From its advent in mid-20th century, machine learning constantly improves the user experience of existing systems. It can be used in almost every field such as weather, sports, business, IoT, medical care, etc. Wireless sensor networks are often placed in hostile environments to observe change in surroundings. Since these communicate wirelessly, many problems such as localisation of nodes, security of data being routed create barriers for proper functioning of system. Extending the horizon of machine learning to WSN creates wonders and adds credibility to the system. This chapter aids to present various machine learning aspects applied on wireless sensor networks and the benefits and drawbacks of applying machine learning to WSN. It also describes various data aggregation and clustering techniques that aim to reduce power consumption and ensure confidentiality, authentication, integrity, and availability amongst sensor nodes. This could contribute to design and alter pre-existing ML algorithms to improve overall performance of wireless sensor networks.
RSSI and LQI Data Clustering Techniques to Determine the Number of Nodes in Wireless Sensor Networks
