关键词： Mobile edge computing  

摘要： With the proliferation of computation-extensive and latency-critical applications in the 5G and beyond networks, mobile-edge computing (MEC) or fog computing, which provides cloud-like computation and/or storage capabilities at the network edge, is envisioned to reduce computation latency as well as to conserve energy for wireless devices (WDs). This paper studies a novel device-To-device (D2D)-enabled multi-helperMEC system, in which a local user solicits its nearbyWDs serving as helpers for cooperative computation. We assume a time division multiple access (TDMA) transmission protocol, under which the local user offloads the tasks to multiple helpers and downloads the results from them over orthogonal pre-scheduled time slots. Under this setup, we minimize the computation latency by optimizing the local user's task assignment jointly with the time and rate for task offloading and results downloading, as well as the computation frequency for task execution, subject to individual energy and computation capacity constraints at the local user and the helpers. However, the formulated problem is a mixed-integer nonlinear program (MINLP) that is difficult to solve. To tackle this challenge, we propose an efficient algorithm by first relaxing the original problem into a convex one, and then constructing a suboptimal task assignment solution based on the obtained optimal one. Next, we consider a benchmark scheme that endows the WDs with their maximum computation capacities. To further reduce the implementation complexity, we also develop a heuristic scheme based on the greedy task assignment. Finally, numerical results validate the effectiveness of our proposed algorithm, as compared against the heuristic scheme and other benchmark ones without either joint optimization of radio and computation resources or task assignment design. Copyright © 2019, The Authors. All rights reserved.

关键词： Mobile edge computing  

摘要： Mobile Social Networks (MSNs) have been evolving and enabling various fields in recent years. Recent advances in mobile edge computing, caching, and device-to-device communications, can have significant impacts on 5G systems. In those settings, identifying central users is crucial. It can provide important insights into designing and deploying diverse services and applications. However, it is challenging to evaluate the centrality of nodes in MSNs with dynamic environments. In this paper, we propose a Social-Relation based Centrality (SoReC) measure, in which social network information is used to quantify the influence of each user in MSNs. We first introduce a new metric to estimate direct social relations among users via direct contacts, and then extend the metric to explore indirect social relations among users bridging to their neighbors. Based on direct and indirect social relations, we detect the influence spheres of users and quantify their influence in the networks. Simulations on real-world networks show that the proposed measure can perform well in identifying future influential users in MSNs. Copyright © 2019, The Authors. All rights reserved.

关键词： collaborative task execution   mobile edge computing   offloading decision   platooning  

摘要： In this paper, we study the offloading decision of collaborative task execution between platoon and Mobile Edge Computing (MEC) server. The mobile application is represented by a series of fine-grained tasks that form a linear topology, each of which is either executed on a local vehicle, offloaded to other members of the platoon, or offloaded to a MEC server. The objective of the design is to minimize the cost of tasks offloading and meets the deadline of tasks execution. The cost minimized task decision problem is transformed into the shortest path problem, which is limited by the deadline of the tasks on a directed acyclic graph. The classical Lagrangian Relaxation-based Aggregated Cost (LARAC) algorithm is adopted to solve the problem approximately. Numerical analysis shows that the scheduling method of the tasks decision can be well applied to the platoon scenario and execute the tasks in cooperation with the MEC server. In addition, compared with task local execution, platoon execution and MEC server execution, the optimal offloading decision for collaborative task execution can significantly reduce the cost of task execution and meet deadlines.

关键词： Cloudlet computing   Crowdsensing   Crowdsourcing   Edge computing   Fog computing   Internet of things   Mobile cloud   Mobile cloud computing   Mobile edge computing   Opportunistic sensing   Participatory sensing   Semantic web of things   Web of things   Wisdom web of things  

摘要： Currently, we are experiencing a technological shift, which is expected to change the way we program and interact with the world. Cloud computing and mobile computing are two prominent research areas that have already had such an impact. The Internet of Things (IoT), which is concerned with building a network of Internet-enabled devices to promote a smart environment, is another promising area of research. Numerous emerging computing paradigms related to those areas of research and/or their intersections have come into play. These include Mobile Cloud Computing (MCC), cloudlet computing, mobile clouds, mobile IoT computing, IoT cloud computing, fog computing, Mobile Edge Computing (MEC), edge computing, the Web of Things (WoT), the Semantic WoT (SWoT), the Wisdom WoT (W2T), opportunistic sensing, participatory sensing, mobile crowdsensing, and mobile crowdsourcing. Unfortunately, those paradigms suffer from the lack of standard definitions, and so we frequently encounter a single term referring to various paradigms or several terms referring to a single paradigm. Accordingly, this paper attempts to disambiguate those paradigms and explain how and where they fit in the above three areas of research and/or their intersections before it becomes a serious problem. They are tracked back to their inception as much as possible. This is in addition to discussing research directions in each area. The paper also introduces technologies related to the IoT such as ubiquitous and pervasive computing, the Internet of Nano Things (IoNT), and the Internet of Underwater Things (IoUT).

关键词： Internet of Things   Network functions virtualization   Virtual Object   Mobile Edge Computing  

摘要： The Internet of Things paradigm will certainly be one of the main drivers of the tomorrow's 5th generation (5G) wireless networks. In order to support the algorithms required for real time exchange and analysis of great amounts of data among the involved smart devices, Virtual Objects (VO) have become a key component to improve the objects energy management efficiency, as well as to address heterogeneity and scalability issues. Following the research trend of exploiting on-demand cloud computing resources to augment the processing and storage capabilities of IoT devices, this paper addresses the design of a novel infrastructure and paradigm to support the deployment of new personal IoT services inside the infrastructure provider premises. The goal is to bring cloud-computing services much closer to the end users and to be able to replace physical IoT devices with their "Virtual Images". Among other benefits, this approach will ensure a longer lifetime to IoT constrained devices and enable the inclusion of new protocols making the development of logic and the configuration of IoT smart application environment technology-agnostic. To achieve this goal, we have developed an open-source software platform that exploits OpenStack APIs and leverages a web interface providing all the functionality typically available in a home gateway through the OpenWRT Linux distribution. Results show this approach brings a manifest reduction in the amount of data transmitted, with benefits in terms of reduced workload and power consumption as well as extended device lifetime. (C) 2018 Elsevier B.V. All rights reserved.

关键词： Evolutionary game theory   Mobile edge computing   Task offloading  

摘要： The mobile edge computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in mobile terminals by offloading computation-intensive and delay-sensitive tasks to nearby edge nodes. However, limited computation resources in edge nodes may not be sufficient to serve excessive offloading tasks exceeding the computation capacities of edge nodes. Therefore, multiple edge clouds with a complementary central cloud coordinated to serve users is the efficient architecture to satisfy users’ Quality-of-Service (QoS) requirements while trying to minimize some network service providers’ cost. We study a dynamic, decentralized resource-allocation strategy based on evolutionary game theory to deal with task offloading to multiple heterogeneous edge nodes and central clouds among multi-users. In our strategy, the resource competition among multi-users is modeled by the process of replicator dynamics. During the process, our strategy can achieve one evolutionary equilibrium, meeting users’ QoS requirements under resource constraints of edge nodes. The stability and fairness of this strategy is also proved by mathematical analysis. Illustrative studies show the effectiveness of our proposed strategy, outperforming other alternative methods. © 2019 by the authors. Licensee MDPI, Basel, Switzerland.

关键词： task offloading   mobile edge computing   evolutionary game theory  

摘要： The mobile edge computing (MEC) paradigm provides a promising solution to solve the resource-insufficiency problem in mobile terminals by offloading computation-intensive and delay-sensitive tasks to nearby edge nodes. However, limited computation resources in edge nodes may not be sufficient to serve excessive offloading tasks exceeding the computation capacities of edge nodes. Therefore, multiple edge clouds with a complementary central cloud coordinated to serve users is the efficient architecture to satisfy users' Quality-of-Service (QoS) requirements while trying to minimize some network service providers' cost. We study a dynamic, decentralized resource-allocation strategy based on evolutionary game theory to deal with task offloading to multiple heterogeneous edge nodes and central clouds among multi-users. In our strategy, the resource competition among multi-users is modeled by the process of replicator dynamics. During the process, our strategy can achieve one evolutionary equilibrium, meeting users' QoS requirements under resource constraints of edge nodes. The stability and fairness of this strategy is also proved by mathematical analysis. Illustrative studies show the effectiveness of our proposed strategy, outperforming other alternative methods.

关键词： Computation offloading   mobile edge computing   resource allocation   stochastic optimization   vehicular networks  

摘要： The proliferation of smart vehicular terminals (VTs) and their resource hungry applications impose serious challenges to the processing capabilities of VTs and the delivery of vehicular services. Mobile Edge Computing (MEC) offers a promising paradigm to solve this problem by offloading VT applications to proximal MEC servers, while TV white space (TVWS) bands can be used to supplement the bandwidth for computation offloading. In this paper, we consider a cognitive vehicular network that uses the TVWS band, and formulate a dual-side optimization problem, to minimize the cost of VTs and that of the MEC server at the same time. Specifically, the dual-side cost minimization is achieved by jointly optimizing the offloading decision and local CPU frequency on the VT side, and the radio resource allocation and server provisioning on the server side, while guaranteeing network stability. Based on Lyapunov optimization, we design an algorithm called DDORV to tackle the joint optimization problem, where only current system states, such as channel states and traffic arrivals, are needed. The closed-form solution to the VT-side problem is obtained easily by derivation and comparing two values. For MEC server side optimization, we first obtain server provisioning independently, and then devise a continuous relaxation and Lagrangian dual decomposition based iterative algorithm for joint radio resource and power allocation. Simulation results demonstrate that DDORV converges fast, can balance the cost-delay tradeoff flexibly, and can obtain more performance gains in cost reduction as compared with existing schemes.

关键词： resource allocation   wireless power transfer   mobile edge computing   Internet of Things  

摘要： Wireless devices in Internet of Things (IoT) applications, such as wireless sensors and Radio Frequency Identifications (RFIDs), are faced with challenges of heavy computation tasks and limited energy, which can be solved by the importation of mobile edge computing (MEC) and wireless power transfer (WPT) techniques. As MEC can effectively enhance computation capability, and the wireless power transfer can ensure a sustainable supply of energy, it has drawn significant research interest in IoT applications. In this paper, we will study the resource allocation problem in the wireless-powered MEC system for IoT applications with one access point (AP) and many other wireless devices, and propose a Stackelberg dynamic game model to obtain the optimal allocated resource for the nodes in the IoT environment. The AP is a wireless power source that can charge wireless devices based on wireless power transfer techniques. The AP is also integrated with a MEC server that can carry out computation tasks that offload from wireless devices. The wireless devices can use the harvested energy to execute and offload computation tasks to the AP. Based on the proposed game model, the AP and wireless devices can control their optimal transmit power for energy transfer, and computation tasks offloading to the AP, respectively. The numerical simulation results show the correctness and effectiveness of the proposed model.

关键词： Internet of Things   mobile edge computing   cloud computing   consensus problem   interactive consistency problem  

摘要： Driven by the vision of the Internet of Things (IoT), there has been a dramatic shift in mobile computing in recent years from centralized mobile cloud computing (MCC) to mobile edge computing (MEC). The main features of MECs are to promote mobile computing, network control, and storage to the edge of the network in order to achieve computationally intensive and latency-critical applications on resource-constrained mobile devices. Therefore, MEC is proposed to enable computing directly at the edge of the network, which can deliver new applications and services, especially for the IoT. In order to provide a highly flexible and reliable platform for the IoT, a MEC-based IoT platform (MIoT) is proposed in this study. Through the MIoT, the information asymmetrical symmetry between the consumer and producer can be reduced to a certain extent. Because of the IoT platform, fault tolerance is an important research topic. In order to deal with the impact of a faulty component, it is important to reach an agreement in the event of a failure before performing certain special tasks. For example, the initial time of all devices and the time stamp of all applications should be the same in a smart city before further processing. However, previous protocols for distributed computing were not sufficient for MIoT. Therefore, in this study, a new polynomial time and optimal algorithm is proposed to revisit the agreement problem. The algorithm makes all fault-free nodes decide on the same initial value with minimal rounds of message exchanges and tolerate the maximal number of allowable faulty components in the MIoT.