# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 1 Introduction  
As the future trend of city development, the smart city (SC) can provide convenient services for people [1]. The SC covers many domains, such as urban infrastructure, resident living environment, transportation management, medical treatment, shopping, security assurance, and so on. It is worth noting that the study of the SC starts with the smart home (SH), which is often treated as the basic unit of the SC. In the early days of SH, the concept of smart product is proposed and these products are often work without networking and inter-connection [2]. Recently, as the development of the Internet of Things (IoT), multiple products/components of SHs are connected to work in a collaborative fashion, and the SH can provide more meticulous services [3], for example, energy management [4], patient assistance [5], real-time product labelling [6] and subscribing [7]. Today, a massive number of smart devices are joining the Internet, and the IoT technologies are in empowering various applications in the SC beyond the SH [8]. In addition to physical devices, some virtual objects are also included in SCs (e.g., properties of objects, data generated by devices and human characteristics). The SC may consist of various scenarios, as shown in Figure 1, including the Smart Grid (SG), Intelligent Transportation System (ITS), Intelligent Medical Diagnosis (IMD), Shopping Recommender System (SRS), and etc. To elaborate further, the SG can optimize the power supply to reduce the total energy consumption. The ITS can provide the best trip route for travelers. The IMD, on the other hand, can suggest reasonable medical plans according to the patient's situation. Finally, the SRS can recommend appropriate products according to users' demands. It is noted that these typical application scenarios in SC are based on the intelligent transmission and processing of amassive amount of data collected from various devices/objects in the SC. Moreover, the intelligent services that help users to make smarter decisions are increasingly popular, because the quality of experience (QoE) is increasingly important in SC applications [9]. However, the demand for intelligent services requires an extremely strong data processing capability, which motivates the artificial intelligence (AI) empowered system in the SH [10]. More specifically, AI uses various learning techniques to facilitate automatic resource provision and judicious decision making. Therefore, the SC empowered with AI is expected to be intelligent [11]. In academia, AI has been studied for over 60 years and more and more practical applications are emerging in recent years. One of the key applications is to make quick and optimal decisions according to real-time situations, and it has been demonstrated recently that AI can outperform human beings in many areas of interests, for example, the Google's recent AI application Alpha Go [12]. In general, the AI application can be divided into two modules. The first and the most important one is the model-learning module, which is mainly responsible for effective data collection, data training and modeling. For example, the model learning module of the Alpha Go program should learn how to win the game. It must be trained by learning a large number of game situations/solutions in the stage of data collection and training. The corresponding solutions/strategies are summarized, and the corresponding learn-and-decide model is constructed in the modeling stage. On the other hand, another module is the predicting module, which is responsible for making actions to respond to the current situation. For example, the predicting module of Alpha Go can analyze the current game situation according to existing solution models, then, the best solution is implemented based on the analysis result. Conventional AI applications are usually developed for specific application scenarios or objects, in addition, they require in-depth customization [13].