Self-calibration methods for uncontrolled environments in sensor networks: A reference survey

被引:65
作者
Barcelo-Ordinas, Jose M. [1 ]
Doudou, Messaoud [2 ]
Garcia-Vidal, Jorge [1 ]
Badache, Nadjib [3 ]
机构
[1] Univ Politecn Cataluna, ES-08034 Barcelona, Spain
[2] Univ Technol Compigne, Sorbonne Univ, Heudiasyc Lab, Compigne, France
[3] CERIST Res Ctr, Algiers, Algeria
基金
欧盟地平线“2020”;
关键词
Wireless sensor networks (WSN); Calibration; Low-cost sensors; Uncontrolled environments; Quality of information (Qol); QUALITY MONITORING. PART; BLIND CALIBRATION; FIELD CALIBRATION; AVAILABLE SENSORS; PERFORMANCE; OZONE; PROTOCOLS; CLUSTER; SYSTEM;
D O I
10.1016/j.adhoc.2019.01.008
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
Growing progress in sensor technology has constantly expanded the number and range of low-cost, small, and portable sensors on the market, increasing the number and type of physical phenomena that can be measured with wirelessly connected sensors. Large-scale deployments of wireless sensor networks (WSN) involving hundreds or thousands of devices and limited budgets often constrain the choice of sensing hardware, which generally has reduced accuracy, precision, and reliability. Therefore, it is challenging to achieve good data quality and maintain error-free measurements during the whole system lifetime. Self-calibration or recalibration in ad hoc sensor networks to preserve data quality is essential, yet challenging, for several reasons, such as the existence of random noise and the absence of suitable general models. Calibration performed in the field, without accurate and controlled instrumentation, is said to be in an uncontrolled environment. This paper provides current and fundamental self-calibration approaches and models for wireless sensor networks in uncontrolled environments. (C) 2019 Elsevier B.V. All rights reserved.
引用
收藏
页码:142 / 159
页数:18
相关论文
共 80 条
  • [31] Ihler AT, 2004, IPSN '04: THIRD INTERNATIONAL SYMPOSIUM ON INFORMATION PROCESSING IN SENSOR NETWORKS, P225
  • [32] A Longitudinal Study of Vibration-Based Water Flow Sensing
    Kim, Younghun
    Park, Heemin
    Srivastava, Mani B.
    [J]. ACM TRANSACTIONS ON SENSOR NETWORKS, 2012, 9 (01)
  • [33] Kotsev A., 2016, SENSORS, V16
  • [34] On-line fault detection of sensor measurements
    Koushanfar, F
    Potkonjak, M
    Sangiovanni-Vincentelli, A
    [J]. PROCEEDINGS OF THE IEEE SENSORS 2003, VOLS 1 AND 2, 2003, : 974 - 979
  • [35] Distributed Bayesian algorithms for fault-tolerant event region detection in wireless sensor networks
    Krishnamachari, B
    Iyengar, S
    [J]. IEEE TRANSACTIONS ON COMPUTERS, 2004, 53 (03) : 241 - 250
  • [36] Automatic Sensor drift detection and correction using Spatial Kriging and Kalman filtering
    Kumar, Dheeraj
    Rajasegarar, Sutharshan
    Palaniswami, Marimuthu
    [J]. 2013 9TH IEEE INTERNATIONAL CONFERENCE ON DISTRIBUTED COMPUTING IN SENSOR SYSTEMS (IEEE DCOSS 2013), 2013, : 183 - 190
  • [37] LaMarca A., MAKING SENSOR NETWOR, P152
  • [38] A Blind Calibration Scheme Exploiting Mutual Calibration Relationships for a Dense Mobile Sensor Network
    Lee, Byung-Tak
    Son, Seung-Chul
    Kang, Kyungran
    [J]. IEEE SENSORS JOURNAL, 2014, 14 (05) : 1518 - 1526
  • [39] Self-calibration and biconvex compressive sensing
    Ling, Shuyang
    Strohmer, Thomas
    [J]. INVERSE PROBLEMS, 2015, 31 (11)
  • [40] Lipor John, 2014, 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), P4244, DOI 10.1109/ICASSP.2014.6854402