Titan: A Tiny Task Network for Dynamically Reconfigurable Heterogeneous Sensor Networks

Lombriser, Clemens; Roggen, Daniel; Stäger, Mathias; Tröster, Gerhard

doi:10.1007/978-3-540-69962-0_11

Cited by 48 publications

(32 citation statements)

References 14 publications

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“…A complex service can thus be interpreted as a task graph, where the different simple services represent tasks. Using task graphs for distributed execution in Wireless Sensor Networks has been used before for data fusion [6], macroprogramming abstraction [3], or context recognition [8]. Upon a request for the retrieval of context information, the e-SENSE system translates the request into a task graph composed of services registered in the system.…”

Section: Distributed Processingmentioning

confidence: 99%

“…The abstraction provides a good trade-off [8] between a fast custom application implementation, and adaptability to a range of different algorithm implementations on heterogeneous.…”

Section: Distributed Processingmentioning

confidence: 99%

“…• Speed -Due to a compact configuration format and preprogrammed tasks, the Node Manager can reconfigure a sensor node in less than 1 ms [8].…”

Section: Distributed Processingmentioning

confidence: 99%

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Organizing Context Information Processing in Dynamic Wireless Sensor Networks

Lombriser

Marin-Perianu

et al. 2007

2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information

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Section: Distributed Processingmentioning

confidence: 99%

“…The abstraction provides a good trade-off [8] between a fast custom application implementation, and adaptability to a range of different algorithm implementations on heterogeneous.…”

Section: Distributed Processingmentioning

confidence: 99%

See 1 more Smart Citation

Organizing Context Information Processing in Dynamic Wireless Sensor Networks

Lombriser

Marin-Perianu

et al. 2007

2007 3rd International Conference on Intelligent Sensors, Sensor Networks and Information

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“…This adds additional challenges, as the type and number of available sensors is difficult to foresee and thus, the context algorithms implemented need to be made adaptable to the environment. We developed a framework that supports the dynamic adaptation of algorithms by reconfiguration of sensor nodes, called Titan [19]. It allows to express context recognition algorithms using interconnected data processing tasks.…”

Section: Introductionmentioning

confidence: 99%

On-body activity recognition in a dynamic sensor network

Lombriser¹,

Bharatula²,

Roggen³

et al. 2007

Proceedings of the Second International Conference on Body Area Networks BodyNets

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Recognizing user activities using body-worn, miniaturized sensor nodes enables wearable computers to act contextaware. This paper describes how online activity recognition algorithms can be run on the SensorButton, our miniaturized wireless sensor platform. We present how the activity recognition algorithms have been optimized to be run online on our sensor platform, and how the execution can be distributed to the wireless sensor network. The resulting algorithm has been implemented as a custom, platform-specific executable as well as integrated into TinyOS. A comparison shows that the TinyOS executable is using about 7kB more code memory, while both implementations classify the activity in up to 18 classifications per second.

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“…Kumar et al present work in (non-automatic) task assignment [10] in a sensor network environment containing two classes of processor. J-Orchestra [11] is an automatic partitioning system used for splitting up ubiquitous computing applications; the Titan framework [12] has been developed to aid in dynamically reconfiguring which task is allocated to which sensor node.…”

Section: Related Workmentioning

confidence: 99%

Scalable Inter-vehicular Applications

Davies

Beresford

On the Move to Meaningful Internet Systems 2007: OTM 2007 Workshops

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Abstract. Many pervasive inter-vehicular applications involve the collation, processing and summarisation of sensor data originating from vehicles. When and where such processing takes place is an explicit designstage decision. Often some processing occurs on vehicles, and some on backend servers, but it is hard for the programmer to optimise this distribution for feasibility or performance. This paper investigates automated task assignment: we define a computational model which captures data aggregation and summarisation explicitly, allowing a compiler to automatically optimise the assignment of processing tasks to particular vehicles and servers. Our model allows a compiler to apply program transformations to data processing, which can further improve task assignment.Modern motor vehicles contain a plethora of on-board computing equipment. Today's cars have a variety of microprocessors governing diverse aspects of the vehicle's operation. We believe that trends in decreasing power requirements, size, and cost of manufacture mean that in future we can expect vehicles to provide embedded computing platforms supporting the execution of general applications. As cars become increasingly connected-to each other and to the Internet-these applications will evolve beyond disconnected intra-vehicle applications and will help to improve the safety, efficiency and comfort of using transport [1]. This vision of communicating vehicles will enable applications involving multiple participants, such as:Collection of vehicle position data. Known as floating car data, information regarding the locations and velocities of vehicles using the road network can be used to identify levels of road congestion and used as input to journeytime prediction applications [2]. Real-time weather map. Most modern vehicles already contain thermometers. If data from vehicles' onboard weather sensors could be aggregated, a real-time weather map of high resolution could be composed. Real-time road map updates. Traditional techniques for updating road maps involve manual surveying and data entry. Timely integration of changes to the road network can instead be done automatically based on vehicles' location traces [3]. Road hazard detection. Acceleration data collected from vehicles containing accelerometers can be used to build a map of road hazards by noting points in the road network where many vehicles have been found to swerve or brake sharply [4].

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Titan: A Tiny Task Network for Dynamically Reconfigurable Heterogeneous Sensor Networks

Cited by 48 publications

References 14 publications

Organizing Context Information Processing in Dynamic Wireless Sensor Networks

Organizing Context Information Processing in Dynamic Wireless Sensor Networks

On-body activity recognition in a dynamic sensor network

Scalable Inter-vehicular Applications

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