关键词： Crowdsourcing   Crowdsourced systems   Cloud services   Crowdcloud   Mobile edge computing  

摘要： The widespread adoption of truly portable, smart devices and Do-It-Yourself computing platforms by the general public has enabled the rise of new network and system paradigms. This abundance of well-connected, well-equipped, affordable devices, when combined with crowdsourcing methods, enables the development of systems with the aid of the crowd. In this work, we introduce the paradigm of Crowdsourced Systems, systems whose constituent infrastructure, or a significant part of it, is pooled from the general public by following crowdsourcing methodologies. We discuss the particular distinctive characteristics they carry and also provide their canonical architecture. We exemplify the paradigm by also introducing Crowdcloud, a crowdsourced cloud infrastructure where crowd members can act both as cloud service providers and cloud service clients. We discuss its characteristic properties and also provide its functional architecture. The concepts introduced in this work underpin recent advances in the areas of mobile edge/fog computing and co-designed/co-created systems.

关键词： Combinatorial optimization problem   indoor localization   mobile edge computing   multimodel framework   NP-hardness  

摘要： Location estimation technology under the wireless environment has become a vital technology in the field of mobile edge computing. Especially, under the mobile edge of entire networks environment, indoor location estimation is gradually getting the interest research and application topic, due to technical constraints of global positioning system technology for indoor environment and the popularity of the mobile edge computing servers. In this paper, the widely used single-model framework for indoor localization is presented as an introduction, which consists of three stages: 1) sample data collection;2) model building;and 3) localization estimation. And then, through analyzing of the actual scene of indoor localization, a new framework for indoor localization under mobile edge computing environment, named Multimodel, is proposed from the theoretical perspective. It is mainly based on the observation that the environment of the sample data collection and that of localization data collection may change seriously. In order to make up for the shortcomings of this framework, two combinatorial optimization problems are proposed. Later, we discuss the NP-hardness of them in several different cases. In addition, two heuristic algorithms are given, and the performance of which are illustrated by the corresponding experimental results.

关键词： mobile edge computing   active safe driving   cooperative platoon driving   cooperative adaptive cruise control  

摘要： Cooperative adaptive cruise control (CACC) for human and autonomous self-driving aims to achieve active safe driving that avoids vehicle accidents or traffic jam by exchanging the road traffic information (e.g., traffic flow, traffic density, velocity variation, etc.) among neighbor vehicles. However, in CACC, the butterfly effect is encountered while exhibiting asynchronous brakes that easily lead to backward shock-waves and are difficult to remove. Several critical issues should be addressed in CACC, including (i) difficulties with adaptive steering of the inter-vehicle distances among neighbor vehicles and the vehicle speed, (ii) the butterfly effect, (iii) unstable vehicle traffic flow, etc. To address the above issues in CACC, this paper proposes the mobile edge computing-based vehicular cloud of the cooperative adaptive driving (CAD) approach to avoid shock-waves efficiently in platoon driving. Numerical results demonstrate that the CAD approach outperforms the compared techniques in the number of shock-waves, average vehicle velocity, average travel time and time to collision (TTC). Additionally, the adaptive platoon length is determined according to the traffic information gathered from the global and local clouds.

关键词： Blockchain smart contract   crowd-intelligence ecosystem   hybrid human-machine   mobile edge computing   reward and penalty   strong Nash equilibrium   trustless  

摘要： Crowd intelligence tries to gather, process, infer, and ascertain massive useful information by utilizing the intelligence of crowds or distributed computers, which has great potential in Industrial Internet of Things. A crowd-intelligence ecosystem involves three stakeholders, namely the platform, workers (e.g., individuals, sensors, or processors), and task publisher. The stakeholders have no mutual trust but interest conflict, which means bad cooperation of them. Due to lack of trust, transferring raw data (e.g., pictures or video clips) between publisher and workers requires the remote platform center to serve as a relay node, which implies network congestion. First, we use a reward-penalty model to align the incentives of stakeholders. Then the predefined rules are implemented using blockchain smart contract on many edge servers (ES) of the mobile edge computing network, which together function as a trustless hybrid human-machine crowd-intelligence platform. As ES are near to workers and publisher, network congestion can be effectively improved. Further, we proved the existence of the only one strong Nash equilibrium, which can maximize the interests of involved ES and make the ecosystem bigger. Theoretical analysis and experiments validate the proposed method, respectively.

关键词： caching   computation offloading   mobile edge computing   priority   energy efficient   delay efficient  

摘要： Mobile Edge Computing (MEC) is relatively a novel concept in the parlance of Computational Offloading. MEC signifies the offloading of intensive computational tasks to the cloud which is generally positioned at the edge of a mobile network. Being in an embryonic stage of development, not much research has yet been done in this field despite its potential promises. However, with time the advantages are gaining growing attention and MEC is gradually taking over some of the resource-intensive functionalities of a traditional centralized cloud-based system. Another new idea called Task Caching is emerging rapidly with the offloading policy. This joint optimization idea of task offloading and caching is relatively a very new concept. It has been in use for reducing energy consumption and delay time for mobile edge computing. Due to the encouraging offshoots from some of the current research on the joint optimization problem, this research initiative aims to take the progress forward. The work improves upon the "prioritization of the tasks" by adopting a very practical approach discussed forward, and proposes a different way for task offloading and caching to the edge of the cloud, thereby bringing a significant enhancement to the QoS of MEC. We propose a novel priority-based offloading and caching model considering the joint optimization of offloading and caching along with the local computing policy. The simulation results show that our proposed model is able to reduce more delay time and energy cost of MEC than any other model proposed earlier.

关键词： Internet of Things (IoT)   mobile edge computing   secrecy provisioning  

摘要： Mobile edge computing (MEC), which enables smart terminals to actively offload computation workloads to computational servers deployed at the edge of networks, has provided an efficient approach to address the intensive computation requirement in mobile Internet applications. In this paper, we investigate the delay-aware computation offloading via MEC for Internet of Things (IoT) with secrecy provisioning. Specifically, we consider a scenario where a malicious eavesdropper intentionally overhears the IoT devices' offloaded computational data. Taking into account the secrecy outage due to the eavesdropper's overhearing, we formulate a joint optimization of the secrecy-provisioning, computation offloading, and radio resource allocation (including time and power allocations), with the objective of minimizing the overall delay in finishing the computation requirement of the IoT device. Despite the nonconvexity of the joint optimization problem, we propose an efficient algorithm to compute the optimal computation offloading solution. By exploiting the optimal offloading decision of each IoT device, we further consider the scenario of a group of IoT devices offloading computation workloads to the edge server, and investigate how the edge server optimally selects the devices for providing the computation offloading service while subject to the limited energy budget and the time-slot budget of the edge server. We propose an efficient algorithm to find the optimal selection of the devices. We present extensive numerical results to validate the effectiveness of our proposed algorithms and show the impact of the secrecy requirement.

关键词： Distributed machine learning   federated learning   mobile edge computing   wireless networking  

摘要： Emerging technologies and applications including Internet of Things, social networking, and crowd-sourcing generate large amounts of data at the network edge. Machine learning models are often built from the collected data, to enable the detection, classification, and prediction of future events. Due to bandwidth, storage, and privacy concerns, it is often impractical to send all the data to a centralized location. In this paper, we consider the problem of learning model parameters from data distributed across multiple edge nodes, without sending raw data to a centralized place. Our focus is on a generic class of machine learning models that are trained using gradient-descent-based approaches. We analyze the convergence bound of distributed gradient descent from a theoretical point of view, based on which we propose a control algorithm that determines the best tradeoff between local update and global parameter aggregation to minimize the loss function under a given resource budget. The performance of the proposed algorithm is evaluated via extensive experiments with real datasets, both on a networked prototype system and in a larger-scale simulated environment. The experimentation results show that our proposed approach performs near to the optimum with various machine learning models and different data distributions.

关键词： Vehicular network   Cloud   Mobile edge computing   Contract theory  

摘要： Mobile Edge Computing (MEC) is a promising solution to improve vehicular services through offloading computation to cloud servers in close proximity to mobile vehicles. However, the self-interested nature together with the high mobility characteristic of the vehicles make the design of the computation offloading scheme a significant challenge. In this paper, we propose a new Vehicular Edge Computing (VEC) framework to model the computation offloading process of the mobile vehicles running on a bidirectional road. Based on this framework, we adopt a contract theoretic approach to design optimal offloading strategies for the VEC service provider, which maximize the revenue of the provider while enhancing the utilities of the vehicles. To further improve the utilization of the computing resources of the VEC servers, we incorporate task priority distinction as well as additional resource providing into the design of the offloading scheme, and propose an efficient VEC server selection and computing resource allocation algorithm. Numerical results indicate that our proposed schemes greatly enhance the revenue of the VEC provider, and concurrently improve the utilization of cloud computing resources.

关键词： Mobile edge computing   Cloudlet   Computation offloading   Offloading failure   Reinforcement learning  

摘要： Cloud of Things (CoT) is a significant paradigm for bridging cloud resource and mobile terminals. Mobile edge computing (MEC) is a supporting architecture for CoT. The objectives of this paper are to describe and evaluate a method to handle the computation offloading problem during user mobility which minimizes the offloading failure rate in heterogeneous network. Furthermore, users' mobility and their choices for offloading lead to the everchanging condition of wireless network and opportunistic resource available. By modeling such dynamic mobile edge environment, quantizing the user cost, failure penalty and diversified QoS requirements, computation offloading problem is converted into an online decision-making problem in a stochastic process. We divide the decision-making into two phases: offloading planning phase and offloading running phase. In both phases the learning agent can continuously improve the control policy. We also conduct a failure recovery policy to tackle different types of failure and is included in the decision-making process. The numerical results show that the proposed online learning offloading method for mobile users can derive the optimal offloading scheme compared with the baseline algorithms. (C) 2019 Published by Elsevier Inc.

关键词： Internet of Things   named data networking   mobile edge computing   information-centric networking  

摘要： Edge computing is a key paradigm to offload the core network and effectively process massive Internet of Things (IoT) raw data without sending them to the cloud. This paradigm normally relies on a set of purpose-built and pre-planned servers, which host storage and processing resources to provide IoT services close to the data sources, thus saving core network resources and offloading the remote cloud infrastructure. In this paper, we propose to turn the network edge into a dynamic, distributed computing environment that supports the provisioning of IoT services, by exploiting the recent evolution of named data networking (NDN), supporting both name-based data retrieval and computation. Specific name structure and novel NDN forwarding mechanisms are designed;a distributed strategy is also engineered to select the service executor among edge nodes, with the objectives to: 1) limit the raw IoT data traffic crossing the network and 2) allocate the service execution according to the nodes' available processing resources. Numerical analysis shows that the performance of the proposed framework approaches the one of the optimal solution of a formulated integer linear programming problem. System-level ndnSIM simulations confirm that the proposal also outperforms the considered state-of-the-art benchmark solutions in terms of service provisioning time.