  Traditional distributed detection systems are often designed for a single
target application. However, with the emergence of the Internet of Things (IoT)
paradigm, next-generation systems are expected to be a shared infrastructure
for multiple applications. To this end, we propose a modular, cascade design
for resource-efficient, multi-task detection systems. Two (classes of)
applications are considered in the system, a primary and a secondary one. The
primary application has universal features that can be shared with other
applications, to reduce the overall feature extraction cost, while the
secondary application does not. In this setting, the two applications can
collaborate via feature sharing. We provide a method to optimize the operation
of the multi-application cascade system based on an accurate resource
consumption model. In addition, the inherent uncertainties in feature models
are articulated and taken into account. For evaluation, the twin-comparison
argument is invoked, and it is shown that, with the optimal feature sharing
strategy, a system can achieve 9$\times$ resource saving and 1.43$\times$
improvement in detection performance.
