Localization Techniques Using Machine Learning Algorithms

Wireless sensor networks monitor environments that amendment apace over time. This dynamic behavior of the networks is either caused by external factors or initiated by the system itself. Machine learning techniques help us to work with extreme conditions and assist in avoiding the redesign of the network. The prominent feature of training the machine or network itself to modify according to such kinds of environments is being introduced in the sensor networks using machine learning techniques. However, the performance of the sensor networks has many constraints like energy efficiency, information measure or bandwidth, etc. Localization of nodes is one of the major issues that have to be worked on, as proper placement of nodes solves above-mentioned performance issues. The sensors in wireless networks gather knowledge regarding the objects they are to be sensed by which machine learning algorithms conjointly evoke several sensible solutions for localization of nodes that maximize resource utilization and prolong the lifetime of the network. The machine learning algorithms are categorized into three categories, namely supervised learning, unsupervised learning and reinforcement learning algorithms. As localization is the method of deciding the geographic coordinates of network’s nodes and its relevant components as position awareness of sensing element of every sensor nodes plays a vital role in network communication for further process. In this chapter, we are going to focus on how the localization issue in wireless sensor networks can be solved using the three categorized machine learning algorithms.