Fault tolerance and scalability of data aggregation in sensor networks

Sensor networks are finding significant applications in large scale distributed systems. One of the basic operations in sensor networks is data aggregation. Among the various approaches to in-network aggregation, such as gossip and tree, including the hash-based techniques, the tree-based approaches have better performance and energy-saving characteristics. However, sensor networks are highly prone to failures. A few techniques are suggested in literature to counteract the effect of failures, but have not been carefully analyzed for fault tolerance and scalability. Our work is geared toward analyzing the fault tolerance of such aggregation algorithms, and proposing scalable techniques for data aggregation. To this end, we make the following distinct contributions. First, we propose a simple fault model for analysis of various aggregation techniques. We then utilize this fault model to analyze the fault tolerance of existing techniques such as tree and gossip aggregation, and also weigh the performance gain of various techniques suggested to improve fault tolerance, such as multiple trees and local fixes. We also do the cost-benefit analysis of using the hash-based schemes which are based on FM sketches. Then, we propose a novel hybrid aggregation technique that combines the best characteristics of tree and gossip aggregation and improves fault-tolerance and scalability. Finally, we analyze the impact of using a few of the more reliable, though expensive, nodes—such as the Intel XScale—called microservers, in addition to the standard motes, on the fault tolerance and scalability of the aggregation algorithms in sensor networks. We show that our work can be effectively used as a design tool for maximizing fault tolerance while designing scalable aggregation algorithms for sensor networks.