Machine Learning in Wireless Sensor Networks for Smart Cities: A Survey

Artificial intelligence (AI) and machine learning (ML) techniques have huge potential to efficiently manage the automated operation of internet things (IoT) nodes deployed in smart cities. In cities, major IoT applications are traffic monitoring, waste management, buildings patient healthcare monitoring. The small size based on low power Bluetooth (IEEE 802.15.1) standard wireless sensor networks (WSN) 802.15.4) generally used for transmission data a remote location using gateways. WSN (WSN-IoT) design problems include network coverage connectivity issues, energy consumption, bandwidth requirement, lifetime maximization, communication protocols state art infrastructure. this paper, authors propose methods as an optimization tool regular WSN-IoT city applications. As per author’s knowledge, is first in-depth literature survey all ML field consumption results unique article show that supervised algorithms been most widely (61%) compared reinforcement (27%) unsupervised (12%)