internet of things and artificial intelligence, human–computer interaction (HCI) is playing an increasingly important role in the daily life. As an important component of HCI, hand gesture recognition (HGR) system is usually combined with edge computing server, utilizing machine learning, including neural network, decision tree, integrated learning, to achieve low latency and high reliability service. High precision HGR with low computational complexity is prerequisite for the commercialization of gesture recognition. Therefore, this paper proposed a high-precision parameter correction algorithm based on the established scattered-point model and the outlier detection scheme, and a recognition algorithm with multivariable decision tree is then presented for the dynamic hand gestures. The experimental results show that the proposed algorithms can improve the recognition accuracy and effectively reduce the running time, which is conducive to algorithm transplantation and model deployment in edge servers. [ABSTRACT FROM AUTHOR]

Propelling energy-constrained sensor tasks to edge servers in Sensor Mobile Edge Computing (SMEC) subjugates Mobile Devices' (MDs) resource limitation menace. Most of the existing studies focused only on the offloading issues. However, a task may hinge on some allied tasks executed in the prior edge server in the trajectory of MDs. Task execution accomplishes by the assemblage of dependent data. This study imparts the dynamic selection of edge cloud for offloading tasks and checks the task's dependencies in a multiuser, multichannel environment. The proposed dynamic edge server selection for the inter-edge dependent task scheme piles up data from multiple allied edge nodes to finish the execution. This paper employs a Genetic Algorithm (GA) based optimization technique in the SMEC environment (GAME) to discern the optimal solution. The performance of our proposal is analyzed and compared with the other offloading policies exerting standard datasets. The result of this study manifests with the depletion of energy consumption and computational delay within the allowable range of transmission latency, despite appraising multiple task dependencies. [ABSTRACT FROM AUTHOR]

Accelerating Visual Neural Networks in Edge Computing environments is crucial for processing image and video data. Visual Neural Networks, including Convolutional Neural Networks and Vision Transformers, are central to image recognition, video analysis, and object detection tasks. Deploying these networks on edge devices and accelerating them can significantly enhance data processing speed and efficiency. The large number of parameters, complex computational flows, and various structural variants of Transformer models present both opportunities and challenges. We propose Vis-TOP (Vision Transformer Overlay Processor) , an overlay processor designed for all types of Vision Transformer models. Vis-TOP, distinct from coarse-grained general-purpose accelerators like GPUs and fine-grained custom designs, encapsulates Vision Transformer characteristics into a three-layer, two-level mapping structure, enabling flexible model switching without hardware architecture modifications. Concurrently, we designed a corresponding instruction bundle and hardware architecture within this mapping structure. We implemented the overlay processor design on the ZCU102 after quantizing the Swin Transformer model to 8-bit fixed points (fix_8). Experimentally, our throughput surpasses GPU implementation by 1.5 times. Our throughput per DSP is 2.2 to 11.7 times higher than that of existing Transformer-like accelerators. Overall, our approach satisfies real-time AI requirements in resource consumption and inference speed. Vis-TOP offers a cost-effective image processing solution for Edge Computing on reconfigurable devices, enhancing computational resource utilization, saving data transfer time and costs, and reducing latency. • We propose a three-layer and two-level transformation structure. • We propose and design a hardware architecture that is compatible with a three-layer and two-level transformation structure. • By analyzing the data storage, data selection and data computation of these visual Transformers in Edge Computing, fixed and variable modules reflecting the characteristics of different visual Transformers are designed in this paper. [ABSTRACT FROM AUTHOR]

• Innovative integration of small-scale vertical farming for edge computing. • Modular and expandable design for flexibility. • Enhanced PM filtration for free cooling system in edge computing centres. • Experimental study on PM removal efficiency. • Promoting sustainability in computing facilities. This paper introduces the integrated Small-Scale Plant-Based Air Filtration (SPAF) system, a novel integration of air filtration and urban agriculture, specifically designed for edge computing free cooling in urban environments. The SPAF's modular design enables extensive customization to meet various air quality requirements and agricultural needs. Through evaluation across different scenarios, the system has proven its effectiveness in particulate matter reduction, with a single unit showcasing substantial initial filtration efficiency, and even greater performance when multiple units are utilised in series. Beyond its air filtration capabilities, the SPAF system also contributes to urban agriculture, expanding planting areas and supporting the growth of local produce, thereby aligning with global sustainability objectives. Despite its promising applications, challenges such as the variability in filtration performance across different plant species, external climates, and the inability to ensure complete particulate matter removal, underscore the need for further research and potential integration with other purification technologies. [ABSTRACT FROM AUTHOR]

In recent years, the use of UAVs to carry mobile edge computing servers has become an emerging solution to the problem of collaborative ground-to-air communication. However, due to the limited energy consumption of UAVs and the open nature of wireless communication, UAV-based mobile edge computing faces issues such as limited operation time and the potential for insecure connections during data transmission. In this paper, we model a secure data transmission model for UAVs with the goal of minimizing energy consumption. First, we decompose the system energy consumption optimization problem into two sub-problems: ground user energy consumption overhead and UAV energy consumption overhead. We consider the limitations of UAV flight range, computational power, and transmission capability in relation to ground user and UAV, solve the energy consumption optimization problem by deep reinforcement learning, and propose a solution based on the SAC algorithm. The solution applies the idea of maximum entropy to explore the optimal policy and use efficient iterative updates to obtain the optimal policy, enhancing the exploration capability of the algorithm and improving the convergence speed of the training process by retaining all policies with high return values. Secondly, considering the existence of insecure connection nodes in practical application scenarios, a ground user data protection scheme based on Paillier encryption and blockchain is used to achieve secure data transmission. Simulation results show that the proposed method can achieve secure data offloading, effectively reduce the average energy consumption of users, and have good stability and convergence compared with existing methods. [ABSTRACT FROM AUTHOR]

Mobile edge computing can use the wireless access network to provide the services required by telecom users and cloud computing functions nearby, thereby creating a service environment with high performance, low latency and high bandwidth. Computational task offloading is a core issue in mobile edge computing. In order to improve the security when computing tasks are partially offloaded in non-orthogonal multiple access-based mobile edge computing systems, this paper studies the physical layer security of MEC networks considering the presence of eavesdroppers. We employ the secrecy outage probability to measure the secrecy performance of computation offloading. Under the premise of comprehensively considering the joint constraints of transmit power, local task calculation and confidentiality outage probability, we introduce the energy consumption weight factor to balance the transmission energy consumption and calculation energy consumption, and finally achieve the minimum weighted sum of system energy consumption. On the premise of satisfying two user priorities, in order to reduce system overhead, we design a joint task offloading and resource allocation mechanism. The mechanism uses binary search-based iterative optimization algorithm to seek the optimal solution after problem transformation, and obtain optimal task offloading and power allocation. A large number of simulation results show that the algorithm proposed in this paper can effectively reduce system energy consumption. [ABSTRACT FROM AUTHOR]

Modern sensor technologies increasingly enrich studies in wildlife behavior and ecology. However, constraints on weight, connectivity, energy and memory availability limit their implementation. With the advent of edge computing, there is increasing potential to mitigate these constraints, and drive major advancements in wildlife studies. [ABSTRACT FROM AUTHOR]

Dynamic Adaptive Streaming over HTTP (DASH) is a representative scheme to improve Quality of Experience (QoE) of video streaming users. However, the QoE fairness is degraded when multiple users stream videos over the same network. The reasons of this problem are a lack of coordination among clients and bitrate adaptation with the fixed heuristics. In this paper, we propose an HTTP adaptive streaming scheme with reinforcement learning in edge computing environments. The proposed scheme generates a policy based on reinforcement learning to optimize QoE fairness and utilizes edge computing to assist policy generation. We evaluated the proposed scheme through simulations under various network conditions. The experimental results show that the proposed scheme improves QoE fairness of multiple clients compared with the existing scheme. [ABSTRACT FROM AUTHOR]

The computation offloading problem in mobile edge computing (MEC) has received a lot of attention, but service caching is also a research topic that cannot be ignored in MEC. Due to the limited resources available on the Edge Server (ES), a wise computation offloading and service caching policy must be formulated in order to maximize system offload efficiency. In this paper, a many-objective joint optimization computation offloading and service caching model (MaJOCOSC) is designed. The model takes into account the limited computing and storage resources of ES, the delay and energy consumption constraints of different types of tasks, and multiple processing modes of user tasks, and sets delay, energy consumption, task hit service rate, service cache balancing, and load balancing as the five optimization objectives of MaJOCOSC. Meanwhile, a non-dominated sorting genetic algorithm (NSGAIII-ASF&WD) based on achievement scalar function (ASF) and the k-nearest neighbor weighted distance mating selection strategy is proposed for better solving the model. The ASF ensures that the given strategy performs well for each objective value, and the k-nearest neighbor weighted distance provides the user with a diversity of strategies. Simulation results show that NSGAIII-ASF&WD can obtain better objective values when solving the model compared with other many-objective evolutionary algorithms, and a suitable computation offloading and service caching strategy is obtained. [ABSTRACT FROM AUTHOR]

Roadside units deployment strategy in vehicular edge computing (VEC) environments plays a crucial role in balancing deployment expenses with user experience. This paper tackles this challenge by formulating the communication and computation cost of offloading vehicle tasks to RSUs and, subsequently, optimizing the RSU deployment problem to minimize the average task completion time. We propose the spectral clustering-based deployment strategy (SCBDS), which determines the optimal number and precise locations of RSUs by identifying clusters of vehicular traffic demands. Performance evaluation through simulations in real-world traffic scenarios demonstrates that SCBDS outperforms existing algorithms in terms of task completion time, showcasing its effectiveness in enhancing VEC efficiency and user experience. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is a promising paradigm that helps mobile terminals save energy and increase battery life by offloading tasks to MEC servers. To obtain full benefit from MEC, the joint optimization of offloading decision-making and resource allocation, while considering user mobility, is crucial and challenging. The objective of this paper was to propose mobility-aware and energy-efficient offloading schemes for MEC in cellular networks. The proposed schemes helped to make offloading decisions, allocate transit power, and schedule transmissions among multiple moving users. An optimal scheme was proposed, in terms of minimizing total energy consumed by all users, which was based on Mixed-Integer Programming (MIP). Considering the high computational complexity of solving MIP problems, a practical heuristic scheme was also proposed. Simulation results showed the effectiveness in reducing energy consumption of both the proposed optimal and heuristic schemes and demonstrated the importance of mobility awareness as well. • The variations in transmission power caused by end-user mobility is considered. • An optimal solution for joint resource allocation is proposed. • A low-complexity heuristic algorithm is proposed for practical applications. • It is demonstrated that mobility awareness enhances energy efficiency effectively. [ABSTRACT FROM AUTHOR]

• A new in-situ monitoring system was proposed to upscale resolution and extract geometric characteristics during AM process. • The proposed ViTSR performs better than the SOTA methods in terms of PSNR, which may reduce the cost of high-speed camera. • The molten pool and plasma arc can be simultaneously and accurately extracted by FCN with input of reconstructed frames. • The low-cost AI edge board reduces the inference time of ViTSR to 50.97 ms, which reduce the cost of computing in AM process. In additive manufacturing (AM), in-situ monitoring systems are vital for ensuring process quality. However, the widespread adoption of traditional high-speed camera-based monitoring systems is hindered by the prohibitively high cost of the required high-speed cameras. This paper introduces an innovative low-cost in-situ monitoring system that utilizes AI edge computing boards to expedite digital image processing without requiring high resolution (HR) video sequences. The system integrates a visual transformer-based video super resolution (ViTSR) network for reconstructing high resolution video frames and employs a fully convolutional network (FCN) to extract geometric characteristics of the molten pool and plasma arc simultaneously during AM processes. Comparing ViTSR with six state-of-the-art super-resolution methods, it achieved the highest peak signal-to-noise ratio (PSNR) of 38.16 dB on the test data. Moreover, the FCN utilized the reconstruction results from ViTSR, demonstrating an accuracy of 96.34% in multi-object extraction tasks. Through operator fusion and model pruning, the inference time of ViTSR and FCN on the AI edge board is optimized to 50.97 ms and 67.86 ms, respectively. Consequently, the proposed system achieves a total inference time of 118.83 ms, meeting the real-time quality monitoring needs of AM processes. Furthermore, this approach effectively reduces the expenses associated with high-speed camera-based monitoring systems, thereby promoting the widespread adoption of in-situ monitoring systems within the realm of AM. [ABSTRACT FROM AUTHOR]

With the explosive growth of global data volume, the centralized cloud computing computing model cannot provide low-latency, high-efficiency video surveillance services. Therefore, a distributed computing model is proposed, which directly processes peripheral video data to reduce transmission pressure, the burden of the central cloud server and the processing delay of the video surveillance system. Combined with the federated learning algorithm, a light neural network model is proposed. The light neural network is used to generate calculation models in different scenarios, and the generated models are reasonably arranged in edge devices. With the continuous development of technology, human motion analysis has become a hot research field in recent years, and human motion recognition is developed on the basis of human motion analysis. With the development of society, the level of Chinese Taekwondo has improved compared to before. Therefore, Chinese Taekwondo classes have gradually developed, but behind this phenomenon there is a problem of uneven teaching quality. In order to help solve the teaching problems in the learning process of Taekwondo and further improve the level of Chinese Taekwondo, the author carried out specific research in conjunction with D.A. Cooper's empirical learning theory. Future work will continue to study federated learning algorithms, optimize general neural network weight update methods, further improve detection results, and introduce neural network compression acceleration algorithms to make the system meet real-time requirements. This research uses scientific and technological means as a guide to study technical actions and strategies, and apply these strategies to specific teaching experiments for testing. [ABSTRACT FROM AUTHOR]

To the superiority of low cost and convenience, large-scale Internet of things has been deployed for various monitoring pervasively. Typically cloud platform is utilized as a remote data and control center. However, it is a challenge to the bandwidth and real-time feedback to the server because of mass data with uploading and processing. In this paper, to cope with the above problems, we design a gateway based on edge computing. The gateway not only retains the function of connecting multiple sensors and the interaction between devices and cloud, but also can provide real-time local services for terminal devices. It also can drop abnormal data through sensors' correlation analysis and reduce the amount of data upload by diverting the computing tasks of a server. In this paper, an anomaly detection method based on the bag of words model is introduced. At first, the gateway makes a bag of words representation by the data collected from each sensor, including normal and abnormal data. Then it aggregates these class predictions using the bag of words to calculate the final prediction. Through the simulation analysis based on the data such as humidity, temperature, and lighting monitored by sensors in agricultural greenhouses, the results show that the bag of words model can detect abnormal sensor data. [ABSTRACT FROM AUTHOR]

The implementation of the "Internet +" policy advocated by the state has also led to rapid development of Internet finance. In order to promote changes in business development models, as a pioneering work for banks serving the real economy, supply chains are being developed to address small and medium-sized enterprises. The financing of enterprises, the transformation and development needs of banks themselves, and the promotion of logistics technology. Edge computing refers to an open platform that integrates network, data processing, storage and application core functions, and can provide the closest end-of-page service near the object data source to meet real-time, application intelligence, security and privacy Sexual needs. The core of supply chain financing is to establish an optimized plan that can effectively control supply chain financing. By integrating the financing literature of the supply chain, the settlement cost in the supply chain can be solved. Based on theoretical research, this article analyzes supply chain financing and block chain technology. Combined with the current specific situation of block chain in supply chain financing, the management system, cash flow of the supply chain, and risk control system are analyzed. All parties to the supply chain financing optimize the supply chain financing risk control system while reducing business costs and improving corporate efficiency, which greatly reduces the risks of all parties in the supply chain financing. The block chain Iota environment based on shared data and advanced data processing has very powerful theoretical and practical significance for promoting the development of commercial banks and enterprises. [ABSTRACT FROM AUTHOR]

Big data will cause the system for business public opinion to be overburdened, and dynamic changes in computing resources will cause tasks to be delayed. To end the situation, this study assigns non-fixed execution time to each task node based on the task soft deadline and task constraints and solves the problem of difficult task scheduling caused by task dependency constraints. Aiming at the problem of task delay caused by dynamic changes of computing resources, this paper proposes an adaptive offloading and scheduling algorithm for dependent tasks in the mobile edge computing environment. This study takes the economic efficiency analysis system as an example and uses the resource allocation management algorithm based on linked lists and edge servers to study the communication resource allocation management of the economic efficiency analysis system. In addition, this study designs experiments to perform performance analysis of the algorithm proposed by this study. The research results show that the proposed algorithm has an obvious effect. [ABSTRACT FROM AUTHOR]

The availability of Software Defined Network's (SDNs) flow rule entry in the flow table is considered a key factor in the timely delivery of a certain flow. The controller is approached for instructions on how to deal with the flow when the rule for such flow is missing. The controller then updates the flow table accordingly at the switch so that flow could be dealt with locally. It becomes problematic when no rule is defined yet at the controller by the application plane. In most of these cases, such a situation is handled by programming the controller with wildcard rules. However, handling many flows at once under wildcard rules severely hampers the network performance. Flow rules formation by the application plane is sometimes critical and time-consuming which increases the latency ratio by creating a bottleneck at the switch level. To avoid the bottlenecks due to rule absence, in this paper, rather than waiting for the application plane's response and putting the pending traffic flows in the buffer, which may be dropped, the controller is programmed in a way that has the built-in mechanism of self-flow rule formation. This atomized mechanism is based on the previously available traces of the same flows when they were forwarded on the network using the wildcard rules. To assess the performance of the proposed work, it is emulated and benchmarked with the latest research. The results show considerable performance achievement. [ABSTRACT FROM AUTHOR]

As a branch of cloud computing, edge computing has received extensive attention. Edge computing offloads computing-intensive tasks to the computing-capable devices (i.e., edge servers) at the network edge of the cloud, and the edge servers assist in mobile devices to perform data analysis, which can significantly reduce the execution time of computing-intensive tasks on mobile devices. However, due to the poor quality of wireless channel communication, it may degrade the overall quality of the edge offloading when the mobile devices are linked to edge servers, that is, the communication link between the mobile device and the edge server may become a bottleneck. In this study, we propose a new edge computing scheme to support remote object detection by splitting the deep neural network (DNN), and we design a framework to reduce the amount of data transmitted over wireless links. Specially, the DNN is divided into a head modular and a tail modular, which are deployed on mobile devices and edge servers, respectively. Furthermore, the wireless link transmits the output of the last layer in the head modular to the edge server, instead of sending all the initial tensors of images to the edge server. And we employ three object detection models with complex structures, that is, Faster RCNN (regions with CNN features), Mask RCNN and Keypoint RCNN as the basic models, and modify their network architectures from the following aspects: 1) we introduce the information bottleneck principle and split the network architecture into a head modular and a tail modular, and we perform knowledge distillation to compress the head modular; 2) we embed a neural filter (NF) into the head modular to pre-filter images with no detection objects by using a simple convolutional neural (CNN) network, and the NF performs a binary classification task in the head modular. Lastly, we conduct experiments on the real COCO 2017 dataset and the PASCAL VOC 2012 dataset, and the results show that the proposed method outperforms other baselines. Especially, in person keypoint detection, the mAP (mean average precision) of the proposed method is at least 1.64% higher than other baselines on the COCO 2017 dataset. In addition, the prediction latency of the proposed splitting scheme at the data transmission rate of 1Mbps is at least 49.17% lower than that of local computing scheme. [ABSTRACT FROM AUTHOR]