Scalable topology control for deployment-support networks

Beutel, Jan; Dyer, Matthias; Meier, Lennart; Thiele, Lothar

doi:10.1109/ipsn.2005.1440949

Cited by 14 publications

(12 citation statements)

References 6 publications

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“…Beutel et al have attached powerful nodes to sensor nodes with adapter cables for direct process monitoring [58]. This enables the retrieval of nodes' state information without packet exchange over the LowPAN.…”

Section: ) Remote Debuggersmentioning

confidence: 99%

“…A deployment support network (DSN) was proposed to capture the network communication, providing a Bluetooth radio front-end in addition to the low-power radio [67]. The time-synchronised capturing nodes form a robust Bluetooth scatternet with a laptop equipped with a Bluetooth radio serving as the system sink [58]; data is therefore communicated back to the sink with little network interference. Further work has shown how data analysis and filtering of network packets captured in-situ can benefit from an integration within Wireshark [28], providing a standard monitoring environment, showing packets as if they were captured at the network sink [69].…”

Section: ) Distributed Packet Sniffersmentioning

confidence: 99%

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Debugging Low-Power and Lossy Wireless Networks: A Survey

Schoofs

O’Hare

Ruzzelli

2012

IEEE Commun. Surv. Tutorials

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Section: ) Remote Debuggersmentioning

confidence: 99%

Section: ) Distributed Packet Sniffersmentioning

confidence: 99%

Debugging Low-Power and Lossy Wireless Networks: A Survey

Schoofs

O’Hare

Ruzzelli

2012

IEEE Commun. Surv. Tutorials

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“…IEEE 802.15.1. Mote sensors designed with Bluetooth radio running TinyOS have been reported in the literature [18]. Since, the CodeBlue uses Telos B motes we envisage implementing EEG sensors using Tmote [19] motes with IEEE 802.15.4 radio and 12 bit A/D conversion hardware.…”

Section: Medical Sensor Network With Wireless Eegsmentioning

confidence: 99%

Integrating wireless EEGs into medical sensor networks

Sarikaya

Alim

Rezaei

2006

Proceedings of the 2006 International Conference on Wireless Communications and Mobile Computing

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Wireless Electroencephalograms (EEG) are currently being used to wirelessly transmit the data from brain sensors to a computer and they carry huge potential for many future medical applications. This paper presents the design of a hybrid medical sensor network with Tmote Sky motes as wireless EEG sensor nodes at the lowest level collecting EEG signals and sending them to Stargate PDAs at the next level. Stargates perform artifact removal, Fourier transformation and feature extraction and the final machine intelligence algorithms are run at a PC server. Several features of the CodeBlue medical sensor network like query processing, routing layer are used in our design. The advantages of the hybrid medical sensor network integrating wireless EEGs include the capability to have the brain monitoring functionality incorporated into the medical sensor networks.

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“…A Vehicle Adhoc Network(VANET) is a special type of mobile adhoc network [1], where all nodes are vehicles and move regularly at high speed. The VANET has unique requirements with respect to the applications, self organization and communication.…”

Section: Introductionmentioning

confidence: 99%

“…Each sensor is capable of collecting valuable information and transmits the data to others [5]. These devices are very small, low cost and can be deployed in a large numbers in the network [1]. Failure of a single device does not affect the network performance.…”

Section: Introductionmentioning

confidence: 99%

Design and development of a wireless sensor model for vehicular area networks

Umesh¹,

Varaprasad²

2008

SIGBED Rev.

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Vehicular area network provides vehicle-to-vehicle, vehicle-to-infrastructure, and vehicle-to-person communications. Its aim is to increase the road safety and transport facility efficiency. It also provides a ubiquitous wireless environment for the end users. The vehicle area network is considered as one of the major applications for wireless networks. Here, each vehicle has a unique identifier and eavesdropper to accumulate the locations of vehicles. If the vehicle changes its pseudonym from time to time, then the long-term tracking is to be avoided. The proposed model automatically monitors the flow of vehicles and sends the data to the control room via gateway nodes. It uses number of sensors to reduce the traffic at important junctions while forwarding the vehicle from one place to another.

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Scalable topology control for deployment-support networks

Cited by 14 publications

References 6 publications

Debugging Low-Power and Lossy Wireless Networks: A Survey

Debugging Low-Power and Lossy Wireless Networks: A Survey

Integrating wireless EEGs into medical sensor networks

Design and development of a wireless sensor model for vehicular area networks

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