An Active Resource Orchestration Framework for PAN-Scale, Sensor-Rich Environments

In this paper, we present Orchestrator, an active resource orchestration framework for a PAN-scale sensor-rich mobile computing platform. Incorporating diverse sensing devices connected to a mobile phone, the platform will serve as a common base to accommodate personal context-aware applications. A major challenge for the platform is to simultaneously support concurrent applications requiring continuous and complex context processing, with highly scarce and dynamic resources. To address the challenge, we build Orchestrator, which actively coordinates applications' resource uses over the distributed mobile and sensor devices. As a key approach, it adopts an active resource use orchestration, which prepares multiple alternative plans for application requests and selectively applies them according to resource availability and demands at runtime. Through the selection, it resolves resource contention among applications and helps them efficiently share resources. With such system-level supports, applications become capable of providing long-running services under dynamic circumstances with scarce resources. Also, the platform can host a number of applications stably, exploiting its full resource capacity. We build an Orchestrator prototype on off-the-shelf mobile devices and sensor motes and show its effectiveness in terms of application supportability and resource use efficiency.

1 Introduction

A smart mobile device expands its role as a gateway for personal pervasive services. It will form a PAN-scale sensor rich environment with diverse wearable or space-embedded sensors, for example, e-watch, sensing garments, and textile electrodes in bed sheets [18], [23]. As a common platform, a mobile device will accommodate various personal context-aware applications, for example, dietary monitoring, life assistant [28], and elderly support [29]. The applications monitor user contexts continuously [16], and provide highly proactive and situational services. The context monitoring often requires multistep complex processing across the mobile and sensor devices (e.g., for a “running” context, acceleration sensing on multiple body-worn sensors, FFT-based feature extraction, and classifying the features through a decision tree [17]).