Calibration as parameter estimation in sensor networks

By Kamin Whitehouse, David E. Culler. ACM International Conference on Wireless Sensor Networks and Applications, 2002

405 citations recorded by the source corpus.

Subfields: Computer Networks and Communications, Electrical and Electronic Engineering, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/bc0d6d0b0ca70e027bf0e13de31c1b99fa190883

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In Computer Networks and Communications

In Electrical and Electronic Engineering

In Ocean Engineering

Citations in the corpus

Localization systems for wireless sensor networks (2007, background): “In these cases, it is normal to make a system calibration [29], where values of RSSI and distances are previously evaluated in a controlled environment.”
Localization Algorithms and Strategies for Wireless Sensor Networks: Monitoring and Surveillance Techniques for Target Tracking (2009, methodology): “RSSI is supported by sensor node hardware, such as the Berkeley motes (Whitehouse and Culler, 2002).”
Exploiting the capture effect for collision detection and recovery (2005, methodology): “This technique is widely used for acoustic and ultrasonic ranging [27, 29].”
Models and solutions for radio irregularity in wireless sensor networks (2006, methodology): “For a system that adopts hardware calibration schemes [Whitehouse and Culler 2002][Hoff and Azuma 2000][Hightower et al.”
The nesC language: a holistic approach to networked embedded systems (2003, background): “Although very resource constrained, motes must be very reactive and participate in complex distributed algorithms, such as data aggregation [19, 29] or spatial localization [50].”
A Collaborative Approach to In-Place Sensor Calibration (2003, background): “The approach described in [1] formulates signal strength calibration as a global optimization problem and as written was not intended for distributed on-line deployment.”
Cyber–Physical Systems: A Perspective at the Centennial (2012, background): “More generally, there is the problem of how nodes in a network can self-calibrate themselves [242].”
The nesC language: A holistic approach to networked embedded systems (2003, background): “Although very resource constrained, motes must be very reactive and participate in complex distributed algorithms, such as data aggregation [19, 29] or spatial localization [50].”
Radio interferometric geolocation (2005, methodology): “The technique relies on a pair of nodes emitting radio waves simultaneously at slightly di.erent frequencies.”
The Emergence of Networking Abstractions and Techniques in TinyOS (2004, background): “They are also commonly used in localization systems based on time of flight measurements [ 41 ].”
Distributed localization in wireless sensor networks: a quantitative compariso (2003, positive): “We selected this error model based on the work of Whitehouse and Culler [18], which shows that, although individual distance measurements tend to overshoot the real distance, a proper calibration procedure yields distance estimates with a symmetric error distribution.”
A practical evaluation of radio signal strength for ranging-based localization (2007, background): “Finally, individual radios can vary significantly in both transmission strength and receptivity, especially in cheap, low-power radios [11, 34].”
TinyOS: An Operating System for Sensor Networks (2005, background): “Sensor networks have been the basis for work on ad hoc networking [34, 37, 38, 47], data aggregation [33, 49], distributed algorithms [25, 46, 59], and primitives such as localization [8, 76, 77], and time synchronization [16, 62].”
Lessons from a Sensor Network Expedition (2004, background): “Ultimately the data collected needs to be meaningful to disciplinary scientists, so sensor design [19] and inthe-field calibration systems are crucial [2,24].”
Handbook of Sensor Networks: Algorithms and Architectures (2005, methodology): “[35] we take the standard deviation of fr(t) to be 0.”
Location, Localization, and Localizability (2010, methodology): “(b) Time Difference of Arrival (TDoA) A more promising technique is the combined use of ultrasound/acoustic and radio signals to estimate distances by determining the Time Difference of Arrival (TDoA) of these signals[25,27-28].”
BeepBeep: a high accuracy acoustic ranging system using COTS mobile devices (2007, background): “To estimate the range between two devices, each will emit a specially-designed sound signal ( Beep ) and collect a simultaneous recording from its microphone.”
Localization of wireless sensor networks with a mobile beacon (2004, methodology): “Another original idea is presented in [32] where auto-calibration is used to improve the accuracy of a localization algorithm.”
Sensor network-based countersniper system (2004, methodology): “The accuracy and range of the first one utilizing a hardware tone detector was not satisfactory for our requirements [22].”