Multi-access Edge Computing (MEC) facilitates the deployment of critical applications with stringent QoS requirements, latency in particular. Our paper considers the problem of jointly planning the availability of computational resources at the edge, the slicing of mobile network and edge computation resources, and the routing of heterogeneous traffic types to the various slices. These aspects are intertwined and must be addressed together to provide the desired QoS to all mobile users and traffic types still keeping costs under control. We formulate our problem as a mixed-integer nonlinear program (MINLP) and we define a heuristic, named Neighbor Exploration and Sequential Fixing (NESF), to facilitate the solution of the problem. The approach allows network operators to fine tune the network operation cost and the total latency experienced by users. We evaluate the performance of the proposed model and heuristic against two natural greedy approaches. We show the impact of the variation of all the considered parameters (viz., different types of traffic, tolerable latency, network topology and bandwidth, computation and link capacity) on the defined model. Numerical results demonstrate that NESF is very effective, achieving near-optimal planning and resource allocation solutions in a very short computing time even for large-scale network scenarios.
With the rapid proliferation of mobile devices, explosive mobile applications (apps) are developed in the past few years. However, the functions of mobile apps are varied and the designs of them are not well understood by end users, especially the activities and functions related to user privacy. Therefore, understanding how much danger of mobile apps with respect to privacy violation to mobile users is becomes a critical issue when people use mobile devices. In this paper, we evaluate the mobile app privacy violation of mobile users by computing the danger coefficient. In order to help people reduce the privacy leakage, we combine both the user preference to mobile apps and the privacy risk of apps and propose a mobile app usage recommendation method named AppURank to recommend the secure apps with the same function as the “dangerous” one for people use. The evaluation results show that our recommendation can reduce the privacy leakage by 50%.
Addressing the overheating fault detection and alarming of insulated busways in buildings, a system based on sensing volatile gases generated by the thermal degradation of the busduct insulation layer was proposed. By monitoring the concentration of volatile gases in the environment, the overheating fault of the busducts can be found early. The thermal degradation process of the busway insulating layer is analyzed, and the pyrolysis characteristic gas at low temperature is studied. The experimental platform has been built, by which the relation between the concentration of volatile gases and temperature of the insulated layer has been studied. By testing, the concentration of volatile organic compounds (VOCs) is proposed as the basis for judging the overheating fault in the alarming system. With the collected samples for training and testing, the AdaBoost classifier is used to identify the overheating fault. Finally, the design of the overheating fault alarming system is given.
Mobile Edge Computing (MEC) is a key technology for the deployment of next generation (5G and beyond) mobile networks. The computational power it provides at the edge could allow providers to fulfill the requirements of use cases in need of ultra-low latency, high bandwidth, as well as real-time access to the radio network. However, this potential needs to be carefully administered as the edge is certainly limited in terms of computation capability, as opposed to the cloud which holds the promise of a virtually infinite power. MECs, though, could still try to exploit not only their local capacity, but also the one that the neighbor MECs could offer. Considering that the 5G scenario assumes an ultra-dense distribution of MECs, this possibility could be feasible, provided that we find an effective way to carefully allocate the resources available at each edge node.In this paper, we provide an optimization framework that considers several key aspects of the resource allocation problem with cooperating MECs. We carefully model and optimize the allocation of resources, including computation and storage capacity available in network nodes as well as link capacity. Specifically, our proposed model jointly optimizes (1) the user requests admission decision (2) their scheduling, also called calendaring (3) and routing as well as ( 4) the decision of which nodes will serve such user requests and (5) the amount of processing and storage capacity reserved on the chosen nodes. Both an exact optimization model and an effective heuristic, based on sequential fixing, are provided.Furthermore, we propose a distributed approach for our problem, based on the Alternating Direction Method of Multipliers (ADMM), so that resource allocation decisions can be made in a distributed fashion by edge nodes with limited overhead.We perform an extensive numerical analysis in several real-size network scenarios, using real positions for radio access points of a mobile operator in the Milan area. Results demonstrate that the heuristic performs close to the optimum in all considered network scenarios, while exhibiting a low computing time. This provides an evidence that our proposal is an effective framework for optimizing resource allocation in next-generation mobile networks.
Distribution transformer is the key equipment in the distribution power grid, and sampling inspection is the main method used to ensure quality control. Inspection comprises many testing processes. Due to various reasons, the testing results may occasionally have errors and be implausible. Only a few errors can be detected empirically by inspectors. To solve this problem, anomaly detection is proposed in this paper to determine implausible inspection reports and assist in re-inspecting. The well-known and representative anomaly detection algorithms, which are now used in many application domains are introduced. These algorithms include single Gaussian distribution and multivariate Gaussian distribution, local outlier factor, one-class SVM. Based on the distribution transformer inspection reports collected, anomaly samples are constructed manually. Train datasets and test datasets are then formulated. By comparing the testing results, the one-class SVM algorithm can detect anomaly samples in testing datasets correctly. Thus, it can be used to distinguish the abnormal samples (i.e., reports with measurement errors suspected) in the inspection reports of distribution transformer, which can help inspectors complete their inspecting work correctly and effectively.
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