Self-Calibration Methods for Uncontrolled Environments in Sensor Networks: A Reference Survey

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

데이터 기반 센서 보정 및 모바일 센서 배치를 통한 도시 미세먼지 센서네트워크 정확도 향상

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 기계공학부, 2020. 8. 이동준.Particulate matter (PM) sensor has been widely deployed to increase spatiotemporal resolution in the urban environment. As a cost-effective PM monitoring solution, low-cost PM sensor ideally stands for dense sensor network nodes. However, low-cost PM sensor remains the doubt of its data reliability. In this paper, we investigate the accuracy of low-cost PM sensor by co-locating a governmental beta attenuation monitor (BAM) for 7.5 months and increase the accuracy with data-driven calibration. We research linear/nonlinear calibration (i.e. multiple linear regression (MLR)/multilayer perceptron (MLP)) and introduce a novel combined calibration. The methods are evaluated by field experiments and are compared with other methods and studies. Also, the data-driven calibration model can utilize for but only a co-located sensor node but also other sensor nodes by using a sensor network. The feasibility of sensor network calibration has been evaluated with experiments.도시 대기 질 측정의 시공간 해상도를 증가시키기 위해 미세먼지 센서가 광범위하게 배치되고 있다. 고해상도의 미세먼지 측정을 위한 현실적인 대안으로 저가형 미세먼지가 대표적으로 이용되고 있다. 하지만 저가형 미세먼지 센서의 측정 데이터 신뢰성에 대한 의문점은 해결되지 않고 있다. 본 연구는 저가형 미세먼지 센서의 장기간 정확도 평가를 수행하였으며, 이를 위하여 멀티 센서 플랫폼을 제작하고 이를 고신뢰도의 정부 관측소에 함께 배치하였다. 선형/비선형 추정 모델인 다중 선형회귀 모델과 인공신경망인 다층 퍼셉트론을 적용하여 데이터 기반 모델을 생성하였으며, 이를 통합한 추정 모델을 개발하였다. 이 방법들은 실외 배치 실험을 통해 평가되었으며 타 추정 모델과 타 연구와의 비교 분석을 수행하였다. 또한 관측소에 배치하여 생성된 데이터 기반 모델은 센서 네트워크를 통해 다른 노드에 전달하여 활용할 수 있으며, 이러한 접근에 대한 타당성 평가는 실험을 통해 확인하였다.1 Introduction 1 2 System Description 3 2.1 System Elements 3 2.1.1 Beta Attenuation Monitor 3 2.1.2 Multi-Sensor Platform 4 2.2 System Con guration 8 2.2.1 Sensor Platform Deployment 8 2.2.2 Calibration Procedures and Evaluation 9 3 Data-Driven Sensor Calibration 12 3.1 Related Studies 12 3.1.1 w/o Calibration Model 12 3.1.2 Previous Researches 13 3.2 Linear/Nonlinear Calibration 15 3.2.1 Linear Calibration: Multiple Linear Regression 15 3.2.2 Nonlinear Calibration: Multilayer Perceptron 17 3.2.3 Limitation on Linear/Nonlinear Calibration 19 3.3 SMART calibration 21 3.3.1 Concepts of Calibration 21 3.3.2 Procedures of SMART Calibration 24 3.4 Experiments and Results 26 3.4.1 Comparison w/ Other Calibration Methods 28 3.4.2 Comparison w/ Other Studies 30 3.4.3 Further Analysis of Calibration Model 31 4 Sensor Network Calibration 33 4.1 Related study 34 4.1.1 Sensor Network Calibration 34 4.1.2 Mobile Sensor Node 35 4.2 Transfer Calibration 35 4.2.1 Concepts of Transfer Calibration 35 4.3 Rendezvous Calibration 36 4.4 Experiments and Results 37 5 Conclusion and Future Work 40 5.1 Conclusion 40 5.2 Future Work 41Maste

Towards Odor-Sensitive Mobile Robots

J. Monroy, J. Gonzalez-Jimenez, "Towards Odor-Sensitive Mobile Robots", Electronic Nose Technologies and Advances in Machine Olfaction, IGI Global, pp. 244--263, 2018, doi:10.4018/978-1-5225-3862-2.ch012 Versión preprint, con permiso del editorOut of all the components of a mobile robot, its sensorial system is undoubtedly among the most critical ones when operating in real environments. Until now, these sensorial systems mostly relied on range sensors (laser scanner, sonar, active triangulation) and cameras. While electronic noses have barely been employed, they can provide a complementary sensory information, vital for some applications, as with humans. This chapter analyzes the motivation of providing a robot with gas-sensing capabilities and also reviews some of the hurdles that are preventing smell from achieving the importance of other sensing modalities in robotics. The achievements made so far are reviewed to illustrate the current status on the three main fields within robotics olfaction: the classification of volatile substances, the spatial estimation of the gas dispersion from sparse measurements, and the localization of the gas source within a known environment

Seismic response trends evaluation and finite element model calibration of an instrumented RC building considering soil-structure interaction and non-structural components

Peer reviewedPostprin

Enhanced Indoor Localization System based on Inertial Navigation

An algorithm for indoor localization of pedestrians using an improved Inertial Navigation system is presented for smartphone based applications. When using standard inertial navigation algorithm, errors in sensors due to random noise and bias result in a large drift from the actual location with time. Novel corrections are introduced for the basic system to increase the accuracy by counteracting the accumulation of this drift error, which are applied using a Kalman filter framework. A generalized velocity model was applied to correct the walking velocity and the accuracy of the algorithm was investigated with three different velocity models which were derived from the actual velocity measured at the hip of walking person. Spatial constraints based on knowledge of indoor environment were applied to correct the walking direction. Analysis of absolute heading corrections from magnetic direction was performed . Results show that the proposed method with Gaussian velocity model achieves competitive accuracy with a 30\% less variance over Step and Heading approach proving the accuracy and robustness of proposed method. We also investigated the frequency of applying corrections and found that a 4\% corrections per step is required for improved accuracy. The proposed method is applicable in indoor localization and tracking applications based on smart phone where traditional approaches such as GNSS suffers from many issues

Evaluating indoor positioning systems in a shopping mall : the lessons learned from the IPIN 2018 competition

The Indoor Positioning and Indoor Navigation (IPIN) conference holds an annual competition in which indoor localization systems from different research groups worldwide are evaluated empirically. The objective of this competition is to establish a systematic evaluation methodology with rigorous metrics both for real-time (on-site) and post-processing (off-site) situations, in a realistic environment unfamiliar to the prototype developers. For the IPIN 2018 conference, this competition was held on September 22nd, 2018, in Atlantis, a large shopping mall in Nantes (France). Four competition tracks (two on-site and two off-site) were designed. They consisted of several 1 km routes traversing several floors of the mall. Along these paths, 180 points were topographically surveyed with a 10 cm accuracy, to serve as ground truth landmarks, combining theodolite measurements, differential global navigation satellite system (GNSS) and 3D scanner systems. 34 teams effectively competed. The accuracy score corresponds to the third quartile (75th percentile) of an error metric that combines the horizontal positioning error and the floor detection. The best results for the on-site tracks showed an accuracy score of 11.70 m (Track 1) and 5.50 m (Track 2), while the best results for the off-site tracks showed an accuracy score of 0.90 m (Track 3) and 1.30 m (Track 4). These results showed that it is possible to obtain high accuracy indoor positioning solutions in large, realistic environments using wearable light-weight sensors without deploying any beacon. This paper describes the organization work of the tracks, analyzes the methodology used to quantify the results, reviews the lessons learned from the competition and discusses its future

3D Reconstruction & Assessment Framework based on affordable 2D Lidar

Lidar is extensively used in the industry and mass-market. Due to its measurement accuracy and insensitivity to illumination compared to cameras, It is applied onto a broad range of applications, like geodetic engineering, self driving cars or virtual reality. But the 3D Lidar with multi-beam is very expensive, and the massive measurements data can not be fully leveraged on some constrained platforms. The purpose of this paper is to explore the possibility of using cheap 2D Lidar off-the-shelf, to preform complex 3D Reconstruction, moreover, the generated 3D map quality is evaluated by our proposed metrics at the end. The 3D map is constructed in two ways, one way in which the scan is performed at known positions with an external rotary axis at another plane. The other way, in which the 2D Lidar for mapping and another 2D Lidar for localization are placed on a trolley, the trolley is pushed on the ground arbitrarily. The generated maps by different approaches are converted to octomaps uniformly before the evaluation. The similarity and difference between two maps will be evaluated by the proposed metrics thoroughly. The whole mapping system is composed of several modular components. A 3D bracket was made for assembling of the Lidar with a long range, the driver and the motor together. A cover platform made for the IMU and 2D Lidar with a shorter range but high accuracy. The software is stacked up in different ROS packages.Comment: 7 pages, 9 Postscript figures. Accepted by 2018 IEEE International Conference on Advanced Intelligent Mechatronic

A smart city-smart bay project - establishing an integrated water monitoring system for decision support in Dublin Bay

Environmental and water quality monitoring is key to measuring and understanding the chemical and biological quality of water and for taking reactive remedial action. Over the coming years, monitoring of water bodies will increase within Europe, in order to comply with the requirements of the Water Framework Directive (WFD, Council Directive 2000/60/EC), and globally owing to pressure from climate change. The establishment of high quality long-term monitoring programmes is regarded as essential if the implementation of the WFD is to be effective. However, the traditional spot/grab sampling using conventional sampling and laboratory based techniques can introduce a significant financial burden, and is unlikely to provide a reasonable estimate of the true maximum and/or mean concentration for a particular physico-chemical variable in a water body with marked temporal variability. When persistent fluctuations occur, it is likely only to be detected through continuous measurements, which have the capability of detecting sporadic peaks of concentration. The aim of this work is to demonstrate the potential for continuous monitoring data in decision support as part of a smart city project. The multi-modal data system shows potential for low-cost sensing in complex aquatic environments around the city. Continuous monitoring data from both visual and water quality sensors is collected and data from grab samples collected support the observations of trends in water quality

Mems based bridge monitoring supported by image-assisted total station

In this study, the feasibility of Micro-Electro-Mechanical System (MEMS) accelerometers and an image-assisted total station (IATS) for short-and long-term deformation monitoring of bridge structures is investigated. The MEMS sensors of type BNO055 from Bosch as part of a geo-sensor network are mounted at different positions of the bridge structure. In order to degrade the impact of systematic errors on the acceleration measurements, the deterministic calibration parameters are determined for fixed positions using a KUKA youBot in a climate chamber over certain temperature ranges. The measured acceleration data, with a sampling frequency of 100 Hz, yields accurate estimates of the modal parameters over short time intervals but suffer from accuracy degradation for absolute position estimates with time. To overcome this problem, video frames of a passive target, attached in the vicinity of one of the MEMS sensors, are captured from an embedded on-axis telescope camera of the IATS of type Leica Nova MS50 MultiStation with a practical sampling frequency of 10 Hz. To identify the modal parameters such as eigenfrequencies and modal damping for both acceleration and displacement time series, a damped harmonic oscillation model is employed together with an autoregressive (AR) model of coloured measurement noise. The AR model is solved by means of a generalized expectation maximization (GEM) algorithm. Subsequently, the estimated model parameters from the IATS are used for coordinate updates of the MEMS sensor within a Kalman filter approach. The experiment was performed for a synthetic bridge and the analysis shows an accuracy level of sub-millimetre for amplitudes and much better than 0.1 Hz for the frequencies. © 2019 M. Omidalizarandi et al