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.
It is worthwhile to note that the cascade detection system of interest here is different from the serial detector network in the distributed detection literature @cite_3 @cite_1 @cite_24 , in which the decision of a current module is treated as an extra observation, instead of as a control signal to subsequent modules and conserve resources.
@cite_24 @cite_1 @cite_3
2069595316 2042286431 2162408301
The problem of distributed detection involving N sensors is considered. The configuration of sensors is serial in the sense that the Jth sensor decides using the decision it receives along with its own observation. When each sensor uses the Neyman-Pearson test, the probability of detection is maximized for a given probability of false alarm, at the Nth stage. With two sensors, the serial scheme has a performance better than or equal to the parallel fusion scheme analyzed in the literature. Numerical examples illustrate the global optimization by the selection of operating thresholds at the sensors. > Distributed detection on the serial (or tandem) topology is considered with the probability of error performance criterion. Previously published efforts, while presenting probability of detection versus false alarm results, limited the number of array elements to two or three. For the detection of known, equally likely signals in additive, symmetric noise, the author presents simple recursive expressions for the threshold values and the performance of the system. Examples for known signals in Gaussian and Laplacian noise show the degradation in performance due to the array structure. > A distributed binary detection problem with binary communications is considered, wherein the nodes (sensors and decision-makers) of the system are organized in a series configuration. It is shown that this problem can be reformulated as a deterministic multistage nonlinear optimal control problem. The necessary and sufficient conditions of optimality using Bayes' risk as the optimization criterion are then derived, and a physical interpretation of how the costates relate to the decision threshold at each node is provided. Using the min-H method of optimal control theory, a computationally efficient algorithm with linear complexity in the number of nodes per iteration is proposed to solve for the optimal decision strategy. The algorithm is then extended to solve a Neyman-Pearson version of the problem to obtain the optimal team (network) receiver operating characteristic curve. Two easily implemented suboptimal decision rules termed the asymptotic decision strategy and the constant control strategy are proposed and their properties are investigated. >