Search

With the development of IoT, the concept of intelligent services has gradually come to the fore. Intelligent services usually involve a large number of computation intensive tasks with data dependencies that are often modelled as directed acyclic graphs (DAGs), and the offloading of DAG tasks is complex and has proven to be an NP hard challenge. As a key research issue, the task offloading process migrates the computation intensive tasks from resource-constrained IoT devices to nearby edge servers, and pursuing a lower delay and energy consumption. However, data dependencies among tasks are complex, and it is challenging to coordinate the computation intensive tasks among multiple edge servers. In this paper, a flexible and generic DAG task model is built to support the associative task offloading process with complex data dependencies in IoT edge computing environments. Additionally, a priority-based DAG task offloading algorithm and a secondary resource allocation algorithm are proposed to minimize the response delay and improve the resource utilization of edge servers. Experimental results demonstrate that the proposed method can well support the DAG task offloading process with the shortest response delay, while outperforming all the benchmark policies, which is suitable for IoT edge computing environments. [ABSTRACT FROM AUTHOR]

Along with the rising popularity of pay-as-you-go cloud services, many businesses and communities are deploying their business or scientific workflow applications on cloud-based computing platforms. The primary responsibility of cloud service providers is to reduce the monetary cost and execution time of Infrastructure as a Service (IaaS) cloud services. The majority of current solutions for cost and makespan reduction were developed for conventional cloud platforms and are incompatible with heterogeneous computing systems (HCS) having service-based resource management approaches and pricing models. Fog-cloud infrastructures (FCI) have emerged as desirable target areas for workflow automation across several fields of application. In heterogeneous FCI, the execution of workflows involving tasks having different properties might influence the performance in terms of resource usage. The primary goal of this research is to efficiently offload the computational task and optimally schedule the workflow in such diverse computing environment. In this article, we present a novel strategy for building an environment that includes techniques for offloading and scheduling while balancing competing demands from the user and the resource providers. In order to address the issue of uncertainty, our approach incorporates a fuzzy dominance-based task clustering and offloading technique. To construct a suitable execution sequence of tasks that helps to limit the precedence relationship, by preserving dependency constraints among the tasks, a novel algorithm for tasks segmentation is employed. To simplify the problem of the complexity, a hybrid-heuristics based on Harmony Search Algorithm (HSA) and Genetic Algorithm (GA) for resource scheduling algorithm is used. The multi-objective optimization using three competing objectives is taken into consideration for investigation in heterogeneous FCI. The fitness function derived includes minimization of makespan and cost along with maximization of resource utilization. We performed experimental research using five workflow datasets in order to investigate and verify the efficacy of our proposed technique. We contrasted our proposed strategy with the primary, closely comparable strategies. Extensive testing using scientific workflows confirms the effectiveness of our offloading approach. Our solution provided a substantially better cost-makespan tradeoffs, while achieving significantly less energy consumption and can execute marginally quicker than the existing algorithms. [ABSTRACT FROM AUTHOR]

With the development of deep learning foundation model technology, the types of computing tasks have become more complex, and the computing resources and memory required for these tasks have also become more substantial. Since it has long been revealed that task offloading in cloud servers has many drawbacks, such as high communication delay and low security, task offloading is mostly carried out in the edge servers of the Internet of Things (IoT) network. However, edge servers in IoT networks are characterized by tight resource constraints and often the dynamic nature of data sources. Therefore, the question of how to perform task offloading of deep learning foundation model services on edge servers has become a new research topic. However, the existing task offloading methods either can not meet the requirements of massive CNN architecture or require a lot of communication overhead, leading to significant delays and energy consumption. In this paper, we propose a parallel partitioning method based on matrix convolution to partition foundation model inference tasks, which partitions large CNN inference tasks into subtasks that can be executed in parallel to meet the constraints of edge devices with limited hardware resources. Then, we model and mathematically express the problem of task offloading. In a multi-edge-server, multi-user, and multi-task edge-end system, we propose a task-offloading method that balances the tradeoff between delay and energy consumption. It adopts a greedy algorithm to optimize task-offloading decisions and terminal device transmission power to maximize the benefits of task offloading. Finally, extensive experiments verify the significant and extensive effectiveness of our algorithm. [ABSTRACT FROM AUTHOR]

This research is the first application of Unmanned Aerial Vehicles (UAVs) equipped with Multi-access Edge Computing (MEC) servers to offshore wind farms, providing a new task offloading solution to address the challenge of scarce edge servers in offshore wind farms. The proposed strategy is to offload the computational tasks in this scenario to other MEC servers and compute them proportionally, which effectively reduces the computational pressure on local MEC servers when wind turbine data are abnormal. Finally, the task offloading problem is modeled as a multi-intelligent deep reinforcement learning problem, and a task offloading model based on Multi-Agent Deep Reinforcement Learning (MADRL) is established. The Adaptive Genetic Algorithm (AGA) is used to explore the action space of the Deep Deterministic Policy Gradient (DDPG), which effectively solves the problem of slow convergence of the DDPG algorithm in the high-dimensional action space. The simulation results show that the proposed algorithm, AGA-DDPG, saves approximately 61.8%, 55%, 21%, and 33% of the overall overhead compared to local MEC, random offloading, TD3, and DDPG, respectively. The proposed strategy is potentially important for improving real-time monitoring, big data analysis, and predictive maintenance of offshore wind farm operation and maintenance systems. [ABSTRACT FROM AUTHOR]

MEC and IoT are rapidly expanding technologies that offer numerous opportunities to enhance efficiency and application performance. However, the huge volume of data generated by IoT devices, coupled with computational and latency constraints, poses data processing challenges. To address this within the MEC architecture, deploying computing servers at the network edge near IoT devices is a promising approach. This reduces latency and traffic load on the core network while improving the user experience. However, offloading computations task from IoT devices to MEC servers and efficiently allocating computing resources is a complex problem. IoT tasks may have specific requirements in terms of latency, bandwidth and energy efficiency, while computing resources and capacities maybe limited or shared between several users. We propose an approach called FedMeta2Ag, which we evaluate using the MNIST database. With 20 epochs, the training accuracy reached 91.5%, while the test accuracy achieved 92.0%. Performance consistently improved during the initial 20 iterations and gradually stabilized thereafter. Additionally, we compared the performance of our proposed model with existing methods, finding that our approach outperforms existing models in predicting performance more accurately. Thus, this approach effectively meets the demanding performance requirements of wireless communication systems. [ABSTRACT FROM AUTHOR]

In vehicular edge computing, both edge and cloud can provide computing services (i.e., tasks). The edge can reduce vehicular task delay by processing data nearby, but is resource‐constrained and cannot handle too many tasks simultaneously. The resource‐rich cloud can handle massive tasks, but is far from users and has low quality of service and energy efficiency. Currently, some work has been done on task offloading for edge‐cloud collaboration. However, either the collaboration among multiple devices is not considered and the load is easily imbalanced; or edge‐cloud collaboration is required for offloading decisions with too many parameters, affecting tasks to be processed efficiently. To address these issues, we learn from the swarm intelligence evolution of sparrow foraging, improve a sparrow search algorithm by integrating three strategies of flyer refine producer update, sin/cos perturbation follower update, and adaptive adjustment agitator update, to optimize the vehicular task offloading location for edge‐cloud collaboration. Furthermore, we combine delay relaxation variables and delay‐energy penalty operator to design a lightweight heuristic task offloading algorithm, and greedily compare the task preoffload location sets with different delay constraints based on the improved sparrow search algorithm, to obtain the optimal task offloading with integrated delay and energy. Simulation experiments verify that our approach can improve the algorithm's optimization accuracy, convergence speed, and robustness. Moreover, the average task delay, total task energy consumption, and load balance degree of our approach outperform the five benchmark algorithms, and can comprehensively optimize task delay and energy consumption, and achieve edge devices load balancing. [ABSTRACT FROM AUTHOR]

With the fast development of Mobile Internet, data traffic generated by end devices is anticipated to witness substantial growth in the future years. However, processing tasks locally will cause latency due to the limited resources of the end devices. Edge-cloud collaboration, an effective solution for latency-sensitive applications, is attracting greater attention from both industry and academia. It combines the advantages of the cloud center with abundant computing resources and edge nodes with low-latency capabilities. In this paper, we propose a two-stage task offloading framework with edge-cloud collaboration to assist end devices processing latency-sensitive tasks either on the edge servers or in the cloud center. As for homogeneous task offloading, in the first stage, the competitive end devices offload tasks to the edge gateways. We formulate the selfish task offloading problem among end devices as a potential game. In the second stage, the edge nodes request resources from the cloud center to process end devices tasks due to their limited resources. Then, we consider the heterogeneous task offloading problem and use intelligent optimization algorithm to obtain the optimal offloading strategy. Simulation results show that the service prices of edge nodes influence the decisions and task offloading costs of end devices. We also verify the intelligent optimization algorithm can achieve optimal performance with low complexity and fast convergence. [ABSTRACT FROM AUTHOR]

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

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]

This paper proposes a novel UAV-based edge computing system for augmented reality (AR) applications, addressing the challenges posed by the limited resources in mobile devices. The system uses UAVs equipped with edge computing servers (UECs) specifically to enable efficient task offloading and resource allocation for AR tasks with dependent relationships. This work specifically focuses on the problem of dependent tasks in AR applications within UAV-based networks. This problem has not been thoroughly addressed in previous research. A dual network architecture-based task offloading (DNA-TO) algorithm is proposed, leveraging the DNA framework to enhance decision-making in reinforcement learning while mitigating noise. In addition, a Karush–Kuhn–Tucker-based resource allocation (KKT-RA) algorithm is proposed to optimize resource allocation. Various simulations using real-world movement data are conducted. The results indicate that our proposed algorithm outperforms existing approaches in terms of latency and energy efficiency. [ABSTRACT FROM AUTHOR]

The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%. [ABSTRACT FROM AUTHOR]

Fog computing, specialized in handling latency-sensitive and resource-hungry tasks, emerges as a pivotal paradigm for Internet of Things (IoT). Task offloading encompasses judgment and forwarding. However, current research predominantly concentrates on the former, neglecting forwarding and the potential occurrence of errors in this process. Additionally, existing models commonly employ continuous-time queuing models. To overcome these limitations, we propose a task offloading strategy considering forwarding errors based on cloud–fog collaboration. The strategy aims to offload tasks according to a predefined offloading ratio. We incorporate forwarding error ratios for all tasks, prioritize access for latency-sensitive tasks, and devise a discrete-time queueing model. Through numerical experiments, we analyze performance trends with offloading ratio. Additionally, a system profit function is employed to ascertain the optimal offloading ratio balancing the key metrics. Our findings underscore the significant advantages of cloud–fog collaboration over traditional pure cloud computing, notably increasing throughput rate while decreasing the blocking rate. [ABSTRACT FROM AUTHOR]

Mobile edge computing (MEC) is considered one of the key technologies for large-scale network services. Task scheduling helps to improve the task completion rate of MEC, by properly mapping tasks generated by devices onto MEC resources. However, the mobility of devices introduces complexities, potentially resulting in failed task offloading or unavailable task results. To tackle this issue, we propose a mobile-aware task scheduling scheme. We first model the trajectory of mobile devices and introduce a strategy for the fastest task offloading, coupled with an efficient result return method. Subsequently, to improve the task completion rate, we present a task scheduling model based on task migration and formulate the relevant problem as a Mixed Integer Non-linear Programming (MINLP) problem. To achieve a solution within a reasonable time complexity, we propose a Particle Swarm Optimization and Genetic Algorithm with a Rescheduling operator (PSOGAR). In PSOGAR, particles update their positions using a mating operator, while maintaining diversity by a mutation operator. In addition, a rescheduling operator is used to further improve the task completion rate. Finally, through simulation experiments, compare PSOGAR with state-of-the-art and classic algorithms. The experimental results show that PSOGAR can improve the task completion rate by 18–31% and can be applied to scenarios with tight task deadlines. [ABSTRACT FROM AUTHOR]

In this paper we propose an auction-based offloading market in the HMC (Heterogeneous Mobile Cloud) environment where mobile devices, Cloudlets, and public cloud form a shared resource pool. In this market, users can register their service offers or submit their offloading requests to the auction manager. Then, the auction manger makes buyer_seller threads as the source–destination of task offloading process. In any made buyer_seller thread a trade-off between conflicting utility functions of buyer and seller has to be guaranteed. Furthermore, auction manager should impose various policies in order to maintain market stability, increase market competition, and reduce monopolies. A stable market is a market where the chance of uncommitted sellers to be considered in the made buyer_seller thread is kept low. Also, almost equal opportunities are made for the users in a competitive and non-monopolized market. To address these issues, first of all, a new task offloading approach based on Hill Climbing Algorithm is designed to assure a trade-off between buyers' and sellers' utilities. Then, a new factor in name reputation is defined to make competitive market while maintain market stability. Furthermore, a Cloudlet selection algorithm is developed to find the Cloudlet as a new offloading destination in a case of reneging on the offloading commitment, leading to market stability improvement. Finally, awarding bonus to new participants is another strategy imposed by auction manager to make competitive and non-monopolized market. Extensive amounts of simulation were carried out to evaluate the performance of our proposed offloading market against (Zhou et al. in J Syst Softw 152:151–164, 2019). The results show that our designed market outperforms (Zhou et al. 2019). [ABSTRACT FROM AUTHOR]

The presence of limitations such as processing and energy in many of end devices has resulted in task offloading to other sources. To address these limitations, offloading can be performed in diverse environments such as fog or cloud. This article reviews the multi-objective optimization of task offloading, which is a crucial challenge in fog computing. First, a task offloading model is presented for the simultaneous optimization of response time, energy consumption, and cost criteria, while considering availability criteria. This model utilizes a virtual controller to monitor all three layers of IoT, Fog, and Cloud. In the following, a developed version of the multi-objective crow search algorithm called the Improved Non-Dominated Sorting Crow Search Algorithm (INSCSA) is presented. An advantage of this research, compared to previous works, is the investigation of the optimality of the whole system. Evaluations demonstrate that our method has achieved superior results compared to the other methods. [ABSTRACT FROM AUTHOR]

As machine learning (ML) technologies continue to evolve, there is an increasing demand for data. Mobile crowd sensing (MCS) can motivate more users in the data collection process through reasonable compensation, which can enrich the data scale and coverage. However, nowadays, users are increasingly concerned about their privacy and are unwilling to easily share their personal data. Therefore, protecting privacy has become a crucial issue. In ML, federated learning (FL) is a widely known privacy‐preserving technique where the model training process is performed locally by the data owner, which can protect privacy to a large extent. However, as the model size grows, the weak computing power and battery life of user devices are not sufficient to support training a large number of models locally. With mobile edge computing (MEC), user can offload some of the model training tasks to the edge server for collaborative computation, allowing the edge server to participate in the model training process to improve training efficiency. However, edge servers are not fully trusted, and there is still a risk of privacy leakage if data is directly uploaded to the edge server. To address this issue, we design a local differential privacy (LDP) based data privacy‐preserving algorithm and a deep reinforcement learning (DRL) based task offloading algorithm. We also propose a privacy‐preserving distributed ML framework for MEC and model the cloud‐edge‐mobile collaborative training process. These algorithms not only enable effective utilization of edge computing to accelerate machine learning model training but also significantly enhance user privacy and save device battery power. We have conducted experiments to verify the effectiveness of the framework and algorithms. [ABSTRACT FROM AUTHOR]

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

Vehicular fog computing (VFC) is an innovative computing paradigm with an exceptional ability to improve the vehicles' capacity to manage computation-intensive applications with both low latency and energy consumption. Moreover, more and more Artificial Intelligence (AI) technologies are applied in task offloading on the Internet of Vehicles (IoV). Focusing on the problems of computing latency and energy consumption, in this paper, we propose an AI-based Vehicle-to-Everything (V2X) model for tasks and resource offloading model for an IoV network, which ensures reliable low-latency communication, efficient task offloading in the IoV network by using a Software-Defined Vehicular-based FC (SDV-F) architecture. To fit to time-critical data transmission task distribution, the proposed model reduces unnecessary task allocation at the fog computing layer by proposing an AI-based task-allocation algorithm in the IoV layer to implement the task allocation of each vehicle. By applying AI technologies such as reinforcement learning (RL), Markov decision process, and deep learning (DL), the proposed model intelligently makes decision on maximizing resource utilization at the fog layer and minimizing the average end-to-end delay of time-critical IoV applications. The experiment demonstrates the proposed model can efficiently distribute the fog layer tasks while minimizing the delay. [ABSTRACT FROM AUTHOR]

Edge computing is a promising technology, especially for offloading users' computationally heavy tasks. The close proximity of edge computing units to users minimizes network latency, thereby enabling delay-sensitive applications. Although optimal resource provisioning and task offloading in edge computing are widely studied in the literature, there are still some critical research gaps. In this study, we propose a traffic-aware optimal association and task-offloading approach. The proposed method does not rely solely on the average rate of offloading requests, which can differ from actual values in real time. Instead, it uses an intelligent, high-precision prediction model to forecast future offloading requests, allowing resource provisioning to be based on future sequences of requests rather than average values. Additionally, we propose an optimization-based approach that can meet task deadlines, which is crucial for mission-critical applications. Finally, the proposed approach distributes the computing load over multiple time steps, ensuring future resource scheduling and task-offloading decisions can be made with a certain level of flexibility. The proposed approach is extensively evaluated under various scenarios and configurations to validate its effectiveness. As a result, the proposed deep learning model has resulted in a request prediction error of 0.0338 (RMSE). In addition, compared to the greedy approach, the proposed approach has reduced the use of local and cloud computing from 0.02 and 18.26 to 0.00 and 0.62, respectively, while increasing edge computing usage from 1.31 to 16.98, which can effectively prolong the lifetime of user devices and reduce network latency. [ABSTRACT FROM AUTHOR]

With the continuous expansion of the scale of the industrial Internet, edge computing has become an indispensable part of the industrial Internet. In order to quickly handle the massive computation tasks of production devices and monitoring devices in industrial production and ensure the safety and efficiency of industrial production. This article considers the Joint Task Offloading and Resource Allocation (JTORA) optimization problem, which is measured by the weighted sum of task completion time and energy consumption, and includes the joint optimization of task offloading decision, uplink power allocation, and computing resource allocation. Also further decompose the JTORA problem into (i) a Resource Allocation (RA) problem with fixed task offloading decision and (ii) a Task Offloading (TO) problem that optimizes the optimal-value function corresponding to the RA problem for hierarchical optimization. This paper adopts Deep Reinforcement Learning (DRL) algorithm to solve the RA problem and a heuristic algorithm for the TO problem. Simulation experimental results show that the proposed JTORA optimization scheme can significantly reduce the production time and device energy consumption in industrial production over traditional approaches. [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]

Earthquake disasters are usually very destructive and pose a great threat to human life and property. Based on the relatively mature technology of unmanned aerial vehicles (UAVs) and their high flexibility, these devices are widely used for information collection and processing in post-disaster relief operations. However, UAVs are limited by their battery capacity, which makes it hard for them to perform both large-scale information gathering and data processing at the same time. Nowadays, smartphones (SPs), which have become portable devices for people, have the characteristics of strong computing power, rich communication means and wide distribution. Therefore, in this study, we developed SPs to assist UAVs in computation incentive-based task execution. To balance the cost of UAVs and the execution utility of SPs during the task execution process, a multi-objective optimization problem was established, and the Multi-Objective Mutation-Immune Bat (MOMIB) algorithm was developed to optimize the proposed problem. Additionally, considering the diversity of tasks in real-world scenarios, Quality of Service (QoS) coefficients were introduced to ensure the performance requirements of different types of tasks. A large number of simulation experiments show that the task-offloading scheme that we propose is effective. [ABSTRACT FROM AUTHOR]

The rapid development of Internet of Things (IoT) technology has led to a significant increase in the computational task load of Terminal Devices (TDs). TDs reduce response latency and energy consumption with the support of task-offloading in Multi-access Edge Computing (MEC). However, existing task-offloading optimization methods typically assume that MEC's computing resources are unlimited, and there is a lack of research on the optimization of task-offloading when MEC resources are exhausted. In addition, existing solutions only decide whether to accept the offloaded task request based on the single decision result of the current time slot, but lack support for multiple retry in subsequent time slots. It is resulting in TD missing potential offloading opportunities in the future. To fill this gap, we propose a Two-Stage Offloading Decision-making Framework (TSODF) with request holding and dynamic eviction. Long Short-Term Memory (LSTM)-based task-offloading request prediction and MEC resource release estimation are integrated to infer the probability of a request being accepted in the subsequent time slot. The framework learns optimized decision-making experiences continuously to increase the success rate of task offloading based on deep learning technology. Simulation results show that TSODF reduces total TD's energy consumption and delay for task execution and improves task offloading rate and system resource utilization compared to the benchmark method. [ABSTRACT FROM AUTHOR]

In current research on task offloading and resource scheduling in vehicular networks, vehicles are commonly assumed to maintain constant speed or relatively stationary states, and the impact of speed variations on task offloading is often overlooked. It is frequently assumed that vehicles can be accurately modeled during actual motion processes. However, in vehicular dynamic environments, both the tasks generated by the vehicles and the vehicles' surroundings are constantly changing, making it difficult to achieve real-time modeling for actual dynamic vehicular network scenarios. Taking into account the actual dynamic vehicular scenarios, this paper considers the real-time non-uniform movement of vehicles and proposes a vehicular task dynamic offloading and scheduling algorithm for single-task multi-vehicle vehicular network scenarios, attempting to solve the dynamic decision-making problem in task offloading process. The optimization objective is to minimize the average task completion time, which is formulated as a multi-constrained non-linear programming problem. Due to the mobility of vehicles, a constraint model is applied in the decision-making process to dynamically determine whether the communication range is sufficient for task offloading and transmission. Finally, the proposed vehicular task dynamic offloading and scheduling algorithm based on muti-agent deep deterministic policy gradient (MADDPG) is applied to solve the optimal solution of the optimization problem. Simulation results show that the algorithm proposed in this paper is able to achieve lower latency task computation offloading. Meanwhile, the average task completion time of the proposed algorithm in this paper can be improved by 7.6% compared to the performance of the MADDPG scheme and 51.1% compared to the performance of deep deterministic policy gradient (DDPG). [ABSTRACT FROM AUTHOR]

Copyright of Journal of Chongqing University of Posts & Telecommunications (Natural Science Edition) is the property of Chongqing University of Posts & Telecommunications 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.)

Abstract The convergence of artificial intelligence and in-vehicle wireless communication technologies, promises to fulfill the pressing communication needs of the Internet of Vehicles (IoV) while promoting the development of vehicle applications. However, making real-time dependency-aware task offloading decisions is difficult due to the high mobility of vehicles and the dynamic nature of the network environment. This leads to additional application computation time and energy consumption, increasing the risk of offloading failures for computationally intensive and latency-sensitive applications. In this paper, an offloading strategy for vehicle applications that jointly considers latency and energy consumption in the base station cooperative computing model is proposed. Firstly, we establish a collaborative offloading model involving multiple vehicles, multiple base stations, and multiple edge servers. Transferring vehicular applications to the application queue of edge servers and prioritizing them based on their completion deadlines. Secondly, each vehicular application is modeled as a directed acyclic graph (DAG) task with data dependency relationships. Subsequently, we propose a task offloading method based on task dependency awareness in deep reinforcement learning (DAG-DQN). Tasks are assigned to edge servers at different base stations, and edge servers collaborate to process tasks, minimizing vehicle application completion time and reducing edge server energy consumption. Finally, simulation results show that compared with the heuristic method, our proposed DAG-DQN method reduces task completion time by 16%, reduces system energy consumption by 19%, and improves decision-making efficiency by 70%.

In most practical applications, the feature space of the training datasets and the target domain datasets are inconsistent, or the data distribution between them is inconsistent, which leads to the problem of data starvation and makes it difficult for terminal devices to obtain high accurate results. Aiming at the problems of limited terminal device resources, low accuracy of data processing results, and unsatisfactory processing speed, a Heterogeneous Multi-access Edge Computing (MEC) Framework based on Transfer Learning (TL) is proposed, abbreviated as HMECF-TL. This framework adopts a cloud-edge-end three-layer architecture. It uses model transfer to optimize the Convolutional Neural Networks (CNN) model at each layer to achieve the goal of improving data processing speed and accuracy. Furthermore, a multi-agent Deep Reinforcement Learning Algorithm having Attention Mechanism (DRLAAM) is designed to further increase the timeliness performance of computation-intensive applications. The performance of HMECSF-TL framework is verified by simulation experiments, which not only reduces the delay by more than 24.66 %, but also improves the accuracy by more than 8.34 %. The framework not only increase the computing capacity to solve the shortage of terminal device resources, but also improve the quality of data processing to solve the problem of data starvation.

Mobile Augmented Reality (MAR) applications enhance user experiences by providing realistic information about the current location through mobile devices. However, MAR applications are computationally intensive, leading to high energy consumption and latency issues. To address these challenges, this research presents a Hybrid Monte Carlo Tree Search (HMCTS) based task offloading scheme, combining a genetic algorithm with Monte Carlo tree search for efficient task management. The proposed method uses YoloV7 for object recognition and aims to reduce energy consumption, response time, and migration time. Experimental results demonstrate that the HMCTS approach significantly reduces energy consumption to 1290 kJ, response time to 24 ms, and migration time to 0.52 ms, outperforming existing techniques. These improvements highlight the potential of the HMCTS method for enhancing the performance of MAR applications. Proposed hybrid approach aims to improve the efficiency and effectiveness of task offloading in MAR applications. The HMCTS model dynamically offloads tasks to edge servers, optimizing scheduling time, response time, and energy consumption.

Internet of things (IoT) and fog and cloud computing are the most widespread technologies that have become a part of our day-to-day activities. The integration of these technologies will become crucial components for the future of the Internet as their usage and acceptance rapidly increase. IoT devices (or terminal nodes (TNs)) have limited resource capability to carry out their generated tasks. Therefore, they depend on the cloud to assist them in completing all their tasks. However, the distance between TNs and the cloud may lead to network congestion and uneven delay. As a result, fog nodes (FNs) work as an intermediary between TNs and the cloud to minimize delay in completing the TNs’ tasks. In this context, previous studies assign the TNs’ tasks to FNs based on various criteria, namely energy, delay and priority among the tasks, without combining them. Fair task offloading (FTO) recently combines these criteria to assign the TN's tasks to FNs without significantly considering load balancing among FNs. This paper introduces a multi-objective task offloading algorithm called energy, delay and priority-aware task offloading (EDP-TO) by considering all the criteria and load balancing. The proposed algorithm uses the multi-objective function to select the FNs for offloading. It divides the tasks into multiple subtasks and assigns them to the chosen FNs, minimizing the overall delay. The performance of the proposed algorithm is shown without and with load balancing, called EDP-TO-WLB and EDP-TO-LB, and it is compared with FTO by considering three scenarios and five performance metrics. The comparison results show the EDP-TO improves a maximum of 3% energy, 5% delay and 45% fairness over the FTO. [ABSTRACT FROM AUTHOR]

Edge computing holds considerable potential to augment the efficiency of computational and energy resources in mobile edge computing (MEC) through the application of task offloading technology. Task offloading involves the delegation of multiple tasks, generated by devices with limited resources, to an edge server or other devices equipped with enhanced capabilities, which facilitates more efficient processing times and energy utilization. This paper addresses significant challenges in MEC environments, particularly the issue of inaccurately perceived task rewards due to queuing delays in task offloading, which adversely affects the performance of deep reinforcement learning (DRL) strategies. We introduce the reward-aware proximal policy optimization (RAPPO) algorithm, which is specifically designed to enhance the accuracy of reward perception by refining data sampling methods. This adjustment enables RAPPO to accurately associate rewards with their respective tasks despite inherent delays, thereby enhancing decision-making and improving overall system performance in dynamic and uncertain environments. Simulation results demonstrate that the proposed algorithm achieves competitive performance compared to existing baseline schemes. [ABSTRACT FROM AUTHOR]

Due to the rapid development of the Internet of Vehicles (IoV), the combination of IoV and edge computing, known as vehicle edge computing (VEC), has received considerable attention from both academia and industry. However, task offloading in diverse intersection scenarios remains suffering from inefficiency of resource allocation and low quality of service for task execution due to the imbalance of traffic flow and the rigid requirement of latency. To address these issues, we develop a task offloading strategy by a fuzzy decision-making algorithm to handle uncertainty and imprecision. This task offloading strategy comprises two components: (1) A VEC resource pool with available vehicles at each intersection is constructed when taking the rotating direction of the recognition region. Then, we introduce a fuzzy decision-making algorithm to select a set of high-quality service vehicles from this VEC resource pool as an auxiliary edge server (AS). (2) We employ an edge service provider (ESP) to manage the computational resources of a main edge server (MS) and an AS deployed at a traffic intersection. The negotiation between the ESP and the task vehicles is modeled as a Stackelberg game. We prove the existence of the unique perfect Nash equilibrium, and a genetic algorithm is applied to find the optimum. Finally, we conduct simulation experiments with datasets collected in real-world scenarios. The results demonstrate that our scheme decreases task execution time by 9.73% compared to the cloud server scheme and reduces energy consumption by 13.78% compared to the state-of-the-art reinforcement learning (RL) strategy. [ABSTRACT FROM AUTHOR]

The recent widespread of AI-powered real-time applications necessitates the use of edge computing for inference task offloading. Power constrained edge devices are required to balance between processing inference tasks locally or offload to edge servers. This decision is determined according to the time constraint demanded by the real-time nature of applications, and the energy constraint dictated by the device's power budget. This problem is further exacerbated in the case of systems leveraging multiple local inference models varying in size and accuracy. In this work, we tackle the problem of assigning inference models to inference tasks either using local inference models or by offloading to edge servers under time and energy constraints while maximizing the overall accuracy of the system. This problem is shown to be strongly NP-hard and therefore, we propose a hybrid genetic algorithm (HGSTO) to solve this problem. We leverage the speed of simulated annealing (SA) with the accuracy of genetic algorithms (GA) to develop a hybrid, fast and accurate algorithm compared with classic GA, SA and Particle Swarm Optimization (PSO). Experiment results show that HGSTO achieved on-par or higher accuracy than GA while resulting in significantly lower scheduling times compared to other schemes. [ABSTRACT FROM AUTHOR]

With the explosive growth of electronic information technology, mobile devices generate massive amounts of data and requirements, which poses a significant challenge to mobile devices with limited computing and battery capacity. Task offloading can transfer computing-intensive tasks from resource-constrained mobile devices to resource-rich servers, thereby significantly reducing the consumption of task execution. How to optimize the task offloading strategy in complex environments with multi-layers and multi-devices to improve efficiency becomes a challenge for the task offloading problem. We optimize the vertical assignment of tasks in a multi-layer system using deep reinforcement learning algorithms, which encompass the cloud, edge, and device layers. To balance the load among multiple devices, we employ the KNN algorithm. Subsequently, we introduce a task state discrimination method based on fuzzy control theory to enhance the performance of computing nodes under high load conditions. By optimizing task offloading policies and execution orders, we successfully reduce the average task execution time and energy consumption of mobile devices. We implemented the proposed algorithm in the PureEdgeSim simulator and performed simulations using different device densities to verify the algorithm's scalability. The simulation results show that the method we proposed outperforms the methods in previous work. Our method can significantly improve performance in high-device density scenarios. [ABSTRACT FROM AUTHOR]

Edge computing is gaining prominence as a solution for IoT data management and processing. Task offloading, which distributes the processing load across edge devices, is a key strategy to enhance the efficiency of edge computing. However, traditional methods often overlook the dynamic nature of the edge environment and the interactions between devices. While reinforcement learning-based task offloading shows promise, it can sometimes lead to an imbalance by favoring weaker servers. To address these issues, this paper presents a novel task offloading method for federated learning that leverages the β-skeleton graph in edge computing. This model takes into account spatial and temporal dynamics, optimizing task assignments based on both the processing and communication capabilities of the edge devices. The proposed method significantly outperforms five state-of-the-art methods, showcasing substantial improvements in both initial and long-term performance. Specifically, this method demonstrates a 63.46% improvement over the Binary-SPF-EC method in the initial rounds and achieves an average improvement of 76.518% after 400 rounds. Moreover, it excels in sub-rewards and total latency reduction, underscoring its effectiveness in optimizing edge computing communication and processing tasks. These results underscore the superiority of the proposed method, highlighting its potential to enhance the efficiency and scalability of edge computing systems. This approach, by effectively addressing the dynamic nature of the edge environment and optimizing task offloading, contributes to the development of more robust and efficient edge computing frameworks. This work paves the way for future advancements in federated learning and edge computing integration, promising better management and utilization of IoT data. [ABSTRACT FROM AUTHOR]

A resource allocation strategy for the low earth orbit (LEO) satellite Internet based on terminal traffic prediction was proposed to address the problems of blind coverage spots in ground network and the low resource utilization of satellite network. An improved LSTM-ARIMA algorithm was proposed with real datasets by the strategy to accurately predict the data traffic generated in the ground area over a certain period of time in the future. Two communication models, differentiated data transmission and task offloading were constructed through Stackelberg games, taking into the data processing latency and energy consumption account. By solving the Nash equilibrium, the optimal ratio for users to transmit data or unload tasks through the LEO satellite Internet, as well as the optimal pricing for satellites to provide network services, were obtained. Extensive simulation results verify that the proposed strategy can increase the revenue by approximately 40% in data transmission services and 50% in task offloading services.

Beyond 6G services and applications demand high and efficient processing capacity due to the massive connectivity of users equipment (UEs). However, the high computational capability and energy consumption of UEs are limited, which becomes a main challenge to overcome. Multi-access edge computing (MEC) has recently been studied widely as it can potentially assist complex tasks executed at UEs. Furthermore, several techniques have been proposed to optimize task offloading among users. Thus, another challenge in MEC is emerging due to the fact that mobile users do not always have a line-of-sight (LoS) to the base station (BS) due to the blocking object. Therefore, it can affect users data rate and result in incremental energy consumption. This research introduces the concept of reconfigurable intelligence surfaces (RIS) to support multiple-input-single-output (MISO) base stations (BS) in both uplink (UL) and downlink (DL) using BCD algorithms. While previous studies concentrate on enhancing task offloading and neglecting inter-user interference, this study suggests an optimization approach for UL and DL data rates, as well as minimizing task offloading delays. The results indicate that optimizing task placement, phase shift, and precoding can reduce the duration of task offloading.

To address the issues of ineffective task offloading decisions caused by multitasking and resource constraints in vehicular networks, the Quasi-Newton method deep reinforcement learning dual-phase online offloading (QNRLO) algorithm was proposed. The algorithm was designed by initially incorporating batch normalization techniques to optimize the training process of deep neural networks. Subsequently, optimization was performed using the Quasi-Newton method to effectively approximate the optimal solution. Through this dual-stage optimization, performance was significantly enhanced under conditions of multitasking and dynamic wireless channels, improving computational efficiency. By introducing Lagrange multipliers and a reconstructed dual function, the non-convex optimization problem was transformed into a convex optimization problem of the dual function, ensuring the global optimality of the algorithm. Additionally, system transmission time allocation in the vehicular network model was considered, enhancing the practicality of the algorithm. Compared to existing algorithms, the proposed algorithm improves the convergence and stability of task offloading significantly, addresses task offloading issues in vehicular networks effectively, and offers high practicality and reliability.

In response to the diverse business needs of the future in all domains and scenarios, 6G networks need to provide scenario-based and personalized service capabilities. Aiming at the problem of quality assurance of fine-grained business services in the future, a joint optimization algorithm for 6G network task offloading and fine-grained slice resource scheduling was proposed, which jointly considered the calculation offloading of multiple MECs and the resource scheduling of network slices, and minimized the execution delay and energy consumption cost of the task within limited resources. Then the A3C reinforcement learning algorithm of asynchronous training was used to solve it. The simulation results show that, compared with the traditional algorithm, the proposed algorithm can reduce the computing cost while meeting the business needs of users. Additionally, the algorithm converges fast and can realize fast decision-making.

Abstract The integration of new Internet of Things (IoT) applications and services heavily relies on task offloading to external devices due to the constrained computing and battery resources of IoT devices. Up to now, Cloud Computing (CC) paradigm has been a good approach for tasks where latency is not critical, but it is not useful when latency matters, so Multi-access Edge Computing (MEC) can be of use. In this work, we propose a distributed Deep Reinforcement Learning (DRL) tool to optimize the binary task offloading decision, this is, the independent decision of where to execute each computing task, depending on many factors. The optimization goal in this work is to maximize the Quality-of-Experience (QoE) when performing tasks, which is defined as a metric related to the battery level of the UE, but subject to satisfying tasks’ latency requirements. This distributed DRL approach, specifically an Actor-Critic (AC) algorithm running on each User Equipment (UE), is evaluated through the simulation of two distinct scenarios and outperforms other analyzed baselines in terms of QoE values and/or energy consumption in dynamic environments, also demonstrating that decisions need to be adapted to the environment’s evolution.

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]

Unmanned aerial vehicles (UAVs) have increasingly become integral to multi-access edge computing (MEC) due to their flexibility and cost-effectiveness, especially in the B5G and 6G eras. This paper aims to enhance the quality of experience (QoE) in large-scale UAV-MEC networks by minimizing the shrinkage ratio through optimal decision-making in computation mode selection for each user device (UD), UAV flight trajectory, bandwidth allocation, and computing resource allocation at edge servers. However, the interdependencies among UAV trajectory, binary task offloading mode, and computing/network resource allocation across numerous IoT nodes pose significant challenges. To address these challenges, we formulate the shrinkage ratio minimization problem as a mixed-integer nonlinear programming (MINLP) problem and propose a two-tier optimization strategy. To reduce the scale of the optimization problem, we first design a low-complexity UAV partition coverage algorithm based on the Welzl method and determine the UAV flight trajectory by solving a traveling salesman problem (TSP). Subsequently, we develop a coordinate descent (CD)-based method and an alternating direction method of multipliers (ADMM)-based method for network bandwidth and computing resource allocation in the MEC system. Extensive simulations demonstrate that the CD-based method is simple to implement and highly efficient in large-scale UAV-MEC networks, reducing the time complexity by three orders of magnitude compared to convex optimization methods. Meanwhile, the ADMM-based joint optimization method achieves approximately an 8% reduction in shrinkage ratio optimization compared to baseline methods. [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]

Copyright of Journal of Zhengzhou University (Natural Science Edition) is the property of Journal of Zhengzhou University (Natural Science Edition) 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.)

A privacy protection task offloading algorithm combining discrete differential evolution and convex optimization is proposed to resolve the new position privacy leakage problem for mobile terminal due to transferring tasks to multiple edge servers for computation via wireless networks. First, the privacy cost of offloading decision is defined for the characteristics of the position privacy problem combined with the standard deviation theory. Second, the objective problem is modeled as a joint optimization of position privacy protection, offloading decision and transmission power, and the total cost of task offloading is an optimization objective. Finally, to handle the objective problem efficiently, offloading decision variable is decoupled from the transmission power variable to decrease the computational complexity of the problem; the discrete differential evolution algorithm is designed to accumulate the approximate optimal offloading decision, and the optimal transmission power is efficiently solved by variable substitution method and subgradient method. Simulations reveal that the algorithm has excellent convergence; it improves the position privacy protection level of mobile terminal and reduces the total cost of task offloading while maintaining lower energy consumption and latency. [ABSTRACT FROM AUTHOR]

In this study, we address the challenge of efficiently processing cloud robot tasks within cloud-edge collaborative systems by designing a collaborative offloading system. Our focus lies in jointly optimizing system completion time and energy consumption through well-calibrated trade-offs between the two objectives. To this end, we developed a task-offloading model for binary (indivisible) tasks, fostering cooperation between multiple edge and central cloud servers. We employed game theory to strategize offloading of computing tasks to local devices, edge clouds, or central clouds in multi-robot scenarios. An improved game-theoretic algorithm with polynomial time complexity was proposed, transforming the task-offloading challenge into a game that dynamically updates the offloading strategies and identifies the Nash equilibrium of the system to determine the optimal offloader strategy. Simulation experiments validate the effectiveness of our strategy, demonstrating its ability to reduce energy consumption, shorten the completion time of computational tasks, fully utilize system resources, and significantly improve the quality of cloud-edge collaboration services. [ABSTRACT FROM AUTHOR]

With the increasing complexity of applications, many delay-sensitive and compute-intensive services have posed significant challenges to mobile devices. Addressing how to efficiently allocate heterogeneous network resources to meet the computing and delay requirements of terminal services is a pressing issue. In this paper, a new cooperative twin delayed deep deterministic policy gradient and deep-Q network (TD3-DQN) algorithm is introduced to minimize system latency by optimizing computational offloading and caching placement asynchronously. Specifically, the task-partitioning technique divides computing tasks into multiple subtasks, reducing the response latency. A DQN intelligent algorithm is presented to optimize the offloading path to edge servers by perceiving network resource status. Furthermore, a TD3 approach is designed to optimize the cached content in the edge servers, ensuring dynamic popularity content requirements are met without excessive offload decisions. The simulation results demonstrate that the proposed model achieves lower latency and quicker convergence in asymmetrical cloud–edge collaborative networks compared to other benchmark algorithms. [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]

The popularity of wireless communication technology and smart devices make emerging tasks tend to be computationally intensive. Unfortunately, mobile devices are often computationally resource-constrained. Mobile edge computing is proposed to offer computing power for these resource-limited devices to solve the computing requirement of their tasks. The unmanned aerial vehicle (UAV) enabled edge networks are flexible and low-cost, so they are considered to provide more flexible computing service for mobile devices. However, UAV-enabled edge networks are limited by the weak wireless propagation environment. To this end, we introduce intelligent reflecting surface (IRS) into the UAV-enabled edge networks in which IRS is used to construct a stronger link between the mobile devices and the UAV for task offloading. We formulate the IRS-aided offloading problem as an optimization problem to optimize the overall delay by jointly optimizing UAV movement, offloading decision, IRS configuration, and UAV's computation resource. To solve the problem more efficiently, we use the deep reinforcement learning (DRL) model to explore the intelligent action that can minimize the task processing time. Our simulation demonstrates the DRL scheme is more effective compared with the benchmarks. [ABSTRACT FROM AUTHOR]

Going to a stadium to see a match or a race is a thrilling experience since these magnificent structures provide an atmosphere where we can truly immerse ourselves in the competition. In today's world, stadiums play an important role in social gatherings and entertainment areas, which are capable of comfortably sheltering anywhere from 10,000 to more than 100,000 people. While the smart stadium is also sometimes called the "connected" arena with smart navigation, real-time data, better protection for athletes and so on. Smart stadium is the future of sports entertainment with the growing technology of 5G and edge computing (EC). Along with the advancements of 5G and EC, the introduction of Internet of Things (IoT) in sports will promote the use of IoT sensors for enabling preventive maintenance in stadium. Sensors deployed in the stadium can monitor the athlete's health state. Mobile EC (MEC) deploys computing resources at the edge of the network to provide computing tasks for sensors, which effectively improves the network environment and reducing transmission latency. At first, a combined power consumption, bandwidth and computing resource allocation optimization model is proposed. Then, given the limitations of probability mixing in optimization model, Markov inequality is introduced to transform for probability constraints. Finally, task offloading and resource allocation algorithm based on Lyapunov by using 5G network slicing is proposed. The simulation results demonstrate that the proposed algorithms outperform four benchmarks in terms of time-power average return, average end-to-end latency and task processing latency. [ABSTRACT FROM AUTHOR]