urban infrastructure

Background: Smart city technology solutions have recently ramped up the utilization of drones with Internet of Things (IoT) technologies for improving smart city systems. IoT sensors combined with real-time communication ad hoc network drones are also another area with great potential including traffic monitoring, environment management, disaster management, etc. Nevertheless, issues regarding energy consumption and density, the number of nodes that can be incorporated into the network, as well as the issue of avoiding collisions between the signal sent by one node with the signals that may be transmitted by other nodes are still observed as essential impediments to the wide application of WSNs. Objective: The article seeks to propose and assess algorithms for operating drone-IoT systems whilst dealing with issues like energy efficiency, real-time data communication, avoiding mid-air collisions, and dealing with the increasing number of systems in crowded urban areas. Methods: This study utilizes a two-time algorithm technique that was adopted from the prior study. The first algorithm provides a method for speed and position control of drones, ensuring that the distance between the drones is sufficient and not violable. The second algorithm is centered on energy reduction, which selects the precise energy usage by employing path planning in real time. The effectiveness of these algorithms was determined using simulation models with respect to metrics including latency, energy consumption, and scalability. Results: The proposed system revealed the systems’ improvements in energy efficiency, fewer collisions, and strong scalability of drone management. Main conclusions possible to conclude during the experiment reveal the system’s generic aptitude to the different urban situations and its stability in changing traffic conditions. Conclusion: The article presents a scalable and efficient solution for extending drone applications to smart cities using IoT platforms. In this way, the results can serve as the further theoretical and experimental base for investigating the trends of management and the infrastructure of cities.

Background: Digital twin (DT) technologies have become significant enablers of urban management, utilising real-time information, data analytics, and IoT connectivity to manage challenging urban issues. Nonetheless, existing studies reveal the capacity of the DTs, while their generalization, flexibility, and cross-disciplinary application for various urban environments are not thoroughly studied yet. Objective: This article aims to evaluate the effectiveness of DT technologies in improving traffic management, energy efficiency, infrastructure maintenance, and public safety across six case study cities: There are Singapore, Helsinki, Barcelona, Dubai, New York, and Tokyo. The study examines how DTs can be extended and implemented to target urban issues and how their use operational performance might be optimized. Methods: The study used quantitative data processing, on-line data analysis with factorization and machine learning, and assessment of the case studies. Quantitative measures which included traffic flow, energy loss, down time, and response to emergency situations were investigated pre and post DT application. The improvements mentioned were statistically confirmed, and the metrics of scalability and adaptability were evaluated in the course of the cities. Results: DT technologies increased traffic flow by up to 42.9%, reduced energy losses by 35%, minimum down time was 42%, emergency response was 44.9%. This was the case because the network had high IoT coverage and because DTs were applied to the context when it specifically needed them. Conclusion: The study proves that DTs can be implemented in different environments due to their flexibility to accommodate different urban conditions. AI and cross domain integration can add to the effectiveness of DT in general and both are inarguably now crucial for the management of contemporary urban environment.