关键词： Mobile edge computing   multi-hop cooperative offloading   trust propagation   completion latency variability  

摘要： Cooperative offloading in mobile edge computing enables resource-constrained edge clouds to help each other with computation-intensive tasks. However, the power of such offloading could not be fully unleashed, unless trust risks in collaboration are properly managed. As tasks are outsourced and processed at the network edge, completion latency usually presents high variability that can harm the offered service levels. By jointly considering these two challenges, we propose OLCD, an Online Learning-aided Cooperative offloaDing mechanism under the scenario where computation offloading is organized based on accumulated social trust. Under co-provisioning of computation, transmission, and trust services, trust propagation is performed along the multi-hop offloading path such that tasks are allowed to be fulfilled by powerful edge clouds. We harness Lyapunov optimization to exploit the spatial-temporal optimality of long-term system cost minimization problem. By gap-preserving transformation, we decouple the series of bidirectional offloading problems so that it suffices to solve a separate decision problem for each edge cloud. The optimal offloading control can not materialize without complete latency knowledge. To adapt to latency variability, we resort to the delayed online learning technique to facilitate completion latency prediction under long-duration processing, which is fed as input to queued-based offloading control policy. Such predictive control is specially designed to minimize the loss due to prediction errors over time. We theoretically prove that OLCD guarantees close-to-optimal system performance even with inaccurate prediction, but its robustness is achieved at the expense of decreased stability. Trace-driven simulations demonstrate the efficiency of OLCD as well as its superiorities over prior related work.

关键词： Mobile edge computing   Low load base station group   Distributed task offload strategy   Game theory   Communication resources   Computing resources  

摘要： Due to the limited computing resources and battery capacity of existing mobile devices, it cannot meet the requirements of low load base station group for computing capacity and delay, and the emergence of mobile edge computing (MEC) technology provides the possibility for it. Therefore, a distributed task unloading strategy to low load base station group under MEC environment is proposed. Firstly, the communication resource, computing resource and task queue of low load base station group are modeled to quantify the energy cost in the process of task unloading. Then, the game theory is introduced, and the potential game model is used to solve the problem of distributed task unloading. The target function of energy optimization based on delay limitation is transformed into the potential game equation, and the mobile device selects MEC nodes according to the game results to calculate the unloading. Finally, based on the MATLAB platform, the algorithm is simulated, and the results show that the proposed potential game equation can converge to the Nash equilibrium. Compared with other algorithms, the proposed distributed task unloading algorithm can effectively save the energy consumption of task unloading.

关键词： Mobile edge computing   Soft time window   Offloading   Biogeography-based optimization algorithm  

摘要： Task offloading is a core function of mobile edge computing to make up shortage of energy in mobile terminal. Scholars deploy techniques including cloud servers to improve offloading process with hard time window constraints, as there is strict deadline, which is exclusive to the flexibility of users facing task time-out. To increase the flexibility, this paper examines the time-varying features of users' tolerance as the soft time windows perform. Pay of task processing varies because of completion time, which is a combinatorial optimization problem. This paper proposes a Hybrid Genetic Algorithm and Biogeography-Based Optimization (HGABBO) offloading algorithm. The experiments show that the proposed algorithm is effective to multitask processes and outperforms the Artificial Fish algorithm and Ant System algorithm.

关键词： Mobile edge computing   Game theory   Computing offloading   Resource allocation   IoT device   Nash equilibrium  

摘要： With the explosive growth of Internet of Things (IoT) devices and the popularization of intelligent terminals, the computing resources of various new applications are intensive, and existing work offloads them to mobile edge centers for computing. However, due to limitations in the coverage and resource capabilities of edge devices, there is still a problem of unbalanced load in the network. Therefore, in order to balance the load reasonably, this paper proposes a computing offloading and resource allocation algorithm for IoT devices using game theory in Mobile Edge Computing (MEC). Firstly, a multi-user multi-MEC computing offloading and resource allocation model is designed, which manages a large number of devices by multiple base stations and multiple MEC partitions, and models the system including computing model and task model. Then, a prediction method of MEC server waiting time is proposed according to the task queue in base stations. The task processing sequence, time and frequency are arranged to improve the calculation efficiency of this algorithm, which makes rational use of computing resources for devices and MEC. Finally, the problem of multi-user and multi-MEC computing offloading and resource allocation is transformed into a multi-user game problem, and it is concluded that there is always a Nash equilibrium in this game problem. Furthermore, the optimal solution of this problem, that is, the optimal device computing offloading and resource allocation scheme, is obtained when the system is in Nash equilibrium state. The simulation results based on MATLAB platform show that our proposed algorithm is effective compared with other algorithms. It can converge reliably and has obvious optimization in energy consumption and time delay;besides the performance of this algorithm is improved.

关键词： Mobile edge computing  

摘要： Social cognition requires neural processing, yet a unifying method linking particular brain activities and social behaviors is lacking. Here, we embedded mobile edge computing (MEC) and light emitting diodes (LEDs) on a neurotelemetry headstage, such that a particular neural event of interest is processed by the MEC and subsequently an LED is illuminated, allowing simultaneous temporospatial visualization of that neural event in multiple, socially interacting mice. As a proof of concept, we configured our system to illuminate an LED in response to gamma oscillations in the basolateral amygdala (BLA gamma) in freely moving mice. We identified (i) BLA gamma responses to a spider robot, (ii) affect-related BLA gamma during conflict, and (iii) formation of defensive aggregation under a threat by the robot, and reduction of BLA gamma responses in the inner-located mice. Our system can provide an intuitive framework for examining brain-behavior connections in various ecological situations and population structures.

关键词： Bi-objective Optimization   Computation Offloading   Energy   Heuristic   Mobile Edge Computing   Multi-task   Processing time  

摘要： This article describes a processing time, energy and computing resources optimization in a Mobile Edge Computing (MEC). We consider a mobile user MEC system, where a smart mobile device (SMD) demands computation offloading to a MEC server. For that, we consider a SMD contains a set of heavy tasks that can be offloadable. The formulated optimization problem takes into account both the dedicated energy capacity and the processing times. We proposed a heuristic solution schema. To evaluate our solution, we realized a range of simulation experiments. The results obtained in terms of treatment time and energy consumption are very. © 2020. All Rights Reserved.

关键词： Edge computing   Mobile edge computing   Elliptic curve cryptography   Key agreement   Security   Privacy  

摘要： Mobile Edge Computing (MEC) is an emergent paradigm that brings computation, storage, and network resources close to mobile users. It allows applications to execute near the user, which are delay-sensitive and content-aware. Since the MEC systems comprise distributed edge devices managed by self-governing people, they are more liable to security and privacy threats. Therefore, acure and efficient authentication mechanism is required for MEC. In this paper, we construct a mutual authentication and key agreement scheme without the involvement of a trusted third party for MEC, which ensures mutual authentication between a user and edge servers and generates a secure session key. This scheme achieves important security attributes such as secure communication, user anonymity in mutual authentication and session key agreement. The performance analysis of the proposed protocol indicates better efficiency. Moreover, we prove the security of the protocol in the random oracle model and simulate the protocols using AVISPA simulator. The analysis of security shows that the proposed protocol is safe against known attacks.

关键词： Cloud computing   E-health   Mobile Edge Computing   Object retrieval   Personal Health Records  

摘要： In this paper, a QR Code Identity Tag System designed for Turkish healthcare and served through the cloud is presented. The system designed is a distributed information management system as the medical data objects are geographically distributed over the system. The data objects are geographically distributed as they are placed on servers closest to the geographical location where they are most frequently required. This enables that the data is always available and the access is provided in minimum time. Additionally, to improve the performance the system employs Mobile Edge Computing technology in the form of cloudlets. The simulation results show that system performance improves as the number of cloudlets used increases and popularity threshold decreases.

关键词： Service recommendation   QoS prediction   fuzzy clustering   features learning   mobile edge computing  

摘要： In recent years, deep neural networks have achieved exciting results in a variety of tasks, and many fields try to introduce neural network techniques. In mobile edge computing, there are not many attempts that build neural network models in service recommendation or QoS (quality-of-service) prediction. The method proposed in this article is an attempt to employ neural network technique for QoS prediction. Compared to the pure use of QoS records, the exploration for context information in QoS prediction also still needs a lot of efforts. But an increasing number of features are highly likely to result in overfitting problem, especially in the case that the data size is small. To solve those problems, in this article, we propose several new techniques, including denoising auto-encoder with fuzzy clustering (DAFC) and recombination embedding network, focusing on how to use context information and how to alleviate overfitting problem. DAFC uses the denoising auto-encoder, which helps the fuzzy clustering algorithm overcome the defect that the performance is easy to be impacted by the number of clusters. Extensive experiments under different data densities show that these two network structures indeed improve the performance and reduce the overfitting problem.

关键词： Mobile Cloud Computing   Mobile Edge Computing   Computation Offloading   Stochastic Offloading   Markov   Semi-Markov   MDP   SMDP   HMM   HHMM  

摘要： Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT networks, and vehicular networks running different specific applications such as Augmented Reality (AR), Virtual Reality (VR), and positioning systems, demand more and more processing and storage resources. Offloading is a promising technique to cope with the inherent limitations of such devices by which the resource-intensive code or at least a part of it will be transferred to the nearby resource-rich servers. Different approaches have been proposed to help make better decisions in respect of whether, where, when, and how much to offload and to improve the efficiency of the offloading process in the literature. On the other hand, the dynamic behavior of mobile devices running on-demand applications faces the offloading to the new challenges, which could be described as stochastic behaviors. Therefore, various stochastic offloading models have been proposed in the literature. However, to the best of the author's knowledge, despite the existence of plenty of related offloading studies in the literature, there is not any systematic, comprehensive, and detailed survey paper focusing on stochastic-based offloading mechanisms. In this paper, we propose a survey paper concerning the stochastic-based offloading approaches in various computation environments such as Mobile Cloud Computing (MCC), Mobile Edge Computing (MEC), and Fog Computing (FC) in which to identify new mechanisms, a classical taxonomy is presented. The proposed taxonomy is classified into three main fields: Markov chain, Markov process, and Hidden Markov Models. Then, open issues and future unexplored or inadequately explored research challenges are discussed, and the survey is finally concluded.