Conference Paper

Poster abstract: Enabling reliable and high-fidelity data center sensing

DOI: 10.1145/1602165.1602213 Conference: Proceedings of the 8th International Conference on Information Processing in Sensor Networks, IPSN 2009, April 13-16, 2009, San Francisco, California, USA
Source: DBLP


RACNet is a sensor network that monitors a data center's environmental conditions at high temporal and spatial resolutions. The data RACNet collects can improve the energy efficiency of data centers, currently one of the fastest growing energy consumers in the U.S. RACNet overcomes the challenge of reliable and low-latency data gathering from dense networks deployed in harsh RF environments through rDCP, a novel network protocol that decouples data collection from topology control. Furthermore, rDCP automatically partitions the network to multiple routing trees, each operating at a different frequency channel. The combination of these features enables rDCP to scale up while maintaining high data yields. Preliminary results from a production deployment of 694 sensors (including 174 wireless nodes) show that rDCP achieves a data yield of 99%, while delivering 90% of the measurements in less than 30 seconds.

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Available from: Andreas Terzis, Oct 05, 2015
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    ABSTRACT: Despite being a core networking primitive, collection pro-tocols today often suffer from poor reliability (e.g., 70%) in practice, and heavily used protocols have never been eval-uated in terms of communication efficiency. Using detailed experimental studies, we describe three challenges that cause existing collection protocols to have poor reliability and waste energy: inaccuracies in link estimation, link dynam-ics, and transient loops. In this paper we present CTP, a robust, efficient, and hardware-independent collection protocol. CTP uses three novel techniques to address these challenges. CTP's link es-timator addresses the inaccuracies in link estimation by us-ing feedback from both the data and control planes, using information from multiple layers through narrow, platform-independent interfaces. Second, CTP addresses link dynam-ics by using the Trickle algorithm for control traffic, send-ing few beacons in stable topologies yet quickly adapting to changes. Finally, CTP addresses transient loops by using data traffic as active topology probes, quickly discovering and fixing routing failures. CTP runs on six different mote platforms and we have tested it on four testbeds. In most experiments, CTP achieves 99% reliability, and in some cases 99.9%. In the most chal-lenging testbed, the state-of-the-art collection protocol to-day (MultiHopLQI) achieves 70% reliability: CTP achieves 97%. CTP achieves this ten-fold reduction in dropped pack-ets with 25% fewer transmissions. CTP works seamlessly on top of existing low-power MAC layers. Together, these re-sults suggest that CTP can be the robust, efficient collection layer that so many sensor network applications and protocols need.
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    ABSTRACT: We present Koala, a reliable data retrieval system designed to operate at permille (.1%) duty cycles, essential for long term environmental monitoring networks. Koala achieves these low duty cycles by letting the network's nodes sleep most of the time and reviving them through an efficient wake-up strategy whenever the gateway performs a bulk data download. Unlike other systems which consume energy to maintain consistent network state (e.g. routes, sleep schedules, etc.) across the network's nodes, Koala maintains no persistent routing state on the motes. Instead, a basestation calculates the network paths using reachability information collected by the motes. The flexible control protocol (FCP), a protocol we developed, is then used to install this routing information on the network's nodes. This paradigm of operation not only eliminates the overhead of maintaining routing state, but also significantly reduces the complexity of the networking code running on the motes. Results from simulation and an actual implementation on TinyOS 2 indicate that Koala can achieve very low duty cycles under a wide range of download and network sizes.
    Information Processing in Sensor Networks, 2008. IPSN '08. International Conference on; 05/2008
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    ABSTRACT: RACNet is a monitoring sensor network to provide high-fidelity visibility, in terms of spatial and temporal resolu-tion, of data center environmental conditions for energy efficiency improvement. RACNet overcomes the high node density and harsh RF environment challenges in data centers to achieve over 99% reliable data yield and short data collection latency. It does so through a novel network architecture that decouples data collection from the construction of the routing tree. This design, cou-pled with the use of different frequencies along neigh-boring data collection trees, enables RACNet to sup-port large-scale, dense networks while maintaining per-fect data reliability. Results from simulations, testbed, and real world deployments indicate that RACNet out-performs previous data collection systems, especially as node density increases.