An Efficient Information Maximization Based Adaptive Congestion Control Scheme In Wireless Sensor Network

An Efficient Information Maximization Based Adaptive Congestion Control Scheme in Wireless Sensor Network

Abstract:

Intelligent communication technology can enhance user experience and improve people’s lifestyle. However, due to the limited resources of wireless sensor networks (WSNs), the congestion occurs when the node’s traffic load exceeds its available capacity. Congestion could cause serious problems such as high packet loss and low throughput, which is an extremely deleterious impact on the performance of WSNs. To tackle this issue, a differentiated rate control data collection (DRCDC) scheme is proposed to avoid or alleviate the network congestion. Different from the previous scheme, the DRCDC scheme can mitigate the congestion by intelligently reducing the data that contains less information, and maintain the distortion rate of information collected by the network is small. The DRCDC mainly avoids or mitigates congestion on the spatial and temporal through information entropy theory, and at the same time makes the information collected by the network has the lowest distortion rate. Congestion mitigation in spatial. Since there is a spatial correlation between sensing data, so in the case of collecting a certain amount of data, some missing data in space can be recovered from another spatially acquired knowledge by matrix completion theory. Therefore, within the DRCDC theme, once congestion happens, the number of data transmitted (forwarded) for nodes are congested will be reduced by an intelligent approach, thereby having the ability to mitigate congestion. Congestion mitigation in temporal. Once congestion occurs, reducing the data collection of time slots containing small information to reduce the flow rate that needs to be transmitted, thereby avoiding congestion while minimizing data distortion rate of the network