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OSDI '02 Proceedings of the 5th symposium on Operating systems design and implementationCopyright restrictions prevent ACM from being able to make the PDFs for this conference available for downloading
Nano-RK: An Energy-Aware Resource-Centric RTOS for Sensor Networks
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ISCC '04 Proceedings of the Ninth International Symposium on Computers and Communications 2004 Volume 2 (ISCC"04) - Volume 02
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Data & Knowledge Engineering
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In recent years, wireless sensor networking has shown great promise in applications ranging from industrial control, environmental monitoring and inventory tracking. Given the resource-constrained nature of sensor devices and the dynamic wireless channel used for communication, a sensor networking protocol needs to be compact, energy efficient and highly adaptable. In this paper we present SAMPL, a simple aggregation and message passing layer, aimed at flexible aggregation of sensor information over a long period of time, and supporting sporadic messages from mobile devices. SAMPL is a compact network layer that operates on top of a low-power CSMA/CA based MAC protocol. The protocol has been designed with extensibility in mind to support new transducer devices and unforeseen applications without requiring reprogramming of the entire network. SAMPL uses a highly adaptive tree-based routing scheme to achieve highly robust operation in a time-varying environment. The protocol supports peer-to-peer data transactions, local storage of data similar to what many RFID systems provide as well as secure gateway to infrastructure communication. SAMPL is built on top of the Nano-RK[1] operating system that runs on the FireFly sensor networking platform. Nano-RK's resource management primitives are used to create virtual energy budgets within SAMPL that enforce application lifetimes. As of October 2008, SAMPL has been operating as part of the Sensor Andrew project at Carnegie Mellon University with battery powered sensor nodes for over seven months and continues to be actively used as a research testbed. We describe our deployment tools and network health monitoring strategies necessary for configuring and maintaining long-term operation of a sensor network. Our approach has led to sustainable average packet success rate of 94% across the entire network.