Solar, a pub-sub system for context-aware computing (2000-2009)

This project is no longer active; this page is no longer updated.

Related keywords: [context-aware], [security], [sensors], [survey]


To succeed without distracting the user, pervasive-computing applications must be aware of the 'context' in which they execute, and automatically adapt as that context changes. More specificically, such applications need to be aware of the context in which they execute, or the context of the applications' users. For example, an application may behave differently when its user is at home than at the office, or outdoors; alone, or with other people; driving or eating or walking. In the Solar project we explored this challenge through four parallel research threads, described below.

The Solar system was a comprehensive middleware framework for the development of context-aware applications. Solar was based on a publish-subscribe model, allowing applications to subscribe to streams of events carrying context data. The applications could deploy a distributed network of operators that transformed raw sensor data, as published by sources, into the desired context. Through a novel naming system, applications could identify the desired sources, which themselves could be a named output of a tree of operators that aggregate many other sources. Solar also included means for data-flow management, recognizing that some sensor-based context systems may produce far more data (events) than can be carried by an underlying wireless network or can be consumed by operators and applications. Solar included a mechanism for filtering data at the context source in a way that recognizes the overlapping goals of the many subscribers to the source, and an inline filtering and summarization technique that managed the flow of events through the Solar system. This research was most completely described in Chen's dissertation [chen:thesis], and in a retrospective journal paper [chen:jsolar]; see also [chen:abstraction, chen:abstraction-tr, chen:bnaming, chen:dependency, chen:dependency-tr, chen:fusenet, chen:naming, chen:pack, chen:pack-tr, chen:pervasive, chen:pervasive-tr, chen:solar, chen:solar-tr, white-abram:thesis, chen:survey-tr].

We developed a theory and implementation of "context-sensitive authorization", in which authorization policies (e.g., for access to physical resources like a room or virtual resources like a database) depend on the context (e.g., location or activity) of the person requesting access to the resource. Our work recognized that the sources of context information are inherently distributed, and that the context used (such as a person's location) is sensitive information that must remain confidential. Our techniques allowed an authorization query to be evaluated in a distributed fashion while respecting confidentiality and integrity policies imposed by the many parties involved. This research was most completely described in Minami's dissertation [minami:thesis]; see also [minami:aclprop-tr, minami:csa, minami:csa-tr, minami:handbook, minami:jcsa, minami:scalability].

We also proposed a "group-aware stream filtering" approach that exploited two overlooked, yet important, properties of monitoring applications: 1) many of them can tolerate some degree of "slack" in their data quality requirements, and 2) there may exist multiple subsets of the source data satisfying the quality needs of an application. We can thus choose the "best alternative" subset for each application to maximize the data overlap within the group to best benefit from multicasting. After proving the group-aware filtering problem NP-hard, we provided a general framework with a suite of heuristics-based algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth. Our evaluation (based on real-world data traces) showed that quality-managed group-aware filtering is effective in trading CPU time for bandwidth savings, compared with self-interested stream filtering. This work was most completely described by Li's dissertation [mingli:thesis]; see also [li:ijcnds, li:jfilter, li:quality, li:wwasn07].

Applications: Finally, Solar was deployed as a data-dissemination middleware in the Automated Remote Triage and Emergency Management Information System (ARTEMIS) project at ISTS [see, for example, McGrath]. We found that these mission-critical applications needed 1) real-time monitoring services (in the form of trigger-based continuous queries) and 2) analytical probing services (in the form of one-shot queries based on historical sensor data as well as real-time sensor streams).

In another application, we explored an application to meeting detection and resource discovery [wang:meeting, wang:meeting-tr, wang:thesis]. In 2002-03, we developed several other applications (e.g., location tracking and campus-wide 'graffiti' apps) as a proof of concept. In general we found that Solar was scalable and efficient enough for high-volume real-life sensor-monitoring applications.

Finally, we developed "SmartReminder", a context-sensitive appointment-reminder system, as a case-study in context-sensitive applications [mathias:thesis].


Download the 1.0.6 version of Solar (gzip tar file).


Guanling Chen, David Kotz, Ming Li, Kazuhiro Minami, and Jue Wang, with Adrian Hartline, Chris Masone, Arun Mathias, Cal Newport, Abe White, and Lin Zhong.

Funding and acknowledgements

This research was supported by DARPA contract F30602-98-2-0107, by DoD MURI contract F49620-97-1-03821, by Microsoft Research, by the Cisco Systems University Research Program, and by USENIX Scholars Program. This project was also supported by Dartmouth's Institute for Security, Technology, and Society (ISTS) under Award No. 2000-DT-CX-K001 from the Office for Domestic Preparedness, U.S. Department of Homeland Security.

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Papers (tagged 'solar')

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[Kotz research]