Storage and indexing issues in sensor networks

Storage management and coordinated event monitoring issues in sensor networks have been explored by many researchers in recent years. This area involves wide range of challenges due to the resource constraints of small sized, low-power sensor nodes. In the first part of this thesis, we focus on the issue of efficient storage management for adaptable resolution data. In the second part, we consider energy efficient coordination and indexing among sensor nodes with the help of a coarse-grained event-detection mechanism.; Our first contribution targets storage-constrained sensor network applications, in which the monitored data is typically stored in adaptable resolution fashion. This allows reclamation of some storage space when needed by reducing the quality of stored data (sacrificing some of the precision). Existing sensor network file systems are optimized for sequential logging of the data. However, flash memories have a number of unique properties that require careful consideration in file system design. In this work, we show that in applications where adjustable resolution occurs, sequential logging file-systems result in an inefficient implementation of adjustable resolution. We propose an alternative implementation of the file system where data components are grouped with each other according to resolution. Thus, reducing resolution is implemented by simply erasing the pages with the excess resolution components. We have implemented the proposed scheme on crossbow Mica 2 sensor nodes. In addition, using TOSSIM simulations, we show that as compared to the existing approach, the proposed scheme results in significant savings in read and write operations to the flash (thereby in turn saving energy, and reducing wear). Further, we show that wear leveling can be maintained over time by assigning the most significant data to the most frequently used pages.; In our second contribution, we focus on an important class of storage-bound sensor network applications, in which sensor nodes coordinate among themselves to monitor and store the important events. In this work, we use a coarse-grained event detection scheme and propose a light-weight indexing mechanism, markers, that indexes the interesting periods in the time series data. We study various interesting properties that this time based index provides, such as index compression and event detection across multiple streams. Further, we incorporate markers into an online coordination system among sensors for monitoring as well as storing important events, and show that markers can be used in this system to improve overall energy and storage efficiency. We have implemented the proposed indexing mechanism and the online coordination system on TOSSIM, and shown that the sensor coordination using markers results in significant energy savings than the coordination using time series data.