Energy-efficiency is highly desirable for sensing systems in the Internet of Things (IoT). A common approach to achieve low-power systems is duty-cycling, where components in a system are turned off periodically to meet an energy budget. However, this work shows that such an approach is not necessarily optimal in energy-efficiency, and proposes as a fundamentally better alternative. The proposed approach offers 1) explicit modeling of performance uncertainties in system internals, 2) a realistic resource consumption model, and 3) a key insight into the superiority of guided-processing over duty-cycling. Generalization from the cascade structure to the more general graph-based one is also presented. Once applied to optimize a large-scale audio sensing system with a practical detection application, empirical results show that the proposed approach significantly improves the detection performance (up to @math and @math reduction in false-alarm and miss rate, respectively) for the same energy consumption, when compared to the duty-cycling approach.
In stream mining, @cite_17 employed a cascade of Gaussian mixture model (GMM)-based classifiers and formulated a problem to find both the number of mixture components and the threshold in each classifier that maximize the system detection rate subject to constraints on false alarm, memory and CPU. The solution in @cite_17 takes a person-by-person approach where it iterates between 1) finding optimal numbers of mixture components, i.e. resource allocation, for all classifiers given thresholds, and 2) finding optimal thresholds for a given resource allocation. However, this approach failed to capture the direct dependence of the cascade's resource consumption on its thresholds, and is inherently suboptimal.
@cite_17
2158368213
Networks of classifiers are capturing the attention of system and algorithmic researchers because they offer improved accuracy over single model classifiers, can be distributed over a network of servers for improved scalability, and can be adapted to available system resources. This work provides a principled approach for the optimized allocation of system resources across a networked chain of classifiers. We begin with an illustrative example of how complex classification tasks can be decomposed into a network of binary classifiers. We formally define a global performance metric by recursively collapsing the chain of classifiers into one combined classifier. The performance metric trades off the end-to-end probabilities of detection and false alarm, both of which depend on the resources allocated to each individual classifier. We formulate the optimization problem and present optimal resource allocation results for both simulated and state-of-the-art classifier chains operating on telephony data.