Sensor data fusion and clustering : A congestion detection and avoidance approach in wireless sensor networks

Abstract

A wireless sensor network is gaining more popularity as it is easy to maintain, manage and inexpensive to establish. A group of sensor nodes with sensing and computing capability are deployed in network. When these nodes are deployed in remote areas, an adequate energy of the nodes becomes major issue. As the sensor nodes are battery operated, so energy is an important resource that must be utilized efficiently. Therefore, the most preferable method to tackle such issue efficiently is to divide the network into clusters and data communication by data fusion techniques. Data fusion is a technique which enables gathering data from several sensor nodes thus to provide a unified scenario in order to reduce redundancy of data which consumes lots of energy. The proposed method is clustered based data fusion to avoid congestion. One node from each cluster is elected as cluster head which is elected after observing various parameters like enduring energy of the node, distance from base station, and chances of traffic in near future. The cluster head node gathers data from cluster members hence applies local level data fusion on this information and then forward it to base station for final decision level data fusion. This method improves the reliability of data, reduced traffic and improve network link. The level of congestion is predicted periodically. It is taken as congestion indicator. This method helps in reducing power consumption and packet drop rate. The performance of this technique is investigated to prove its superiority to different state of art algorithms. The performance is validated using numerical analysis derivations and MATLAB.