Abstract: Data gathering is one of most important functions provided by WSNs, where sensor readings have to be collected from sensor nodes to one or few data collection sinks node. Due to the fact that there may exist high correlations among these sensor readings, it is inefficient to directly deliver raw data to the destination(s). In this paper the application of CS with random walks for data gathering in WSNs. The proposed system adopts the standard random walk algorithm to collect random measurements along multiple random paths. However, such an approach will lead to the non-uniform selection of measurements and it is still unknown whether such an approach can be used to recover sparse signals in a WSN scenario. However, previous works focus on finding the movement patterns of each single object or all objects. This survey of efficient distributed mining algorithm to identify a discover their movement patterns and group of moving objects in wireless sensor networks. The compression algorithm includes a sequence of merge and entropy reduction phases. In the sequence merge phase, a Merge algorithm is to merge and compress the location data of a group of moving objects. The survey results show that the proposed compression algorithm leverages the group movement patterns to reduce the amount of delivered data effectively and efficiently.
Keywords: Compressive Sensing, Data Gathering, Random Walk, Wireless Sensor Network.