Enabled by the widespread deployment of wireless sensor networks and the ubiquity of smart phones, large-scale sensing has played a critical role in many aspects of our life, such as environment monitoring, post disaster assessment, and biomedical sensing, etc. Such applications usually generate astronomical volumes of data, which impose formidable challenges for efficient processing, storage, and transmission of data. This project will address these challenges by developing a new paradigm of energy-aware sparse sensing scheme. The proposed scheme can significantly reduce the amount of data to be collected and processed in sensor networks powered by extremely limited energy sources, such as devices harvesting energy from light, vibration, or heat. The sparse sensing is enabled by the fact that some information is more important than others, thus the amount of collected information can be significantly reduced by sampling only the most essential information. In addition, the sparse sensing scheme can also be used for extracting useful information from an ocean of data that have already been collected by a system. This is especially important for crowd-sensing applications where a huge number of users voluntarily contribute sensing data with their smart phones or tablet computers. The proposed research will accelerate the wide range deployment of large-scale participatory sensing and energy-harvesting sensor networks. Results obtained from this project can be applied to a wide range of applications, such as disaster relief, stock market analysis, surveillance, and pollution monitoring, etc. This research will contribute to public safety, improve homeland security, and promote the social and economic development of the United States.
The goal of this project is to conquer the 'big data' challenge in large-scale energy-constrained sensing applications through a new paradigm of energy-aware sparse sensing, which dynamically and sparsely samples a random field by adapting to the energy availability of sensors and the time-varying nature of the monitored objects. The energy-aware sparse sensing scheme can significantly reduce the amount of data to be collected and processed, bridge the gap between energy supplies and energy demands in energy-constrained systems, and provide energy-efficient and scalable solutions to large-scale sensing applications. The specific research thrusts leading to this goal include: 1) Create a new paradigm of dynamic sparse sensing. The dynamic sparse sensing is enabled by 'information diversity', i.e., data collected at different space locations and time instants have different impacts on a certain design objective. Thus the number of required samples can be significantly reduced by sampling only the most informative data, which can be adaptively identified by learning through previous sensing results. 2) Design energy-aware sparse sensing techniques for systems powered by energy harvesting devices such as photovoltaic or thermal devices. For systems with energy harvesting devices, there is energy diversity due to non-uniform energy supplies across the sensors. The energy-aware sparse sensing is achieved by aligning energy diversity with information diversity, so that the limited energy resources are used to collect the most informative data.
|Effective start/end date||8/1/14 → 7/31/19|
- National Science Foundation: $362,394.00