# Artificial Intelligence-Based Semantic Internet of Things in a User-Centric Smart City
## 2 Related Work  
Recently, the study of SC architecture has attracted ample attention from both academia and industry. The main goal of the SC is to understand the requirements of users and provide appropriate services accordingly. The requirements of users can be acquired by analyzing user activities. For example, healthcare requirements can be discovered according to some special activities of patients, e.g., those who suffer from [PERSON]'s disease show a typical syndrome. Disease impairs people's daily activities, and some novel SC applications should be developed to aid the patients. [PERSON] et al. proposed a clinical assessment method according to the behavioral data in a smart home [14]. The method constructs an activity assessment model that evaluates the activities of daily living based on related clinician-provided cognitive assessment scores. The abnormal activity can be discovered, and the related diseases are predicted according to the evaluation scores. Similarly, [PERSON] et al. proposed a Bayesian activity model based on the temporal energy consumption patterns of applications in smart homes [15]. Interestingly, the anomalous activity can be recognized by analyzing the energy usage changes, and the healthcare services can be provided accordingly. [PERSON] et al. proposed a behavioral pattern discovering method based on the time and space factors in the smart home [16]. The associations between different behavioral patterns are considered, and the pattern transitions are modeled by the third order Markov chain. The safety requirements can also be addressed by analyzing the environmental data detected and collected from people's activities, smart applications and building monitors. [PERSON] et al. proposed an approach to support judicious decision for safety services based on the semantic ontology model in a wireless sensor network [17]. The approach is able to explore the target context and recognize risk factors through reasoning, and a series of ontology models are constructed. In the aspect of intelligent service provisions, some IoT and SC architectures have been proposed in recent literature. [PERSON] et al. proposed an IoT architecture that integrates smart homes and smart cities through the Cloud-of-Things (CoT) [18]. The architecture updates data from IoT devices to the cloud, and the intelligent service is provided through the AI system. [PERSON] et al. proposed an intelligent resource inquisition framework with three layers, which are perception, discovery, and application [19]. The architecture can access the context information and provide services through a semantic match-making engine based on ontology models. [PERSON] et al. proposed a simplified architecture that provided services through a hybrid data processing model, including historical data analysis and real-time analysis [20]. This architecture supports data ingestion, data retrieval and machine learning to determine the services to be provided. [PERSON] et al. proposed a semantic rule engine (SRE) for industrial gateways [21]. The SRE can handle semantic queries and infer the required services. In the aspect of mobile crowd sensing for SC, [PERSON] et al. [22; 23; 24] presented a review on Mobile Crowd Sensing (MCS) and proposed a novel framework of Mobile Crowd Sensing and Computing (MCSC), which allow mobile users share their personal data. The framework can realize explicit/implicit sensing and heterogeneous, cross-space data mining. [PERSON] et al. also presented the fusion of human and machine intelligence to select the proper human to meet the specific needs. [PERSON] et al. [25] proposed a MCS platform in SC to sense users' activities and opinions, give suggestions about the noise abatement interventions to city managers and provide low-cost, large-scale monitoring tool for potential noise pollution risks. [PERSON] et al.