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Owing to the rich processing, multi-modal sensing, and versatile networking capabilities, smartphones are increasingly used to build data-intensive embedded sensing applications. However, various challenges must be systematically addressed before smartphones can be used as a generic embedded sensing platform, including high power consumption, lack of real-time functionality and user-friendly embedded programming support. This paper presents ORBIT, a smartphone-based platform for data-intensive embedded sensing applications. ORBIT features a tiered architecture, in which a smartphone can interface to an energy-efficient peripheral board and/or a cloud service. ORBIT as a platform addresses the shortcomings of current smartphones while utilizing their strengths. ORBIT provides a profile-based task partitioning that allows it to intelligently dispatch the processing tasks among the tiers to minimize the system power consumption. ORBIT also provides a data processing library that includes two mechanisms namely adaptive delay-quality trade-off and data partitioning via multi-threading to optimize resource usage. Moreover, ORBIT supplies an annotation-based programming API for developers that significantly simplifies the application development and provides programming flexibility. Extensive microbenchmark evaluation and three case studies including seismic sensing, visual tracking using an ORBIT robot, and multi-camera 3D reconstruction, validate the generic design of ORBIT.

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Appliance-level power usage monitoring may help conserve electricity in homes. Several existing systems achieve this goal by exploiting appliances’ power usage signatures identified in labor-intensive in situ training processes. Recent work shows that autonomous power usage monitoring can be achieved by supplementing a smart meter with distributed sensors that detect the working states of appliances. However, sensors must be carefully installed for each appliance, resulting in a high installation cost. This article presents Supero —the first ad hoc sensor system that can monitor appliance power usage without supervised training. By exploiting multisensor fusion and unsupervised machine learning algorithms, Supero can classify the appliance events of interest and autonomously associate measured power usage with the respective appliances. Our extensive evaluation in five real homes shows that Supero can estimate the energy consumption with errors less than 7.5%. Moreover, nonprofessional users can quickly deploy Supero with considerable flexibility.