Adaptive Cluster-Based Data Collection in Sensor Networks with Direct Sink Access.

Recently, wireless sensor networks featuring direct sink access have been studied as an efficient architecture to gather and process data for numerous applications. In this paper, we focus on the joint effect of clustering and data correlation on the performance of such networks. More specifically, we propose a novel cluster-based data collection scheme for sensor networks with direct sink access (CDC-DSA) and provide an analytical framework to evaluate its performance in terms of energy consumption, latency, and robustness. In our scheme, cluster heads use a low-overhead and simple medium access control (MAC) conceptually similar to ALOHA to contend for the reachback channel to the data sink. Since, in our model, data is collected periodically, packet arrival is not modeled by a continuous random process, and therefore, we base our framework on a transient analysis rather than a steady-state analysis. Using random geometry tools, we study how the optimal average cluster size and energy savings, under the proposed MAC, vary according to the level of data correlation in the network. Extensive simulations for various protocol parameters show that our analysis is fairly accurate for a wide range of parameters. Our results suggest that, despite the trade-off between energy consumption and latency, both can be substantially reduced by a proper clustering design. [ABSTRACT FROM AUTHOR]