Special issue on selected technologies and applications in smart city computing

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, 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. The submitted manuscripts were reviewed by experts from both academia and industry. After two rounds of reviewing, the highest quality manuscripts were accepted for this special issue. Totally, 14 papers are accepted. This special issue will be published by CCPE as special issues. Arunarani et al propose a security and cost aware scheduling algorithm for heterogeneous tasks in scientific workflow executed in a cloud. A core semantics extraction model (CSEM) is proposed by Liu et al to improve the novel and rich semantics of multi‐document summary. Zhou et al study to enhance the service reliability by designing a novel network failure–aware redundant virtual machine placement approach in a cloud data center. Ding et al attempt to answer the fundamental question of “how much information regarding the dynamic property of the original time series can be extracted from these networks.” Lai et al study the issue of improving the performance of Markov chain Monte Carlo method to solve local PageRank problem under General Purpose Graphics Processing Unit environment. In order to effectively solve this problem, Bayes belief model is applied by Xing et al to generate the initial dispensation plan, and learnable ant colony optimization is proposed to solve task scheduling subproblem. Bai analyzes the characteristics and meaning of intelligent manufacturing systems, which leads to problems facing the Intelligent Manufacturing System and then dispersing the benefits of SDN. Two models of the scheme are verifiable by Tang and Cai, supporting location query on homomorphic encrypted ciphertext with searching index and trapdoor. Yang et al aim to present a method to make calibration procedure intelligent and automatic by machine. Wang et al identify that the optimum resolution in the study area is 10 m in digital elevation model. Yan et al formulate the contaminant source identification problem into an optimization problem and then design the cultural algorithm to solve it by considering different sizes of water supply networks as the experimental data. The discussion on the causes of educational service quality was complemented by Li et al from the perspective of social network, especially through analysis at the team level. Zhang et al study the complexity and information flow of stock time series and construct the stock influence network on the basis of transfer entropy. Aiming at lacking in trend prediction and evaluation of network public opinion, the attributes of monitoring index system of network public opinion are reduced by Chen et al to using rough set theory and construct a more scientific monitoring index system of network public opinion combined with the real life, determine the weight of the index using the analytic hierarchy process, and present a novel method of trend prediction and evaluation of network public opinion on the basis of fuzzy comprehensive evaluation model from the aspects of the quantitative and qualitative.