Systematic literature review of context-awareness applications supported by smart cities’ infrastructures

Smart City Applications to Promote Citizen Participation in City Management and Governance: A Systematic Review

This systematic review aimed to provide a comprehensive view of (1) the purposes of research studies using smart city infrastructures to promote citizen participation in the cities’ management and governance, (2) the characteristics of the proposed solutions in terms of data sources, data quality, and data security and privacy mechanisms, as well, as strategies to incentivize citizen participation, and (3) the development stages of the applications being reported. An electronic search was conducted combining relevant databases and keywords, and 76 studies were included after a selection process. The results show a current interest in developing applications to promote citizen participation to identify urban problems and contribute to decision-making processes. Most of the included studies considered citizens as agents able to report issues (e.g., issues related to the maintenance of urban infrastructures or the mobility in urban spaces), monitor certain environmental parameters (e.g., air or acoustic pollution), and share opinions (e.g., opinions about the performance of local authorities) to support city management. Moreover, a minority of the included studies developed collaborative applications to involve citizens in decision-making processes in urban planning, the selection of development projects, and deepening democratic values. It is possible to conclude about the existence of significant research related to the topic of this systematic review, but also about the need to deepen mechanisms to guarantee data quality and data security and privacy, to develop strategies to incentivize citizen participation, and to implement robust experimental set-ups to evaluate the impact of the developed applications in daily contexts.

Edge Computing Based on Federated Learning for Machine Monitoring

This paper focused on providing a general solution based on edge computing and cloud computing in IoT to machine monitoring in manufacturing of small and medium-sized factory. For real-time consideration, edge computing and cloud computing models were seamlessly cooperated to perform information capture, event detection, and adaptive learning. The proposed IoT system processed regional low-level features for detection and recognition in edge nodes. Cloud-computing including fog computing was responsible for mid- and high-level features by using the federated learning network. The system fully utilized all resources in the integrated deep learning network to achieve high performance operations. The edge node was implemented by a simple camera embedded on Terasic DE2-115 board to monitor machines and process data locally. Learning-based features were generated by cloud computing through the data sent from edge and the identification results could be obtained by combining mid- and high-level features with the nonlinear classifier. Therefore, each factory could monitor the real-time condition of machines without operators and keep its data privacy. Experimental results showed the efficiency of the proposed method when compared with other methods.