关键词： Mobile edge computing   Cloud computing   Biosurveillance systems   Edge cloud data management   Multi-Agent systems  

摘要： The purposeful acts of bioterrorism and the emergence of new pathogens call for developing efficient biosurveillance systems that are capable of detecting health security threats and localizing their origins. For this sake, several biosurveillance systems have been developed and deployed in different regions around the world. The objectives of these systems are to efficiently detect threats and effectively manage responses. Although modern biosurveillance systems start to leverage recent advances in internet-of-things and communication technologies to detect natural biological outbreaks, it is not evident whether these systems can be generalized to detect threats related to purposeful biological attacks. As opposed to natural disease outbreaks, biological attacks can be triggered concurrently at different locations and in vital areas that are traversed by a large number of humans. In this paper, we propose an efficient framework to detect multiple concurrent threats and localize their origins based on real-time information and mobile-edge computing support. The proposed framework is organized into a hierarchical architecture. At the micro-level, monitored humans are equipped with wearable sensors that periodically capture the monitored human vital signs. To process the captured information efficiently, the monitored environment is partitioned into cells. Each cell is assigned a mobile-edge computing (MEC) component that is responsible for processing the monitoring information within its cell. The MEC components then send the processed information to a super component for final processing and to detect possible threats. The experimental results show that the proposed framework accurately detects multiple concurrent health security threats and localize their origins.

关键词： Internet of Things   Computation offloading   Mobile edge computing   Evolutionary game theory   Reinforcement learning  

摘要： Due to the limited computing resources and energy of IoT devices, complex computing tasks are offloaded to sufficient computing servers, such as Cloud Center. However, offloading may increase the latency and congestion of the IoT network. Mobile Edge Computing (MEC) is a promising approach, which can decrease the delay and energy consumption of IoT devices significantly. In this paper, we investigate the problem of multi-user computation offloading under dynamic environment. Considering the channel interference when multiple IoT devices offload computing tasks via wireless channels at the same time, we formulate the computation offloading as an evolutionary game model. We use the replicator dynamics to analyze the evolutionary process of IoT devices and prove that multi-user computation offloading exists unique Evolutionary Stability Strategy (ESS). Finally, we design an evolutionary game algorithm based on reinforcement learning in practical application scenarios. Experiments can verify the convergence and performance of the proposed algorithm in multi-user scenarios. (C) 2020 Elsevier GmbH. All rights reserved.

关键词： deployment   infrastructure   mobile edge computing   network function virtualization   software-defined networks   5G  

摘要： This paper presents the design and prototype implementation of the SELFNET fifth-generation (5G) mobile edge infrastructure. In line with the current and emerging 5G architectural principles, visions, and standards, the proposed infrastructure is established primarily based on a mobile edge computing paradigm. It leverages cloud computing, software-defined networking, and network function virtualization as core enabling technologies. Several technical solutions and options have been analyzed. As a result, a novel portable 5G infrastructure testbed has been prototyped to enable the preliminary testing of the integrated key technologies and to provide a realistic execution platform for further investigating and evaluating software-defined networking- and network function virtualization-based application scenarios in 5G networks.

关键词： Deep learning   Computational modeling   Edge computing   Data models   Informatics   Robustness   5G mobile communication   Adversarial examples   deep learning   generative adversarial networks (GAN)   industrial artificial intelligence systems (IAISs)   mobile edge computing  

摘要： Individually reinforcing the robustness of a single deep learning model only gives limited security guarantees especially when facing adversarial examples. In this article, we propose DeSVig, a decentralized swift vigilance framework to identify adversarial attacks in an industrial artificial intelligence systems (IAISs), which enables IAISs to correct the mistake in a few seconds. The DeSVig is highly decentralized, which improves the effectiveness of recognizing abnormal inputs. We try to overcome the challenges on ultralow latency caused by dynamics in industries using peculiarly designated mobile edge computing and generative adversarial networks. The most important advantage of our work is that it can significantly reduce the failure risks of being deceived by adversarial examples, which is critical for safety-prioritized and delay-sensitive environments. In our experiments, adversarial examples of industrial electronic components are generated by several classical attacking models. Experimental results demonstrate that the DeSVig is more robust, efficient, and scalable than some state-of-art defenses.

关键词： Mobile edge computing  

摘要： This paper considers a wireless powered multiuser mobile edge computing (MEC) system, in which a multi-antenna hybrid access point (AP) wirelessly charges multiple users, and each user relies on the harvested energy to execute computation tasks. We jointly optimize the energy beamforming and remote task execution at the AP, as well as the local computing and task offloading, aiming to minimize the total system energy consumption over a finite time horizon, subject to causality constraints for both energy harvesting and task arrival at the users. In particular, we consider a practical scenario with casual task state information (TSI) and channel state information (CSI), i.e., only the current and previous TSI and CSI are available, but the future TSI and CSI can only be predicted subject to certain errors. To solve this real-time resource allocation problem, we propose an offline-optimization inspired online design approach. First, we consider the offline optimization case by assuming that the TSI and CSI are perfectly known a-priori. In this case, the energy minimization problem corresponds to a convex problem, for which the semi-closed-form optimal solution is obtained via the Lagrange duality method. Next, inspired by the optimal offline solution, we propose a sliding-window based online resource allocation design in practical cases by integrating with the sequential optimization. Finally, numerical results show that the proposed joint wireless powered MEC designs significantly improve the system’s energy efficiency, as compared with the benchmark schemes that consider a sliding window of size one or without such joint optimization. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： This article provides an overview of mobile edge computing (MEC) and artificial intelligence (AI) and discusses the mutually-beneficial relationship between them. AI provides revolutionary solutions in nearly every important aspect of the MEC offloading process, such as resource management and scheduling. On the other hand, MEC servers are utilized to avail a distributed and parallelized learning framework, namely mobile edge learning. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is a new paradigm that provides cloud computing services at the edge of networks. To achieve better performance with limited computing resources, peer offloading between cooperative edge servers (e.g. MEC-enabled base stations) has been proposed as an effective technique to handle bursty and spatially imbalanced arrival of computation tasks. While various performance metrics of peer offloading policies have been considered in the literatures, the worst-case response time, a common Quality of Service(QoS) requirement in real-time applications, yet receives much less attention. To fill the gap, we formulate the peer offloading problem based on a stochastic arrival model and propose two online algorithms for cases with and without prior knowledge of task arrival rate. Our goal is to maximize the utility function of time-average throughput under constraints of energy consumption and worst-case response time. Both theoretical analysis and numerical results show that our algorithms are able to produce close to optimal performance. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile edge computing (MEC) is an emerging communication scheme that aims at reducing latency. In this paper, we investigate a green MEC system under the existence of an eavesdropper. We use computation efficiency, which is defined as the total number of bits computed divided by the total energy consumption, as our main metric. To alleviate stringent latency requirement, a joint secure offloading and computation model is proposed. Additionally, we formulate an optimization problem for maximizing computation efficiency, under several practical constraints. The non-convex problem is tackled by successive convex approximation and an iterative algorithm. Simulation results have verified the superiority of our proposed scheme, as well as the effectiveness of our problem solution. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： In recent years, mobile edge computing (MEC) has attracted tremendous research thanks to its advantage in handling computation intensive latency critical tasks. To overtake the bad channel condition in the process of task offloading, multiple-relay assisted MEC system is considered in this paper. In specific, three cases including TDMA scenario, FDMA scenario in decode- and-forward (DF) mode and amplify-and-forward (AF) mode are investigated. The target is to minimize the overall energy consumption of mobile user and relays by jointly optimizing offloading data amount, transmit power and slot duration (in TDMA, or bandwidth allocation in FDMA, or amplitude gain in AF). In the scenario of TDMA, we show the associated problem is convex and solve it in a easier way through the manner of bi-level optimization. In the upper level, the optimal data amount for offloading is acquired, which corresponds to a simpler convex optimization problem, while in the lower level, the optimal solution of the rest of variable are found via KKT conditions. In the scenario of FDMA, the associated optimization problem is non-convex. Global optimal solution is found with the help of bi-level optimization and monotonic programming. For AF mode, bi-level optimization is also utilized in which neither of the two levels is convex. To this end, geometric programming and successive convex approximation (SCA) is used to find the convergent solution of the lower level while monotonic programming is adopted in the upper level. Numerical results proves the effectiveness of the proposed strategies under various scenarios investigated in this paper. Copyright © 2020, The Authors. All rights reserved.

关键词： Mobile edge computing   energy-aware application placement   quality of service   multi-stage stochastic programming   sample average approximation   parallel algorithms  

摘要： The Quality of Service (QoS) in Mobile Edge Computing (MEC) systems is significantly dependent on the application offloading and placement decisions. Due to the movement of users in MEC networks, an optimal application placement might turn into the least efficient placement in few minutes. Thus, it is crucial to take the dynamics of the system into account when designing application placement mechanisms. On the other hand, energy consumption of servers is a significant component of the cost of services in MEC systems and must also be considered in the design of the mechanisms. In this article, we model the problem of energy-aware application placement in edge computing systems as a multi-stage stochastic program. The objective is to maximize the QoS of the system while taking into account the limited energy budget of the edge servers. To solve the problem, we design a novel parallel Sample Average Approximation (SAA) algorithm. We conduct an extensive experimental analysis to evaluate the performance of the proposed algorithm using real-world trace data.