Utilization of IoT-assisted Computational Strategies in Wireless Sensor Networks for Smart Infrastructure Management

At the moment, Internet of things (IoT) breakthroughs and implementations are facilitating intelligent city initiatives and operations all over the world. The core theme of IoT has been supporting the design of sustainable building models with standard architecture to boost optimal energy conservation and its efficiency. Wireless sensor networks (WSNs), which are associated with IoT, represent functional networks in assisting the monitoring, tracking and sensing different environmental activities. Sensors characteristics play the leading role in designing and applying any WSN. The main problem of coverage factor, predictive event score, dependability ratio, error effect, and energy usage in the sensing node relies on the average node timings in WSN for the power-sharing and processing of communication networks that mainly includes information exchange. This study primarily dealt with formulating and implementing a Hybridized IoT-Assisted Hierarchy-based Computation Strategy (HIHCS) technique and an Active Randomized Optimization Strategy (AROS) to help alleviate the energy problems in a Smart City surveillance using WSN. The energy-controlled sensor node also negotiates many network-related tasks and improves energy utilization and detection precision throughout data processing by adopting an optimal cluster node. The experiments revealed that both the HIHCS and AROS methods could enhance energy savings, especially during cluster node selection in WSN. Ultimately, the result proves that the proposed system improvises the performance of WSN-bound IoT based smart city applications, which are evident through the average resultants: performance ratio (83.97%), event predictive score (77%), dependability ratio (86.23%), energy consumption (58.96%), coverage factor (70.43), and error effect (27.91%).