关键词： Servers   Wireless communication   Task analysis   Image edge detection   Computational modeling   Energy consumption   Resource management   Mobile edge computing   computation offloading   service caching   personalized service preference   computation latency   sum-utility maximization  

摘要： Mobile edge computing (MEC) has emerged as an attractive solution by executing computation-intensive services at a powerful edge server instead of mobiles. Two types of data are necessary to this end. One is user-specific data acquired from mobiles, called computation offloading (CO). The other is service-specific data downloaded from a central cloud, called service caching (SC). It is noteworthy that CO and SC decisions are coupled when each user's service preference (SP) is personalized. Specifically, noting that the optimal SC is to cache services likely to be requested more frequently, the resultant SC tends to be biased to the SP of the user whose offloading rate is high. On the other hand, such an SC decision causes longer computing latency of users with a relatively low offloading rate, which ultimately limits a CO decision for agile MEC services. This work tackles this issue from a sum-utility maximization perspective under radio-resource and computation-latency constraints. The average computation latency is first derived in closed-form by modeling a computation as a stochastic process following a hyper-exponential distribution. Based on it, we first consider the case for homogeneous SP where CO and SC decisions are decoupled. Thus, SC can be deterministically controlled using the homogeneous SP, while CO decision is independently determined, lying between water-filling and channel-inversion allocations. Next, we design a joint CO-and-SC policy for heterogeneous SP. CO and SC decisions are iteratively optimized with the other fixed by leveraging the homogeneous SP's result. The optimal stopping rules are derived, guaranteeing the sum-utility enhancement. The proposed algorithm's effectiveness is verified by simulations that the proposed CO-and-SC design for heterogenous SP always outperforms that for homogeneous SP.

关键词： Task analysis   Sensors   Internet of Things   Optimization   Batteries   Energy harvesting   Quality of service   Mobile edge computing   energy harvesting   cognitive radio system   online optimization algorithm  

摘要： Mobile edge computing (MEC) has recently become a prevailing technique to alleviate the intensive computation burden in Internet of Things (IoT) networks. However, the limited device battery capacity and stringent spectrum resource significantly restrict the data processing performance of MEC-enabled IoT networks. To address the two performance limitations, we consider in this paper an MEC-enabled IoT system with a wireless device (WD) replenishing its battery by means of energy harvesting (EH) and opportunistically accessing the licensed spectrum of an overlaid primary communication link to offload its sensing data to an MEC server (MS) for edge processing. Under time-varying fading channel, random energy arrivals, and stochastic ON-OFF state of the primary link, we aim to design an online algorithm to jointly control the cognitive data sensing rate and processing method (i.e., local and edge processing) without knowing future system information. In particular, we aim to maximize the long-term average sensing rate of the WD subject to quality of service (QoS) requirement of primary link, average power constraint of MS and data queue stability of both MS and WD. We formulate the problem as a multi-stage stochastic optimization and propose an online algorithm named PLySE that applies the perturbed Lyapunov optimization technique to decompose the original problem into per-slot deterministic optimization problems. For each per-slot problem, we derive the closed-form optimal solution of data sensing and processing control to facilitate low-complexity real-time implementation. Interestingly, our analysis finds that the optimal solution exhibits an threshold-based structure related to the current energy state, secondary queueing backlogs and primary link activity. Simulation results collaborate with our analysis and demonstrate more than 46.7% data sensing rate improvement of the proposed PLySE over representative benchmark methods.

关键词： mobile edge computing   low latency   deep reinforcement learning   partial offloading   resource allocation  

摘要： In the dense data communication environment of 5G wireless networks, with the dramatic increase in the amount of request computation tasks generated by intelligent wireless mobile nodes, its computation ability cannot meet the requirements of low latency and high reliability. Mobile edge computing (MEC) can utilize its servers with mighty computation power and closer to tackle the computation tasks offloaded by the wireless node (WN). The physical location of the MEC server is closer to WN, thereby meeting the requirements of low latency and high reliability. In this paper, we implement an MEC framework with multiple WNs and multiple MEC servers, which consider the randomness and divisibility of arrival request tasks from WN, the time-varying channel state between WN and MEC server, and different priorities of tasks. In the proposed MEC system, we present a decentralized multi-agent deep reinforcement learning-based partial task offloading and resource allocation algorithm (DeMADRL) to minimize the long-term weighted cost including delay cost and bandwidth cost. DeMADRL is a model-free scheme based on Double Deep Q-Learning (DDQN) and can obtain the optimal computation offloading and bandwidth allocation decision-making policy by training the neural networks. The comprehensive simulation results show that the proposed DeMADRL optimization scheme has a nice convergence and outperforms the other three baseline algorithms.

关键词： Dynamic searchable encryption   Mobile edge computing   Oblivious RAM  

摘要： In recent years, edge-cloud computing is regarded as a promising solution to meet the requirements of mobile computing and Internet-of-Things (IoT). However, due to the limited storage resources of edge equipment, there is a security threat when users outsource their sensitive data to the cloud computing center. The users usually adopt a data-encryption approach, Oblivious RAM (ORAM), which enables a user to read/write her outsourced private data without access pattern leakage. Not all users like the fully functional ORAM all the time since the ORAM protocol is usually highly interactive or occupies large edge storage space. We show that forward-private/backward-private (FP/BP) ORAMs are good alternatives for secure storage outsourcing. We introduce the FP/BP-ORAM definitions and present LL-ORAM, the first FP/BP-ORAM that achieves near-zero edge storage, single-round-trip read/write, and worst-case sublinear access time. For any outsourced record, LL-ORAM provides both an oblivious-access interface and a nonoblivious-access interface. FP-ORAM concerns more data-write privacy than data-read privacy. BP-ORAM concerns more data-read privacy. The constructions involve a tree data structure named LL-tree, which supports fast computation in the cloud with an access-pattern-reduced leakage profile. The security analysis shows that LL-ORAM meets the proposed forward and backward security model. The experimental results demonstrate that LL-ORAM is round-efficient and can be deployed on edge-cloud computing systems. (c) 2022 Elsevier Inc. All rights reserved.

关键词： Task analysis   Delays   Cloud computing   Energy consumption   Computational modeling   Heuristic algorithms   Game theory   Computation offloading   mobile edge computing   game theory   task migration   I2I  

摘要： Roadside units (RSUs), which have strong computing capability and are close to vehicle nodes, have been widely used to process delay- and computation-intensive tasks of vehicle nodes. However, due to their high mobility, vehicles may drive out of the coverage of RSUs before receiving the task processing results. In this paper, we propose a mobile edge computing-assisted vehicular network, where vehicles can offload their tasks to a nearby vehicle via a vehicle-to-vehicle (V2V) link or a nearby RSU via a vehicle-to-infrastructure link. These tasks are also migrated by a V2V link or an infrastructure-to-infrastructure (I2I) link to avoid the scenario where the vehicles cannot receive the processed task from the RSUs. Considering mutual interference from the same link of offloading tasks and migrating tasks, we construct a vehicle offloading decision-based game to minimize the computation overhead. We prove that the game can always achieve Nash equilibrium and convergence by exploiting the finite improvement property. We then propose a task migration (TM) algorithm that includes three task-processing methods and two task-migration methods. Based on the TM algorithm, computation overhead minimization offloading (COMO) algorithm is presented. Extensive simulation results show that the proposed TM and COMO algorithms reduce the computation overhead and increase the success rate of task processing.

关键词： mobile edge computing   offloading   wireless power transfer   full-duplex   half-duplex  

摘要： In this paper, we investigate a resource allocation and computation offloading problem in a heterogeneous mobile edge computing (MEC) system. In the considered system, a wireless power transfer (WPT) base station (BS) with an MEC sever is able to deliver wireless energy to the mobile devices (MDs), and the MDs can utilize the harvested energy for local computing or task offloading to the WPT BS or a Macro BS (MBS) with a stronger computing server. In particular, we consider that the WPT BS can utilize full- or half-duplex wireless energy transmission mode to empower the MDs. The aim of this work focuses on optimizing the offloading decision, full/half-duplex energy harvesting mode and energy harvesting (EH) time allocation with the objective of minimizing the energy consumption of the MDs. As the formulate problem has a non-convex mixed integer programming structure, we use the quadratically constrained quadratic program (QCQP) and semi-definite relaxation (SDR) methods to solve it. The simulation results demonstrate the effectiveness of the proposed scheme.

关键词： Wireless communication   Real-time systems   Wireless sensor networks   Servers   Energy consumption   Uplink   Renewable energy sources   SWIPT   mobile edge computing   real-time energy trading   wireless powered MEC system  

摘要： In this brief, we consider a wireless powered mobile edge computing (MEC) system, where the base station (BS) is integrated with a MEC server to support computation offloading. Meanwhile, the BS is equipped with renewable energy resource and can transfer energy to the wireless devices (WDs) and trade energy with the grid. The objective is to minimize the total energy cost of the operator under the offloading delay constraint by jointly optimizing the BS's spot-market energy demand and transmit power as well as users' offloading data size and transmit power. Both the perfect and imperfect channel state information (CSI) are considered. For the former, we propose a low computational-complexity algorithm for solving the formulated problem. For the latter, the non-convex problem is transformed into a convex one by using the successive convex approximation technique, and then an iterative algorithm is proposed. Simulation results verify the effectiveness of our proposed algorithms.

关键词： internet of vehicles   mobile edge computing   edge-cloud collaboration   deep deterministic policy gradient networks   computational offloading  

摘要： With the continuous development of intelligent transportation system technology, vehicle users have higher and higher requirements for low latency and high service quality of task computing. The computing offloading technology of mobile edge computing (MEC) has received extensive attention in the Internet of Vehicles (IoV) architecture. However, due to the limited resources of the MEC server, it cannot meet the task requests from multiple vehicle users simultaneously. For this reason, making correct and fast offloading decisions to provide users with a service with low latency, low energy consumption, and low cost is still a considerable challenge. Regarding the issue above, in the IoV environment where vehicle users race, this paper designs a three-layer system task offloading overhead model based on the Edge-Cloud collaboration of multiple vehicle users and multiple MEC servers. To solve the problem of minimizing the total cost of the system performing tasks, an Edge-Cloud collaborative, dynamic computation offloading method (ECDDPG) based on a deep deterministic policy gradient is designed. This method is deployed at the edge service layer to make fast offloading decisions for tasks generated by vehicle users. The simulation results show that the performance is better than the Deep Q-network (DQN) method and the Actor-Critic method regarding reward value and convergence. In the face of the change in wireless channel bandwidth and the number of vehicle users, compared with the basic method strategy, the proposed method has better performance in reducing the total computational cost, computing delay, and energy consumption. At the same time, the computational complexity of the system execution tasks is significantly reduced.

关键词： Mobile edge computing  

摘要： With the help of 5G and mobile edge computing, business owners, manufacturing facility managers and control engineers are experiencing the benefits of transforming traditional, manual processes into automated, efficient technological solutions. Massive transformation is expected to change the way manufacturing facilities operate with the help of 5G in the coming years as manufacturers consider how leveraging technology.

关键词： Mobile edge computing   Scheduling algorithms   Quality of service   Power consumption   Optimization   Cloud computing  

摘要： Mobile edge computing (MEC) is a promising technology that has the potential to meet the latency requirements of next-generation mobile networks. Since MEC servers have limited resources, an orchestrator utilizes a scheduling algorithm to decide where and when each task should execute so that the quality of service (QoS) of each task is achieved. The scheduling algorithm should use the least possible resources required to meet the service demands. In this paper, we develop a two-level cooperative scheduling algorithm with a centralized orchestrator layer. The first scheduling level is used to schedule tasks locally on MEC servers. In contrast, the second level resides at the orchestrator and assigns tasks to a neighboring base station or the cloud. The tasks serve in accordance with their priority, which is determined by the latency and required throughput. We also present a resource optimization algorithm for determining resource distribution in the system in order to ensure satisfactory service availability at the minimum cost. The resource optimization algorithm contains two variations that can be employed depending on the traffic model. One variant is used when the traffic is uniformly distributed, and the other is used when the traffic load is unbalanced among base stations. Numerical results show that the cooperative model of task scheduling outperforms the non-cooperative model. Furthermore, the results show that the suggested scheduling algorithm performs better than other well-known scheduling algorithms, such as shortest job first scheduling and earliest deadline first scheduling.