Integrating IoT-Driven Decision Systems for Adaptive Urban Planning in Smart Cities

The Smart City (SC) concept is enabled by the rapid growth of connected devices, driving the Internet of Things (IoT). Multiple components, such as intelligent transportation, smart belonging, competent medical care, and smart grid, integrated within SC design, seek to enhance urban residents' Quality of Life (QoL). The demands of real-time processing and the exponential increase in data hinder the implementation of SC. The research offers an experimental design for SC incorporating Big Data (BD) Analytics (BDA). The The BDA-embedded SC involves two basic aspects. First, it uses Urban Big Data (UBD) for planning, construction and maintenance of SC infrastructure. Secondly, it engages BDA to handle and analyze extensive UBD to improve the quality of urban operations. The suggested architecture consists of three levels for data gathering, real-time data administration, and service supply. Integrating data normalization and filtration algorithms accelerates the proposed study's offline and online analysis activities. The research acquired the threshold levels necessary for urban planning and municipal operations management by analysing verified datasets. The efficiency measurements for on- and off-line information processing of the suggested dual-node Hadoop cluster are derived using the previously described genuine databases. Investigating the current works' throughput and processing time confirms the proposed work's efficiency and superiority. Therefore, the research asserts the feasibility and dependability of using the suggested BDA-embedded SC design in practical settings.