关键词： Mobile edge computing   DDPG   GA   Partial offloading   Computation offloading  

摘要： Edge computing, which provides lightweight cloud computing and storage capabilities at the edge of the network, has become a new computing paradigm. A key research challenge for edge computing is to design an efficient offloading strategy for offloading decision-making and resource allocation. Although many researches attempt to address this challenge, the traditional offloading strategies cannot adapt to complex environments, and the offloading strategies based on reinforcement learning require centralized control or the pursuit of the user's best interests, which is impractical. Individual users should rationally pursue benefits in order to create a high-quality offloading environment to obtain long-term benefits. In this paper, we first separate the offloading process into a two-step offloading framework, and reverse the order of solving offloading decision and resource allocation problems to reduce the dimensionality of the action and state space. We formulate the resource allocation as a Markov Decision Process (MDP) and use the Deep Deterministic Policy Gradient Algorithm (DDPG) to adjust load balancing of the edge server and reduce the transmission energy and delay, and then use the genetic algorithm (GA) to search for decisions and use Fully-Connected Network (FCN) to fit the decision-making process, thereby avoiding excessive response time caused by iteration. Simulation results show that compared with baseline methods, the proposed algorithm is more stable, flexible, adaptable and suitable for practical applications. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Blockchains   Throughput   Wireless communication   Servers   Optimization   Security   Resource management   Internet of Things   mobile edge computing   blockchain   transaction throughput  

摘要： The integration of blockchain and mobile edge computing (MEC), as a secure, efficient, and reliable edge computing paradigm, has been widely applied in many applications, such as large-scale Internet of Things (IoT), Internet of Vehicles (IoV), and smart grid. However, due to the restricted transaction throughput of blockchain, the combination of blockchain and MEC in most existing works cannot support applications with frequent transaction requirements. In this paper, we propose an integrated blockchain and MEC (IBM) framework based on a space-structured ledger to meet the transaction demands for IoT applications. In the framework, a collaborative mining process is designed, where we consider the cooperation between mobile devices (MDs) and MEC servers. To promote mining efficiency, we further develop a high-performance consensus mechanism called reputation-based proof of work (Re-PoW), in which differentiated mining targets are assigned according to the reputation of MDs. In the Re-PoW consensus mechanism, heterogeneous capabilities and historical behaviors of MDs are all considered for accurately evaluating their reputation. In addition, we present an alternating optimization algorithm by jointly optimizing bandwidth allocation and computation resource allocation to further enhance the performance of the proposed scheme. Simulation results show that the proposed approach can achieve significant throughput improvement.

关键词： Task analysis   Network topology   Costs   Resource management   Topology   Servers   Dynamic scheduling   Resource allocation   deep Q learning   hierarchical learning   latency   mobile edge computing   offloading   network topology   robustness  

摘要： We consider a resource allocation and offloading decision-making problem in a mobile edge computing (MEC) network. Since the locations of user equipments (UEs) vary over time in practice, we consider a dynamic network, where the UEs could leave or join the network coverage at any location. Since the joint offloading decision that minimizes the network cost also varies with the topology, the expected best offloading decision for the previous topology would not match the new topology. Consequently, the system suffers from recurring cost peaks due to the topology change. Thus, we propose a robust distributed hierarchical online learning approach to enhance the algorithm's robustness and reduce the cost peaks. Specifically, the UEs learn the utility of each offloading decision via deep Q networks (DQNs) from their interaction with the MEC network. Meanwhile, the computational access points (CAPs) train their deep neural networks (DNNs) online with the real-time data collected from the UEs to predict their corresponding Q-value vectors. Therefore, the UEs and CAPs form a hierarchical collaborative-learning structure. When the topology changes, each UE downloads its Q-value vector as the Q-bias vector and learns its difference from the actual Q-value vector via its DQN. With different agents learning distributedly, both the peak and sum costs are reduced as the joint offloading decision could start from a near-local-optimal point. In simulations, our robust approach successfully reduces the peak cost and sum cost by up to 50% and 30%, respectively. This demonstrates the need for a robust learning algorithm design in a practical dynamic MEC network.

关键词： edge service allocation   latency   mobile edge computing   service migration   user mobility  

摘要： As a key technology in the 5G era, mobile edge computing (MEC) has developed rapidly in recent years. MEC aims to reduce the service delay of mobile users, while alleviating the processing pressure on the core network. MEC can be regarded as an extension of cloud computing on the user side, which can deploy edge servers and bring computing resources closer to mobile users, and provide more efficient interactions. However, due to the user's dynamic mobility, the distance between the user and the edge server will change dynamically, which may cause fluctuations in Quality of Service. Therefore, when a mobile user moves in the MEC environment, certain approaches are needed to schedule services deployed on the edge server to ensure the user experience. In this article, we model service scheduling in MEC scenarios and propose a delay-aware and mobility-aware service management approach based on concise probabilistic methods. This approach has low computational complexity and can effectively reduce service delay and migration costs. Furthermore, we conduct experiments by utilizing multiple realistic datasets and use iFogSim to evaluate the performance of the algorithm. The results show that our proposed approach can optimize the performance on service delay, with 8%-20% improvement and reduce the migration cost by more than 75% compared with baselines during the rush hours.

关键词： Resources allocation   Mobile Edge Computing   Futures market   Spot market   Stochastic information  

摘要： In order to deal with the problem of user diversity in Mobile Edge Computing (MEC) resource trading market, in this paper, we propose a hybrid market-based resource transaction mechanism consisting of futures market and spot market. Two different types of users have been taken into consideration. One is registered users and another is unregistered users. In futures market, registered users pay a registration fee to the agent and use the reserved resources according to the contract signed exclusively. We design optimal contracts for the registered users by adjusting the registration fee in order to maximize the servers' utility. In spot market, unregistered users compete with one another to purchase the resources on demand. We model the trading process as a multi-seller and multi-buyer market, and propose auction algorithms to match the asking price from servers and the bidding price from unregistered users by assigning computation resources to the users. The agent acts as the auctioneer to host the auction, and the unregistered users bid on computation resources based on the estimated valuation. We study the optimal solution under both complete and incomplete information scenarios, depending on whether the agent can observe the users' private information. Simulation results demonstrate the existences of the asking price and registration fee for the servers to maximize utility. (C) 2021 Elsevier B.V. All rights reserved.

关键词： Energy consumption   Servers   Task analysis   Turning   Unmanned aerial vehicles   Roads   Edge computing   Deep reinforcement learning   energy efficiency   mobile edge computing   unmanned aerial vehicle relay  

摘要： As the general mobile edge computing (MEC) scheme cannot adequately handle the emergency communication requirements in vehicular networks, unmanned aerial vehicle (UAV)-assisted vehicular edge computing networks (VECNs) are envisioned as the reliable and cost-efficient paradigm for the mobility and flexibility of UAVs. UAVs can perform as the temporary base stations to provide edge services for road vehicles with heavy traffic. However, it takes a long time and huge energy consumption for the UAV to fly from the stay charging station to the mission areas disorderly. In this paper, we design a pre-dispatch UAV-assisted VECNs system to cope with the demand of vehicles in multiple traffic jams. We propose an optimal UAV flight trajectory algorithm based on the traffic situation awareness. The cloud computing center (CCC) server predicts the real-time traffic conditions, and assigns UAVs to different mission areas periodically. Then, a flight trajectory optimization problem is formulated to minimize the cost of UAVs, while both the UAV flying and turning energy costs are mainly considered. In addition, we propose a deep reinforcement learning(DRL)-based energy efficiency autonomous deployment strategy, to obtain the optimal hovering position of UAV at each assigned mission area. Simulation results demonstrate that our proposed method can obtain an optimal flight path and deployment of UAV with lower energy consumption.

关键词： Mobile edge computing   Task offloading   Genetic algorithm   Computing overhead   Allocating schedule  

摘要： As today's Internet of Things (IoT) applications are becoming more complicated and intelligent, IoT devices alone can no longer well support the ever-increasing demand for powerful computation and high energy efficiency. Mobile Edge Computing (MEC) and 5G technology emerge as promising solutions, which enable IoT tasks to be offloaded to edge servers for effective processing. Though desirable, there however exists a mismatch between the massive IoT task workloads and limited wireless bandwidth, making it challenging to achieve an optimal offloading strategy at the mobile edge, e.g., the base station (BS) server. In this paper, we aim to migrate the most suitable offloading tasks to fully obtain the benefits of the MEC task offloading. We first formulate the task offloading model as an optimization problem, and theoretically prove the NP-hardness in achieving the optimal solution. Thus, a Genetic algorithm, named M-COGA, is proposed to solve the task offloading selection in both single and multiple BS scenarios. The algorithm focuses on offloading as many tasks as possible with the maximum cost offloading. The proposed cost function takes into account both the computation overhead and energy consumption. Besides, for the multi-BS coverage scenario, we also consider the approach flexibility as well as link load balance. And an enhanced dynamic task offloading scenario is further discussed. We verify the efficiency of our algorithm under the condition of both uniform and non-uniform distribution of covered nodes. Numerical experiments demonstrate that our dynamic allocating scheme can effectively work in MEC offloading. Besides, it largely outperforms the single BS scenarios and reduces the cost of edge devices.

关键词： mobile crowdsensing   Bloom filter   multi-access edge computing   mobile edge computing  

摘要： Mobile crowdsensing (MCS) is a sensing paradigm that allows ordinary citizens to use mobile and wearable technologies and become active observers of their surroundings. MCS services generate a massive amount of data due to the vast number of devices engaging in MCS tasks, and the intrinsic mobility of users can quickly make information obsolete, requiring efficient data processing. Our previous work shows that the Bloom filter (BF) is a promising technique to reduce the quantity of redundant data in a hierarchical edge-based MCS ecosystem, allowing users engaging in MCS tasks to make autonomous informed decisions on whether or not to transmit data. This paper extends the proposed BF algorithm to accept multiple data readings of the same type at an exact location if the MCS task requires such functionality. In addition, we thoroughly evaluate the overall behavior of our approach by taking into account the overhead generated in communication between edge servers and end-user devices on a real-world dataset. Our results indicate that using the proposed algorithm makes it possible to significantly reduce the amount of transmitted data and achieve energy savings up to 62% compared to a baseline approach.

关键词： Computation offloading   mobile edge computing   nash equilibrium   non-cooperative game   unmanned aerial vehicles  

摘要： In addition to the stationary mobile edge computing (MEC) servers, a few MEC surrogates that possess a certain mobility and computation capacity, e.g., flying unmanned aerial vehicles (UAVs) and private vehicles, have risen as powerful counterparts for service provision. In this article, we design a two-stage online scheduling scheme, targeting computation offloading in a UAV-assisted MEC system. On our stage-one formulation, an online scheduling framework is proposed for dynamic adjustment of mobile users' CPU frequency and their transmission power, aiming at producing a socially beneficial solution to users. But the major impediment during our investigation lies in that users might not unconditionally follow the scheduling decision released by servers as a result of their individual rationality. In this regard, we formulate each step of online scheduling on stage one into a non-cooperative game with potential competition over the limited radio resource. As a solution, a centralized online scheduling algorithm, called ONCCO, is proposed, which significantly promotes social benefit on the basis of the users' individual rationality. On our stage-two formulation, we are working towards the optimization of UAV computation resource provision, aiming at minimizing the energy consumption of UAVs during such a process, and correspondingly, another algorithm, calledWS-UAV, is given as a solution. Finally, extensive experiments via numerical simulation are conducted for an evaluation purpose, by which we show that our proposed algorithms achieve satisfying performance enhancement in terms of energy conservation and sustainable service provision.

关键词： Mobile Edge Computing   Computing and Relaying   Block Coordinate Descent   P2P Communication  

摘要： As a promising technique to support tremendous numbers of Internet of Things devices and a variety of applications efficiently, mobile edge computing (MEC) has attracted extensive studies recently. In this paper, we consider a MEC-assisted peer-to-peer (P2P) mobile communication system where MEC servers are deployed at access points to accelerate the communication process between mobile terminals. To capture the tradeoff between the time delay and the energy consumption of the system, a cost function is introduced to facilitate the optimization of the computation and communication resources. The formulated optimization problem is non-convex and is tackled by an iterative block coordinate descent algorithm that decouples the original optimization problem into two subproblems and alternately optimizes the computation and communication resources. Moreover, the MEC-assisted P2P communication system is compared with the conventional P2P communication system, then a condition is provided in closed-form expression when the MEC-assisted P2P communication system performs better. Simulation results show that the advantage of this system is enhanced when the computing capability of the receiver increases whereas it is reduced when the computing capability of the transmitter increases. In addition, the performance of this system is significantly improved when the signal-to-noise ratio of hop -1 exceeds that of hop -2.