关键词： Remote sensing image analysis   Image fusion   Mobile edge computing   Forest tourism   Ubiquitous cloud   Prediction model  

摘要： With the development of society and the improvement of people's living standards, ecotourism based on forest ecological environment has become an urgent need of urban and rural people. Forest leisure tourism is different from traditional sightseeing tourism. The differences between them are not only reflected in consumption purpose, consumption behavior, consumption grade and consumption form, but also reflected in the differences between tourism products, service system and service quality demand. Therefore, in order to develop forest leisure tourism, we must first fully understand the objective status quo of the leisure tourism market and the real needs of the market, and correctly judge the basic characteristics of the current forest leisure tourism market. At present, the competition of tourism market is upgraded from the competition of tourism price and tourism product to the competition of tourism brand. However, the resource utilization efficiency of forest park tourism is insufficient, and the internal management and external marketing are difficult to adapt to the changes of the market, thus the image and attraction of forest park tourism need to be improved urgently. Therefore, based on remote sensing image analysis and neural computing model, this paper constructs a forest ecotourism evaluation scheme. We designed the novel cloud-based mobile edge computing model to construct the efficient scenario for the prediction. The experimental results show that the model proposed in this paper can effectively evaluate the development plan of forest eco-tourism.

关键词： Task analysis   Energy consumption   Resource management   Collaboration   Servers   Optimization   Delays   Computation offloading   service caching   mobile edge computing   deep deterministic policy gradient  

摘要： Mobile Edge Computing (MEC) meets the delay requirements of emerging applications and reduces energy consumption by pushing cloud functions to the edge of the networks. Service caching is to cache application services and related databases at Edge Servers (ESs) in advance, and then ESs can process the relevant computation tasks. Due to the limited resources in the ESs, how to determine an effective service caching strategy is very crucial. In addition, the heterogeneity of ESs makes it impossible to make full use of the computing and caching resources without considering the collaboration among ESs. This paper considers a joint optimization of computation offloading, service caching, and resource allocation in a collaborative MEC system with multi-users, and formulates the problem as Mixed-Integer Non-Linear Programming (MINLP) which aims at minimizing the long-term energy consumption of the system. To solve the optimization problem, a Deep Deterministic Policy Gradient (DDPG) based algorithm is proposed for determining the strategies of computation offloading, service caching, and resource allocation. Simulation results demonstrate that the proposed DDPG based algorithm can reduce the long-term energy consumption of the system greatly, and can outperform some other benchmark algorithms under different scenarios.

关键词： Mobile edge computing   Computation offloading   Adaptive neighborhood search   Simulated annealing algorithm   Multiuser collaborative model  

摘要： The combination of idle computing resources in mobile devices and the computing capacity of mobile edge servers enables all available devices in an edge network to complete all computing tasks in coordination to effectively improve the computing capacity of the edge network. This is a research hotspot for 5G technology applications and integrating collaborative computing techniques into edge computing. Previous research has focused on the minimum energy consumption and/or delay to determine the formulation of the computational offloading strategy but neglected the cost required for the computation of collaborative devices (mobile devices, mobile edge servers, etc.);therefore, we propose a cost-based collaborative computation offloading model. In this model, when a task requests these devices' assistance in computing, it needs to pay the corresponding calculation cost;and on this basis, the task is offloaded and computed. In addition, for the model, we propose an adaptive neighborhood search based on simulated annealing algorithm (ANSSA) to jointly optimize the offloading decision and resource allocation with the goal of minimizing the sum of both the energy consumption and calculation cost. The adaptive mechanism enables different operators to update the probability of selection according to historical experience and environmental perception, which makes the individual evolution have certain autonomy. A large number of experiments conducted on different scales of mobile user instances show that the ANSSA can obtain satisfactory time performance with guaranteed solution quality. The experimental results demonstrate the superiority of the mobile edge computing (MEC) offloading system. It is of great significance to strike a balance between maintaining the life cycle of smart mobile devices and breaking the performance bottleneck of MEC servers.

关键词： Task offloading   Queuing theory   Industrial Internet of things   Multi -agent deep reinforcement learning   Mobile edge computing  

摘要： In the Industrial Internet of Things (IIoT), there exist numerous sensor devices with weak computing power and small energy storage. To meet the real-time and big data computing requirements of industrial production, EIIoT (Edge computing-based IIoT) that combines mobile edge computing with IIoT has emerged. It is necessary to offload computing tasks to nearby edge servers for data storage and processing in EIIoT, thus inevitably causing the edge servers to overload. To this end, we propose a jointly constrained optimization model of delay and energy consumption based on queuing theory;this model can effectively solve the task offloading problem in EIIoT. Subsequently, to satisfy the unique offloading requirements of EIIoT, we improve the MAPPO (multi agent proximal policy optimization) algorithm structure to form a lightweight optimal task offloading algorithm called Multi-Agent Deep Reinforcement Learning based on Queuing theory (MAQDRL), which is more suitable for EIIoT. In the algorithm, we systematically integrate queuing theory and use Multi-Agent Deep Reinforcement Learning (MADRL) to obtain the optimal offloading strategy in dynamic and random multiuser offloading environments. We also improve the structure of neural networks of MADRL by analyzing the structural characteristics of the input data. As a result, the algorithm that we proposed exhibits good convergence and exceptional performance in terms of the task arrival rate, bandwidth, energy consumption, latency and other indicators. The simulation results indicate that compared with other classical algorithms, MAQDRL is effective for solving the EIIoT offloading problem. (c) 2023 Elsevier Inc. All rights reserved.

关键词： mobile edge computing   network slicing   in-vehicle task offloading   deep reinforcement learning  

摘要： With the surge in tasks for in-vehicle terminals, the resulting network congestion and time delay cannot meet the service needs of users. Offloading algorithms are introduced to handle vehicular tasks, which will greatly improve the above problems. In this paper, the dependencies of vehicular tasks are represented as directed acyclic graphs, and network slices are integrated within the edge server. The Dynamic Selection Slicing-based Offloading Algorithm for in-vehicle tasks in MEC (DSSO) is proposed. First, a computational offloading model for vehicular tasks is established based on available resources, wireless channel state, and vehicle loading level. Second, the solution of the model is transformed into a Markov decision process, and the combination of the DQN algorithm and Dueling Network from deep reinforcement learning is used to select the appropriate slices and dynamically update the optimal offloading strategy for in-vehicle tasks in the effective interval. Finally, an experimental environment is set up to compare the DSSO algorithm with LOCAL, MINCO, and DJROM, the results show that the system energy consumption of DSSO algorithm resources is reduced by 10.31%, the time latency is decreased by 22.75%, and the ratio of dropped tasks is decreased by 28.71%.

关键词： Mobile edge computing   Cooperative MEC   Multi-UAV flight trajectory optimization  

摘要： In this paper, a cooperative MEC system with multi-UAV and a ground access point (AP) is considered, in which UAVs can act as both a computing platform to help Internet of Things devices (IoTDs) deal with their computing tasks and a relay platform to offload some of the task data from IoTDs to the AP with higher computing ability. We aim to minimize the weighted overall energy consumption of UAVs and IoTDs by jointly optimizing connection scheduling, CPU frequency, task offloading bits and the flight trajectory of UAVs. The formulated problem is a Mixed-Integer Nonlinear Programming (MINLP) problem, which is hard to solve. To tackle this problem, we divided it into three sub-problems and resolved them iteratively by the Lagrangian dual method and succession convex approximation (SCA) technique. Finally, an alternately iterative optimization algorithm is proposed. The numerical results show that our proposed algorithm has better performance compared to other benchmark algorithms.& COPY;2023 Elsevier B.V. All rights reserved.

关键词： Mobile edge computing   Mobility management   Handover security   Deep reinforcement learning  

摘要： The experience of users in Mobile Edge Computing (MEC) highly depends on data offloading whose major bottle neck is the design of handover scheme. Existing research advocates that offloading security and quality of service (QoS) should be both considered for an optimal handover. However, recent studies concentrate on improving QoS while neglect security. Due to the complexity of MEC scenarios, generating an optimal handover scheme improving both QoS and security remains a challenge. To solve this problem, we propose SECHO, a secure handover scheme for blockchain-based single-user mobile edge computing system. Then, we design a security policy selection algorithm of offloading tasks to guarantees security performance and controls overhead. Finally, we customize the neural network for a deep reinforcement learning algorithm to realize optimal handover, where a security policy selection method is engaged to collaboratively enhance offloading QoS while decreasing underlying risks. Simulation results show that SECHO leads a 29.6%-302% advantage in overall performance than existing approaches. It proves that our proposal is effective to achieve secure handover while not degrading offloading QoS.

关键词： Task analysis   Delays   Vehicle dynamics   Computational modeling   Bandwidth   Heuristic algorithms   Roads   Cloud computing   computation offloading   deep reinforcement learning (DRL)   Internet of Vehicles (IoV)   mobile edge computing   resource allocation  

摘要： Mobile edge computing (MEC) and cloud computing (CC) have been considered as the key technologies to improve the task processing efficiency for Internet of Vehicles (IoV). In this article, we consider a random traffic flow and dynamic network environment scenario where MEC and CC are collaborated for processing delay-sensitive and computation-intensive tasks in IoV. We study the joint optimization of computation offloading and resource allocation (CORA) with the objective of minimizing the system cost of processing tasks subject to the processing delay and transmission rate constraints. To attack the challenges brought by the dynamic environment, we use the Markov decision process model for formulating the dynamic optimization problem, and apply a deep reinforcement learning (DRL) technique to deal with high-dimensional and continuous states and action spaces. Then, we design a CORA algorithm, which is able to effectively learn the optimal scheme by adapting to the network dynamics. Extensive simulation experiments are conducted, in which we compare the CORA algorithm with both non-DRL algorithms and DRL algorithms. The experimental results show that the CORA algorithm outperforms others with excellent training convergence and performance in processing delay and processing cost.

关键词： Mobile edge computing   Task offloading   Diverse tasks   Performance measures   Improved PHR algorithm  

摘要： With the rapid development for the technology of Mobile edge computing (MEC), tasks tend to be more diversified and personalized, but fewer scholars considered differentiated quality of service requirements from diverse tasks in task offloading studies. In order to guarantee the real-time performance of latency-sensitive tasks and the throughput of latency-tolerant tasks, we propose a task offloading strategy in a MEC system with a two-tier edge structure. We establish a system model composed of a local model and an edge model to capture the workflow of tasks based on our proposed task offloading strategy, and we derive the key performance measures in terms of the average delay of a latency-sensitive task, the average delay of a latency-tolerant task, the utility of MBS Cluster I and the average power of the MEC system. We carry out experiments with analysis and simulation out to evaluate the long-term performance and validate the effectiveness of our proposed task offloading strategy. Finally, by trading off the average delay of a task and the average power of the MEC system, we formulate an optimization problem with inequality constraints for the average delay of a latency-sensitive task and the stability conditions of the MEC system. Furthermore, we develop an improved Powell-Hestenes-Rockafellar algorithm based on Lagrangian multiplier method to jointly optimize the offloading probabilities of the two types of tasks.

关键词： Mobile edge computing   quality of experience   bisection searching   sub-channel matching  

摘要： Most of existing works related to mobile edge computing (MEC) focused on the total energy consumption or task completion time. However, for the applications with multiple players as a whole, the diverse battery levels of players should be considered to achieve better quality of experience (QoE), which is reflected in the metrics of task completion time and the lifespan of service. In this paper, we focus on QoE and diversity of devices. By jointly considering the individual battery level of different devices, we formulate an optimization problem to minimize the task completion time of the system. Firstly, we propose two necessary conditions to achieve the optimal solution of the formulated problem. In addition, we transform the formulated problem into another problem, which can be used to judge whether a given value is feasible for the formulated problem based on the proposed optimal conditions and bisection searching (BSS) algorithm. Then, we propose a method named sub-channel (SC) matching based on virtual computation resource (SMVCR) to solve the transformed problem with low computational complexity. Simulation results indicate that the BSS-SMVCR algorithm has a good performance in task completion time compared with some existing algorithms, and also can significantly extend the lifespan of the corresponding service, which is reflected in the supporting number of offloading tasks.