Sanem Kabadayi

Virtual sensors: abstracting data from physical sensors

Sensor networks are becoming increasingly pervasive. Existing methods of aggregation in sensor networks offer mostly standard mathematical operators over homogeneous data types. In this paper, we instead focus on supporting emerging scenarios in which applications will need to extract abstracted measurements from diverse sets of sensor network nodes. This paper introduces the virtual sensors abstraction that enables an application developer to programmatically specify an applications high-level data requirements. This paper reports on our initial work with the virtual sensors and the results of our prototype implementation

A Local Data Abstraction and Communication Paradigm for Pervasive Computing

As sensor networks are increasingly used to support pervasive computing, we envision an instrumented environment that can provide varying amounts of information to mobile applications immersed within the network. Such a scenario deviates from existing deployments of sensor networks which are often highly application-specific and funnel information to a central collection point. We instead target scenarios in which multiple mobile applications will leverage sensor network nodes opportunistically and unpredictably. Such situations require new communication abstractions that enable immersed devices to interact directly with available sensors, reducing both communication overhead and data latency. This paper introduces scenes, which applications create based on their communication requirements, abstract properties of the underlying network communication, and properties of the physical environment. This paper reports on the communication model, an initial implementation, and its performance in varying scenarios

Scenes: Abstracting interaction in immersive sensor networks

Pervasive computing deployments are increasingly using sensor networks to build instrumented environments that provide local data to immersed mobile applications. These applications demand opportunistic and unpredictable interactions with local devices. While this direct communication has the potential to reduce both overhead and latency, it deviates significantly from existing uses of sensor networks that funnel information to a static central collection point. This pervasive computing driven perspective demands new communication abstractions that enable the required direct communication among mobile applications and sensors. This paper presents the scene abstraction, which allows immersed applications to create dynamic distributed data structures over the immersive sensor network. A scene is created based on application requirements, properties of the underlying network, and properties of the physical environment. This paper details our work on defining scenes, providing an abstract model, an implementation, and an evaluation.

Dynamic Decision Support in Direct-Access Sensor Networks; A Demonstration

This paper describes application demonstrations of a new middleware that supports dynamic decision support over networks of resource-constrained devices. For the purposes of our demonstrations, we tap into intelligent job site applications for the construction domain. The demonstrations are carefully constructed to highlight our middlewares ability to provide on-demand access to local data, aggregation of data across dynamically defined regions, fusion of heterogeneous sensor data, and intelligent application-sensitive sensor clustering. The paper briefly describes the middleware and the details of the demonstrations

An interrelational grouping abstraction for heterogeneous sensors

In wireless sensor network applications, the potential to use cooperation to resolve user queries remains largely untapped. Efficiently answering a users questions requires identifying the correct set of nodes that can answer the question and enabling coordination between them. In this article, we propose a query domain abstraction that allows an application to dynamically specify the nodes best suited to answering a particular query. Selecting the ideal set of heterogeneous sensors entails answering two fundamental questions—how are the selected sensors related to one another, and where should the resulting sensor coalition be located. We introduce two abstractions, the proximity function and the reference function, to precisely specify each of these concerns within a query. All nodes in the query domain must satisfy any provided proximity function, a user-defined function that constrains the relative relationship among the group of nodes (e.g., based on a property of the network or physical environment or on logical properties of the nodes). The selected set of nodes must also satisfy any provided reference function, a mechanism to scope the location of the query domain to a specified area of interest (e.g., within a certain distance from a specified reference point). In this article, we model these abstractions and present a set of protocols that accomplish this task with varying degrees of correctness. We evaluate their performance through simulation and highlight the tradeoffs between protocol overhead and correctness.

An architecture for local decision support in ad hoc sensor networks

SUMMARY: The intelligent jobsite is becoming a reality as applications using sensors and mobile computing devices are being developed and deployed commercially. This creates an opportunity for workers to access data in an ad hoc manner as they move through a site. However, retasking and reuse of sensors in a dynamic setting presents significant challenges including ad hoc identification of sensors in a local environment, generalization of data, and the use of dynamic information for decision support. To achieve a generalized approach to using local data, we describe an architecture that abstracts functionality into three tiers: a layer for physical communication among devices; a layer for data processing and abstraction; and a top layer for decision support. At each tier, the level of abstraction increases, allowing for development of decision support applications at the top level that are not directly tied to specific devices. The industry vision for the intelligent jobsite is driving considerable research and development in mobile technologies. However, the bulk of this development is application specific and directly ties hardware (e.g., RFID tags, sensors) to data processing applications. We seek to decouple sensors from computing hardware and application development by providing a flexible and robust middleware. Realization of the middleware will enable more flexible reuse of data to make it available to a range of decision support applications, while at the same time speeding development of such applications. This paper describes an architecture informed by a working first generation prototype. Details of the prototype, lessons learned, and specific advancements are detailed. Future commercial implementation of the architecture will make construction-specific visions for ubiquitous computing possible by enabling flexible and robust discovery and use of data in an ad hoc manner.