No Arabic abstract
The advancement of various research sectors such as Internet of Things (IoT), Machine Learning, Data Mining, Big Data, and Communication Technology has shed some light in transforming an urban city integrating the aforementioned techniques to a commonly known term - Smart City. With the emergence of smart city, plethora of data sources have been made available for wide variety of applications. The common technique for handling multiple data sources is data fusion, where it improves data output quality or extracts knowledge from the raw data. In order to cater evergrowing highly complicated applications, studies in smart city have to utilize data from various sources and evaluate their performance based on multiple aspects. To this end, we introduce a multi-perspectives classification of the data fusion to evaluate the smart city applications. Moreover, we applied the proposed multi-perspectives classification to evaluate selected applications in each domain of the smart city. We conclude the paper by discussing potential future direction and challenges of data fusion integration.
Data-driven approaches have been applied to many problems in urban computing. However, in the research community, such approaches are commonly studied under data from limited sources, and are thus unable to characterize the complexity of urban data coming from multiple entities and the correlations among them. Consequently, an inclusive and multifaceted dataset is necessary to facilitate more extensive studies on urban computing. In this paper, we present CityNet, a multi-modal urban dataset containing data from 7 cities, each of which coming from 3 data sources. We first present the generation process of CityNet as well as its basic properties. In addition, to facilitate the use of CityNet, we carry out extensive machine learning experiments, including spatio-temporal predictions, transfer learning, and reinforcement learning. The experimental results not only provide benchmarks for a wide range of tasks and methods, but also uncover internal correlations among cities and tasks within CityNet that, with adequate leverage, can improve performances on various tasks. With the benchmarking results and the correlations uncovered, we believe that CityNet can contribute to the field of urban computing by supporting research on many advanced topics.
Public space utilization is crucial for urban developers to understand how efficient a place is being occupied in order to improve existing or future infrastructures. In a smart cities approach, implementing public space monitoring with Internet-of-Things (IoT) sensors appear to be a viable solution. However, choice of sensors often is a challenging problem and often linked with scalability, coverage, energy consumption, accuracy, and privacy. To get the most from low cost sensor with aforementioned design in mind, we proposed data processing modules for capturing public space utilization with Renewable Wireless Sensor Network (RWSN) platform using pyroelectric infrared (PIR) and analog sound sensor. We first proposed a calibration process to remove false alarm of PIR sensor due to the impact of weather and environment. We then demonstrate how the sounds sensor can be processed to provide various insight of a public space. Lastly, we fused both sensors and study a particular public space utilization based on one month data to unveil its usage.
The conventional approach to pre-process data for compression is to apply transforms such as the Fourier, the Karhunen-Lo`{e}ve, or wavelet transforms. One drawback from adopting such an approach is that it is independent of the use of the compressed data, which may induce significant optimality losses when measured in terms of final utility (instead of being measured in terms of distortion). We therefore revisit this paradigm by tayloring the data pre-processing operation to the utility function of the decision-making entity using the compressed (and therefore noisy) data. More specifically, the utility function consists of an Lp-norm, which is very relevant in the area of smart grids. Both a linear and a non-linear use-oriented transforms are designed and compared with conventional data pre-processing techniques, showing that the impact of compression noise can be significantly reduced.
The Internet of Things (IoT) is the enabler for smart city to achieve the envision of the Internet of Everything by intelligently connecting devices without human interventions. The explosive growth of IoT devices makes the amount of business data generated by machine-type communications (MTC) account for a great proportion in all communication services. The fifth-generation (5G) specification for cellular networks defines two types of application scenarios for MTC: One is massive machine type communications (mMTC) requiring massive connections, while the other is ultra-reliable low latency communications (URLLC) requiring high reliability and low latency communications. 6G, as the next generation beyond 5G, will have even stronger scales of mMTC and URLLC. mMTC and URLLC will co-exist in MTC networks for 5G 6G-enabled smart city. To enable massive and reliable LLC access to such heterogeneous MTC networks where mMTC and URLLC co-exist, in this article, we introduce the network architecture of heterogeneous MTC networks, and propose an intelligent hybrid random access scheme for 5G/6G-enabled smart city. Numerical results show that, compared to the benchmark schemes, the proposed scheme significantly improves the successful access probability, and satisfies the diverse quality of services requirements of URLLC and mMTC devices.
Urban conditions are monitored by a wide variety of sensors that measure several attributes, such as temperature and traffic volume. The correlations of sensors help to analyze and understand the urban conditions accurately. The correlated attribute pattern (CAP) mining discovers correlations among multiple attributes from the sets of sensors spatially close to each other and temporally correlated in their measurements. In this paper, we develop a visualization system for CAP mining and demonstrate analysis of smart city data. Our visualization system supports an intuitive understanding of mining results via sensor locations on maps and temporal changes of their measurements. In our demonstration scenarios, we provide four smart city datasets collected from China and Santander, Spain. We demonstrate that our system helps interactive analysis of smart city data.