Orchestrator: An active resource orchestration framework for mobile context monitoring in sensor-rich mobile environments

In this paper, we present Orchestrator, an active resource orchestration framework for mobile context monitoring. Emerging pervasive environments will introduce a PAN-scale sensor-rich mobile platform consisting of a mobile device and many wearable and space-embedded sensors. In such environments, it is challenging to enable multiple context-aware applications requiring continuous context monitoring to simultaneously run and share highly scarce and dynamic resources. Orchestrator enables multiple applications to effectively share the resources while exploiting the full capacity of overall system resources and providing high-quality service to users. For effective orchestration, we propose an active resource use orchestration approach that actively finds appropriate resource uses for applications and flexibly utilizes them depending on dynamic system conditions. Orchestrator is built upon a prototype platform that consists of off-the-shelf mobile devices and sensor motes. We present the detailed design, implementation, and evaluation of Orchestrator. The evaluation results show that Orchestrator enables applications in a resource-efficient way.

I. Introduction

Smart mobile devices have been the pivot for personal services. Many diverse sensors around the mobile device will enable highly proactive services with the help of a lot of personal context-aware applications, e.g., u-Trainer, dietary monitoring, and health monitoring. In such PAN-scale sensor-rich environments, mobile devices will serve as the common computing platform which accommodates diverse wearable sensors or nearby space-embedded sensors, e.g., e-watch, sensing garments, and textile electrodes in bed sheets. Then a mobile device will usually run multiple context-aware applications at a time which mainly focus on continuous monitoring of users' context with diverse sensors [19]. The context monitoring often requires multi-step and complex processing over multiple devices at the same time, e.g., for a ‘running’ context, sensing of body-worn accelerometers, filtering, FFT-based feature extraction, and classifying the features through a decision tree [20].