Distributed Data Collection in Large-Scale Asynchronous Wireless Sensor Networks Under the Generalized Physical Interference Model.

<?Pub Dtl?>Wireless sensor networks (WSNs) are more likely to be distributed asynchronous systems. In this paper, we investigate the achievable data collection capacity of realistic distributed asynchronous WSNs. Our main contributions include five aspects. First, to avoid data transmission interference, we derive an \cal R0-proper carrier-sensing range (\cal R0-PCR) under the generalized physical interference model, where \cal R0 is the satisfied threshold of data receiving rate. Taking \cal R0-PCR as its carrier-sensing range, any sensor node can initiate a data transmission with a guaranteed data receiving rate. Second, based on \cal R0-PCR, we propose a Distributed Data Collection (DDC) algorithm with fairness consideration. Theoretical analysis of DDC surprisingly shows that its achievable network capacity is order-optimal and independent of network size. Thus, DDC is scalable. Third, we discuss how to apply \cal R0-PCR to the distributed data aggregation problem and propose a Distributed Data Aggregation (DDA) algorithm. The delay performance of DDA is also analyzed. Fourth, to be more general, we study the delay and capacity of DDC and DDA under the Poisson node distribution model. The analysis demonstrates that DDC is also scalable and order-optimal under the Poisson distribution model. Finally, we conduct extensive simulations to validate the performance of DDC and DDA. [ABSTRACT FROM AUTHOR]