An Efficient Data Aggregation Method for Event-Driven WSNs: A Modeling and Evaluation Approach

This paper proposes and models an efficient data aggregation method for wireless sensor networks (WSNs). In the proposed data aggregation method, every cluster head (CH) node incorporates a local forwarding history to decide whether to forward or to drop a recently received packet. When a new packet arrives at a CH node, a threshold value is calculated based on the information of the forwarding history; then, a random number is generated and compared with the threshold value to determine whether the packet should be dropped or not. In fact, the CH node forwards the new packet with the probability of 1 − p and drops it with probability of p where the parameter p is determined based on the forwarding history. In order to evaluate the proposed data aggregation method, two approaches consisting of simulation and analytical modeling are used. Various scenarios are considered in simulations conducted with NS2 software to compare the proposed data aggregation method with four previously proposed methods. Results reveal that the proposed method (1) aggregates more than 70 % of redundant packets, (2) reduces the network end-to-end delay by at least 22 %, and (3) reduces the missed event rate compared with the other methods. The proposed method is also evaluated by means of an analytical model based on queuing networks. The model accurately estimates the network performance utilizing the proposed data aggregation method. Comparisons of the results obtained by the proposed model and simulations confirm that the proposed model has at most 7 % prediction error. The proposed model allows WSN designers to easily achieve useful information about their networks before the establishment and manufacturing of the networks.