Information-Centric Networking (ICN) has been widely regarded as a new, promising networking paradigm for smart grids. Accordingly, content security becomes the most critical security goal, because the content is now ...
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Information-Centric Networking (ICN) has been widely regarded as a new, promising networking paradigm for smart grids. Accordingly, content security becomes the most critical security goal, because the content is now decoupled from its location in ICN. As existing security approaches are mainly designed for traditional IP-based smart grids, how to achieve content security in ICN-based smart grid remains unexplored. In this work, we first propose a two-layer ICN architecture to move from host-centric communication to receiver-driven data retrieval. Then, we design a pseudo-identity signcryption scheme based on receiver-driven communication patterns to achieve mutual authentication and various other security goals, assuring content security in an ICN-based smart grid. Experiments and comparison results show the superiority of our proposed scheme.
This New type of power system will be given a variety of renewable energy, energy access needs to implement more comprehensive perception, therefore, in order to solve the traditional Internet of Things (IoT) percepti...
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ISBN:
(纸本)9781665490832
This New type of power system will be given a variety of renewable energy, energy access needs to implement more comprehensive perception, therefore, in order to solve the traditional Internet of Things (IoT) perception research methods in network information perception accuracy of defects under complex conditions, this paper proposes a sense of renewable energy were calculated based on edge and the technical implementation of couplet system comprehensive perception. Firstly, a cloud-edge-end three-layer distributed IoT sensing framework is constructed. Secondly, all kinds of IoT related data of renewable energy are collected and sent to the cloud. Then, RBF-based state monitor and good state baseline are constructed to accurately perceive the IoT of renewable energy. Experimental results show that the proposed model has high accuracy and provides an effective method for IoT sensing of renewable energy.
Along with the increasing challenge of optimal energy dispatch in an integrated electric power substation, this work presents a model to construct a typical substation system. Based on the solution framework of model ...
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Mobile Edge computing (MEC) enables mobile users to offload their computation loads to nearby edge servers, and is seen to be integrated in the 5G architecture to support a variety of low-latency applications and serv...
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ISBN:
(纸本)9781728190747
Mobile Edge computing (MEC) enables mobile users to offload their computation loads to nearby edge servers, and is seen to be integrated in the 5G architecture to support a variety of low-latency applications and services. However, an edge server might soon be overloaded when its computation resources are heavily requested, and would then fail to process all of its received computation loads in time. Unlike most of existing schemes that ingeniously instruct the overloaded edge server to transfer computation loads to the remote cloud, we make use of the spare computation resources from other local edge servers by specially taking the risk of network link failures into account. We measure such link failure risks with the financial risk management metric of Conditional Value-at-Risk (CVaR), and well constrain it to the offloading decisions using a Minimum Cost Flow (MCF) problem formulation. Numerical results validate the enhancement of the MEC service's availability by our risk-aware offloading scheme.
The proceedings contain 35 papers. The special focus in this conference is on computing in Engineering and Technology. The topics include: Dynamic virtual machine provisioning in cloud computing using knowledge-based ...
ISBN:
(纸本)9789811548505
The proceedings contain 35 papers. The special focus in this conference is on computing in Engineering and Technology. The topics include: Dynamic virtual machine provisioning in cloud computing using knowledge-based reduction method;early detection of grape stem borer using IoT;Controlled Privacy-Aware (CPA) protocol for machine-to-machine communication in internet of things;Performance analysis of wireless sensor network (WSN);verification of 32-bit memory using layered testbench with optimum functional coverage and constrained randomization;stochastic model of a sensor node;prevention of replay attack for isolated smart grid;dangers of bias in data-intensive information systems;Performance of routing protocols using mobility models in VANET;Entity-Centric Combined Trust (ECT) algorithm to detect packet dropping attack in vehicular Ad Hoc Networks (VANETs);graph partitioning using heuristic kernighan-lin algorithm for parallelcomputing;experimenting with reordering model of phrase-based machine translation system for english to Hindi;investigation of imbalanced big data set classification: Clustering minority samples over sampling technique;Blended learning and analysis of factors affecting the Use of ICT in education;automation in hydroponics farming ecosystem;low power, low voltage, low drop-out on-chip voltage regulator;optimized low-energy adaptive clustering hierarchy in wireless sensor network;qoS in vertical handoff for wireless network using hungarian model and data parceling technique;Study of three-way solution for node movement in mobile Ad Hoc Networks (MANETS);ioT capable mechanism for crowd analysis;probability analysis of vehicular traffic at city intersection;automated malware identifier and analyzer;preface;UAV communication in FANETs with metaheuristic techniques;animal repellents from agricultural fields.
With the expansion of network scale, traditional routing strategies in Software-Defined Networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network ...
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With the expansion of network scale, traditional routing strategies in Software-Defined Networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes a different routing method, called Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning (RSDRL). Considering the links in the network that can carry large flow are limited, RSDRL can schedule large flow firstly and generate routing strategies adaptively. At the same time, in order to avoid the occurrence of congestion, RSDRL quantifies the degree of congestion in the network to implement routing. Experiments show that the proposed model has better Quality of Service (QoS) performance under the test traffic, realizing a reasonable schedule of traffic.
As one of the core technologies of 5G, mobile edge computing (MEC) has attracted wide attention from the academic community in recent years. In the mobile edge computing environment, users can choose to offload tasks ...
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As one of the core technologies of 5G, mobile edge computing (MEC) has attracted wide attention from the academic community in recent years. In the mobile edge computing environment, users can choose to offload tasks to edge servers that are closer to the user. But during task processing, the user is likely to move out of the coverage of the edge server currently serving him, which will result in additional transmission delay. Therefore, achieving low task processing delay becomes increasingly challenging due to user mobility in mobile edge computing environment. In this paper, we aim to address this challenge and propose a location-aware task offloading strategy based on deep reinforcement learning algorithm in a mobile edge computing environment. The proposed strategy jointly considers user mobility and allocation of bandwidth and computing resources to minimize the average delay of tasks completion. Experimental results show that our method reduces the average delay of tasks completion by about 10% compared to algorithms that do not predict user motion trajectories.
作者：
Lin, WenqingTencent
Interact Entertainment Grp Shenzhen Guangdong Peoples R China
Network embedding has been widely used in social recommendation and network analysis, such as recommendation systems and anomaly detection with graphs. However, most of previous approaches cannot handle large graphs e...
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ISBN:
(纸本)9781450383325
Network embedding has been widely used in social recommendation and network analysis, such as recommendation systems and anomaly detection with graphs. However, most of previous approaches cannot handle large graphs efficiently, due to that (i) computation on graphs is often costly and (ii) the size of graph or the intermediate results of vectors could be prohibitively large, rendering it difficult to be processed on a single machine. In this paper, we propose an efficient and effective distributed algorithm for network embedding on large graphs using Apache Spark, which recursively partitions a graph into several small-sized subgraphs to capture the internal and external structural information of nodes, and then computes the network embedding for each subgraph in parallel. Finally, by aggregating the outputs on all subgraphs, we obtain the embeddings of nodes in a linear cost. After that, we demonstrate in various experiments that our proposed approach is able to handle graphs with billions of edges within a few hours and is at least 4 times faster than the state-of-the-art approaches. Besides, it achieves up to 4.25% and 4.27% improvements on link prediction and node classification tasks respectively. In the end, we deploy the proposed algorithms in two online games of Tencent with the applications of friend recommendation and item recommendation, which improve the competitors by up to 91.11% in running time and up to 12.80% in the corresponding evaluation metrics.
distributed multi-energy systems, in addition to their advantages, pose significant challenges to future energy networks. One of these challenges is how these systems participate in energy markets. To overcome this is...
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ISBN:
(数字)9781665455053
ISBN:
(纸本)9781665455060
distributed multi-energy systems, in addition to their advantages, pose significant challenges to future energy networks. One of these challenges is how these systems participate in energy markets. To overcome this issue, this paper introduces a virtual energy hub plant (VEHP) comprised of multiple energy hubs (EHs) to participate in the energy market in a cost-effective manner. Each EH is equipped with multiple distributed energy resources (DERs) in order to supply electrical, heating and cooling loads. Moreover, an integrated demand response (IDR) program and vehicle-to-grid (V2G) capable electric vehicles (EVs) are taken into consideration to enhance the flexibility to EHs. The manager of the VEHP participates in the existing day-ahead markets on behalf of EHs after collecting their bids. Since EHs are independent entities, a hybrid model of mobile edge computing system and analytical target cascading theory (MECATC) is proposed to preserve data privacy of EHs. Further, to tackle the uncertainty of renewables, a robust optimization method is applied. Obtained results corroborated the proposed scheduling is efficient and could increase the VEHP’s profit about 21.4% in light of using flexible technologies.
In the integrated energy system (IES), distributed energy, flexible load and electric vehicle are widely involved in the energy production, consumption and other aspects of the power system, which makes it an importan...
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