ExScal: Elements of an Extreme Scale Wireless Sensor Network

Arora, Anish; Ramnath, Rajiv; Ertin, Emre; Sinha, Prasun; Bapat, Sandip; Naik, Vinayak; Kulathumani, Vinod; Zhang, Hongwei; Cao, Hui; Sridharan, Karthik; Kumar, Santosh; Seddon, Nick; Anderson, Chris; Herman, Ted; Trivedi, Nishank; Nesterenko, Mikhail; Shah, Romil; Kulkami, S.; Aramugam, Mahesh; Wang, Limin; Gouda, Mohamed G.; Choi, Young-ri; Culler, David; Dutta, Prabal; Sharp, Cory; Tolle, Gilman; Grimmer, M. K.; Ferriera, Bill; Parker, Ken R.

doi:10.1109/rtcsa.2005.47

Cited by 241 publications

(210 citation statements)

References 9 publications

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“…For example, in a forest fire detection system, high partial coverage will ensure that most fires are detected immediately and all fires are detected within a short duration. Likewise, high partial coverage could also be used in applications such as ExScal [6], an application for intruder detection, classification and tracking. ranges can be caused by the hardware (antenna, radio unit, etc.…”

Section: Introductionmentioning

confidence: 99%

Sacrificing a little coverage can substantially increase network lifetime

Wang

Kulkarni

2008

Ad Hoc Networks

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Section: Introductionmentioning

confidence: 99%

Sacrificing a little coverage can substantially increase network lifetime

Wang

Kulkarni

2008

Ad Hoc Networks

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“…Although very many papers have been written about wireless sensor networks, experience papers reporting on real-world deployments are a minority: they include at least Mainwaring et al [19] who monitor seabirds' nesting environment and behaviour, Arora et al [1] who deploy a perimeter control WSN and Werner-Allen et al [23] who monitor an active volcano. Closer to our scenario are Krishnamurthy et al [16] who monitor equipment for early signs of failure and especially Kim et al [14] who monitor the structural health of the Golden Gate bridge.…”

Section: Related Workmentioning

confidence: 99%

Smart bridges, smart tunnels: Transforming wireless sensor networks from research prototypes into robust engineering infrastructure

et al. 2010

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We instrumented large civil engineering infrastructure items, such as bridges and tunnels, with sensors that monitor their operational performance and deterioration. In so doing we discovered that commercial o erings of Wireless Sensor Networks (WSNs) are still geared towards research prototypes and are currently not yet mature for deployment in practical scenarios.We distill the experience gained during this three-year interdisciplinary project into speci c advice for researchers and developers. We discuss problems and solutions in a variety of areas including sensor hardware, radio propagation, node deployment, system security and data visualization. We also point out the problems that are still open and that the community needs to address to enable widespread adoption of WSNs outside the research lab. IntroductionLarge civil engineering infrastructure items such as bridges, highways, tunnels and water pipes are expected to last for decades or even centuries. Over the course of their lifetimes, these structures deteriorate and require timely maintenance in order to prevent further degradation that might lead to accidents, the need for replacement or, in the worst case, collapse. Traditionally, early detection of such deterioration is achieved by visual inspection, either during routine maintenance visits or when a maintenance team is sent to the site to investigate a known or suspected problem. But such inspections are time-consuming and costly and therefore infrequent. An alternative is to equip infrastructure with sensors that are permanently wired up to report back to a central system; but this solution is not adopted very extensively because of the di culty and cost of running data and power cables to each individual sensor in challenging environments such as a subway tunnel or a long suspension bridge.The purpose of our research project, which at the time of writing has been running for almost three years, is to develop a system for continuous monitoring of such infrastructure using wireless sensor networks which, compared to wired systems, are easier and cheaper to deploy and also o er the opportunity for straightforward expansion.How nice it would be if we could just go out and buy a commercial o the shelf (COTS) wireless sensor network (WSN) system and use it for monitoring our structures straight away. Unfortunately, though, the available commercial systems are typically only kits of building blocks and a non-trivial integration e ort is required, together with the development of any missing parts, before arriving at a complete and usable monitoring solution.This experience paper identi es some of the challenges and issues encountered when installing wireless sensor networks in the eld, with speci c but not exclusive reference to civil engineering deployments, and discusses how these challenges can be addressed. Inspired by the format of the instructive and well-presented paper by Barrenetxea et al. [2], which content-wise is largely complementary to ours, we share our experience in the form o...

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“…Another major problem is due to the spatio-temporal nature of the data generation. In several WSN deployments, including environmental monitoring [1], habitat monitoring [2], and especially surveillance systems [3], [4], it has been observed that the phenomena of interest are local both in time and space. Fixing the location of the basestation ignores the nature of the data generation and results in long multihop paths for relaying, which leads to a lot of collisions and data losses.…”

Section: Introductionmentioning

confidence: 99%

Data Spider: A Resilient Mobile Basestation Protocol for Efficient Data Collection in Wireless Sensor Networks

Soysal¹,

Demirbaş²

2010

Distributed Computing in Sensor Systems

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Abstract-Traditional deployments of wireless sensor networks (WSNs) rely on static basestations to collect data. For applications with highly spatio-temporal and dynamic data generation, such as tracking and detection applications, static basestations suffer from communication bottlenecks and long routes, which cause reliability and lifetime to plummet.To address this problem, we propose a holistic solution where the synergy of the WSN and the mobile basestation improves the reliability and lifetime of data collection. The WSN component of our solution is a very lightweight dynamic routing tree maintenance protocol which tracks the location of the basestation to provide an always connected network. Our basestation algorithm complements the dynamic tree reconfiguration protocol by trailing towards the data generation, and hence, reducing the number of hops data needs to travel to the basestation. While both protocols are very simple and lightweight, combined they lead to significant improvements in the reliability and lifetime of data collection. We provide an analytical discussion of our solution together with detailed discrete event simulations.

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ExScal: Elements of an Extreme Scale Wireless Sensor Network

Cited by 241 publications

References 9 publications

Sacrificing a little coverage can substantially increase network lifetime

Sacrificing a little coverage can substantially increase network lifetime

Smart bridges, smart tunnels: Transforming wireless sensor networks from research prototypes into robust engineering infrastructure

Data Spider: A Resilient Mobile Basestation Protocol for Efficient Data Collection in Wireless Sensor Networks

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