SIGNIFICANCE OF DATA FUSION ALGORITHMS IN IOT ENVIRONMENT-A REVIEW - SHENBAGAVADIVU, S.; M. SENTHIL, KUMAR; B. CHIDHAMBARA, RAJAN (I-Manager's Journal on Information Technology, 2019)
Data fusion is a multidisciplinary research area with a boundless range of applications in numerous domains including defense, robotics, automation, Intelligent Transportation Systems (ITS), IoT and machine learning. Detection and tracing of a moving object is a vital task in mobile robotics as well as in the field of ITS. Due to its critical role, data fusion has been extensively studied in the recent decades. Computational Intelligence would become the challenging factor to integrate real time data generated from various sensory nodes to implement Internet of Things (IoT) products such as Smart Agriculture, Smart City, Smart Wheel Chair, Smart Healthcare System, etc. The methods discussed under Data Fusion methods are Kalman Filter and Distributed Kalman Filter. Deep learning and neural network-based fusion at symbol level is analyzed in this paper. This paper also aims to provide high level future research directions of data fusion techniques for Internet of Things environment.