Segmentation and Abstraction of an IoT Enabled Distributed Sensor Network
Yuanhang Shao, Suman Kumar, Takahiro Kawakami · 2019
We propose an area segmentation algorithm which is completely distributed, highly responsive, and exhibits a wide range of application scenarios. The proposed algorithm segments the area based on similarity of local sensor data and therefore, it requires a similarity measure parametrized with selected system indicators. In addition, algorithm creates an energy efficient data aggregation tree with a local highest energy node as a root. The resulting segmented sub-areas represents a level of spatial diversity and an abstraction of the sensor field which has a wide range of large scale distributed applications. Through simulation, the application and working of our scheme is demonstrated.