An Improved Compressive Sensing Data Gathering in Wireless Sensor Networks

In wireless sensor networks (WSNs) the sampling rate of the sensors determines the pace of its energy use since most of the energy is used in sampling and transmission. In wireless sensor network (WSN) there are two main problems in employing conventional compression techniques. Recent advances in technologies have increased the use of wireless sensor networks in different applications like chemical and physical monitoring, healthcare, tracking and soon. The compression performance depends on the organization of the routes for a larger extent. The efficiency of an in-network data compression scheme is not solely determined by the compression ratio, but also depends on the computational and communication overheads. Propose a sparser analysis that depends on modified diffusion wavelets, which exploit sensor readings' spatial correlation in WSNs. In particular, a novel data-gathering scheme with combine routing and CS is presented. A modified ant colony algorithm-based diffusion wavelets (ACBDW), where next hop node selection takes a node’s residual energy and path length into consideration simultaneously. The simulation results, show that our proposed technique improves the delivery ratio while reducing the energy and delay.