## 2 Background
Energy is needed to handle interference during retransmissions for the network's operational lifespan. Interference is a complex routing protocol for choosing the paths to reduce signal interference and guarantee reliable data delivery. The practical deployment challenges comprise node position and coverage optimization, energy consumption management, environmental factors impacting signal quality, network connectivity problems, complexity in replacing or recharging batteries in remote locations, security vulnerabilities, and deployment scenarios. The network is preserved for sufficient sensing coverage for the monitored area. Due to different laws, policies, and guidelines, regulatory hurdles in WSNs refer to the challenges that arise when implementing, managing, and deploying WSNs. These networks often involve wireless communication technologies that must comply with regulations like spectrum usage, data privacy, safety standards, and network security. Various techniques have been proposed for monitoring and forecasting environmental factors using IoT-enabled sensors in urban areas. A method presented in [PERSON]. [PERSON] et al. (2024) [1] focuses on the accurate and reliable monitoring and forecasting of air pollution at various scales, integrating data from IoT sensors and state-run air quality monitoring stations. However, the accuracy of the system was not improved. Another approach discussed by [PERSON] et al. (2024) [2] involves a wireless environmental sensing system developed for monitoring soil moisture dynamics across seven diverse urban green spaces over a year. Despite its utility, the system failed to reduce execution time. A similar wireless sensor network (WSN)-based urban monitoring system was proposed by [PERSON] et al. (2023) [3] for monitoring the structural health of houses located on hillsides prone to land movement. Unfortunately, energy consumption was not reduced as anticipated. Remote sensing technology, as outlined by [PERSON] et al. (2024) [4], uses high-resolution urban green space images, applying an image enhancement algorithm to improve the clarity and recognition of these images. However, this approach did not succeed in reducing computational costs. Furthermore, a new framework proposed by [PERSON] et al. (2024) [5] adopts WSNs for remote sensing and monitoring in smart city applications. Despite its promising capabilities, the framework did not address the underlying time complexity issues. A dependable monitoring network designed by [PERSON] and [PERSON] (2024) [6] aimed to achieve high availability with energy consumption and data assurance, yet it failed to reduce execution time. In the realm of cost-effective architecture, the Smart Sensor Surveillance System (4S-UEM) was developed by [PERSON] et al. (2023) [7] for environmental monitoring in urban zones of smart cities. On a similar note, the research by [PERSON] et al. (2023) [8] presented the status of ocean environment monitoring using WSNs, including the general architecture of WSN-based ocean environment systems. However, it did not succeed in reducing time complexity. Additionally, a smart clustering model combined with trust-based spectrum sensing, introduced by [PERSON] (2025) [9], extends network lifetimes by optimizing energy consumption, but execution time remained unchanged. The study by [PERSON] et al. (2022) [10] explored the time of pollution detection as a random variable. However, the study did not account for the cumulative distribution function of air pollution detection time by mobile sensors, which remains a critical consideration in this context. WSNs often operate in harsh or remote environments, making efficient node deployment critical. Optimizing deployment reduces redundancy and cost while enhancing energy efficiency, coverage, and network lifetime. A recent approach uses perceived environmental awareness in [11] to deploy sensor nodes, effectively minimizing blind spots and adapting to various application scenarios.