Biblio

Nowadays, microservice architecture is known as a successful and promising architecture for smart city applications. Applying microservices in the designing and implementation of systems has many advantages such as autonomy, loosely coupled, composability, scalability, fault tolerance. However, the complexity of calling between microservices leads to problems in security, accessibility, and data management in the execution of systems. In order to address these challenges, in recent years, various researchers and developers have focused on the use of microservice patterns in the implementation of microservice-based systems. Microservice patterns are the result of developers’ successful experiences in addressing common challenges in microservicebased systems. However, hitherto no guideline has been provided for an in-depth understanding of microservice patterns and how to apply them to real systems. The purpose of this paper is to investigate in detail the most widely used and important microservice patterns in order to analyze the function of each pattern, extract the behavioral signatures and construct a service dependency graph for them so that researchers and enthusiasts use the provided guideline to create a microservice-based system equipped with design patterns. To construct the proposed guideline, five real open source projects have been carefully investigated and analyzed and the results obtained have been used in the process of making the guideline.

Technological innovations are expanding rapidly in the public safety sector providing opportunities for more targeted and comprehensive urban crime deterrence and detection. Yet, the spatial dispersion of crimes may vary over time. Therefore, it is unclear whether and how sensors can optimally impact crime rates. We developed a Smart City Digital Twin-based method to dynamically place license plate reader (LPR) sensors and improve their detection and deterrence performance. Utilizing continuously updated crime records, the convolutional long short-term memory algorithm predicted areas crimes were most likely to occur. Then, a Monte Carlo traffic simulation simulated suspect vehicle movements to determine the most likely routes to flee crime scenes. Dynamic LPR placement predictions were made weekly, capturing the spatiotemporal variation in crimes and enhancing LPR performance relative to static placement. We tested the proposed method in Warner Robins, GA, and results support the method's promise in detecting and deterring crime.