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Integrated communication–sensing–computing (ICSC) satellites, which integrate edge computing servers on Earth observation satellites to process collected data directly in orbit, are attracting growing attention. Nevertheless, some monitoring tasks involve sequential sub-tasks like target observation and movement prediction, leading to data dependencies. Moreover, the limited energy supply on satellites requires the sequential execution of sub-tasks. Therefore, inappropriate assignments can cause circular waiting among satellites, resulting in deadlocks. This paper formulates task offloading in ICSC satellites with data-dependent constraints as a mixed-integer linear programming (MILP) problem, aiming to minimize service latency and energy consumption simultaneously. Given the non-centrality of ICSC satellites, we propose a distributed deadlock-free task offloading (DDFTO) algorithm. DDFTO operates in parallel on each satellite, alternating between sub-task inclusion and consensus and sub-task removal until a common offloading assignment is reached. To avoid deadlocks arising from sub-task inclusion, we introduce the deadlock-free insertion mechanism (DFIM), which strategically restricts the insertion positions of sub-tasks based on interval relationships, ensuring deadlock-free assignments. Extensive experiments demonstrate the effectiveness of DFIM in avoiding deadlocks and show that the DDFTO algorithm outperforms benchmark algorithms in achieving deadlock-free offloading assignments. [ABSTRACT FROM AUTHOR]

In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches. [ABSTRACT FROM AUTHOR]

Mobile Edge Computing (MEC) has been widely applied in various Internet of Things (IoT) scenarios due to its advantages of low latency and low energy consumption. However, the offloading of tasks generated by terminal devices to edge servers inevitably raises privacy leakage concerns. Given the limited resources in MEC networks, this paper proposes a task scheduling strategy, named DQN-DP, to minimize location privacy leakage under the constraint of offloading costs. The strategy is based on a differential privacy location obfuscation probability density function. Theoretical analysis demonstrates that the probability density function employed in this study is valid and satisfies ϵ -differential privacy in terms of security. Numerical results indicate that, compared to existing baseline approaches, the proposed DQN-DP algorithm effectively balances privacy leakage and offloading cost. Specifically, DQN-DP reduces privacy leakage by approximately 20% relative to baseline approaches. [ABSTRACT FROM AUTHOR]

With the rapid advancement of the Internet of Things and the emergence of 6G networks in smart city environments, a growth in the generation of data, commonly known as big data, is expected to consequently lead to higher latency. To mitigate this latency, mobile edge computing has been proposed to alleviate a portion of the workload from mobile devices by offloading it to nearby edge servers equipped with appropriate computational resources. However, existing solutions often exhibit poor performance when confronted with complex network topologies. Thus, this paper introduces a decentralized mechanism aimed at determining the locations of network edge nodes in such complex network topologies, characterized by lengthy execution times. Our proposal provides performance improvements and offers scalability and flexibility as networks become more complex. Experimental evaluations are conducted using the Shanghai Telecom dataset to validate our proposed approach. [ABSTRACT FROM AUTHOR]

Task offloading and resource allocation is a research hotspot in cloud-edge collaborative computing. Many existing pieces of research adopted single-agent reinforcement learning to solve this problem, which has some defects such as low robustness, large decision space, and ignoring delayed rewards. In view of the above deficiencies, this paper constructs a cloud-edge collaborative computing model, and related task queue, delay, and energy consumption model, and gives joint optimization problem modeling for task offloading and resource allocation with multiple constraints. Then, in order to solve the joint optimization problem, this paper designs a decentralized offloading and scheduling scheme based on "task-oriented" multi-agent reinforcement learning. In this scheme, we present information synchronization protocols and offloading scheduling rules and use edge servers as agents to construct a multi-agent system based on the Actor–Critic framework. In order to solve delayed rewards, this paper models the offloading and scheduling problem as a "task-oriented" Markov decision process. This process abandons the commonly used equidistant time slot model but uses dynamic and parallel slots in the step of task processing time. Finally, an offloading decision algorithm TOMAC-PPO is proposed. The algorithm applies the proximal policy optimization to the multi-agent system and combines the Transformer neural network model to realize the memory and prediction of network state information. Experimental results show that this algorithm has better convergence speed and can effectively reduce the service cost, energy consumption, and task drop rate under high load and high failure rates. For example, the proposed TOMAC-PPO can reduce the average cost by from 19.4% to 66.6% compared to other offloading schemes under the same network load. In addition, the drop rate of some baseline algorithms with 50 users can achieve 62.5% for critical tasks, while the proposed TOMAC-PPO only has 5.5%. [ABSTRACT FROM AUTHOR]

Device-to-device (D2D) is a pivotal technology in the next generation of communication, allowing for direct task offloading between mobile devices (MDs) to improve the efficient utilization of idle resources. This paper proposes a novel algorithm for dynamic task offloading between the active MDs and the idle MDs in a D2D–MEC (mobile edge computing) system by deploying multi-agent deep reinforcement learning (DRL) to minimize the long-term average delay of delay-sensitive tasks under deadline constraints. Our core innovation is a dynamic partitioning scheme for idle and active devices in the D2D–MEC system, accounting for stochastic task arrivals and multi-time-slot task execution, which has been insufficiently explored in the existing literature. We adopt a queue-based system to formulate a dynamic task offloading optimization problem. To address the challenges of large action space and the coupling of actions across time slots, we model the problem as a Markov decision process (MDP) and perform multi-agent DRL through multi-agent proximal policy optimization (MAPPO). We employ a centralized training with decentralized execution (CTDE) framework to enable each MD to make offloading decisions solely based on its local system state. Extensive simulations demonstrate the efficiency and fast convergence of our algorithm. In comparison to the existing sub-optimal results deploying single-agent DRL, our algorithm reduces the average task completion delay by 11.0% and the ratio of dropped tasks by 17.0%. Our proposed algorithm is particularly pertinent to sensor networks, where mobile devices equipped with sensors generate a substantial volume of data that requires timely processing to ensure quality of experience (QoE) and meet the service-level agreements (SLAs) of delay-sensitive applications. [ABSTRACT FROM AUTHOR]

Federated learning (FL) can break the problem of data silos and allow multiple data owners to collaboratively train shared machine learning models without disclosing local data in mobile edge computing. However, how to incentivize these clients to actively participate in training and ensure efficient convergence and high test accuracy of the model has become an important issue. Traditional methods often use a reverse auction framework but ignore the consideration of client volatility. This paper proposes a multi-dimensional reverse auction mechanism (MRATR) that considers the uncertainty of client training time and reputation. First, we introduce reputation to objectively reflect the data quality and training stability of the client. Then, we transform the goal of maximizing social welfare into an optimization problem, which is proven to be NP-hard. Then, we propose a multi-dimensional auction mechanism MRATR that can find the optimal client selection and task allocation strategy considering clients' volatility and data quality differences. The computational complexity of this mechanism is polynomial, which can promote the rapid convergence of FL task models while ensuring near-optimal social welfare maximization and achieving high test accuracy. Finally, the effectiveness of this mechanism is verified through simulation experiments. Compared with a series of other mechanisms, the MRATR mechanism has faster convergence speed and higher testing accuracy on both the CIFAR-10 and IMAGE-100 datasets. [ABSTRACT FROM AUTHOR]

Cybertwin (CT) is an innovative network structure that digitally simulates humans and items in a virtual environment, significantly influencing Cybertwin instances more than regular VMs. Cybertwin-driven networks, combined with Mobile Edge Computing (MEC), provide practical options for transmitting IoT-enabled data. This research introduces a hybrid methodology integrating deep learning with Cybertwin-driven resource allocation to enhance energy-efficient workload offloading and resource management in MEC networks. Offloading work is essential in MEC networks since several applications require significant resources. The Cybertwin-driven approach considers user mobility, virtualization, processing power, load migrations, and resource demand as crucial elements in the decision-making process for offloading. The model optimizes job allocation between on-premises and distant execution using a task-offloading strategy to reduce the operating burden on the MEC network. The model uses a hybrid partitioning approach and a cost function to optimize resource allocation efficiently. This cost function accounts for energy consumption and service delays associated with job assignment, execution, and fulfilment. The model calculates the cost of several segmentation and offloading procedures and chooses the lowest cost to enhance energy efficiency and performance. The approach employs a deep learning architecture called "CNN-LSTM-TL" to accomplish energy-efficient task offloading, utilizing pre-trained transfer learning models. Batch normalization is used to speed up model training and improve its robustness. The model is trained and assessed using an extensive mobile edge computing public dataset. The experimental findings confirm the efficacy of the proposed methodology, indicating a 20% decrease in energy usage compared to conventional methods while achieving comparable or superior performance levels. Simulation studies emphasize the advantages of incorporating Cybertwin-driven insights into resource allocation and workload-offloading techniques. This research enhances energy-efficient and resource-aware MEC networks by incorporating Cybertwin-driven techniques. [ABSTRACT FROM AUTHOR]

The rapid increase in the number of computing intensive tasks has led to an overload of SMD (Smart Mobile Devices) computing tasks. By using MEC(Mobile Edge Computing Servers) and idle ED (Edge Devices) in the network, SMD with limited computing power can offload computing tasks to MEC and ED collaboration, and enhance system security based on the DPoR (Delegated Proof of Reputation) consensus mechanism. This study proposes a blockchain mobile edge computing offloading model based on BS AFSA (Bird Swarm Artificial Fish Swarm Algorithm), which transforms the task offloading problem into an optimization objective function to reduce the computational overhead. The improved BS AFSA is used to optimize the task delay and energy consumption, and the behavior parameters in the algorithm are constructed and the crowding factor is improved to elevate the local search accuracy in the later iteration. The simulation results show that compared with other benchmark algorithms, the proposed algorithm reduces the possibility of falling into local optimum and effectively reduces the total system cost of the joint offloading scheme. [ABSTRACT FROM AUTHOR]

Maritime transport plays a critical role in global logistics. Compared to road transport, the pace of research and development is much slower for maritime transport. It faces many major challenges, such as busy ports, long journeys, significant accidents, and greenhouse gas emissions. The problems have been exacerbated by recent regional conflicts and increasing international shipping demands. Maritime Autonomous Surface Ships (MASSs) are widely regarded as a promising solution to addressing maritime transport problems with improved safety and efficiency. With advanced sensing and path-planning technologies, MASSs can autonomously understand environments and navigate without human intervention. However, the complex traffic and water conditions and the corner cases are large barriers in the way of MASSs being practically deployed. In this paper, to address the above issues, we investigated the application of Large Language Models (LLMs), which have demonstrated strong generalization abilities. Given the substantial computational demands of LLMs, we propose a framework for LLM-assisted navigation in connected MASSs. In this framework, LLMs are deployed onshore or in remote clouds, to facilitate navigation and provide guidance services for MASSs. Additionally, certain large oceangoing vessels can deploy LLMs locally, to obtain real-time navigation recommendations. To the best of our knowledge, this is the first attempt to apply LLMs to assist with ship navigation. Specifically, MASSs transmit assistance requests to LLMs, which then process these requests and return assistance guidance. A crucial aspect, which has not been investigated in the literature, of this safety-critical LLM-assisted guidance system is the knowledge and safety performance of the LLMs, in regard to ship handling, navigation rules, and skills. To assess LLMs' knowledge of navigation rules and their qualifications for navigation assistance systems, we designed and conducted navigation theory tests for LLMs, which consisted of more than 1500 multiple-choice questions. These questions were similar to the official theory exams that are used to award the Officer Of the Watch (OOW) certificate based on the Standards of Training, Certification, and Watchkeeping (STCW) for Seafarers. A wide range of LLMs were tested, which included commercial ones from OpenAI and Baidu and an open-source one called ChatGLM, from Tsinghua. Our experimental results indicated that among all the tested LLMs, only GPT-4o passed the tests, with an accuracy of 86%. This suggests that, while the current LLMs possess significant potential in regard to navigation and guidance systems for connected MASSs, further improvements are needed. [ABSTRACT FROM AUTHOR]

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Online detection devices, powered by artificial intelligence technologies, enable the comprehensive and continuous detection of high-speed railways (HSRs). However, the computation-intensive and latency-sensitive nature of these detection tasks often exceeds local processing capabilities. Mobile Edge Computing (MEC) emerges as a key solution in the railway Internet of Things (IoT) scenario to address these challenges. Nevertheless, the rapidly varying channel conditions in HSR scenarios pose significant challenges for efficient resource allocation. In this paper, a computation offloading system model for detection tasks in the railway IoT scenario is proposed. This system includes direct and relay transmission models, incorporating Non-Orthogonal Multiple Access (NOMA) technology. This paper focuses on the offloading strategy for subcarrier assignment, mode selection, relay power allocation, and computing resource management within this system to minimize the average delay ratio (the ratio of delay to the maximum tolerable delay). However, this optimization problem is a complex Mixed-Integer Non-Linear Programming (MINLP) problem. To address this, we present a low-complexity subcarrier allocation algorithm to reduce the dimensionality of decision-making actions. Furthermore, we propose an improved Deep Deterministic Policy Gradient (DDPG) algorithm that represents discrete variables using selection probabilities to handle the hybrid action space problem. Our results indicate that the proposed system model adapts well to the offloading issues of detection tasks in HSR scenarios, and the improved DDPG algorithm efficiently identifies optimal computation offloading strategies. [ABSTRACT FROM AUTHOR]

With the proliferation of intelligent applications, mobile devices are increasingly handling computation-intensive tasks but often struggle with limited computing power and energy resources. Mobile Edge Computing (MEC) offers a solution by enabling these devices to offload computation-intensive tasks to resource-rich edge servers, thus reducing processing latency and energy consumption. However, existing task-offloading strategies often neglect critical security concerns. In this paper, we propose a security-aware task-offloading framework that utilizes Deep Reinforcement Learning (DRL) to solve these challenges. Our framework is designed to minimize the latency of task accomplishment and energy consumption while ensuring data security. We model system utility as a Markov Decision Process (MDP) and design a Proximal Policy Optimization (PPO)-based algorithm to derive optimal offloading strategies. Experimental results demonstrate that the proposed algorithm outperforms traditional methods regarding task execution latency and energy consumption. [ABSTRACT FROM AUTHOR]

Abstract In recent years, mobile applications have proliferated across domains such as E-banking, Augmented Reality, E-Transportation, and E-Healthcare. These applications are often built using microservices, an architectural style where the application is composed of independently deployable services focusing on specific functionalities. Mobile devices cannot process these microservices locally, so traditionally, cloud-based frameworks using cost-efficient Virtual Machines (VMs) and edge servers have been used to offload these tasks. However, cloud frameworks suffer from extended boot times and high transmission overhead, while edge servers have limited computational resources. To overcome these challenges, this study introduces a Microservices Container-Based Mobile Edge Cloud Computing (MCBMEC) environment and proposes an innovative framework, Optimization Task Scheduling and Computational Offloading with Cost Awareness (OTSCOCA). This framework addresses Resource Matching, Task Sequencing, and Task Scheduling to enhance server utilization, reduce service latency, and improve service bootup times. Empirical results validate the efficacy of MCBMEC and OTSCOCA, demonstrating significant improvements in server efficiency, reduced service latency, faster service bootup times, and notable cost savings. These outcomes underscore the pivotal role of these methodologies in advancing mobile edge computing applications amidst the challenges of edge server limitations and traditional cloud-based approaches.

To tackle the problem of resource competition among service migrations caused by limited edge server resources in mobile edge computing (MEC) scenarios, a service migration optimization method for resource competition based on Lyapunov and game theory (OMRC-LG) was proposed. Considering the system's limited migration costs and the difficulty of predicting trajectories when the number of users was large, the service migration was modeled as an optimization problem with migration cost constraints and used the Lyapunov technique to transform it into an online problem without user trajectory prediction. To alleviate resource competition among users, a distributed method based on game theory was proposed. By sharing user service migration decisions, the method obtained accurate information on available edge server resources and would continuously update these decisions to optimize service migration. Simulation results show that the OMRC-LG method can reduce the average service delay while satisfying the migration cost constraints.

In a single UAV-assisted mobile edge computing system, in order to enable the UAV to serve all user devices in a large area, the large area can be divided into a plurality of sub-areas and the UAV can be set to fly between the sub-areas with a fixed route to provide computing services for the user devices. Considering the scarcity of computational resources for user devices and the fact that users outside the coverage area of the UAV may choose to move to the coverage area for task offloading in order to maximize their own utility, the partial offloading problem of user devices can be transformed into the problem of maximizing the utility of each user device. The mixed-strategy game and the submodular game are used to determine the movement probability of user devices and the amount of offloaded data, so as to derive the optimal offloading strategy, and the existence of mixed-strategy Nash equilibrium and pure-strategy Nash equilibrium is proved, respectively. Simulation results show that the proposed scheme can effectively improve the utility of user device compared with classical schemes such as MBO (Binary Offloading Based on Mixed Strategy Game), and its convergence and stability are verified.

Abstract Cybertwin (CT) is an innovative network structure that digitally simulates humans and items in a virtual environment, significantly influencing Cybertwin instances more than regular VMs. Cybertwin-driven networks, combined with Mobile Edge Computing (MEC), provide practical options for transmitting IoT-enabled data. This research introduces a hybrid methodology integrating deep learning with Cybertwin-driven resource allocation to enhance energy-efficient workload offloading and resource management in MEC networks. Offloading work is essential in MEC networks since several applications require significant resources. The Cybertwin-driven approach considers user mobility, virtualization, processing power, load migrations, and resource demand as crucial elements in the decision-making process for offloading. The model optimizes job allocation between on-premises and distant execution using a task-offloading strategy to reduce the operating burden on the MEC network. The model uses a hybrid partitioning approach and a cost function to optimize resource allocation efficiently. This cost function accounts for energy consumption and service delays associated with job assignment, execution, and fulfilment. The model calculates the cost of several segmentation and offloading procedures and chooses the lowest cost to enhance energy efficiency and performance. The approach employs a deep learning architecture called “CNN-LSTM-TL” to accomplish energy-efficient task offloading, utilizing pre-trained transfer learning models. Batch normalization is used to speed up model training and improve its robustness. The model is trained and assessed using an extensive mobile edge computing public dataset. The experimental findings confirm the efficacy of the proposed methodology, indicating a 20% decrease in energy usage compared to conventional methods while achieving comparable or superior performance levels. Simulation studies emphasize the advantages of incorporating Cybertwin-driven insights into resource allocation and workload-offloading techniques. This research enhances energy-efficient and resource-aware MEC networks by incorporating Cybertwin-driven techniques.

Home appliance manufacturers are increasingly focusing on building smarter home systems by incorporating user feedback to enhance products and services. To support this, we designed a federated learning (FL) system that includes a reputation mechanism to help manufacturers leverage customer data to train machine learning models. First, it downloads the initial model provided by the manufacturer and trains it with local data. Then, it asks customers to sign their models and upload them to the blockchain. To protect customer privacy, we implemented differential privacy and introduced a new normalization technique. In addition, we also attract more customers to participate in crowdsourced FL tasks by rewarding their contributions, thereby ensuring that the datasets for model training are robust and diverse. This system not only promotes collaboration between customers and manufacturers, but also facilitates the development of more responsive and smarter home appliance systems through advanced FL and blockchain technologies.

Home appliance manufacturers are increasingly focusing on building smarter home systems by incorporating user feedback to enhance products and services. To support this, we designed a federated learning (FL) system that includes a reputation mechanism to help manufacturers leverage customer data to train machine learning models. First, it downloads the initial model provided by the manufacturer and trains it with local data. Then, it asks customers to sign their models and upload them to the blockchain. To protect customer privacy, we implemented differential privacy and introduced a new normalization technique. In addition, we also attract more customers to participate in crowdsourced FL tasks by rewarding their contributions, thereby ensuring that the datasets for model training are robust and diverse. This system not only promotes collaboration between customers and manufacturers, but also facilitates the development of more responsive and smarter home appliance systems through advanced FL and blockchain technologies. [ABSTRACT FROM AUTHOR]

In the domain of 5G/6G wireless communications, mobile edge computing (MEC) technology is widely utilized for efficient data transmission. Unmanned aerial vehicles (UAVs) have emerged as the most recent transmission carriers in this landscape. However, the challenges associated with UAV deployment and path planning are often regarded as NP-hard, nonconvex, and nonlinear problems. Traditional optimization techniques struggle to address these complexities effectively. To address this issue, this study proposes "Edge-UAV", a novel mobile edge computing system specifically designed for UAVs. The primary objective of Edge-UAV is to minimize energy consumption and optimize the operational efficiency of UAVs. To achieve this goal, this study introduces a two-stage optimization strategy and a nonhovering transmission strategy. First, the coordinate updating operation of the UAVs' hovering points is decoupled from the path planning task. Additionally, the perturbation-inheritance algorithm is employed to enhance the coordinate updating process. In the second stage, the nonhovering transmission strategy enables UAVs to perform data transmission tasks while in flight, effectively optimizing their working time. This paper provides a detailed elucidation of the roles played by these innovative strategies within the Edge-UAV system. To assess the superiority of the proposed strategies, eight sets of comparative experiments involving 60–200 individual devices awaiting data transmission are conducted. The experimental results demonstrate the significant advantages of the Edge-UAV system in terms of reducing total energy consumption and optimizing the operational hours of UAVs. Through comprehensive experimentation and analysis, this study contributes to the advancement of UAV-assisted mobile edge computing in 5G/6G wireless communication networks. The proposed strategies exhibit promising potential for enhancing the efficiency and performance of UAVs. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape.

Abstract With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model’s predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions.

Abstract Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments.

Abstract The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users’ privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations.

With the increasing complexity of autonomous driving tasks, the computational demands on single vehicular computing units have escalated, more and more tasks need to be offloaded to the edge. These tasks vary in latency sensitivity: real-time tasks, critical for passenger safety, require strict deadline adherence, whereas the latency of standard tasks mainly affects the user experience and has more flexible constraints. Addressing the challenge of selecting suitable edge computing nodes to enhance the offloading success rate of real-time tasks amidst a vast and heterogeneous cluster becomes crucial. This paper introduces the adaptive priority-based hierarchical task offloading (APHTO) algorithm, which optimizes task offloading strategies by accounting for the diverse latency constraints of different task types. Experiments demonstrate that under optimal performance conditions, APHTO significantly outperforms existing algorithms such as Min–Min, Max–Min, CUS, and FMS in reducing task latency by 20.31%, increasing offloading success rates by 35.83%, and improving resource utilization by 30.21%, marking a substantial advancement in task offloading strategies for autonomous driving integrated with MEC. [ABSTRACT FROM AUTHOR]

Edge servers frequently manage their own offline digital twin (DT) services, in addition to caching online digital twin services. However, current research often overlooks the impact of offline caching services on memory and computation resources, which can hinder the efficiency of online service task processing on edge servers. In this study, we concentrated on service caching and task offloading within a collaborative edge computing system by emphasizing the integrated quality of service (QoS) for both online and offline edge services. We considered the resource usage of both online and offline services, along with incoming online requests. To maximize the overall QoS utility, we established an optimization objective that rewards the throughput of online services while penalizing offline services that miss their soft deadlines. We formulated this as a utility maximization problem, which was proven to be NP-hard. To tackle this complexity, we reframed the optimization problem as a Markov decision process (MDP) and introduced a joint optimization algorithm for service caching and task offloading by leveraging the deep Q-network (DQN). Comprehensive experiments revealed that our algorithm enhanced the utility by at least 14.01% compared with the baseline algorithms. [ABSTRACT FROM AUTHOR]

In recent years, many mobile edge computing network solutions have enhanced data privacy and security and built a trusted network mechanism by introducing blockchain technology. However, this also complicates the task-offloading problem of blockchain-enabled mobile edge computing, and traditional evolutionary learning and single-agent reinforcement learning algorithms are difficult to solve effectively. In this paper, we propose a blockchain-enabled mobile edge computing task-offloading strategy based on multi-agent reinforcement learning. First, we innovatively propose a blockchain-enabled mobile edge computing task-offloading model by comprehensively considering optimization objectives such as task execution energy consumption, processing delay, user privacy metrics, and blockchain incentive rewards. Then, we propose a deep reinforcement learning algorithm based on multiple agents sharing a global memory pool using the actor–critic architecture, which enables each agent to acquire the experience of another agent during the training process to enhance the collaborative capability among agents and overall performance. In addition, we adopt attenuatable Gaussian noise into the action space selection process in the actor network to avoid falling into the local optimum. Finally, experiments show that this scheme's comprehensive cost calculation performance is enhanced by more than 10% compared with other multi-agent reinforcement learning algorithms. In addition, Gaussian random noise-based action space selection and a global memory pool improve the performance by 38.36% and 43.59%, respectively. [ABSTRACT FROM AUTHOR]

Zero-touch networks (ZTNs) can provide autonomous network solutions by integrating software-based solutions for various emerging 5G and 6G applications. The current literature does not provide any suitable end-to-end network management and resource-slicing solutions for service function chaining (SFC) and user intent-based (time and cost preference) 6G/non-6G application execution over ZTNs enabled by mobile edge computing, network function virtualization, and software-defined networking. To tackle these challenges, this work initiates an end-to-end network management and user intent-aware intelligent network resource-slicing scheme for SFC-based 6G/non-6G application execution over ZTNs, taking into account various virtual and physical resources, task workloads, service requirements, and task numbers. The results depicted that at least 25.27% average task implementation delay gain, 6.15% energy gain, and 11.52% service monetary gain are realized in the proposed scheme over the compared schemes. [ABSTRACT FROM AUTHOR]

With the development of digital economy technologies, mobile edge computing (MEC) has emerged as a promising computing paradigm that provides mobile devices with closer edge computing resources. Because of high mobility, unmanned aerial vehicles (UAVs) have been extensively utilized to augment MEC to improve scalability and adaptability. However, with more UAVs or mobile devices, the search space grows exponentially, leading to the curse of dimensionality. This paper focus on the combined challenges of the deployment of UAVs and the task of offloading mobile devices in a large-scale UAV-assisted MEC. Specifically, the joint UAV deployment and task offloading problem is first modeled as a large-scale multiobjective optimization problem with the purpose of minimizing energy consumption while improving user satisfaction. Then, a large-scale UAV deployment and task offloading multiobjective optimization method based on the evolutionary algorithm, called LDOMO, is designed to address the above formulated problem. In LDOMO, a CSO-based evolutionary strategy and a MLP-based evolutionary strategy are proposed to explore solution spaces with different features for accelerating convergence and maintaining the diversity of the population, and two local search optimizers are designed to improve the quality of the solution. Finally, simulation results show that our proposed LDOMO outperforms several representative multiobjective evolutionary algorithms. [ABSTRACT FROM AUTHOR]

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multihierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and contentpopularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments. [ABSTRACT FROM AUTHOR]

Satellite edge computing (SEC) plays an increasing role in earth observation, due to its global coverage and low-latency computing service. In SEC, it is pivotal to offload diverse observation-data-processing tasks to the appropriate satellites. Nevertheless, due to the sparse intersatellite link (ISL) connections, it is hard to gather complete information from all satellites. Moreover, the dynamic arriving tasks will also influence the obtained offloading assignment. Therefore, one daunting challenge in SEC is achieving optimal offloading assignments with consideration of the dynamic delay-sensitive tasks. In this paper, we formulate task offloading in SEC with delay-sensitive tasks as a mixed-integer linear programming problem, aiming to minimize the weighted sum of deadline violations and energy consumption. Due to the limited ISLs, we propose a fully-decentralized method, called the PI-based task offloading (PITO) algorithm. The PITO operates on each satellite in parallel and only relies on local communication via ISLs. Tasks can be directly offloaded on board without depending on any central server. To further handle the dynamic arriving tasks, we propose a re-offloading mechanism based on the match-up strategy, which reduces the tasks involved and avoids unnecessary insertion attempts by pruning. Finally, extensive experiments demonstrate that PITO outperforms state-of-the-art algorithms when solving task offloading in SEC, and the proposed re-offloading mechanism is significantly more efficient than existing methods. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the Enhanced Whale Optimization Algorithm (EWA) to optimize dependent task offloading in a multi-edge cloud computing environment. Our proposed algorithm aims to identify the most suitable offloading scenario for dependent tasks, focusing on minimizing total processing latency, energy consumption, and associated costs. We operate within a system comprising many decentralized Mobile Edge Computing servers (MECs) and a centralized cloud server. Two novel improvement operations, namely Frame Shifting (FS) and Load Redistribution Strategy (LRS), are introduced to enhance the performance of the whale algorithm. Through simulation, our results demonstrate the superior performance of EWA. Specifically, compared to the Whale Optimization Algorithm (WOA), EWA achieves a remarkable reduction in latency by 22.84%, a substantial decrease in energy consumption by 78.28%, and a notable reduction in cost usage by 61.47%. These outcomes underscore the efficacy and practical significance of the proposed EWA in addressing the challenges posed by dependent task offloading in the multi-edge cloud computing landscape. [ABSTRACT FROM AUTHOR]

In this paper, a joint optimization algorithm of offloading decision, energy harvesting time, and unmanned aerial vehicle (UAV) location is proposed for user equipment (UEs)'s task completion latency problem in a communication–sensing–computing integration scenario with wireless energy supply. Under the constraints of causality of energy harvesting consumption by the UEs and conditional mutual information, the total latency minimization problem of the UEs is established. Firstly, the optimization variables of the problem are transformed from three variables of offloading decision, energy harvesting time, and UAV location to two variables of offloading decision and UAV location by means of the derived closed expression, and then the transformed optimization problem is decomposed into the offloading decision optimization sub-problem and the UAV location optimization sub-problem to be solved alternately and iteratively. The genetic algorithm is employed to tackle the optimization sub-problem of offloading decisions, and the successive convex approximation algorithm is applied to the drone positioning optimization sub-problem. Simulation results show that the proposed algorithm in this paper reduces the average task completion latency by 35 percent and 15 percent, respectively, compared to the two baseline algorithms for different numbers of UEs. [ABSTRACT FROM AUTHOR]

With the boom in mobile internet services, computationally intensive applications such as virtual and augmented reality have emerged. Mobile edge computing (MEC) technology allows mobile devices to offload heavy computational tasks to edge servers, which are located at the edge of the network. This technique is considered an effective approach to help reduce the burden on devices and enable efficient task offloading. This paper addresses a dynamic real-time task-offloading problem within a stochastic multi-user MEC network, focusing on the long-term stability of system energy consumption and energy budget constraints. To solve this problem, a task-offloading strategy with long-term constraints is proposed, optimized through the construction of multiple queues to maintain users' long-term quality of experience (QoE). The problem is decoupled using Lyapunov theory into a single time-slot problem, modeled as a Markov decision process (MDP). A deep reinforcement learning (DRL)-based LMADDPG algorithm is introduced to solve the task-offloading decision. Finally, Experiments are conducted under the constraints of a limited MEC energy budget and the need to maintain the long-term energy stability of the system. The results from simulation experiments demonstrate that the algorithm outperforms other baseline algorithms in terms of task-offloading decisions. [ABSTRACT FROM AUTHOR]

The present and next-generation networks of service offloading present exciting issues. The traditional network optimisation techniques still require rigorous heuristic tuning to obtain a good result. Traditional methods employ data as an input and produce solutions that are close to ideal. These methods only operate with tiny networks and exhibit exponential computation times. Thus, we are driven to understand the behaviour of conventional optimisation methods while boosting service quality and meeting next-generation applications. The Optimization problem in Virtual Mobile Edge Computing using Twin Delayed Deep Deterministic Policy Gradient-based Intelligent Computation Offloading (TD3PG-ICO). Optimal Stopping Theory is used in virtual mobile edge computing for the best service offloading (OST). To support the use case, a service offloading protocol is also described. We use Software Defined Networking (SDN) and Network Function Virtualization (NFV) ideas to manage and virtualise network components. To deal with dense Internet of Things (IoT) networks, a TD3PG-OST-based offloading is therefore suggested. There are extensive analyses and comparisons with cutting-edge methods. Results demonstrate the effectiveness of the proposed methods in terms of networking, resource utilisation, and service offloading. [ABSTRACT FROM AUTHOR]

To address the current challenge of smart devices in healthcare Internet of things (IoT) struggling to efficiently process intensive applications in real-time, a collaborative cloud-edge offloading model tailored for ultra-dense edge computing (UDEC) networks is developed. While numerous studies have delved into the optimization of offloading in mobile edge computing (MEC), it is imperative to consider non-orthogonal multiple access (NOMA) as a physical technology when addressing the offloading optimization process in MEC. The multiuser sharing of spectrum resources in NOMA can enhance the network spectrum utilization and reduce the computational delay when users transmit computing tasks. Consequently, a model for NOMA-assisted UDEC systems is proposed. The model takes into account joint offloading decisions, computational resources, and sub-channel resources and is modeled as a complex nonlinear mixed-integer programming problem. The aim is to decrease the task execution delay and energy consumption of smart devices while ensuring that users' maximum acceptable delay for processing medical computational tasks is met efficiently and in a timely manner. Deep deterministic policy gradient (DDPG), a deep reinforcement learning method, is employed to solve the joint optimization problem. The final simulation results show that the algorithm converges well. The proposed offloading scheme can reduce the system cost by 54.5 and 69.9% in comparison with scenarios where users solely perform local computations and offload their tasks to the base station (BS). The application of NOMA communication in our offloading scheme boosts network spectrum utilization and trims down the system cost by 87.09% when contrasted with orthogonal multiple access (OMA). [ABSTRACT FROM AUTHOR]

Vehicular Edge Computing (VEC) is a crucial application of Mobile Edge Computing (MEC) in vehicular networks. In VEC networks, the computation tasks of vehicle terminals (VTs) can be offloaded to nearby MEC servers, overcoming the limitations of VTs' processing power and reducing latency caused by distant cloud communication. However, a mismatch between VTs' demanding tasks and MEC servers' limited resources can overload MEC servers, impacting Quality of Service (QoS) for computationally intensive tasks. Additionally, vehicle mobility can disrupt communication with static MEC servers, further affecting VTs' QoS. To address these challenges, this paper proposes a vehicle collaborative partial computation offloading model. This model allows VTs to offload tasks to two types of service nodes: collaborative vehicles and MEC servers. Factors like a vehicle's mobility, remaining battery power, and available computational power are also considered when evaluating its suitability for collaborative offloading. Furthermore, we design a deep reinforcement learning-based strategy for collaborative partial computation offloading that minimizes overall task delay while meeting individual latency constraints. Experimental results demonstrate that compared to traditional approaches without vehicle collaboration, this scheme significantly reduces latency and achieves a significant reduction (around 2%) in the failure rate under tighter latency constraints. [ABSTRACT FROM AUTHOR]

With the advent of Mobile Edge Computing (MEC), shifting data processing from cloud centers to the network edge presents an advanced computational paradigm for addressing latency-sensitive applications. Specifically, in radar systems, the real-time processing and prediction of radar echo data pose significant challenges in dynamic and resource-constrained environments. MEC, by processing data near its source, not only significantly reduces communication latency and enhances bandwidth utilization but also diminishes the necessity of transmitting large volumes of data to the cloud, which is crucial for improving the timeliness and efficiency of radar data processing. To meet this demand, this paper proposes a model that integrates a spatiotemporal Attention Module (STAM) with a Long Short-Term Memory Gated Recurrent Unit (ST-ConvLSGRU) to enhance the accuracy of radar echo prediction while leveraging the advantages of MEC. STAM, by extending the spatiotemporal receptive field of the prediction units, effectively captures key inter-frame motion information, while optimizations to the convolutional structure and loss function further boost the model's predictive performance. Experimental results demonstrate that our approach significantly improves the accuracy of short-term weather forecasting in a mobile edge computing environment, showcasing an efficient and practical solution for processing radar echo data under dynamic, resource-limited conditions. [ABSTRACT FROM AUTHOR]

The application of blockchain technology to the field of e-commerce has solved many dilemmas, such as low transparency of transactions, hidden risks of data security and high payment costs. Mobile edge computing(MEC) can provide computational power for blockchain, and can meet the demand for high real-time and low latency in e-commerce transaction systems. However, there are still some constraints in the MEC enabled e-commerce consortium blockchain, such as the leakage of user privacy information, low security of consensus algorithm and other security issues. In this paper, we propose a secure transaction model suitable for MEC enabled e-commerce consortium blockchain, aiming to ensure the efficiency of system transaction processing while improving the security of users' privacy information and transaction data. The model adopts the lightweight Paillier encryption algorithm to protect the security of user privacy information and transaction data to prevent the leakage of user privacy information, and optimizes the security of leader election phase of Raft consensus algorithm by introducing the shamir secret sharing protocol to improve the anti-Byzantine failure capabilities of Raft consensus algorithm. The effectiveness of the scheme proposed in this paper is demonstrated by experimental simulations. [ABSTRACT FROM AUTHOR]

Sensitive data identification represents the initial and crucial step in safeguarding sensitive information. With the ongoing evolution of the industrial internet, including its interconnectivity across various sectors like the electric power industry, the potential for sensitive data to traverse different domains increases, thereby altering the composition of sensitive data. Consequently, traditional approaches reliant on sensitive vocabularies struggle to adequately address the challenges posed by identifying sensitive data in the era of information abundance. Drawing inspiration from advancements in natural language processing within the realm of deep learning, we propose a transferable Sensitive Data Identification method based on Product Quantization, named PQ-SDI. This innovative approach harnesses both the composition and contextual cues within textual data to accurately pinpoint sensitive information within the context of Mobile Edge Computing (MEC). Notably, PQ-SDI exhibits proficiency not only within a singular domain but also demonstrates adaptability to new domains following training on heterogeneous datasets. Moreover, the method autonomously identifies sensitive data throughout the entire process, eliminating the necessity for human upkeep of sensitive vocabularies. Extensive experimentation with the PQ-SDI model across four real-world datasets, resulting in performance improvements ranging from 2% to 5% over the baseline model and achieves an accuracy of up to 94.41%. In cross-domain trials, PQ-SDI achieved comparable accuracy to training and identification within the same domain. Furthermore, our experiments showcased the product quantization technique significantly reduces the parameter size by tens of times for the subsequent sensitive data identification phase, particularly beneficial for resource-constrained environments characteristic of MEC scenarios. This inherent advantage not only bolsters sensitive data protection but also mitigates the risk of data leakage during transmission, thus enhancing overall security measures in MEC environments. [ABSTRACT FROM AUTHOR]

In the context of the rapid development of the Internet of Vehicles, virtual reality, automatic driving and the industrial Internet, the terminal devices in the network show explosive growth. As a result, more and more information is generated from the edge of the network, which makes the data throughput increase dramatically in the mobile communication network. As the key technology of the fifth-generation mobile communication network, mobile edge caching technology which caches popular data to the edge server deployed at the edge of the network avoids the data transmission delay of the backhaul link and the occurrence of network congestion. With the growing scale of the network, distributing hot data from cloud servers to edge servers will generate huge energy consumption. To realize the green and sustainable development of the communication industry and reduce the energy consumption of distribution of data that needs to be cached in edge servers, we make the first attempt to propose and solve the problem of edge caching data distribution with minimum energy consumption (ECDDMEC) in this paper. First, we model and formulate the problem as a constrained optimization problem and then prove its NP-hardness. Subsequently, we design a greedy algorithm with computational complexity of O (n 2) to solve the problem approximately. Experimental results show that compared with the distribution strategy of each edge server directly requesting data from the cloud server, the strategy obtained by the algorithm can significantly reduce the energy consumption of data distribution. [ABSTRACT FROM AUTHOR]

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The evolution of science has given rise to many technologies that have changed the world. The upcoming Six-Generation (6G) mobile network indicates a fundamental transformation in wireless technologies, enhancing connectivity and data transmission rates. In this circumstance, Mobile Edge Computing (MEC) is a paradigm technology that emerges as a key major supporter of enhancing mobility awareness. Edge computing offers improved efficiency for service migration from the edge node to the user. However, mobility management in MEC is a complex challenge as seamless handovers between edge nodes must be efficiently executed to ensure uninterrupted service for mobile devices, demanding intricate coordination and low-latency decision-making. To the best of the author's knowledge, there has been no comprehensive work on the most recent developments in mobility awareness using mobile edge computing in 6G. However, this paper aims to present a general overview of the intersection between mobility awareness and MEC over 6G networks. The general concept of MEC in 6G mobile networks is comprehensively introduced. This will highlight the integration between MEC and 6G for bringing more efficient network and service migration to the edge, reducing latency, and enhancing the user experience. Meanwhile, this survey discusses augmented reality with MEC applications. This survey discusses the integration of mobility awareness and mobile edge computing in upcoming mobile applications and emphasizes the need for 6G networks. This integration results in providing seamless communication during handovers between the serving base station and the target base station. This study contributes to understanding the upcoming trends that will enable the operation of mobility awareness and MEC operation in the 6G mobile communication. Furthermore, we delve into a comprehensive overview of the challenges and future research directions for mobility management with MEC in 6G mobile networks, underlining the complexities and potentials of integrating mobility awareness and mobile edge computing.

This research is concerned with the fusion of artificial intelligence (AI) and machine learning within multi-hierarchical caching systems, specifically targeting vehicular and edge caching domains. This study introduces an innovative architecture harmonizing Thompson sampling learning-based caching policies with advanced vehicle clustering and content-popularity prediction methods (TS-MMCM). Simulations show substantial performance improvements and a big impact of the proposed approach on system efficiency in dynamic network environments. The proposal demonstrates a notable gain in cache hit rates and decreased latency levels, highlighting the potential of AI to improve caching techniques in dynamic network environments.

Clients can increase or decrease the number of resources they use dynamically over time due to the elasticity of cloud resources. As a result, variations in resource demands and predefined VM sizes result in a lack of resource utiliation, load imbalances, and excessive power consumption. A framework of efficient resource management is proposed to address these issues, balancing the load accordingly and anticipating the resource utilization of the servers. By optimizing resource utilization and minimizing the number of active servers, this technique facilitates power savings. Under/overloaded servers reduce energy consumption, execution delay, and performance degradation through a resource prediction system that is deployed at the CPU. Moreover, OCL-MEC load-balancing and resource allocation algorithms are proposed to reduce data center network traffic and power consumption. Experiments on real-world workload datasets, namely Bitsbrain VM traces, are conducted to evaluate the proposed framework. Different performance metrics demonstrate the superiority of the proposed framework over state-of-the-art approaches. Power savings of up to 98 % can be achieved by the OCL-MEC framework using a decision tree load balancing model based on HMM prediction systems.

The Industrial Internet of Things integrates modern technologies such as intelligent terminals, computer technology, and big data, achieving low cost and high applicability in industrial production processes, and improving industrial production efficiency. The offloading of IoT data traffic requires significant energy consumption due to limited mobile terminal device resources. For this reason, the author designs a method of IoT data traffic unloading based on mobile edge computing. The initialization simulation parameters include the number of mobile users, the number of base stations equipped with edge servers, fixed bandwidth, base station height, etc. Calculate the distance and channel power gain from each base station to mobile users, and optimize power allocation through algorithms such as simulated annealing and PSO. The binary PSO algorithm is used to maximize the welfare in edge computing. Finally, by comparing with local offloading, utilizing layered offloading methods, and collaborative computing offloading methods based on fiber wireless networks. Simulation results: The energy consumption of SAPA is generally higher than that of PSO, and with the increase of mobile users, the energy consumption of both algorithms shows a significant growth trend. Especially, SAPA's energy consumption is significantly higher than PSO when the number of users is 40 and 60. This indicates that in the mobile network environment, PSO algorithm has more advantages in energy consumption than SAPA, By using particle swarm optimization algorithm to further optimize energy consumption, it greatly saves energy consumption. In comparison to local execution, the proposed offloading method yields substantial energy savings of nearly 62.5 %. The maximum difference in energy consumption between the proposed method and the collaborative computing offloading method based on fiber optic wireless networks is 268 J, while the maximum difference compared to the layered offloading method is 150 J. These results highlight the strategy's ability to enhance the computational efficiency of high-priority tasks on edge servers, concurrently reducing latency and energy consumption for task completion. This underscores the effectiveness of the proposed offloading method in conserving energy and emphasizes its practical significance.

With the help of mobile edge computing (MEC) and network virtualization technology, the mobile terminals can offload the computing, storage, transmission and other resource required for executing various complex applications to the edge service nodes nearby, so as to obtain more efficient service experience.For edge service providers, the optimal energy consumption decision-making problem when deploying complex tasks was comprehensively investigated.Firstly, the problem of deploying complex tasks to multiple edge service nodes was modeled as a mixed integer programming (MIP) model, and then a deep reinforcement learning (DRL) solution strategy that integrated graph to sequence was proposed.Potential dependencies between multiple subtasks through a graph-based encoder design were extracted and learned, thereby automatically discovering common patterns of task deployment based on the available resource status and utilization rate of edge service nodes, and ultimately quickly obtaining the deployment strategy with the optimal energy consumption.Compared with representative benchmark strategies in different network scales, the experimental results show that the proposed strategy is significantly superior to the benchmark strategies in terms of task deployment error ratio, total power consumption of MEC system, and algorithm solving efficiency.

The next generation of wireless communication technologies will enhance the quality of user experience by supporting services such as intelligent transportation, intelligent healthcare, virtual/augmented reality, and more.These emerging services are typically computation-intensive and delay-sensitive, and must meet stringent delay, energy consumption, and reliability requirements that cloud-based services struggle to meet.In order to solve the above problems, mobile edge computing (MEC) technology was proposed to solve the problems of high latency and high bandwidth consumption when sending offloading requests back to the cloud computing centers.As an important research direction of 6G , space-air-ground integrated network (SAGIN) could make up for the huge coverage gap in the world, and had been widely concerned.This paper introduced MEC technology, federated learning technology and artificial intelligence technology into SAGIN to efficiently manage massive and heterogeneous resources in the network, and builded low-delay, low-energy consumption and high-reliability SAGIN to support various emerging services.

Abstract In the field of remote sensing image interpretation, automatically extracting water body information from high-resolution images is a key task. However, facing the complex multi-scale features in high-resolution remote sensing images, traditional methods and basic deep convolutional neural networks are difficult to effectively capture the global spatial relationship of the target objects, resulting in incomplete, rough shape and blurred edges of the extracted water body information. Meanwhile, massive image data processing usually leads to computational resource overload and inefficiency. Fortunately, the local data processing capability of edge computing combined with the powerful computational resources of cloud centres can provide timely and efficient computation and storage for high-resolution remote sensing image segmentation. In this regard, this paper proposes PMNet, a lightweight deep learning network for edge-cloud collaboration, which utilises a pipelined multi-step aggregation method to capture image information at different scales and understand the relationships between remote pixels through horizontal and vertical spatial dimensions. Also, it adopts a combination of multiple decoding branches in the decoding stage instead of the traditional single decoding branch. The accuracy of the results is improved while reducing the consumption of system resources. The model obtained F1-score of 90.22 and 88.57 on Landsat-8 and GID remote sensing image datasets with low model complexity, which is better than other semantic segmentation models, highlighting the potential of mobile edge computing in processing massive high-resolution remote sensing image data.