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This project proposes a sensor monitoring system design with adaptive sampling strategies for efficient data collection. The high-level statement includes exploring hierarchical sampling approaches and user state-based sampling guidance. The sub-problems involve determining optimal sampling rates and thresholds, as well as integrating learning algorithms for parameter adjustments. Existing solutions, drawbacks, and challenges in sensor monitoring are discussed. The approach emphasizes the need for adaptive sampling to reduce wasteful continuous monitoring. Design rationale involves comparing alternatives and tradeoffs to optimize scanning efficiency. Details of protocols and services for sensor monitoring are outlined, along with potential benefits of employing domain-specific knowledge for sampling optimization. The project aims to evaluate the effectiveness of adaptive sampling through simulations and preliminary implementations.
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Project Presentation Notes • Problem Statement: • High level statement • Sub-problems • Existing solutions, drawbacks, challenges • Approach, promising new ideas (contributions) • Design rationale, comparing/contrasting alternatives and tradeoffs • Details of protocols & services • Evaluation details (as applicable) • Math analysis – simulations – implementation • Initial results and findings (if applicable)
Project-related note: Scanning, sensing efficiency • On monitoring of sensors (e.g., accelerometers, or others, location, bluetooth,…): • 1- continuous monitoring is wasteful and may not be needed always • 2- a constant rate of sampling may not be needed, and an adaptive/adjustable rate may be better (how to adjust and which rates/thresholds to use is a challenge and could be a design element in your project) • [check the efficient scanning mode in iTrust and the paper to see how Udayan approached it... but that's not the only way to do it] • 3- one can employ a hierarchical sampling approach, where you start with coarse grained sensing, and if/once you sense something 'potentially' interesting, then you turn on a finer (higher fidelity) sampling • 4- one can use the state of the user/patient and make use of high level constraints and domain-specific knowledge to guide the sampling to be efficient yet effective (e.g., getting location estimates without new GPS or other scanning by estimating speed, using a finite state machine for the activity of the patient,... etc.). • 5- one can employ a learning algorithm to adjust the parameter settings based on the patients feedback and or information.
