CCSS: Learning-Driven Scheduling and Communications in Edge-Assisted Battery-Free Wireless Sensor Networks

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The recent significant progresses of information-sensing techniques, wireless communications, and embedded systems have greatly accelerated the generation of sensory data, which provides a nourishing fertile ground for the development of deep learning and machine learning in data-hungry applications. Nevertheless, the inherent limitations of traditional Wireless Sensor Networks (WSNs) including limited lifetime, difficult battery replacement, and centralized network architecture, become an unavoidable obstacle to the wide deployment and adoption of sensory data. Moreover, due to the big volume and high complexity of sensory data, processing sensory data in a centralized manner would increase the consumption of network resources and the risk of privacy leakage. To tackle the aforementioned limitations as well as to satisfy the needs of managing massive sensory data in real applications, developing power-optimized, sustainably-reliable, and efficiently-distributed solutions has become an essential task.This project explores the energy characteristics of battery-free WSNs, capacities of edge-assisted sinks, and advantages of distributed multi-task learning, which will result in the following technical innovations. (1) The seamless integration of battery-free sensors, edge/cloud computing and machine learning can break through technique imprisonments in traditional WSNs, in which new problems are defined and new methodologies are developed. (2) The energy correlation of battery-free sensors in the temporal and spatial domains is exploited to predict sensor’s dynamic sensing ability for scheduling sensing activities, in which the schemes of time-energy-correlated sensing, time-space cooperative data acquisition and energy-accompanied data acquisition will be developed. (3) The interference of battery-free sensors in the temporal, spatial and energy domains is utilized to construct multi-dimensional conflict graphs for interference-free transmission scheduling, in which the algorithms of associating battery-free sensors with edge-assisted sinks and scheduling data collection from sensors to sinks will be designed. (4) The diverse capacities of computation and communication on edge-assisted sinks are employed to schedule learning process so that multiple related tasks can be simultaneously completed in a distributed fashion. (5) The validation is well planned, where an analog simulator and a prototype system will be built to perform the designed simulations and real-data experiments, respectively.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.