… machine learning lies in exploiting historical data to improve the performance of sensor
networks … More specifically, machine learning is important in WSN applications for the following …

… Routing data place a major role in improving the network lifetime. The dynamic behavior of
sensor network requires dynamic routing mechanisms to enhance the system performance. …

… , bandwidth requirement, network lifetime maximization, communication protocols and state
of the art infrastructure. In this paper, the authors propose machine learning methods as an …

… sensor networks (WSNs) are typically used with dynamic conditions of task-related environments
for sensing(monitoring) and gathering of raw sensor … problem, machine learning (ML) …

… This paper provides a convenient reference for wireless sensor network infrastructure and
… from machine learning algorithms by reducing the security costs of wireless sensor networks …

… sensor network from a machine learning and signal processing perspective. Unlike current
sensor network books, this book has contributions from world-famous sensing experts that …

… Usually, sensor nodes are deployed in hazardous environments where we leave the network
… challenges in Wireless Sensor Networks that are handled by machine learning techniques. …

… of the environment where nodes in the network are deployed. … sensor networks and
diverse opportunities that are available to overcome these challenges using machine learning …

… machine learning algorithms to detect anomalous readings in medical WSNs. We will compare
the performance of different machine learning … detection and machine learning algorithms …

… As sensor nodes have limited power, information transmission, packet latency and … network.
This review discusses various WSNs challenges with the system based on machine learning…