Implicit Study of Techniques and Tools for Data Analysis of Complex Sensory Data

The utility as well as contribution of applications in Wireless Sensor Network (WSN) has been experienced by the users from more than a decade. However, with the evolution of time, it has been found that there is a massive growth of data generation even in WSN. The smaller size of sensor with limited battery life and minimal computational capability cannot handle processive such a massive stream of complex data efficiently. Although, there are various types of mining techniques being practiced today, but such tools and techniques cannot be efficiently used for analyzing such complex and massively growing data. This paper therefore discusses about the generation of large data and issues of the existing research techniques by reviewing the literatures and frequently used tools. The study finally briefs about the significant research gap that calls for need of data analytical tools in extracting knowledge from complex sensory data

Analysis cloud: Running sensor data analysis programs on a cloud computing infrastructure

Sensors have been used for many years to gather information about their environment. The number of sensors connected to the internet is increasing, which has led to a growing demand of data transport and storage capacity. In addition, evermore emphasis is put on processing the data to detect anomalous situations and to identify trends. This paper presents a sensor data analysis platform that executes statistical analysis programs on a cloud computing infrastructure. Compared to existing batch and stream processing platforms, it adds the notion of simulated time, i.e. time that differs from the actual, current time. Moreover, it adds the ability to dynamically schedule the analysis programs based on a single timestamp, recurring schedule, or on the sensor data itself