The field of measurement technology in the sensors domain is rapidly changing due to the availability of statistical tools to handle many variables simultaneously. The phenomenon has led to a change in the approach of generating dataset from sensors. Nowadays, multiple sensors, or more specifically multi sensor data fusion (MSDF) are more favourable than a single sensor due to significant advantages over single source data and has better presentation of real cases. MSDF is an evolving technique related to the problem for combining data systematically from one or multiple (and possibly diverse) sensors in order to make inferences about a physical event, activity or situation. Mitchell (2007) defined MSDF as the theory, techniques, and tools which are used for combining sensor data, or data derived from sensory data into a common representational format. The definition also includes multiple measurements produced at different time instants by a single sensor as described by (Smith & Erickson, 1991).

Although the concept of MSDF was first introduced in the 1960s and implemented in the 1970s in the robotic and defense application, today, the application of MSDF has proliferated into various nonmilitary applications. However the method is still disparate, where it is impossible to create a one-fits-all data fusion framework. The applications of MSDF are now multidisciplinary in nature. Some specific applications of MSDF include multimodal biometric systems using face and palm-print (Raghavendra et al., 2011); renewable energy system (Li et al., 2010); color texture analysis (Wu et al., 2007); face and voice outdoor multibiometric system (Vajaria et al., 2007); medical decision making (Harper, 2005); image recognition (Sun et al., 2005), road traffic accidents (Sohn et al., 2003); and personal authentication (Duc et al., 1997; Kumar et al., 2006).