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Special Issue on Internet-of-Things for Smart Cities

ieee INTERNET OF THINGS JOURNAL 2018年第2期5卷 468-472页

作者： Hu, Jia Yang, Kun Marin, Sergio Toral Sharif, Hamid Univ Exeter Dept Comp Sci Exeter EX4 4QF Devon England Univ Essex Sch Comp Sci & Elect Engn Colchester CO4 3SQ Essex England Univ Seville Dept Elect Engn Seville 41092 Spain Univ Nebraska Lincoln Coll Engn Omaha NE 68182 USA

The cities in the world are in the process of quick transition toward more smart, automatic, responsive, and flexible societies. The Internet-of-Things (IoT) are expected to improve the intelligence of the cities, promote the interaction between the human and the environment, enhance the reliability, resilience, operational efficiency, and energy efficiency, as well as reduce costs and resource consumption. The development, adoption, and application of IoT technology into smart cities is of huge interest. local authorities have partnered with startups, technology companies, research institutions, and universities to test and deploy IoT across all dimensions of urban life such as smart grid (SG), smart buildings, water management, connected healthcare and patient monitoring, environment/climate monitoring, connected cars, and smart transportation.

关键词： Special issues and sections Smart cities Energy efficiency local government Public infrastructure Environmental monitoring Intelligent transportation systems Urban areas Energy management

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Enhancing Recommendations in Mobile Social Network 13

Enhancing Recommendations in Mobile Social Network

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13th International conference on computer Engineering and Systems, ICCES 2018

作者： Ibrahim, Hadeer Hassan Abdelkader, Tamer Gohary, Rania El Faculty of Computer and Information Sciences Ain Shams University Cairo Egypt

ISBN: (纸本)9781538651117

In Mobile Social networks (MSNs), people contact each other through mobile devices, such as smartphones and tablets, while they move freely. The communication takes place on-the-fly by the opportunistic contacts between mobile users via local wireless bandwidth, such as Bluetooth or WiFi without a network infrastructure. Social Multicast is an important routing service in MSNs where data transmission is addressed to a group of users according to their social features. The aim of this paper is to find and recommend mobile nodes that can efficiently relay and consume messages based on their social features. Efficiency in this context is to achieve high delivery ratio while reducing considering resources constraints and limitations such as power and space. The proposed algorithm, TESS, measures social similarity based on Time-based Encounter of Socially Similar nodes. We compare the proposed algorithm with the known social multicast algorithms: Multi-CSDO, EncoCent and Epidemic. Simulations results show that the proposed algorithm outperforms others in terms of delivery ratio and network overhead. © 2018 ieee.

关键词： Wi-Fi

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An Improved LFM Algorithm Based on Fitness Function and Community Similarity

An Improved LFM Algorithm Based on Fitness Function and Comm...

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ieee International conference on Big Data and Cloud Computing (BdCloud)

作者： Lingxiao Zhang Xuefeng Yan College of Computer Sciences and Technology Nanjing University of Aeronautics and Astronautics Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing University of Aeronautics and Astronautics Nanjing China

ISBN: (数字)9781728143286

ISBN: (纸本)9781728143293

Detecting community structures has become very important to help us understand the characteristics of the complex networks. local Fitness Method (LFM) may generate some homeless nodes because of its backtracking step. local Fitness Method Extension (LFMEX) was proposed to improve LFM by adopting a new fitness value of nodes that determines if the neighbor node should be joined into the expanding community. The new fitness value of nodes removes the backtracking step and considers the indirect relationship, direct relationship, exclusive relationship between the node and community. By locally optimizing the fitness function, the raw community partition is got, which may has some near duplicated communities. These duplicated communities reduce the modularity of community partition and need to be merged together. A new community similarity criteria is proposed to evaluate the similarity between two communities from the random graph based probability theory. It considered the difference between real edges and expected edges between communities. The community similarity measure can be used to merge similar communities efficiently and improve the modularity of final community partition. Experiments showed that the method improves modularity performance and normalized mutual information in community detection.

关键词： Partitioning algorithms Backtracking Image edge detection Merging Optimization Solids Complex networks

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Distributed algorithm for solving linear algebraic equations: An implicit gradient neural network approach

Distributed algorithm for solving linear algebraic equations...

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Control Technology and Applications (CCTA),

作者： Peijun Wang Wenwu Yu Guanghui Wen School of Mathematics Southeast University Nanjing China

This brief investigates the online solving problem for linear algebraic equation Ax = b by means of the principle of consensus in multi-agent systems, where A ∈ ℝ m×n and b ∈ Rn. To be specific, we choose m autonomous agents and agent i knows only the i-th row of [A b] under a fixed and connected undirected communication topology. Under local interactions, by designing an implicit gradient neural network based algorithm, it is shown that all the agents' states which starting from any different initial conditions can converge exponentially fast to one of the solutions to Ax = b, if the matrix A has full row rank. It is worth noting that the proposed algorithm is fully distributed. In addition, it is shown that the proposed algorithm is effective in obtaining least square solutions for no-solution cases. Finally, computer simulations verify and demonstrate the efficiency of the proposed methods for solving linear algebraic equations.

关键词： Mathematical model Neural networks Signal processing algorithms Convergence Distributed algorithms Heuristic algorithms Iterative methods

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Design and Field Implementation of a Multi-Agent System for Voltage Regulation Using Smart Inverters and Data Distribution Service (DDS)

Design and Field Implementation of a Multi-Agent System for ...

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2018 ieee Electrical Power and Energy conference, EPEC 2018

作者： Saxena, Shivam El-Taweel, Nader A. Farag, Hany E. Hilaire, Leigh St. Electrical Engineering and Computer Sciencee York University Toronto Canada Toronto and Region Conservation Authority Sustainable Tech. Eval Program Toronto Canada

ISBN: (纸本)9781538654194

This paper proposes a multi-agent scheme to regulate voltage in smart distribution grids using smart inverters and Data Distribution Service (DDS). DDS is an Internet of Things (IoT) based communication framework that is well suited to handle the demanding requirements of inter-agent communication within smart grid applications, including: Asynchronous and low-latency messaging, high scalability, and seamless interoperability. Coupling smart inverters with DDS results in a fully distributed control and communication platform that can significantly increase the penetration of distributed and renewable energy resources in distribution networks. The proposed multi-agent scheme is tested at a real-world microgrid to show its ability to mitigate voltage regulation challenges using both local and coordinated control. © 2018 ieee.

关键词： Distributed power generation

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Joint Resource Optimization for Adaptive Multimedia Services in MEC-based Vehicular networks

Joint Resource Optimization for Adaptive Multimedia Services...

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ieee Global Communications conference

作者： Penglin Dai Kai Liu Xiao Wu Huanlai Xing Jing Xu Victor C. S. Lee School of Information Science and Technology Southwest Jiaotong University Chengdu China College of Computer Science Chongqing University Chongqing China School of Electronic Information and Communications Huazhong University of Science and Technology China City University of Hong Kong Hong Kong China

ISBN: (数字)9781728109626

ISBN: (纸本)9781728109633

Mobile edge computing (MEC) has been an emerging paradigm to support low-latency applications in vehicular networks by offloading resources at network edge. However, it is still challenging to apply MEC-based architecture to implement multimedia services due to varying wireless communication, high vehicle mobility and heterogeneous resource integration. In this paper, we investigate adaptive-bitrate (ABR)-based multimedia services (MS) in MEC-based vehicular networks, where each multimedia file is divided into multiple chunks and can be requested at different bitrate levels. Further, MEC servers can satisfy local vehicular requests by integrating heterogeneous cache and communication resources. Based on the above observation, we formulate joint resource optimization (JSO) problem by synthesizing cache placement, wireless bandwidth allocation and chunk quality adaptation. On this basis, we propose a reinforcement-learning-based cache placement (RLCP) algorithm, which determines the optimal offloaded chunks by learning the global knowledge of cache reward in an iterative way. Further, we design an adaptive-quality-based chunk selection (AQCS) algorithm, which can be adaptive to time-varying wireless channel by dynamically adjusting bandwidth allocation and quality level based on real-time service workload. Lastly, we build the simulation model and conduct an extensive performance evaluation, which demonstrates the superiority of proposed algorithms.

关键词： Servers Wireless communication Delays Streaming media Channel allocation Heuristic algorithms Optimization

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Cloud based Sparse Random Projection for Compressed Imaging

Cloud based Sparse Random Projection for Compressed Imaging

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ieee International conference on Smart Cloud (SmartCloud)

作者： Peihao Yang Linghe Kong Guihai Chen Jianhong Shi Guihua Zeng Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Advanced Optical Communication Systems and Networks Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9781728155067

ISBN: (纸本)9781728155074

Compressed Imaging is a framework for image signal acquisition and reconstruction based on Compressed Sensing theory. Existing reconstruction algorithms in compressed imaging are optimization problem solvers based on specific prior constraints. The computation burdens of these optimization algorithms are enormous and the solutions are usually local optimums. It is also inconvenient to deploy these algorithms on cloud. In this paper, we propose a novel distributed reconstruction algorithm based on the sparse random projection, which enables efficient reconstruction on cloud. We first build the projection matrix according to the sampling procedure and propose the sparse random projection (SRP) reconstruction algorithm. Then we derive ways to accelerate SRP algorithm. We finally generalize the SRP to a distributed version called Cloud-SRP(CSRP). Experiments on real ghost imaging reconstruction reveal that our algorithm is effective.

关键词： Cloud computing Image reconstruction Sparse matrices Image coding Signal processing algorithms Reconstruction algorithms

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Edge-based Differenital Big Data Processing for Sensor-Cloud Systems

Edge-based Differenital Big Data Processing for Sensor-Cloud...

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Signal, Information and Data Processing (ICSIDP), ieee International conference on

作者： Yaxin Mei Tian Wang Ying Ma College of Computer Science and Technology Huaqiao University Xiamen China Key Laboratory of Data Mining and Intelligent Recommendation Fujian Province University Xiamen China

ISBN: (数字)9781728123455

ISBN: (纸本)9781728123462

The rise of big data processing and storage bring new challenges for privacy protection. More data, especially personal information, being hosted online such as in cloud, which is out of control. In this paper, we propose an edge-based model for big data processing, taking sensor-cloud data as an example, in which the raw data from wireless sensor networks (WSNs) is differentially processed by algorithms on edge servers. A small quantity of core data is stored on edge and local servers while the rest is transmitted to cloud for storage. In this way, the benefits are twofold. First, the data privacy is preserved since the original data cannot be retrieved even if the data stored in cloud is leaked. Second, implemented by a differential storage method, compared to the state of the art, the edge-based model sends less data to the cloud and reduces the cost of communication and storage. Both theoretical analyses and extensive experiments validate our proposed method.

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Residual Attention Graph Convolutional Network for Geometric 3D Scene Classification

Residual Attention Graph Convolutional Network for Geometric...

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International conference on computer Vision Workshops (ICCV Workshops)

作者： Albert Mosella-Montoro Javier Ruiz-Hidalgo Universitat Politècnica de Catalunya Image Processing Group - Signal Theory and Communications

ISBN: (数字)9781728150239

ISBN: (纸本)9781728150246

Geometric 3D scene classification is a very challenging task. Current methodologies extract the geometric information using only a depth channel provided by an RGB-D sensor. These kinds of methodologies introduce possible errors due to missing local geometric context in the depth channel. This work proposes a novel Residual Attention Graph Convolutional Network that exploits the intrinsic geometric context inside a 3D space without using any kind of point features, allowing the use of organized or unorganized 3D data. Experiments are done in NYU Depth v1 and SUN-RGBD datasets to study the different configurations and to demonstrate the effectiveness of the proposed method. Experimental results show that the proposed method outperforms current state-of-the-art in geometric 3D scene classification tasks.

关键词： Three-dimensional displays Convolution Task analysis Neural networks Sensors Standards Image edge detection

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Facial expression recognition based on Gabor feature and neural network

Facial expression recognition based on Gabor feature and neu...

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2018 International conference on Security, Pattern Analysis, and Cybernetics, SPAC 2018

作者： Pang, Lei Li, Nianqiang Zhao, Li Shi, Wenxiu Du, Yunpan University of Jinan School of Information Science and Engineering Jinan China

ISBN: (纸本)9781728105512

With the development of human-computer interaction, emotional computing has gradually become a hot issue in computer vision research. Human expressions contain a wealth of information. How to make the computer fully extract facial expression information and understand human emotions is an urgent problem to be solved. The difficulty of facial expression recognition lies in facial expression extraction and facial expression classification. This paper analyzes the local features of facial expression extracted by Gabor wavelet transform and performs the various dimensional reduction on the problem of feature redundancy, which balances the feature dimension and The contribution rate of the feature. After extracting the features, a suitable BP neural network is constructed, and the extracted features are trained as a data set to the neural network to obtain a good facial expression classifier. © 2018 ieee.

关键词： Neural networks

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