关键词： Internet of Things (IoT)   wireless sensor network   optimization algorithm   mobile edge computing  

摘要： The number of sensor nodes in the sensor network is massive, and the network running time is relatively long, with a large number of data sampled, forming the big data of the sensor network. As the energy and communication capacities of the sensor nodes are highly limited, the design of an effective optimization algorithm for the wireless transmission of distributed cable tunnel data and the application of the mobile edge computing method to accomplish the task of big data transmission efficiently has become a major challenge faced by the wireless sensor network design. In this paper, the mobile edge computing method is used to estimate the packet reception ratio (PRR) of the wireless link, and the average number of data transmission times in the wireless link is calculated based on the reception rate. On this basis, a mobile edge computing method is proposed, which can determine the optimal path from all sensor nodes to sink nodes, so as to ensure the minimum sum of the average link transmission times in the optimal path. According to the distributed environment of the sensor network, the mobile edge computing method is improved. The distributed implementation protocol of the mobile edge computing method is proposed to identify the optimal path with the smallest average number of transmission times. The smallest number of transmission times means the minimum energy consumption. This study is of important practical significance for the wireless sensor network with limited energy. Finally, the experiment has verified that the optimization model for the wireless transmission of the distributed cable tunnel data in this paper has relatively high performance and feasibility.

关键词： Autonomous aerial vehicles   Blockchains   Security   Computer architecture   Privacy   Multi-access edge computing   Collaborative work   Unmanned aerial vehicles   mobile edge computing   federated learning   blockchain   privacy  

摘要： Unmanned aerial vehicles (UAVs) extend the traditional ground-based Internet of Things (IoT) into the air. UAV mobile edge computing (MEC) architectures have been proposed by integrating UAVs into MEC networks during the current novel coronavirus disease (COVID-19) era. UAV mobile edge computing (MEC) shares personal data with external parties (such as edge servers) during intelligent medical analytics. However, this technique raises privacy concerns about patients' health data. More recently, the concept of federal learning (FL) has been set up to protect mobile user data privacy. Compared to traditional machine learning, federated learning requires a decentralized distribution system to enhance trust for UAVs. Blockchain technology provides a secure and reliable solution for FL settings between multiple untrusted parties with anonymous, immutable, and distributed features. Therefore, blockchain-enabled FL provides both theories and techniques to improve the performance of intelligent UAV edge computing networks from various perspectives. This survey begins by discussing the current state of research on blockchain and FL. Then, compare the leading technologies and limitations. Second, we will discuss how to integrate blockchain and FL into UAV edge computing networks and the associated challenges and solutions. Finally, we discuss the fundamental research challenges and future directions.

关键词： Task analysis   Energy consumption   Servers   Resource management   Costs   Wireless communication   Optimization   Computation offloading   deep reinforcement learning   energy efficiency   mobile edge computing   resource allocation  

摘要： Mobile edge computation offloading (MECO) has recently emerged as a promising method to support computation-intensive and latency-sensitive applications, significantly saving the battery energy of smart mobile devices (SMDs). However, on the one hand, the energy consumption depends on both the SMD and the MEC server, which makes it necessary to consider these two entities to achieve energy sustainability jointly. On the other hand, for a real-time mobile edge computing (MEC) system, efficient optimization algorithms based on binary offloading have received significant attention, while efficient algorithms for partial offloading under time-varying channels are seldom investigated. In this paper, we propose an energy-efficient algorithm based on deep reinforcement learning to optimize the overall energy cost in a real-time multi-user MEC system. We decompose the energy minimization problem into two sub-problems, where a deep neural network learns the optimal mapping between wireless channels and offloading ratios, and a closed-form solution for the optimal local frequency and a convex optimization algorithm are used to solve the resource allocation sub-problem. Finally, the extensive experiments demonstrate the effectiveness of our proposed algorithm in reducing the total energy consumption of the MECO system against several offloading schemes and achieving low processing latency fit to the time-varying wireless channels.

关键词： Lyapunov theory   mobile edge computing   task offloading   evolutionary algorithm  

摘要： Mobile edge computing (MEC) is a novel technique that can reduce computational burden of local terminal devices by tasks offloading, which emerges as a promising paradigm to provide computing capabilities near mobile users. Furthermore, the computing power consumption and battery capacity in the terminal devices is generally limited in MEC, which directly affect the quality of service (QoS), which brings new challenge in achieving the stability of the system. The trade-off between delay and energy consumption of task offloading process under multi-user MEC scenario is in general a complicated problem that highly correlates to the computation offloading decisions of computation tasks, i.e., whether or not to offload a task for edge execution. To address the above issues, the task offloading decision to solve the queuing problem of tasks to be processed in the local terminals was being considered, with an application of Lyapunov theory to ensure the queue stability in this paper. Then, a trade-off model was formulated to minimize the delay and energy consumption to achieve a minimum execution cost. Moreover, an improved Dynamic Niche-based Self-organizing Learning Algorithm was presented to accelerate the speed of the search process to gain an optimal task offloading scheme. The simulation results provided supporting evidence that the proposed optimal scheme IDO could achieve a lower average energy consumption than LFO and a lower average execution delay than EFO under the scenarios of the waiting queue length varies within [10, 50], the number of users varies within [4, 20], and the number of tasks varies within [20, 100].

关键词： Optimization   Approximation algorithms   Search problems   Multi-access edge computing   Linear programming   Internet of Things   Clustering algorithms   Differential evolution   mobile edge computing   successive convex approximation   unmanned aerial vehicles  

摘要： UAV-enabled mobile edge computing (MEC) is an emerging technology to support resource-intensive yet delay-sensitive applications with edge clouds (ECs) deployed in the proximity to mobile users and UAVs served as computing base stations in the air. The formulated optimization problems therein are highly nonconvex and thus difficult to solve. To tackle the nonconvexity, the successive convex approximation (SCA) technique has been widely used to solve for the nonconvex optimization problems by transforming the nonconvex objective functions and constraints into suitable convex surrogates. However, the optimal solutions are based on the approximated optimization problem not the original one and they are highly dependent on the feasible solution initialization. Unlike SCA, Differential Evolution (DE) is a global optimization method that iteratively updates the best candidate solutions with respect to the predefined objective functions. DE works well especially in unconstrained optimization problems since it can freely search very large regions of possible solutions without considering the convexity of the original problem. However, when it comes to the constrained optimization problem, DE becomes inefficient to find the feasible and optimal solutions within given time limits. In view of the shortcomings incurred in both DE and SCA, we propose an innovative algorithm by jointly applying DE and SCA (DE-SCA) to solve for the nonconvex optimization problems. However, directly using full DE solutions to initialize the SCA-based algorithm will result in worse objective function values as the DE solutions are often infeasible. Therefore, we further design to screen the feasible parts from the DE solutions and utilize them to initialize the SCA-based algorithm. In experimental simulations, we consider a system of UAV-enabled MEC where IoT devices, the UAV and ECs interact with each other. The simulation results demonstrate that our proposed Screened DE-SCA algorithm largely outp

关键词： Data models   Convergence   Training   Servers   Computational modeling   Probabilistic logic   Predictive models   Federated learning   mobile edge computing   fast convergence   node selection  

摘要： Federated Learning (FL) is a distributed learning paradigm that enables a large number of resource-limited nodes to collaboratively train a model without data sharing. The non-independent-and-identically-distributed (non-i.i.d.) data samples invoke discrepancies between the global and local objectives, making the FL model slow to converge. In this paper, we proposed Optimal Aggregation algorithm for better aggregation, which finds out the optimal subset of local updates of participating nodes in each global round, by identifying and excluding the adverse local updates via checking the relationship between the local gradient and the global gradient. Then, we proposed a Probabilistic Node Selection framework (FedPNS) to dynamically change the probability for each node to be selected based on the output of Optimal Aggregation. FedPNS can preferentially select nodes that propel faster model convergence. The convergence rate improvement of FedPNS over the commonly adopted Federated Averaging (FedAvg) algorithm is analyzed theoretically. Experimental results demonstrate the effectiveness of FedPNS in accelerating the FL convergence rate, as compared to FedAvg with random node selection.

关键词： Servers   Resource management   Throughput   Stochastic processes   Optimization   Edge computing   Computational modeling   Mobile edge computing   user allocation   Lyapunov optimization   resource allocation  

摘要： Mobile edge computing (MEC) is a new distributed computing paradigm where edge servers are deployed at, or near cellular base stations in close proximity to end-users. This offers computing resources at the edge of the network, facilitating a highly accessible platform for real-time, latency-sensitive services. A typical MEC environment is highly stochastic with random user arrivals and departures over time. Here, we address the user allocation problem from a service provider's perspective, who needs to allocate its users to the cloud or edge servers in a specific area. A user, who has a multi-dimensional resource requirement, can be allocated to either the remote cloud, which incurs a high latency, or an edge server, which results in a low latency but might require the user to wait in a queue. This article aims to achieve a controllable trade-off between performance (throughput) and several associated costs such as queuing delay and latency costs. We model this problem as a stochastic optimization problem, propose SUAC (Stochastic User AlloCation) - an online Lyapunov optimization-based algorithm, and prove its performance bounds. The experimental results demonstrate that SUAC outperforms existing approaches, effectively allocating users with a desired trade-off while keeping the system strongly stable.

关键词： Task analysis   Quality of experience   Stochastic processes   Energy consumption   Wireless communication   Power control   Optimization   Computation offloading   mobile edge computing   resource allocation  

摘要： As a new computing paradigm, mobile edge computing (MEC) enables users to execute computation-intensive tasks at the network edge nodes (ENs) through computation offloading. Energy consumption of computation offloading is envisioned as a significant metric to satisfy the high quality of experience (QoE). In multi-ENs MEC networks, computation scheduling and power control of each user is tightly coupled with task offloading. Moreover, due to the stochastic task arrivals and unknown wireless channel conditions, it is challenging to allocate resources for efficient offloading without prior information of tasks and channels. In this paper, we propose an individualized utility metric of each user. We formulate the problem of computation scheduling and power control of each user as a stochastic optimization problem. We aim to maximize the long-term averaged utility quality of all users by jointly optimizing the computation scheduling, task-partition factor and power control. We use Lyapunov optimization technique to convert the long-term stochastic problem into a series of deterministic sub-problems in each time slot. We propose an online algorithm for utility quality maximization (OAUQM). The asymptotic optimality and queue stability of our algorithm are analyzed. Experimental simulations are conducted to evaluate the performance of the proposed algorithm against the benchmark offloading algorithms in terms of utility quality and energy consumption.

关键词： Servers   Cloud computing   Resource management   Quality of service   Task analysis   Internet of Things   Delays   Mobile edge computing   cloud computing   data center hierarchy   mixed integer linear programming   genetic algorithm   particle swarm optimization  

摘要： Mobile Edge Computing (MEC) extends Cloud Computing to the edge of the network, creating a hierarchy of data centers. This paradigm provides computing capacity close to final users, relieves backhaul and the leading network, and serves latency-sensitive applications. When providing computing services at different network levels, it becomes necessary to carry out a more efficient distribution of the resources that come to coexist. A random allocation of these resources can lead to a low service acceptance rate and backhaul overhead problems. Problems like these can be solved with MEC. To maximize the service acceptance and ensure a fair distribution of services in the kinds of servers guaranteeing their quality of service requirements, we propose a MILP (Mixed Integer Linear Programming). The model performs an optimal allocation of applications in a two-level hierarchy of data centers: (i) MEC and (ii) cloud computing. On a large scale, the use of MILP becomes unfeasible due to the high computational cost, so we propose a heuristic based on the application profiles. We compare the proposed heuristic with two metaheuristics: (i) Genetic Algorithm and (ii) Particle Swarm Optimization. The solutions are compared in terms of service acceptance rate, large-scale performance, and efficient use of available resources. Results show that the proposed heuristic reaches 91% of the optimal solution and over 140% compared to GA and PSO solutions.

关键词： Task analysis   Privacy   Servers   Optimization   Reinforcement learning   Differential privacy   Delays   Mobile edge computing   task offloading   differential privacy   reinforcement learning  

摘要： Mobile edge computing has been widely used in various IoT devices due to its excellent computing power and good interaction speed. Task offloading is the core of mobile edge computing. However, most of the existing task offloading strategies only focus on improving the unilateral performance of MEC, such as security, delay, and overhead. Therefore, focus on the security, delay and overhead of MEC, we propose a task offloading strategy based on differential privacy and reinforcement learning. This strategy optimizes the overhead required for the task offloading process while protecting user privacy. Specifically, before task offloading, differential privacy is used to interfere with the user's location information to avoid malicious edge servers from stealing user privacy. Then, on the basis of ensuring user privacy and security, combined with the resource environment of the MEC network, reinforcement learning is used to select appropriate edge servers for task offloading. Simulation results show that our scheme improves the performance of MEC in many aspects, especially in security and resource consumption. Compared with the typical privacy protection scheme, the security is improved by 7%, and the resource consumption is reduced by 9% compared with the typical task offloading strategy.