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Aiming at the unloading problem of mobile tasks for the limited edge server resources to maximize the satisfaction of numerous mobile tasks with deadline requirements, a model for cloud-edge-device collaboration was proposed to offload mobile tasks. Firstly, the model analyze the factors that affect the service demand of mobile tasks and the service guarantee of virtual machines, and give the measurement method, as well as the measurement method of the service matching degree between mobile tasks and virtual machines. Secondly, a mobile task offloading strategy was designed for on-demand allocation of physical resources in a dynamic cloud-edge environment. Based on the improved Hungarian algorithm, the purpose of this strategy was to find an offloading plan that could maximize service matching for a batch of tasks, and to further optimize the offloading plan by eliminating resource competition through a limited number of iterations. Finally, the algorithm in this study was compared with the P2PITS algorithm, the ALBOA algorithm and the ESSDSA algorithm from many aspects. Experimental results showed that compared with the P2PITS algorithm, the algorithm in this study reduced the virtual machine load rate by 30. 1%, the average waiting time by 13%, compared with the ALBOA algorithm, the algorithm in this study reduce the average completion time by 38. 6% on average, compared with the ESSDSA algorithm, the algorithm in this study increased the execution success rate by 3. 5% on average. The proposed algorithm could effectively improve resource utilization and reduce the average completion time of tasks while meeting user deadline requirements. [ABSTRACT FROM AUTHOR]

In the cloud-edge collaborative environment, the edge server manager will divide the physical resources based on virtualization technology, so as to deploy multiple applications on the same server. However, due to the imperfect virtualization technology and the complexity and dynamics of the applications deployed on virtual machines (VMs), it is difficult for cloud service providers to evaluate the performance of VMs and thus cannot implement dynamic resource management effectively. To address this problem, this paper proposes an adaptive resource allocation approach. Firstly, we use the profiling tools to collect hardware counters and corresponding performance that reflect the resource usage in real time. Then, we select the data instances that contribute more to the performance prediction based on Gradient-based One Side Sampling (GOSS) to build a VM performance prediction model. When the prediction results indicate the performance cannot meet users' requirements, we further apply one of the reinforcement learning framework-Deep Deterministic Policy Gradient (DDPG) to optimize the allocation of fine-grained resources. Our proposed method enables adaptive allocation of fine-grained resources in cloud environment, and the extensive experiments demonstrate that the average accuracy of performance prediction by our proposed method surpasses 95%, whereas the metrics derived from the others ranges only between 75 and 97.5%. Furthermore, the average accuracy by our proposed method on the several benchmark applications is 88.4%, gaining a performance improvement of 9.1% compared to the suboptimal baseline. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Vehicles (IoV) technology has led to a sharp increase in vehicle data. Traditional cloud computing is no longer sufficient to meet the high bandwidth and low latency requirements of IoV tasks. Ensuring the service quality of applications on in-vehicle devices has become challenging. Edge computing technology moves computing tasks from the cloud to edge servers with sufficient computing resources, effectively reducing network congestion and data propagation latency. The integration of edge computing and IoV technology is an effective approach to realizing intelligent applications in IoV.This paper investigates the deployment of vehicle edge servers in cloud-edge collaborative environment. Taking into consideration the vehicular mobility and the computational demands of IoV applications, the vehicular edge server deployment within the cloud-edge collaborative framework is formulated as a multi-objective optimization problem. This problem aims to achieve two primary objectives: minimizing service access latency and balancing server workload. To address this problem, a model is established for optimizing the deployment of vehicle edge servers and a deployment approach named VSPR is proposed. This method integrates hierarchical clustering and reinforcement learning techniques to effectively achieve the desired multi-objective optimization. Experiments are conducted using a real datasets from Shanghai Telecom to comprehensively evaluate the performance of workload balance and service access latency of vehicle edge servers under different deploy methods. Experimental results demonstrate that VSPR achieves an optimized balance between low latency and workload balancing while ensuring service quality, and outperforms SRL, CQP, K-means and Random algorithm by 4.76%, 44.59%, 40.78% and 69.33%, respectively. [ABSTRACT FROM AUTHOR]

In order to enhance the performance of intelligent image recognition, this study optimizes the image recognition model through lightweight convolutional neural networks (CNNs) and cloud computing technology. The study begins by introducing the relevant theories and models of edge computing (EC) and lightweight CNNs models. Next, this study focuses on optimizing traditional image recognition models. Finally, the effectiveness and reliability of the proposed model are experimentally validated. The experimental results indicate that, when recognizing 1000 images, the average recognition times per image on cloud servers and edge servers are 13.33 ms and 50.11 ms, respectively. Despite the faster speed of cloud servers, the performance of edge servers can be improved by stacking servers. When the number of edge servers reaches 4, their recognition speed surpasses that of the cloud server model. Additionally, comparing the latency and processing time between EC and cloud computing architectures, it is observed that, with an increase in the number of processed images, the average processing time per image in the EC architecture remains relatively stable and consistent. In contrast, the average processing time gradually increases in the cloud computing architecture. This indicates a significant impact of the number of images on the processing rate of the cloud computing architecture. Therefore, as the time gap in processing between cloud computing and EC increases, the advantages of the EC architecture become more apparent. This study's significance lies in advancing the development of deep learning technology and providing possibilities for its widespread practical application. The contribution of this study lies in promoting the development of EC and lightweight neural network models, offering valuable references and guidance for practical applications in related fields. [ABSTRACT FROM AUTHOR]

The power Internet of Things(PIoT) is a smart service system that offers full-state awareness, efficient information processing, and convenient and flexible applications to users. However, these services also pose a risk of privacy leakage. The existing research on privacy protection of power data mainly concentrates on secure aggregation, but seldom addresses the core technology of many basic services, such as KNN query. Unlike traditional relational data, the PIoT collects flowing data of user electricity consumption, and the various power parameters exhibit dynamic correlations. Attackers can use data mining and other methods to infer future trends in data changes. Therefore, this paper proposed a privacy-preserving KNN query method. Firstly, it proposed a similarity measurement model based on bucket distance, and proved the upper and lower bounds of the error between the similarity measurement model based on bucket distance and the similarity measurement model based on Euclidean distance. Through this model, the similarity measurement could be transformed into set intersection operations. Then, it constructed a privacy-preserving function, which could generate different data privacy-preserving functions and query privacy-preserving functions for various smart terminals by substituting different parameters. Based on this, it proposed a data encoding scheme based on bucket partitioning and random number allocation. After being encrypted by the privacy-preserving function, the encoded data possessed the characteristic of ciphertext indistinguishability, and could effectively resist various attacks such as chosen plaintext attacks, data mining attacks, statistical analysis attacks, ICA attacks, and inference prediction attacks. Analysis and simulation demonstrate that the proposed secure KNN query method not only has high security but also has low overhead. [ABSTRACT FROM AUTHOR]

The transfer of cloud-computing capabilities at the network's edge reduces the service latency and energy shortage problem in Mobile Fog Computing. Task offloading from resource-constraint devices to the edge servers addresses the limitations of the battery power and computational capability of Mobile Devices (MDs). Most of the offloading strategies proposed earlier comprehended independent tasks. Today's communication network demands the comprehensive study of collaborative task execution. The offloading of latency-sensitive sensor tasks in collaboration at a multi-access edge network promisingly utilizes the edge servers with increased performance. In this paper, we propose a dynamic edge server selection mechanism for task offloading, which collects all the collaborative tasks from allied edge servers and other end devices. A hybrid metaheuristic based on Greedy Randomized Adaptive Search Procedure (GRASP) and Genetic Algorithm (GA) is used to solve the offloading issues. The GRASP -GA based Collaborative Task Offloading technique implemented in the Mobile Fog Computing environment finds the optimal solution. Simulation results show that the proposed study achieves 11% to 63% better performance in depleting the total execution time and energy consumption compared with other existing state-of-art methods. [ABSTRACT FROM AUTHOR]

Due to the emergence of various new applications, such as short videos and online games, higher requirements of their computing and storage capacity are demanded of mobile networks. The traditional cloud computing paradigm has the shortcomings of large latency and high bandwidth demand of the core network. Therefore, how to mine the hotspot distribution of these applications and reasonably configure 5G edge nodes to reduce latency and core network bandwidth are facing great challenges. To address these issues, we designed a placement method for the 5G edge nodes based on mobile hotspots. In this method, we first cluster all locations from the user trajectories to obtain the cluster areas. Further, we extract the features, such as the number of users and duration time in all cluster areas, and extract the hotspots from all cluster areas based on the features of each cluster. Then, we introduce the base station’s high load utilization rate and the core network’s bandwidth reduction rate as the optimization parameters to construct the mathematical model of multi-objective optimization. Finally, we formalize the model into a 0–1 integer programming problem and design a greedy algorithm to solve this model. We also complete a series of experiments to evaluate our proposed methods using the GeoLife dataset. The experimental results show that the high load utilization rate can be increased up to 7.69%, and the bandwidth reduction rate of the core network can be improved up to 6.34%.

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol's performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined. [ABSTRACT FROM AUTHOR]

Summary: Human‐centered systems play an important role in the modern world, for example, driverless car, autonomous and smart vehicles, drones, and robotics. The internet of things environment demands a faster real‐time response depending on the applications processed in a particular duration. Mobile edge computing (MEC) allows a user to get a real‐time response as compared with cloud computing (CC), although ensuring a number of security attributes in MEC environment remains challenging. In this article, a protocol is designed to achieve mutual authentication, anonymous communication, and security against traceability, as these are very crucial factors to ensure the security of data and user's privacy. Moreover, the proposed scheme ensures mutual authentication between a mobile user and an edge server along with the user's anonymity and untraceability. The proof of security and evaluation of performance of the scheme validates that it ensures security attributes and improves efficiency in terms of communication and computation overheads. [ABSTRACT FROM AUTHOR]

When UAVs are equipped with a deep neural network for power inspection, because the device computing power and battery capacity as well as the deep neural network computation load limit, it cannot independently process the massive amount of image data generated in the inspection task. To address this problem, this paper proposed an unloading strategy for UAV power inspection based on improved hybrid particle swarm algorithm and matching theory, which decomposed the system cost minimization problem into two sub-problems, such as cooperative partitioning of deep neural network computing tasks and edge server selection. For the collaborative partition problem, it proposed a staggered transmission method based on the execution process of deep neural network computational tasks, and an improved hybrid particle swarm algorithm solved the multi-UAV task collaborative partition layer. For the server selection problem, it defined the respective preference functions of the UAV and the server, and established a stable match between them through matching theory according to the preference functions to obtain the server selection policy. Simulation results show that the proposed strategy can effectively reduce the energy consumption and computational task processing delay of UAVs and promote the load balancing of edge servers compared with other offloading strategies. [ABSTRACT FROM AUTHOR]

The Internet of Things‐based smart healthcare provides numerous facilities to patients and medical professionals. Medical professionals can monitor the patient's real‐time medical data and diagnose diseases through the medical health history stored in the cloud database. Any kind of attack on the cloud database will result in misdiagnosis of the patients by medical professionals. Therefore, it becomes a primary concern to secure private data. On the other hand, the conventional data aggregation method for smart healthcare acquires immense communication and computational cost. Edge‐enabled smart healthcare can overcome these limitations. The paper proposes an edge‐enabled efficient privacy‐preserving data aggregation (EEPPDA) scheme to secure health data. In the EEPPDA scheme, captured medical data have been encrypted by the Paillier homomorphic cryptosystem. Homomorphic encryption is engaged in the assurance of secure communication. For data transmission from patients to the cloud server (CS), data aggregation is performed on the edge server (ES). Then aggregated ciphertext data are transmitted to the CS. The CS validates the data integrity and analyzes and processes the authenticated aggregated data. The authorized medical professional executes the decryption, then the aggregated ciphertext data are decrypted in plaintext. EEPPDA utilizes the batch verification process to reduce communication costs. Our proposed scheme maintains the privacy of the patient's identity and medical data, resists any internal and external attacks, and verifies the health data integrity in the CS. The proposed scheme has significantly minimized computational complexity and communication overhead concerning the existing approach through extensive simulation. [ABSTRACT FROM AUTHOR]

Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method. [ABSTRACT FROM AUTHOR]

In an intelligent edge network, data owners encrypt data and outsource it to the edge servers to prevent the leakage of data and user information. It is a research issue to achieve efficient search and data update of the ciphertext stored in the edge servers. For the above problems, we construct a fuzzy multi-keyword search scheme based on a two-level tree index, where the first-level tree index stores keyword tags and the second-level stores encrypted file identifiers and counting bloom filters (CBFs). By constructing a two-level tree index, efficient multi-keyword search is realised, and it also has obvious time advantages when performing the single-keyword search. In addition, the proposed scheme supports index updating by introducing the CBF, and it can realise fuzzy search by calculating the inner product between the CBF in index and search trapdoor. At last, the proposed scheme is proved to be semantically secure under the known ciphertext attack model. Theoretical analysis and simulation test indicate that the proposed scheme spends lower computation overhead than other related schemes, especially in the search phase; as the number of matching files or query keywords grows, it costs less time, at around 2–10 ms. [ABSTRACT FROM AUTHOR]

In the application of mobile edge computing(MEC) in intelligent mine, the mobile users unload tasks to non-optimal edge servers due to unreasonable resource allocation, which leads to extra transmission time and execution delay, thus resulting in the decrease of the total task completion rate. In order to solve the above problem, a two-dimensional dynamic matching algorithm based on preference is proposed to optimize the resource allocation decision in MEC system. The data of the position of a mobile user in MEC system and the calculation amount required by a task in one time slot is sent to the edge server. The preference table of the edge server for the mobile user is formed according to the set preference value. At the same time, the preference table for all the edge servers is formed by the mobile user according to different physical distances. The two preference tables are combined to form a two-dimensional dynamic preference table, which is abstracted into a two-dimensional matrix. The two-dimensional matrix is processed by a two-dimensional dynamic matching algorithm based on preference, and the matching optimization results of mobile users and edge servers are obtained. The simulation results show that compared with the conventional MEC scene unloading algorithm, the preference-based two-dimensional dynamic matching algorithm can effectively alleviate the problem of the decrease of the total task completion rate in a large number of sudden task scenes, and can achieve the total task completion rate of more than 60% in extreme cases.

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.

With the rapid expansion of the Internet of Things (IoT), the necessity for lightweight communication is also increasing due to the constrained capabilities of IoT devices. This paper presents the design of a novel lightweight protocol called the Enhanced Lightweight Security Gateway Protocol (ELSGP) based on a distributed computation model of the IoT layer. This model introduces a new type of node called a sub-server to assist edge layer servers and IoT devices with computational tasks and act as a primary gateway for dependent IoT nodes. This paper then introduces six features of ELSGP with developed algorithms that include access token distribution and validation, authentication and dynamic interoperability, attribute-based access control, traffic filtering, secure tunneling, and dynamic load distribution and balancing. Considering the variability of system requirements, ELSGP also outlines how to adopt a system-defined policy framework. For fault resiliency, this paper also presents fault mitigation mechanisms, especially Trust and Priority Impact Relation for Byzantine, Cascading, and Transient faults. A simulation study was carried out to validate the protocol’s performance. Based on the findings from the performance evaluation, further analysis of the protocol and future research directions are outlined.

Aiming at the security problem of edge server, a trust evaluation algorithm was proposed, which integrated server cooperation reputation and device user feedback to improve the security of edge computing context.In the process of interaction, a fusion algorithm based on objective information entropy theory was used to aggregate the cooperation reputation between servers, and a partial homomorphic encryption algorithm was used to prevent the leakage of user data in the process of interaction.After the interaction, the highly trusted device node was selected to calculate the feedback trust, which overcame the malicious feedback of the traditional mechanism.When calculating the global trust, the expected score of the server and the adaptive weight calculation method were considered, which overcame the limitations of the traditional trust scheme.The experimental results show that the proposed trust calculation scheme has low time complexity and spatial complexity, and can effectively resist the attack of malicious nodes.

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Establishing an accurate edge server power model is helpful for resource providers to predict and optimize power consumption within edge data centers. Considering the fact that the accuracy of the previous energy consumption model is easily affected by the workload types, this paper develops an edge server power model based on BP (back propagation) neural network and feature selection, which is denoted by DSBF. For different task types, DSBF leverages "principal component analysis (PCA)" to analyze the contribution of each energy consumption parameter and selects "representative parameter", and then builds a power model based on BP neural network. In contrast to other power models, DSBF can effectively handle the variable workload. To measure the effectiveness of the DSBF model, a series of experiments were conducted. The results suggest that compared with other energy consumption models, DSBF can better adapt to the changing workload and has advantages in predicting the accuracy of the energy consumption model. • Designing the power modeling process, including parameter selection, parameter analysis and screening, model training and evaluation. • Using the "principal component analysis (PCA)" to analyze the factor contribution of each parameter. • Building a new power model named DSBF based on BP neural network and feature selection. [ABSTRACT FROM AUTHOR]

An intelligent monitoring terminal for power distribution room based on edge computing is designed in this paper, which is important for the power distribution Internet of Things. Compared with the traditional monitoring terminals, it employs an edge server (ES) to store and process the data collected by sensing devices at the edge, such as the voltage, current, state and security information. Furthermore, a FPGA and DSP based embedded digital control system in the monitoring terminal is designed to collect and process the on-site power and environmental data, which overcomes the data delay problem caused by insufficient communication bandwidth of traditional system and the problem of transmitted data dropout caused by low cloud computing capacity. The engineering application results showed that, the designed intelligent monitoring terminal based on edge computing has the advantages such as high detection accuracy, stable communication effect and fast calculation speed, which will reduce the alarm and action response time by more than 5 times.

Aiming at the DoS (denial of service) attacks against edge servers in an edge computing system, a parallel runtime verification approach for DoS attack detection was proposed.First, PPTL (propositional projection temporal logic) formulas were utilized to formally describe expected behaviors of an edge server and DoS attack characteristics.Then, for the PPTL formulas to be verified, a parallel runtime verification framework was adopted to make use of the computing and storage resources of an edge server to conduct anomaly detection and misuse detection.The proposed attack detection approach was performed for an actual P2P (peer-to-peer) network based on smart parking system using edge computing which was supposed to suffer from a DoS attack.Experiments show that the proposed method can accurately and efficiently identify abnormal behaviors of edge servers and types of DoS attacks.

The smart factory is a concept to express the ultimate goal of digitization in manufacturing. The most common definition of a smart factory is a highly digital store floor that collects and shares data on a continuous basis through connected devices and production systems. the proposed system focuses on reducing the delay by using hybrid computing, cloud, and edge servers. The purpose of this study is to investigate Real-time requirements using the Selection Algorithm. The factory is represented by a group of sensors that send data to three servers. (Two are edge servers with the same copies of data and rules, and the third is the cloud). When the sensor's reading reaches the edge servers, scheduling time an selection algorithm is implemented through which a single edge server receives data selecting the least delay the highest priority. Alternatively, if there is any problem or malfunction affects one of the edge servers, the second can complete its work without the need to stop the factory. In turn, the delay is reduced, and the factory performance is improved. When the delay time is reduced, the response time is improved and the service quality will be enhanced. [ABSTRACT FROM AUTHOR]

Due to the rapid development of smart phone utilization, the use of fast response with delay sensitive applications leads to high traffic demands. These issues are not yet fulfill by current researchers. When there is a massive crowd of users gathering in hotspot areas such as, railway stations, corporate centers, and malls requires service accessing social networks, then there cause uncertain traffic with congestion and network delay. In this article, an Efficient Offloading Mechanism (EOM) is proposed to transfer the user's intensive computations to picocells, where each picocell (PE) is integrated with the functionalities of edge. Moreover, a novel distributed multihop mesh middle layer (DMMM) framework is proposed for seamless communication, where the self‐healing network provides high resilience, immediate response, and multiuser content sharing and saves battery power. Generally, a picocell is a distributed antenna system that is an alternative to the repeater utilized to extend wireless services to 100 users. An Adaptive Ant Optimization (AAO) algorithm is developed for backhaul routing conflicts. Simulation outcomes of EdgeCloudSim prove that the offloading tiny devices incorporated with considerable additional computational capacities to fulfill all the demands within 15 ms delay from each network. [ABSTRACT FROM AUTHOR]

In this paper, a modern computer network, cloud-based network, which comprises internet of things (IoT), edge servers, and cloud servers for data transmission, is investigated and evaluated. A cloud-based network is modeled as a graph having a set of nodes and a set of links. Each link represents a transmission route, and each node represents a device, such as an IoT device, edge server, and cloud server. In practical, a transmission route comprises several physical lines or virtual channels. Each physical line (virtual channel) may provide a capacity or may fail to imply several and stochastic states. Such a cloud-based network is called a stochastic flow cloud-based network (SCN) herein. System reliability for an SCN is then evaluated. It is defined as the probability of the data being successfully transmitted through the SCN under edge server capacity and budget constraints. The SCN is modeled firstly in order to elucidate the flow relationship among the whole system; capacity limitation of the edge servers and costs of data transmission/process are also considered. Subsequently, we conclude an algorithm to evaluate system reliability. Supervisors can manage the SCN based on system reliability which presents the system capability with capacity and budget consideration. [ABSTRACT FROM AUTHOR]

The new cellular network standard 5G meets the networking requirements for many industrial use cases due to the advantages of low latency, high bandwidth, and high device density while providing a very good quality of service. These capabilities enable the wireless realization of digital twins (DT), a key element of cyber-physical production systems. A requirement of DT is the bidirectional exchange of information between the digital and the physical world. 5G is the only technology that enables wireless, highly scalable, and flexible realization of even safety- and latency-critical connections (e.g., between a machine tool and its motion control unit). In this paper, a 5G enabled DT of a machine tool for machine control and simulation is developed and implemented. A bidirectional control of the physical machine tool and the DT is realized by offloading the machine control unit to an edge server via 5G. The simulation instances are also offloaded to the edge server and obtain the required information from the control system. In addition, this paper outlines the components for implementing a 5G-enabled DT and aims to discuss the benefits, disadvantages, and potentials of the 5G-enabled DT. [ABSTRACT FROM AUTHOR]

Vehicle-to-Everything (V2X) communications provide opportunities for information sharing among vehicles, edge servers, and cloud services. By the collection and extraction of sensing information from vehicles, such as communication quality or free space size, the edge server in V2X communications can improve its sensing and perception coverage. However, the collection of sensing data from vehicles consumes a large amount of wireless resources and computing resources at the edge server. The objective of this study is to extract object sensing information from vehicles, including the minimum or maximum of the sensing values, with low resource consumption and with high scalability. We propose a method that transforms the extraction of sensing information into a two-level procedure that includes (1) the local sharing and extraction of sensing information among vehicles and (2) the efficient extraction of sensing information at the edge server. Moreover, hybrid communication methods are employed at vehicles, with a short range of communication between vehicles to reduce the consumption of wireless resources for the local sharing of sensing data. The evaluation results show that the proposed method highly reduces the number of reports from the vehicles to the edge server, with a small amount of network resource consumption and scalability. [ABSTRACT FROM AUTHOR]

Considering the influence of gradient statisticsan optimal power control method based on wireless federated learning in fading channel was put forward. The goal was to minimize the aggregation error of over the air computing under the limit of peak power. Firstly, the mean square error expression of gradient aggregation in each iteration of model training was derived. Then, a joint optimization problem of transmitting power of edge devices and denoising factor of edge server was proposed to minimize the peak power constraint of edge devices, and a historical cumulative gradient estimation method which could ignore the communication cost was further proposed. The experimental results on three data sets show that the proposed method has better control accuracy and applicability. [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]

Copyright of Journal of Mine Automation is the property of Industry & Mine Automation Editorial Department and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Internet of Things (IoT) has gained great popularity in various fields including smart warehouse and intelligent manufacturing. As a building block of IoT network, the Radio Frequency IDentification (RFID) technology enables a large and ever-increasing number of physical objects to be monitored across the Internet via tag identification. Efficiently managing massive tags in RFID systems becomes an important research issue for IoT networks. This paper focuses on a fundamental management problem — tag-sorting, which is to (1) put a set $S$ of identified tags into a certain order by informing each tag $t\in S$ of a unique integer $\Delta (t)\in \{1,2,\cdots,|S|\}$ , and meanwhile (2) keep unidentified tags from receiving any of these integers. For RFID systems, it is critical to solve this problem as quickly as possible in the sense that, once sorted, every identified tag $t\in S$ can be manipulated via $t$ ’s $\log _{2}(|S|)$ -bit integer significantly shorter than $t$ ’s 96-bit long tag-ID ( $\log _{2}(|S|)\ll 96$ ), boosting efficiency substantially. The existing works of literature, however, fails to solve this problem rapidly, as they accomplish (1) and (2) separately by using aloha-like protocols and Bloom filters, which incur a long communication time far from the optimum. In this paper, we overcome this drawback by proposing a protocol $P_{{\mathrm {sort}}}$ capable of solving the problem fastly. In particular, this protocol is built with a novel data structure and communication scheme to achieve (1) and (2) simultaneously by using a communication time proven to be much less than the state-of-the-art protocols. The simulation results demonstrate the competence of $P_{{\mathrm {sort}}}$ in achieving about $1.4\times $ speedup than the state-of-the-art solutions.

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

In order to meet the demand for efficient computing services in big data scenarios, a cloud edge collaborative computing allocation strategy based on deep reinforcement learning by combining the powerful computing capabilities of cloud is proposed. First, based on the comprehensive consideration of computing resources, bandwidth, and migration decisions, an optimization problem is constructed that minimizes the sum of all user task execution delays and energy consumption weights. Second, a dynamic offloading scheduling algorithm based on Q -learning is proposed based on the optimization problem. This algorithm makes full use of the computing power for cloud and edge, which effectively meets the demand for efficient computing services in Internet of Things' scenarios. Finally, facing the environment dynamic changes of edge nodes in edge cloud, the algorithm can adaptively adjust the migration strategy. Experiments show that when the number of Internet of Things' devices is 30, the total energy consumption of Internet of Things' devices of proposed algorithm is reduced by 24.67% and 19.44%, respectively, compared with other algorithms. The experimental results show that proposed algorithm can effectively improve the success rate of task offloading and execution, which can reduce the local energy consumption. [ABSTRACT FROM AUTHOR]

With Mobile Edge Computing (MEC), computational intensive applications can be offloaded to the nearby edge servers to support latency-sensitive applications on mobile devices. Different from the cloud, edge servers usually have limited resources, and then selecting which edge server to run the offloaded computation becomes an important issue. Although server selection has received considerable attention, not much work has been done to consider the limited coverage of the edge server and the frequent user movement, which introduce many dynamic changing factors affecting the workload of the edge server and making it hard to achieve long-term optimum in the edge server selection. To deal with these challenges, we model the problem of continuous server selection as a Markov Decision Process (MDP). The difficulty of this problem is that achieving long-term optimum requires future knowledge, such as user mobility, server workload, etc, which is not known a priori. We do not have such knowledge and thus cannot find the optimal policy through traditional methods. To address this problem, we propose a Deep Reinforcement Learning (DRL) based algorithm to learn the selection policy based on the observed performance of past server selections. Specifically, a Long Short-Term Memory (LSTM) based neural network is exploited to encode the historical information which helps infer future knowledge of the dynamically changing factors. Then the DRL model selects the optimal server automatically based on the extracted system states. Extensive trace-driven evaluations demonstrate that the proposed DRL-based algorithm has the lowest overall cost compared to existing solutions. [ABSTRACT FROM AUTHOR]

We consider federated learning (FL) with multiple wireless edge servers having their own local coverage. We focus on speeding up training in this increasingly practical setup. Our key idea is to utilize the clients located in the overlapping coverage areas among adjacent edge servers (ESs); in the model-downloading stage, the clients in the overlapping areas receive multiple models from different ESs, take the average of the received models, and then update the averaged model with their local data. These clients send their updated model to multiple ESs by broadcasting, which acts as bridges for sharing the trained models between servers. Even when some ESs are given biased datasets within their coverage regions, their training processes can be assisted by adjacent servers through the clients in their overlapping regions. As a result, the proposed scheme does not require costly communications with the central cloud server (located at the higher tier of edge servers) for model synchronization, significantly reducing the overall training time compared to the conventional cloud-based FL systems. Extensive experimental results show remarkable performance gains of our scheme compared to existing methods. Our design targets latency-sensitive applications where edge-based FL is essential, e.g., when a number of connected cars/drones must cooperate (via FL) to quickly adapt to dynamically changing environments. [ABSTRACT FROM AUTHOR]

Abstract Content delivery network (CDN) has gained increasing popularity in recent years for facilitating content delivery. Most existing CDN-based works upload the content generated by mobile users to the cloud data center firstly. Then, the cloud data center delivers the content to the proxy server. Finally, the mobile users request the required content from the proxy server. However, uploading all the collected content to the cloud data center increases the pressure on the core network. In addition, it also wastes a lot of bandwidth resources because most of the content does not have to be uploaded. To make up for the shortcomings of existing CDN-based works, this article proposes an edge content delivery and update (ECDU) framework based on mobile edge computing architecture. In the ECDU framework, we deploy a number of content servers to store raw content collected from mobile users, and cache pools to store content that frequently requested at the edge of the network. Thus, it is not necessary to upload all content collected by mobile users to the cloud data center, thereby alleviating the pressure of the core network. Based on content popularity and cache pool ranking, we also propose edge content delivery (ECD) and edge content update (ECU) schemes. The ECD scheme is to deliver content from cloud data center to cache pool, and the ECU scheme is to mitigate the content to appropriate cache pools in terms of its request frequency and cache pool ranking. Finally, a representative case study is provided and several open research issues are discussed.

Recently, low-latency services for large-capacity data have been studied given the development of edge servers and wireless mesh networks. The 3D data provided for augmented reality (AR) services have a larger capacity than general 2D data. In the conventional WebAR method, a variety of data such as HTML, JavaScript, and service data are downloaded when they are first connected. The method employed to fetch all AR data when the client connects for the first time causes initial latency. In this study, we proposed a prefetching method for low-latency AR services. Markov model-based prediction via the partial matching (PPM) algorithm was applied for the proposed method. Prefetched AR data were predicted during AR services. An experiment was conducted at the Nowon Career Center for Youth and Future in Seoul, Republic of Korea from 1 June 2022 to 31 August 2022, and a total of 350 access data points were collected over three months; the prefetching method reduced the average total latency of the client by 81.5% compared to the conventional method.