Design, analysis and implementation of a spatial-temporal, adaptive and multi-replication data centric storage framework for wireless sensor and actor networks

Resumen

This PhD Thesis presents a novel framework for Data-Centric Storage(DCS) in a Wireless Sensor and Actor Network(WSAN) that enables the use of a multiple set of data replication nodes, which also change over the time. This allows reducing the average network traffic and energy consumption by adapting the number of replicas to applications’ traffic, while balancing energy burdens by varying their location. To that end we propose and validate a simple model to determine the optimal number of replicas, in terms of minimizing average traffic/energy consumption, from the measured applications’ production and consumption traffic. Simple mechanisms are proposed to decide when the current set of replication nodes should be changed, to enable new applications and sensor nodes to efficiently bootstrap into a working sensor network, to recover from failing nodes, and to adapt to changing conditions. Extensive simulations demonstrate that our approach can extend a sensor network’s lifetime by at least a 60%, and up to a factor of 10x depending on the lifetime criterion being considered. Furthermore, we have implemented our framework in a real testbed with 20 motes that validates in a small scenario those results obtained via simulation for large WSANs. Finally, we present a heuristic that adapts our framework to scenarios with spatially heterogeneous consumption and/or production traffic distributions providing an effective reduction in the overall traffic, as well as reducing the number of nodes that die over the time. --------------------------------------------------------------------------------------------------------------------------------------------