Online data recovery in wireless sensor networks

Abstract

Each node in a wireless sensor network has some data storage capability that preserves gathered data until that data is either requested by a data collector, or utilized by the network itself. Local storage, where every node stores its own data locally, is disadvantageous because a software or mechanical failure may cause permanent data loss of some or all of the data. Robust distributed networked storage, where each node stores its data amongst other nodes in a redundant manner, offers higher levels of data persistence than local storage. Using a distributed storage scheme, a data collector can reconstruct any node's data by downloading a portion of data from a previously defined threshold number of nodes. Consider a set of nodes storing some data distributively. The failure of any node in that set, reduces the data's level of redundancy. However, if a newcomer joins the network, it can be used to restore the data's level of redundancy, by downloading some data from a threshold number of live nodes. This recovery process is termed regeneration. In the case of a software failure, the node that failed may be reassigned to be the newcomer, if it is able to return to its previous working state. In this paper, we generalize the terms reconstruction and regeneration to the term recovery. We propose a framework that serves as a measure of performance for algorithms targeting data persistence. We then propose one such algorithm and show that it achieves a constant approximation ratio of the optimal, with respect to the framework. Finally, we conclude with simulations analyzing the proposed data recovery framework.