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, CHs 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, the 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 saving...

• Type: Journal
• Year: 2008
• Where: TMC
• Authors: Mahdi Lotfinezhad, Ben Liang, Elvino S. Sousa
• Keywords: Data Correlation, Direct Sink Access, Sensor Networks, TMC 2008