ProFuN TG: Programming Sensornets with Task Graphs for Increased Reliability and Energy-Efficiency
2015 (English)Conference paper, Poster (Refereed)
Sensor network macroprogramming methodologies such as the Abstract Task Graph hold the promise of enabling high-level sensor network application development. However, progress in this area is hampered by the scarcity of tools, and also because of insufficient focus on developing tool support for programming applications aware of performance requirements.
In this demo we present ProFuN TG (Task Graph), a tool for designing sensor network applications using task graphs. ProFuN TG provides automated task mapping, sensor nodefirmware macrocompilation, application simulation, deployment, and runtime maintenance capabilities. It allows users to incorporate performance requirements in the applications, expressed through constraints on task-to-task dataflows. The tool includes middleware that uses an efficient flooding-based protocol to set up tasks in the network, and also enables runtime assurance by keeping track of the constraint conditions.
Through task allocation in a way that optimizes an objective function in a model of the network, and adaptive task reallocation in case of link, node, or sensor failures the tool helps to make sensornet applications both more energy-efficient and reliable.
Place, publisher, year, edition, pages
IEEE Computer Society, 2015.
Communication Systems Embedded Systems
Research subject Computer Science with specialization in Computer Communication
IdentifiersURN: urn:nbn:se:uu:diva-288533OAI: oai:DiVA.org:uu-288533DiVA: diva2:924240
The 40th IEEE Conference on Local Computer Networks (LCN)
FunderSwedish Foundation for Strategic Research , RIT08-0065