Wireless sensor networks (WSNs) have emerged to address unique challenges in urban environments. This survey dives into the challenges faced in urban areas and explores how WSN applications can help overcome these obstacles. The diverse applications of WSNs in urban settings discussed in this paper include gas monitoring, traffic optimization, healthcare, disaster response, and security surveillance. The innovative research is considered in an urban environment, where WSNs such as energy efficiency, throughput, and scalability are deployed. Every application scenario is distinct and examined in details within this paper. In particular, smart cities represent a major domain where WSNs are increasingly integrated to enhance urban living through intelligent infrastructure. This paper emphasizes how WSNs are pivotal in realizing smart cities by enabling real-time data collection, analysis, and communication among interconnected systems. Applications such as smart transportation systems, automated waste management, smart grids, and environmental monitoring are discussed as key components of smart city ecosystems. The synergy between WSNs and smart city technologies highlights the potential to significantly improve the quality of life, resource management, and operational efficiency in modern cities. This survey specifies existing work objectives with results and limitations. The aim is to develop a methodology for evaluating the quality of performance analysis. Various performance metrics are discussed in existing research to determine the influence of real-time applications on energy consumption, network lifetime, end-to-end delay, efficiency, routing overhead, throughput, computation cost, computational overhead, reliability, loss rate, and execution time. The observed outcomes are that the proposed method achieves a higher 16% accuracy, 36% network lifetime, 20% efficiency, and 42% throughput. Additionally, the proposed method obtains 36%, 30%, 46%, 35%, and 32% reduction in energy consumption, computation cost, execution time, error rate, and computational overhead, respectively, compared to conventional methods.

Cloud computing enables users to access and utilize various computing resources over the internet, including servers, storage, databases, and analytics. This paradigm offers flexibility, scalability, and cost efficiency, making it a critical technology for numerous applications. In the realm of the Internet of Things (IoT), cloud computing provides a scalable and flexible infrastructure for managing the vast amount of data generated by IoT devices. Specifically, in Industrial IoT applications (IIoT), predictive maintenance has become a key focus, leveraging advanced technologies to forecast equipment failures and minimize downtime. However, achieving high accuracy in fault prediction remains a challenge. To address this, we propose a novel approach called Brokenstick Regression-based multiobjective dragonfly predictive optimization (BR-MDPO). This method aims to optimize predictive maintenance with enhanced accuracy and execution time (ET). The process begins with IoT devices collecting data, such as vibration, temperature, speed, torque, and operational hours, from industrial machinery. This data is then sent to centralized cloud data centers for predictive analysis. The BR-MDPO technique utilizes the Multiobjective Dragonfly Optimization algorithm, a metaheuristic inspired by the natural behavior of dragonflies, to solve multiobjective optimization problems. Brokenstick regression analyzes the data to optimize various objective functions. The technique identifies potential failures, facilitating proactive maintenance and informed decision-making to ensure continuous productivity. The proposed method shows a significant improvement in accuracy, precision, and recall by 7%, 5%, and 6%, respectively. The observed results reveal a 6%, 4%, and 5% enhancement in the accuracy, precision, and recall. Furthermore, the proposed technique realizes a substantial reduction in error rate by 68%, 15%, and 13% reduction in ET as well as latency compared to conventional methods.