Effective Data Mining with Smart City Scheduling using Recurrent Encoder Neural Networks - Kavitha S. (International Journal of Intelligent Systems and Applications in Engineering, 2024)
Urban areas have drastically increased their population, which results in a shortage of resources like transportation, electricity, water, housing, public services, etc. Therefore, it is important to have a strategy for urban area improvement with the aid of a smart city, which is more apparent when using Wireless Sensor Networks. This study suggests a brand-new, machine learning-based technique for effective data mining for scheduling in smart cities. This works aims to initiate a data prediction method through Recurrent Neural Network, namely recurrent encoder neural netwoks (REncNN) reshold Denoising then remove and discover abstract features of sensory data. Hence predicted data are scheduled in multi-layer network design to contain sensor/device networks in the encoder block of the network. The simulation is done in MATLAB by picking the parameter such as Packet Delivery Ratio (PDR), throughput, network lifetime, Prediction rate, RMSE, RAE. As a result, the proposed REncNN achieves 89.84% of Packet Delivery Ratio, 95.34% of throughput, 81% of network lifetime, 80.94% of prediction rate, 43.12% of MAE, 44.32% of RMSE and 41.92% of RAE.