Impact of AI and Machine Learning in Smart Sensor Networks for Health Care

Abstract

Recently, one of the evolving technologies that has huge influence in the arena of research is Wireless Smart Sensor Networks (WSSN). The WSSN is fortified with an arrangement which incorporates components for observing, calculating and communication. These networks can perceive and respond to procedures or occurrences in an indicated ambience. WSSN signifies the subsequent evolutionary expansion stage in engineering like ecological observation, industrial mechanization, traffic observation and robot control. WSSN has several exclusive features like power ingestion, dimension, low cost, scalability, agility and elasticity. Artificial Intelligence (AI) is the replication of human acumen procedures by computer systems. Of late, AI procedures have been used efficaciously to resolve issues in engineering. AI comprises of skilled systems, speech identification, machine learning, deep learning platforms and robotic process automation. Machine Learning (ML) has been propelled as an exclusive technique for AI. ML can be demarcated as the learning procedures for enhancement of computer models that can augment the act of systems. Recently, use of ML technologies in automation of health care systems has been proficient. For diagnosing the diseases and predicting the risks of critical diseases in advance, ML could be applied in Internet of Things (IoT) based WSN. The ML comprises of supervised, semi supervised, unsupervised and reinforcement learning methods. This chapter offers an exclusive study of machine learning approaches utilised on WSSN which can be applied in health care systems.