• Corpus ID: 7218494

A Computing Perspective on Smart City

@inproceedings{Wang2016ACP,
  title={A Computing Perspective on Smart City},
  author={Lizhe Wang and Shiyan Hu and Gilles Betis and Rajiv Ranjan},
  year={2016}
}

• Lizhe Wang, Shiyan Hu, +1 author R. Ranjan
• Published 2016
• Computer Science

DEVELOPING smart city is the key to the next generation urbanization process for improving the efficiency, reliability, and security of a traditional city. The concept of smart city includes various aspects such as environmental sustainability, social sustainability, regional competitiveness, natural resources management, cybersecurity, and quality of life improvement. With the massive deployment of networked smart devices/sensors, an unprecedentedly large amount of sensory data can be collected and processed by advanced computing paradigms, which are the enabling techniques for smart city. For example, given historical environmental, population, and economic information, salient modeling and analytics are needed to simulate the impact of potential city planning strategies, which will be critical for intelligent decision-making. Analytics are also indispensable for discovering the underlying structure from retrieved data in order to design the optimal policies for real time automatic control in the cyberphysical smart city system. Furthermore, uncertainties and security concerns in the data collected from heterogeneous resources aggravate the problem, which makes smart city planning, operation, monitoring, and control highly challenging. Green, sustainable, and secure computing in smart cities has recently become a very active area of research in academia and has attracted significant industry interest. Since the computing issues for smart cities are highly interdisciplinary and cover various topics, a special section of Smart City Computing in the IEEE Transactions on Computers becomes an ideal forum for presenting and discussing the latest research results. The goal of this special section is to present the outstanding research results dedicated to the topics of green, sustainable, and secure computing for smart cities. We have received 40 manuscript submissions in total and six papers have finally been accepted after several rounds of very constructive and deep reviews. The smart city brings convenience to users through providing personalized yet efficient services. However, it also introduces privacy and security issues. Two different methods are used to counteract these issues. The active method is to prevent the overcollection of private data (Dai et al.), while the passive method is to make private data more secure via certain encryption algorithms (Zhang et al.). Dai et al. study the current state of data overcollection and look at some of the most frequent cases of overcollected data. They present a mobile cloud framework, which is an active approach to eradicate data overcollection. Through putting all users’ data into a cloud, the security of the data can be greatly improved. Zhang et al. use the BGV encryption scheme to encrypt the private data and employ cloud servers to perform the high-order back-propagation algorithm on the encrypted data efficiently for deep computation model training. Furthermore, their proposed scheme approximates the sigmoid function as a polynomial function to support the secure computation of the activation function with the BGV encryption. Data infrastructures play an important role in smart city computing. Two interesting topics are included in this special section, which are wireless sensor network for data collection (Santos et al.) and spatial-temporal database for data storage (Ding et al.), respectively. Santos et al. propose a decentralized algorithm for detecting damage in structures using a WSAN. As key characteristics, beyond presenting a fully decentralized (in-network) and collaborative approach for detecting damage in structures, the algorithm makes use of cooperative information fusion for calculating a damage coefficient, which represents frequency and amplitude shifts simultaneously. Ding et al. propose the Parallel-Distributed Network-constrained Moving Objects Database (PD-NMOD), a general framework that manages big trajectory data in a scalable manner. The PD-NMOD provides an infrastructure that is able to support a wide variety of smart transportation applications, thus benefiting the smart city vision as a whole. Furthermore, two interesting applications are selected to instantiate the effect of smart city computing, which includes smart grid (Wei et al.) and smart railway transportation (Huang et al.). Concerning the application domain of the smart grid, Huang et al. propose a comprehensive framework to integrate the operations of smart buildings into the energy scheduling of bulk power systems through proactive building demand participation. This new scheme enables buildings to proactively express and communicate their energy consumption preferences to smart grid operators rather than passively receive and react to market signals and instructions such as time varying electricity prices. For smart railway transportation, Huang et al. propose an energy-efficient train control framework through integrating both offline and onboard optimization techniques. The offline processing builds a decision-tree based sketchy solution through a complete flow of sequence mining, optimization, and machine learning. The onboard system feeds the train parameters into the decision tree to derive an optimized control sequence. For this special section of the IEEE Transactions on Computers we have selected the above very interesting papers to represent some important advances in smart city computing. As a conclusion, it should be noted that the research L. Wang (corresponding author) is with the School of Computer Science, China University of Geosciences, Wuhan, P.R. China. E-mail: [e-mail]. S. Hu is with the Michigan Technological University. G. Betis is with the EIT ICT Labs, France. R. Ranjan is with the University of Newcastle, United Kingdom.