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
  • [71] Chemical gas sensor drift compensation using classifier ensembles
    Vergara, Alexander
    Vembu, Shankar
    Ayhan, Tuba
    Ryan, Margaret A.
    Homer, Margie L.
    Huerta, Ramon
    [J]. SENSORS AND ACTUATORS B-CHEMICAL, 2012, 166 : 320 - 329
  • [72] Wang C., 2009, Ensuring data storage security in Cloud Computing, P1, DOI [10.4108/ICST.BODYNETS2009.6022, DOI 10.4108/ICST.BODYNETS2009.6022]
  • [73] Blind Drift Calibration of Sensor Networks Using Sparse Bayesian Learning
    Wang, Yuzhi
    Yang, Anqi
    Li, Zhan
    Chen, Xiaoming
    Wang, Pengjun
    Yang, Huazhong
    [J]. IEEE SENSORS JOURNAL, 2016, 16 (16) : 6249 - 6260
  • [74] Whitehouse K, 2002, P 1 ACM INT WORKSH W, P59
  • [75] Validation of low-cost ozone measurement instruments suitable for use in an air-quality monitoring network
    Williams, David E.
    Henshaw, Geoff S.
    Bart, Mark
    Laing, Greer
    Wagner, John
    Naisbitt, Simon
    Salmond, Jennifer A.
    [J]. MEASUREMENT SCIENCE AND TECHNOLOGY, 2013, 24 (06)
  • [76] Calibrate without Calibrating: An Iterative Approach in Participatory Sensing Network
    Xiang, Chaocan
    Yang, Panlong
    Tian, Chang
    Cai, Haibin
    Liu, Yunhao
    [J]. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 2015, 26 (02) : 351 - 361
  • [77] Machine Learning-Based Calibration of Low-Cost Air Temperature Sensors Using Environmental Data
    Yamamoto, Kyosuke
    Togami, Takashi
    Yamaguchi, Norio
    Ninomiya, Seishi
    [J]. SENSORS, 2017, 17 (06)
  • [78] Virtual in-situ calibration method in building systems
    Yu, Yuebin
    Li, Haorong
    [J]. AUTOMATION IN CONSTRUCTION, 2015, 59 : 59 - 67
  • [79] A machine learning calibration model using random forests to improve sensor performance for lower-cost air quality monitoring
    Zimmerman, Naomi
    Presto, Albert A.
    Kumar, Sriniwasa P. N.
    Gu, Jason
    Hauryliuk, Aliaksei
    Robinson, Ellis S.
    Robinson, Allen L.
    Subramanian, R.
    [J]. ATMOSPHERIC MEASUREMENT TECHNIQUES, 2018, 11 (01) : 291 - 313
  • [80] 2012, INT CONF ACOUST SPEE, P2713