Solar project

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 et.al]. 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].

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People

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 the Cisco Systems University Research Program, the US Department of Defense (DARPA) contract F30602-98-2-0107, the US Department of Defense (MURI) contract F49620-97-1-03821, by Microsoft Research, and by the USENIX Scholars Program.

This project was also supported by the US Department of Homeland Security (Office for Domestic Preparedness) under Award No. 2000-DT-CX-K001, through Dartmouth's Institute for Security, Technology, and Society (ISTS).

The views and conclusions contained on this site and in its documents are those of the authors and should not be interpreted as necessarily representing the official position or policies, either expressed or implied, of the sponsor(s). Any mention of specific companies or products does not imply any endorsement by the authors or by the sponsor(s).

Papers (tagged 'solar')

Papers are listed in reverse-chronological order; click an entry to pop up the abstract. For full information and pdf, please click Details link. Follow updates with RSS.

2009:

Kazuhiro Minami and David Kotz. Distributed proof systems for cross-domain authorization. Information Assurance, Security and Privacy Services. 2009. [Details]

The ability to access information resources across organizational boundaries is vital for today’s corporate, military, and educational organizations, which must be able to quickly pool their resources to respond to opportunities and threats. Since each organization protects its resources with its local authorization policies, we need mechanisms for cross-domain authorization to achieve information sharing among multiple organizations. Unfortunately, traditional identity-based authorization approaches are impractical, because the identity of a requester is not a useful clue for authorization in a decentralized environment. Many distributed authorization schemes, therefore, consider a requester’s properties (e.g., employer and physical location) to make an authorization decision and use a logic-based approach to specify authorization policies in a flexible way. Such a distributed proof system makes an authorization decision by constructing a proof with information provided by different entities in a distributed environment. In this chapter, we provide an overview of distributed proof systems for cross-domain authorization, while covering major language constructs and proof-constructing algorithms, and introduce an emerging issue of protecting confidential policies and credentials (facts) in a distributed proof system involving multiple security domains since it is unlikely that a principal in one security domain is willing to release all its local information to any principal in other domains. We finally describe our distributed proof system for cross-domain authorization in detail and show how our cryptographic protocol allows mutually untrusted principals to construct a proof in a decentralized way while preserving each principal’s security policies.

Ming Li and David Kotz. Towards Collaborative Data Reduction in Stream-Processing Systems. International Journal of Communication Networks and Distributed Systems (IJCNDS). June 2009. [Details]

We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a collaborative data-reduction mechanism, “group-aware stream filtering”, used together with multicast, to select a small set of necessary data that satisfy the needs of a group of subscribers simultaneously. We turn data-compressing filters into group-aware filters by exploiting 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. We provide a general framework that treats the group-aware stream filtering problem completely; we prove the problem NP-hard and thus provide a suite of heuristic algorithms that ensure data quality (specifically, granularity and timeliness) while collaboratively reducing data. The framework is extensible and supports a diverse range of filters. Our prototype-based evaluation shows that group-aware stream filtering is effective in trading CPU time for data reduction, compared with self-interested filtering.

2008:

Ming Li and David Kotz. Group-aware Stream Filtering for Bandwidth-efficient Data Dissemination. International Journal of Parallel, Emergent and Distributed Systems (IJPEDS). December 2008. Invited paper. [Details]

In this paper we are concerned with disseminating high-volume data streams to many simultaneous applications over a low-bandwidth wireless mesh network. For bandwidth efficiency, we propose a group-aware stream filtering approach, used in conjunction with multicasting, that exploits two overlooked, yet important, properties of these applications: 1) many applications 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. An evaluation of our prototype implementation shows that group-aware data filtering can save bandwidth with low CPU overhead. We also analyze the key factors that affect its performance, based on testing with heterogeneous filtering requirements.

Ming Li and David Kotz. Event Dissemination via Group-aware Stream Filtering. Proceedings of the International Conference on Distributed Event-Based Systems (DEBS). July 2008. [Details]

We consider a distributed system that disseminates high-volume event streams to many simultaneous monitoring applications over a low-bandwidth network. For bandwidth efficiency, we propose a group-aware stream filtering approach, used together with multicasting, that exploits 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. Here we provide a general framework for the group-aware stream filtering problem, which we prove is NP-hard. We introduce a suite of heuristics-based algorithms that ensure data quality (specifically, granularity and timeliness) while preserving bandwidth. Our evaluation shows that group-aware stream filtering is effective in trading CPU time for bandwidth savings, compared with self-interested filtering.

Ming Li. Group-Aware Stream Filtering. PhD thesis, May 2008. Available as Dartmouth Computer Science Technical Report TR2008-621. [Details]

Recent years have witnessed a new class of monitoring applications that need to continuously collect information from remote data sources. Those data sources, such as web click-streams, stock quotes, and sensor data, are often characterized as fast-rate high-volume “streams”. Distributed stream-processing systems are thus designed to efficiently use system resources to serve the data-acquisition needs of the applications. Most of the state-of-the-art stream-processing systems assume an Ethernet-based network whose bandwidth is abundant, and focus on mechanisms to save computational power and memory. For applications involving wireless networks, particularly multi-hop mesh networks, we recognize that the most limiting factor in efficiently processing streams lies in the network’s highly constrained bandwidth. Hence, this dissertation proposes a group-aware stream filtering approach that saves bandwidth at the cost of increased CPU time, for low-bandwidth data-streaming systems. This approach, used together with multicasting, exploits two overlooked 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 problem NP-hard, we introduce a suite of heuristics-based algorithms that ensure data quality, specifically data granularity and timeliness, in addition to preserving network bandwidth. Our framework for group-aware stream filtering is extensible and supports a diverse range of filtering needs of monitoring applications. We evaluate this approach with a prototype system based on real-world data sets. The results show that quality-managed group-aware filtering is effective in trading CPU time for bandwidth savings, compared with self-interested stream filtering. We also evaluate the effect of each algorithm on temporal freshness of the data. Finally, we discuss other application realms that might benefit from group-aware stream filtering.

Guanling Chen, Ming Li, and David Kotz. Data-centric middleware for context-aware pervasive computing. Pervasive and Mobile Computing. April 2008. [Details]

The complexity of developing and deploying context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggregate, and disseminate contextual data. In this paper, we motivate a data-centric design for such an infrastructure to support context-aware applications. Our middleware system, Solar, treats contextual data sources as stream publishers. The core of Solar is a scalable and self-organizing peer-to-peer overlay to support data-driven services. We describe how different services can be systematically integrated on top of the Solar overlay and evaluate the resource discovery and data-dissemination services. We also discuss our experience and lessons learned when using Solar to support several implemented scenarios. We conclude that a data-centric infrastructure is necessary to facilitate both the development and deployment of context-aware pervasive-computing applications.

2007:

Guanling Chen, Kazuhiro Minami, and David Kotz. Naming and Discovery in Mobile Systems. The Handbook of Mobile Middleware. 2007. [Details]

Middleware supporting mobile applications must provide naming and discovery functionalities to enable anytime and anywhere service access. In this chapter, we survey existing service-discovery standards, identify four challenges for naming and discovery in a mobile environment, and provide a detailed discussion of the approaches that can be used to address each of these challenges.

Ming Li and David Kotz. Group-aware Stream Filtering. Proceedings of the Workshop on Wireless Ad hoc and Sensor Networks (WWASN). June 2007. [Details]

In this paper we are concerned with disseminating high-volume data streams to many simultaneous context-aware applications over a low-bandwidth wireless mesh network. For bandwidth efficiency, we propose a group-aware stream filtering approach, used in conjunction with multicasting, that exploits two overlooked, yet important, properties of these applications: 1) many applications 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. An evaluation of our prototype implementation shows that group-aware data filtering can save bandwidth with low CPU overhead.

2006:

Kazuhiro Minami and David Kotz. Scalability in a Secure Distributed Proof System. Proceedings of the International Conference on Pervasive Computing (Pervasive). May 2006. [Details]

A logic-based language is often adopted in systems for pervasive computing, because it provides a convenient way to define rules that change the behavior of the systems dynamically. Those systems might define rules that refer to the users’ context information to provide context-aware services. For example, a smart-home application could define rules referring to the location of a user to control the light of a house automatically. In general, the context information is maintained in different administrative domains, and it is, therefore, desirable to construct a proof in a distributed way while preserving each domain’s confidentiality policies. In this paper, we introduce such a system, a secure distributed proof system for context-sensitive authorization and show that our novel caching and revocation mechanism improves the performance of the system, which depends on public key cryptographic operations to protect confidential information in rules and facts. Our revocation mechanism maintains dependencies among facts and recursively revokes across multiple hosts all the cached facts that depend on a fact that has become invalid. Our initial experimental results show that our caching mechanism, which maintains both positive and negative facts, significantly reduces the latency for handling a logical query.

Kazuhiro Minami. Secure Context-sensitive Authorization. PhD thesis, February 2006. Available as Dartmouth Computer Science Technical Report TR2006-571. [Details]

Pervasive computing leads to an increased integration between the real world and the computational world, and many applications in pervasive computing adapt to the user’s context, such as the location of the user and relevant devices, the presence of other people, light or sound conditions, or available network bandwidth, to meet a user’s continuously changing requirements without taking explicit input from the users.

We consider a class of applications that wish to consider a user’s context when deciding whether to authorize a user’s access to important physical or information resources. Such a context-sensitive authorization scheme is necessary when a mobile user moves across multiple administrative domains where they are not registered in advance. Also, users interacting with their environment need a non-intrusive way to access resources, and clues about their context may be useful input into authorization policies for these resources. Existing systems for context-sensitive authorization take a logic-based approach, because a logical language makes it possible to define a context model where a contextual fact is expressed with a boolean predicate and to derive higher-level context information and authorization decisions from contextual facts.

However, those existing context-sensitive authorization systems have a central server that collects context information, and evaluates policies to make authorization decisions on behalf of a resource owner. A centralized solution assumes that all resource owners trust the server to make correct decisions, and all users trust the server not to disclose private context information. In many realistic applications of pervasive computing, however, the resources, users, and sources of context information are inherently distributed among many organizations that do not necessarily trust each other. Resource owners may not trust the integrity of context information produced by another domain, and context sensors may not trust others with the confidentiality of data they provide about users.

In this thesis, we present a secure distributed proof system for context-sensitive authorization. Our system enables multiple hosts to evaluate an authorization query in a peer-to-peer way, while preserving the confidentiality and integrity policies of mutually untrusted principals running those hosts. We also develop a novel caching and revocation mechanism to support context-sensitive policies that refer to information in dozens of different administrative domains. Contributions of this thesis include the definition of fine-grained security policies that specify trust relations among principals in terms of information confidentiality and integrity, the design and implementation of a secure distributed proof system, a proof for the correctness of our algorithm, and a performance evaluation showing that the amortized performance of our system scales to dozens of servers in different domains.

2005:

Guanling Chen and David Kotz. Policy-Driven Data Dissemination for Context-Aware Applications. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom). March 2005. [Details]

Context-aware pervasive-computing applications require continuous monitoring of their physical and computational environment to make appropriate adaptation decisions in time. The data streams produced by sensors, however, may overflow the queues on the dissemination path. Traditional flow-control and congestion-control policies either drop data or force the sender to pause. When the data sender is sensing the physical environment, however, a pause is equivalent to dropping data. Instead of arbitrarily dropping data that may contain important events, we present a policy-driven data dissemination service named PACK, based on an overlay-based infrastructure for efficient multicast delivery. PACK enforces application-specified policies that define how to discard or summarize data flows wherever queues overflow on the data path, notably at the mobile hosts where applications often reside. A key contribution of our approach is to uniformly apply the data-stream “packing” abstraction to queue overflow caused by network congestion, slow receivers, and temporary disconnection. We present experimental results and a detailed application study of the PACK service.

Kazuhiro Minami and David Kotz. Secure Context-sensitive Authorization. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom). March 2005. [Details]

There is a recent trend toward rule-based authorization systems to achieve flexible security policies. Also, new sensing technologies in pervasive computing make it possible to define context-sensitive rules, such as “allow database access only to staff who are currently located in the main office.” However, these rules, or the facts that are needed to verify authority, often involve sensitive context information. This paper presents a secure context-sensitive authorization system that protects confidential information in facts or rules. Furthermore, our system allows multiple hosts in a distributed environment to perform the evaluation of an authorization query in a collaborative way; we do not need a universally trusted central host that maintains all the context information. The core of our approach is to decompose a proof for making an authorization decision into a set of sub-proofs produced on multiple different hosts, while preserving the integrity and confidentiality policies of the mutually untrusted principals operating these hosts.

Kazuhiro Minami and David Kotz. Secure Context-sensitive Authorization. Journal of Pervasive and Mobile Computing. March 2005. [Details]

2004:

Kazuhiro Minami and David Kotz. Secure Context-sensitive Authorization. Technical Report, December 2004. [Details]

Jue Wang. Performance Evaluation of a Resource Discovery Service. Master's thesis, October 2004. Available as Dartmouth Computer Science Technical Report TR2004-513. [Details]

In a pervasive computing environment, the number and variety of resources (services, devices, and contextual information resources) make it necessary for applications to accurately discover the best ones quickly. Thus a resource-discovery service, which locates specific resources and establishes network connections as better resources become available, is necessary for those applications. The performance of the resource-discovery service is important when the applications are in a dynamic and mobile environment. In this thesis, however, we do not focus on the resource-discovery technology itself, but the evaluation of the scalability and mobility of the resource discovery module in Solar, a context fusion middleware. Solar has a naming service that provides resource discovery, since the resource names encode static and dynamic attributes. The results of our experiments show that Solar’s resource discovery performed generally well in a typical dynamic environment, although Solar can not be scaled as well as it should. And we identify the implementation issues related to that problem. We also discuss experience, insights, and lessons learned from our quantitative analysis of the experiment results.

Guanling Chen, Ming Li, and David Kotz. Design and implementation of a large-scale context fusion network. Proceedings of the International Conference on Mobile and Ubiquitous Systems: Networking and Services (Mobiquitous). August 2004. [Details]

In this paper we motivate a Context Fusion Network (CFN), an infrastructure model that allows context-aware applications to select distributed data sources and compose them with customized data-fusion operators into a directed acyclic information fusion graph. Such a graph represents how an application computes high-level understandings of its execution context from low-level sensory data. Multiple graphs by different applications inter-connect with each other to form a global graph. A key advantage of a CFN is re-usability, both at code-level and instance-level, facilitated by operator composition. We designed and implemented a distributed CFN system, Solar, which maps the logical operator graph representation onto a set of overlay hosts. In particular, Solar meets the challenges inherent to heterogeneous and volatile ubicomp environments. By abstracting most complexities into the infrastructure, we believe Solar facilitates both the development and deployment of context-aware applications. We present the operator composition model, basic services of the Solar overlay network, and programming support for the developers. We also discuss some applications built with Solar and the lessons we learned from our experience.

Guanling Chen. Solar: Building A Context Fusion Network for Pervasive Computing. PhD thesis, August 2004. Available as Dartmouth Computer Science Technical Report TR2004-514. [Details]

The complexity of developing context-aware pervasive-computing applications calls for distributed software infrastructures that assist applications to collect, aggregate, and disseminate contextual data. In this dissertation, we present a Context Fusion Network (CFN), called Solar, which is built with a scalable and self-organized service overlay. Solar is flexible and allows applications to select distributed data sources and compose them with customized data-fusion operators into a directed acyclic information flow graph. Such a graph represents how an application computes high-level understandings of its execution context from low-level sensory data. To manage application-specified operators on a set of overlay nodes called Planets, Solar provides several unique services such as application-level multicast with policy-driven data reduction to handle buffer overflow, context-sensitive resource discovery to handle environment dynamics, and proactive monitoring and recovery to handle common failures. Experimental results show that these services perform well on a typical DHT-based peer-to-peer routing substrate. In this dissertation, we also discuss experience, insights, and lessons learned from our quantitative analysis of the input sensors, a detailed case study of a Solar application, and development of other applications in different domains.

Jue Wang, Guanling Chen, and David Kotz. A sensor-fusion approach for meeting detection. Proceedings of the MobiSys 2004 Workshop on Context Awareness. June 2004. [Details]

In this paper we present a context-sensing component that recognizes meetings in a typical office environment. Our prototype detects the meeting start and end by combining outputs from pressure and motion sensors installed on the chairs. We developed a telephone controller application that transfers incoming calls to voice-mail when the user is in a meeting. Our experiments show that it is feasible to detect high-level context changes with “good enough” accuracy, using low-cost, off-the-shelf hardware, and simple algorithms without complex training. We also note the need for better metrics to measure context detection performance, other than just accuracy. We propose several metrics appropriate for our application in this paper. It may be useful, however, for the community to define a set of general metrics as a basis to compare different approaches of context detection.

Guanling Chen and David Kotz. Dependency management in distributed settings (Poster Abstract). Proceedings of the International Conference on Autonomic Computing (ICAC). May 2004. [Details]

Ubiquitous-computing environments are heterogeneous and volatile in nature. Systems that support ubicomp applications must be self-managed, to reduce human intervention. In this paper, we present a general service that helps distributed software components to manage their dependencies. Our service proactively monitors the liveness of components and recovers them according to supplied policies. Our service also tracks the state of components, on behalf of their dependents, and may automatically select components for the dependent to use based on evaluations of customized functions. We believe that our approach is flexible and abstracts away many of the complexities encountered in ubicomp environments. In particular, we show how we applied the service to manage dependencies of context-fusion operators and present some experimental results.

Guanling Chen and David Kotz. Dependency management in distributed settings. Technical Report, March 2004. [Details]

Jue Wang, Guanling Chen, and David Kotz. A meeting detector and its applications. Technical Report, March 2004. [Details]

Guanling Chen and David Kotz. Application-Controlled Loss-Tolerant Data Dissemination. Technical Report, February 2004. [Details]

Reactive or proactive mobile applications require continuous monitoring of their physical and computational environment to make appropriate decisions in time. These applications need to monitor data streams produced by sensors and react to changes. When mobile sensors and applications are connected by low-bandwidth wireless networks, sensor data rates may overwhelm the capacity of network links or of the applications. In traditional networks and distributed systems, flow-control and congestion-control policies either drop data or force the sender to pause. When the data sender is sensing the physical environment, however, a pause is equivalent to dropping data. Arbitrary data drops are not necessarily acceptable to the reactive mobile applications receiving sensor data. Data distribution systems must support application-specific policies that selectively drop data objects when network or application buffers overflow.

In this paper we present a data-dissemination service, PACK, which allows applications to specify customized data-reduction policies. These policies define how to discard or summarize data flows wherever buffers overflow on the dissemination path, notably at the mobile hosts where applications often reside. The PACK service provides an overlay infrastructure to support mobile data sources and sinks, using application-specific data-reduction policies where necessary along the data path. We uniformly apply the data-stream “packing” abstraction to buffer overflow caused by network congestion, slow receivers, and the temporary disconnections caused by end-host mobility. We demonstrate the effectiveness of our approach with an application example and experimental measurements.

2003:

Guanling Chen and David Kotz. Context-Sensitive Resource Discovery. Proceedings of the IEEE International Conference on Pervasive Computing and Communications (PerCom). March 2003. [Details]

This paper presents the “Solar” system framework that allows resources to advertise context-sensitive names and for applications to make context-sensitive name queries. The heart of our framework is a small specification language that allows composition of “context-processing operators” to calculate the desired context. Resources use the framework to register and applications use the framework to lookup context-sensitive name descriptions. The back-end system executes these operators and constantly updates the context values, adjusting advertised names and informing applications about changes. We report experimental results from a prototype, using a modified version of the Intentional Naming System (INS) as the core directory service.

2002:

Guanling Chen and David Kotz. Context Aggregation and Dissemination in Ubiquitous Computing Systems. Proceedings of the IEEE Workshop on Mobile Computing Systems and Applications (WMCSA). June 2002. [Details]

Many “ubiquitous computing” applications need a constant flow of information about their environment to be able to adapt to their changing context. To support these “context-aware” applications we propose a graph-based abstraction for collecting, aggregating, and disseminating context information. The abstraction models context information as events, produced by sources and flowing through a directed acyclic graph of event-processing operators and delivered to subscribing applications. Applications describe their desired event stream as a tree of operators that aggregate low-level context information published by existing sources into the high-level context information needed by the application. The operator graph is thus the dynamic combination of all applications’ subscription trees.

In this paper, we motivate and describe our graph abstraction, and discuss a variety of critical design issues. We also sketch our Solar system, an implementation that represents one point in the design space for our graph abstraction.

Guanling Chen and David Kotz. Solar: An Open Platform for Context-Aware Mobile Applications. Proceedings of the International Conference on Pervasive Computing (Pervasive) (Short paper). June 2002. In an informal companion volume of short papers. [Details]

Emerging pervasive computing technologies transform the way we live and work by embedding computation in our surrounding environment. To avoid increasing complexity, and allow the user to concentrate on her tasks, applications in a pervasive computing environment must automatically adapt to their changing context, including the user state and the physical and computational environment in which they run. Solar is a middleware platform to help these “context-aware” applications aggregate desired context from heterogeneous sources and to locate environmental services depending on the current context. By moving most of the context computation into the infrastructure, Solar allows applications to run on thin mobile clients more effectively. By providing an open framework to enable dynamic injection of context processing modules, Solar shares these modules across many applications, reducing application development cost and network traffic. By distributing these modules across network nodes and reconfiguring the distribution at runtime, Solar achieves parallelism and online load balancing.

A. Abram White. Performance and Interoperability In Solar. Technical Report, June 2002. Available as Dartmouth Computer Science Technical Report TR2002-427. [Details]

Ubiquitous computing promises to integrate computers into our physical environment, surrounding us with applications that are able to adapt to our dynamics. Solar is a software infrastructure designed to deliver contextual information to these applications. To serve the large number and wide variety of context-aware devices envisioned by ubiquitous computing, Solar must exhibit both high performance and the ability to interoperate with many computing platforms. We created a testing framework to measure the performance of distributed systems such as Solar, as well as a pluggable data-transfer mechanism to support the dissemination of information to heterogeneous applications. This paper explores the testing framework developed, analyzes its findings concerning the performance of the current Solar prototype, presents several optimizations to Solar and their effects, and finally discusses the design of the pluggable data-transfer mechanism.

Guanling Chen and David Kotz. Context Aggregation and Dissemination in Ubiquitous Computing Systems. Technical Report, February 2002. [Details]

Guanling Chen and David Kotz. Solar: A pervasive-computing infrastructure for context-aware mobile applications. Technical Report, February 2002. [Details]

Emerging pervasive computing technologies transform the way we live and work by embedding computation in our surrounding environment. To avoid increasing complexity, and allow the user to concentrate on her tasks, applications must automatically adapt to their changing context, the physical and computational environment in which they run. To support these “context-aware” applications we propose a graph-based abstraction for collecting, aggregating, and disseminating context information. The abstraction models context information as events, which are produced by sources, flow through a directed acyclic graph of event-processing operators, and are delivered to subscribing applications. Applications describe their desired event stream as a tree of operators that aggregate low-level context information published by existing sources into the high-level context information needed by the application. The operator graph is thus the dynamic combination of all applications’ subscription trees. In this paper, we motivate our graph abstraction by discussing several applications under development, sketch the architecture of our system (“Solar”) that implements our abstraction, report some early experimental results from the prototype, and outline issues for future research.

Kazuhiro Minami and David Kotz. Controlling access to pervasive information in the “Solar” system. Technical Report, February 2002. [Details]

Pervasive-computing infrastructures necessarily collect a lot of context information to disseminate to their context-aware applications. Due to the personal or proprietary nature of much of this context information, however, the infrastructure must limit access to context information to authorized persons. In this paper we propose a new access-control mechanism for event-based context-distribution infrastructures. The core of our approach is based on a conservative information-flow model of access control, but users may express discretionary relaxation of the resulting access-control list (ACL) by specifying relaxation functions. This combination of automatic ACL derivation and user-specified ACL relaxation allows access control to be determined and enforced in a decentralized, distributed system with no central administrator or central policy maker. It also allows users to express their personal balance between functionality and privacy. Finally, our infrastructure allows access-control policies to depend on context-sensitive roles, allowing great flexibility.

We describe our approach in terms of a specific context-dissemination framework, the Solar system, although the same principles would apply to systems with similar properties.

2001:

Guanling Chen and David Kotz. SOLAR: Towards a Flexible and Scalable Data-Fusion Infrastructure for Ubiquitous Computing. Proceedings of the UbiTools workshop at UbiComp 2001. October 2001. [Details]

As we embed more computers into our daily environment, ubiquitous computing promises to make them less noticeable and to avoid information overload. We see, however, few ubiquitous applications that are able to adapt to the dynamics of user, physical, and computational context. The challenge is to allow applications flexible access to these sources, and yet scale to thousands of devices and sensors. In this paper we introduce our proposed infrastructure, Solar. In Solar, information sources produce events. Applications may subscribe to interesting sources directly, or they may instantiate and subscribe to a tree of operators that filter, transform, merge and aggregate events. Applications use a subscription language to describe the tree, based on event streams registered in a context-sensitive naming hierarchy. Solar is flexible: modular operators can be composed to produce new event streams. Solar is scalable: it distributes operators across hosts called Planets, and it re-uses common subgraphs in the operator network.

Arun Mathias. SmartReminder: A Case Study on Context-Sensitive Applications. Technical Report, June 2001. Available as Dartmouth Computer Science Technical Report TR2001-392. [Details]

Designing context-sensitive applications is challenging. We design and implement SmartReminder to explore designing context-sensitive applications and to demonstrate how the SOLAR system can be used in developing such applications. SmartReminder is an application that reminds the user based on contextual information. Current appointment-reminder applications remind the user about their appointments at an arbitrarily specified time. For instance, they might remind the user ten minutes before each appointment. SmartReminder, on the other hand, uses contextual information, like location, to better estimate the appropriate reminder time for each appointment. It reminds the user based on where they are, where they need to be, and how long it will take them to get there. This paper presents SmartReminder as an illustration of how context-sensitive applications can be designed using the SOLAR system for dissemination of contextual information.

Guanling Chen and David Kotz. Supporting Adaptive Ubiquitous Applications with the SOLAR System. Technical Report, May 2001. [Details]

As we embed more computers into our daily environment, ubiquitous computing promises to make them less noticeable and help to prevent information overload. We see, however, few ubiquitous applications that are able to adapt to the dynamics of user, physical, and computational context. We believe that there are two challenges causing this lack of ubiquitous applications: there is no flexible and scalable way to support information collection and dissemination in a ubiquitous and mobile environment, and there is no general approach to building adaptive applications given heterogeneous contextual information. We propose a system infrastructure, Solar, to meet these challenges. Solar uses a subscription-based operator graph abstraction and allows dynamic composition of stackable operators to manage ubiquitous information sources. After developing a set of diverse adaptive applications, we expect to identify fundamental techniques for context-aware adaptation. Our expectation is that Solar’s end-to-end support for information collection, dissemination, and utilization will make it easy to build adaptive applications for a ubiquitous mobile environment with many users and devices.

2000:

Guanling Chen and David Kotz. A Survey of Context-Aware Mobile Computing Research. Technical Report, November 2000. [Details]

Context-aware computing is a mobile computing paradigm in which applications can discover and take advantage of contextual information (such as user location, time of day, nearby people and devices, and user activity). Since it was proposed about a decade ago, many researchers have studied this topic and built several context-aware applications to demonstrate the usefulness of this new technology. Context-aware applications (or the system infrastructure to support them), however, have never been widely available to everyday users. In this survey of research on context-aware systems and applications, we looked in depth at the types of context used and models of context information, at systems that support collecting and disseminating context, and at applications that adapt to the changing context. Through this survey, it is clear that context-aware research is an old but rich area for research. The difficulties and possible solutions we outline serve as guidance for researchers hoping to make context-aware computing a reality.

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

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Papers (tagged 'solar')