Collaborative Storage Management In Sensor Networks

TL;DR

This paper introduces a collaborative storage approach for sensor networks that leverages spatial data correlation to reduce storage needs, extend network lifetime, and balance load across sensors.

Contribution

The paper proposes a novel collaborative storage technique that exploits spatial correlation among sensor data to optimize storage efficiency and load balancing in sensor networks.

Findings

Significant reduction in stored data size compared to local buffering.

Extended network operational lifetime due to storage savings.

Effective load balancing across sensors with varying data rates.

Abstract

In this paper, we consider a class of sensor networks where the data is not required in real-time by an observer; for example, a sensor network monitoring a scientific phenomenon for later play back and analysis. In such networks, the data must be stored in the network. Thus, in addition to battery power, storage is a primary resource: the useful lifetime of the network is constrained by its ability to store the generated data samples. We explore the use of collaborative storage technique to efficiently manage data in storage constrained sensor networks. The proposed collaborative storage technique takes advantage of spatial correlation among the data collected by nearby sensors to significantly reduce the size of the data near the data sources. We show that the proposed approach provides significant savings in the size of the stored data vs. local buffering, allowing the network to run for a longer time without running out of storage space and reducing the amount of data that will eventually be relayed to the observer. In addition, collaborative storage performs load balancing of the available storage space if data generation rates are not uniform across sensors (as would be the case in an event driven sensor network), or if the available storage varies across the network.