LAUSR

The opportunistic data collection paradigm leverages human mobility to improve sensing coverage and data transmission for collecting data from a number of Points of Interest scattered across a large sensing field, enabling many large-scale mobile crowd sensing applications at lower cost. Sensing delay and transmission delay are two critical Quality of Service (QoS) metrics for such applications. However, the existing works only study them separately, and do not fully consider the effects of various sink deployment schemes (i.e., single sink or multiple sinks, and sinks are static or mobile) and transmission schemes (i.e., direct transmission, epidemic transmission or other schemes). In contrast, we provide a unified delay analysis framework for opportunistic data collection, by integrating both the sensing and transmission delays, to describe the QoS more comprehensively. We analyze how the three delays (sensing delay, transmission delay, and a novel metric called data collection delay) vary with several parameters considering the effects of various sink deployment schemes and transmission schemes. Validity of the theoretic analysis is verified by simulations.