Volatility-based diversity awareness for distributed data storage of Mobile Crowd Sensing

Abstract

With the widespread utilization of sensors and the rapid development of Mobile Crowd Sensing (MCS), the framework of distributed data storage, which makes use of the limited storage capacity of sensing devices, is providing an alternative to cloud-based data storage. Since the construction of the distributed storage framework for MCS has to be completed by Compressed Sensing (CS) theory, a reasonable measurement allocation becomes a no-brainer if we want to obtain more recovery precision of data without wasting measurement resources. To more efficiently address the above problems, we present a novel strategy of Volatility-Based Diversity Awareness for CS measurement allocation called VBDA, which collectively achieves lower computational complexity, adaptive sampling rate allocation for non-visual data, and high-quality reconstruction. We first process the target monitoring region in blocks. Then, we calculate the degree of data diversity of each area using the volatility, which is used to assess the importance of the different areas. Worthy note, based on the volatility calculation, we can obtain the magnitude of the variation in diversity that is only present in the real data, even when using very ambiguous recovered data. This is primarily due to the volatility unique design concept, which attempts to offset partially identical errors in adjacent recovered data using volatility calculations. Finally, to rationally allocate measurement resources, a volatility-based sampling rate allocation scheme is proposed. We further provide an implementation for practical deployments of VBDA in various related contexts. Numerous experimental show that the VBDA performs excellently. In comparison to the current state-of-the-art strategy, it improves data recovery accuracy by 12.2% without the use of any prior knowledge, while being adaptable to many different distribution types of data.