Sensors and Data Acquisition

Diego Galar , Uday Kumar , in eMaintenance, 2017

1.3.3.3.2 Environmental Monitoring

Environmental sensor networks have evolved, and there are now many applications of WSNs to earth science research. This includes sensing volcanoes, oceans, glaciers, forests, etc. Some other major areas are listed below ( Bokare and Ralegaonkar, 2012).

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Air Pollution Monitoring:

WSNs have been deployed in several cities to monitor the concentration of dangerous gases. These can take advantage of ad hoc wireless links, making them more mobile for testing readings in different areas.

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Forest Fire Detection:

A network of sensor nodes can be installed in a forest to detect fires. The nodes can be equipped with sensors to measure temperature, humidity, and gases produced by fires in the trees or vegetation. Early detection is crucial; with WSNs, the fire fighters know when a fire starts and how/where it is spreading.

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Landslide Detection:

A landslide detection system makes use of a WSN to detect the slight movements of soil and changes in various parameters that may occur before or during a landslide. With the data, it may be possible to predict the occurrence of a landslide long before it actually happens.

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Biosensors and Internet of Things in smart healthcare applications: challenges and opportunities

Maria Pateraki , ... Dimitrios Koutsouris , in Wearable and Implantable Medical Devices, 2020

2.3.3 Big data and machine learning

With environmental sensors and wearable devices, smart healthcare platforms can continuously monitor the health status and activities of patients and the elderly, as well as the safety and security of the environment [19,20]. This continuous flow of data in combination with individual's medical histories, can support personalized diagnosis and assistance and can automate important tasks, such as medical data archiving and evaluation of the effectiveness of medical interventions. Recent advances in artificial intelligence (AI) and big data processing technologies have allowed the implementation of highly reliable, accurate, and robust infrastructures for data recording and processing [21].

The analysis of data from wearable devices is led by decision-making process requirements, which define the data to be collected and extracted in support of intelligent decision-making. To support intelligent decision-making, the following requirements must be considered: (1) modular and interoperable data ingestion in which devices from different manufacturers contribute to a common data model, (2) parallelization and data stream processing in all steps from data acquisition to storage and processing, (3) use of data analytics and AI to support automatic monitoring and decision-making, (4) privacy-aware data processing to ensure consistent data encryption, database security, as well as secured communication channels.

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STM32 Nucleo Expansion Boards

Dogan Ibrahim , in ARM-Based microcontroller projects using MBED, 2019

17.5 Motion MEMS and Environmental Sensor Expansion Board (X-NUCLEO-IKS01A2)

This is a motion MEMS and environmental sensor expansion board ( Fig. 17.4), equipped with three-dimensional (3D) accelerometer, 3D gyroscope, 3D magnetometer, humidity and temperature sensor, and ambient atmospheric pressure sensor. The board is interfaced via the I2C pin and is compatible with the Arduino UNO R3 connector. The basic features of this expansion board are as follows:

Fig. 17.4

Fig. 17.4. X-NUCLEO-IKS01A2 expansion board.
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LSM6DSL MEMS 3D accelerometer (±   2/±4/±8/±16   g) and 3D gyroscope

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LSM303AGR MEMS 3D accelerometer (±   2/±4/±8/±16   g) and MEMS 3D magnetometer (±   50   G)

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LPS22HB MEMS pressure sensor (260–1260   hPa)

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HTS221 capacitive relative humidity and temperature sensor

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DIL24 socket for additional MEMS sensors (e.g., UV index)

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I2C interface

This board is described in more detail since a project is given in this section which displays the current humidity, temperature, and atmospheric pressure. The X-NUCLEO-IKS01A2 expansion board is equipped with jumpers which allow separate current consumption measurement of each sensor. An additional sensor can be connected as an adapter board to J1 DIL24 socket.

The board functional block diagram is shown in Fig. 17.5. There are a number of jumpers that can be used to configure the various modules on the board. All the modules on the board are configured to operate as I2C slaves by default (JP7: 1, 2, 3, 4 connected, also JP8: 1, 2, 3, 4 connected) and they all share the same bus. The module I2C default addresses are shown in Fig. 17.5 and are repeated below for convenience:

Fig. 17.5

Fig. 17.5. Functional block diagram.
Module I2C Address Module Description
LSM6DSL 0xD6 Accelerometer   +   Gyroscope
LSM303AGR 0x32 Accelerometer
LSM303AGR 0x3C Magnetometer
HTS221 0xBE Humidity   +   Temperature
LPS221HB 0xBA Pressure

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The Role of Smart Homes in Intelligent Homecare and Healthcare Environments

Laura Vadillo Moreno , ... María Lindén , in Ambient Assisted Living and Enhanced Living Environments, 2017

14.3.3 Monitoring in Telehealth Scenarios

Accordingly, there are three typical kinds of monitoring at home in telehealth scenarios:

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Alert detection as a result of monitoring based on environmental sensors (smoke detector, flood detector, fall detector, etc.). This scenario can be reactive or proactive. The intelligence includes basic cause-effect behaviors (e.g. if the smoke detector is activated, a call is sent to the telecare center). This monitoring can also include a major intelligence that appears as a result of the combination of various sensors to detect other possible risk situations (e.g. location sensors at home do not detect movement all day) ( Valero et al., 2009).

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Medical monitoring is based on the monitoring of biomedical variables to obtain the health status and detect emergencies related to health situations, detect early diseases, or take decisions about medication prescription. It is possible also to integrate remote control with clinical decision support systems (CDSS) for medical consultation results (Mouttham et al., 2009; Villarreal et al., 2011; Islam et al., 2015; Hussain et al., 2012).

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Activities monitoring involves applications for activity recognition analyzing the activities that a person carries out at home. Continuous remote monitoring of patients enables the telecare system to track lifestyle changes over time and detect patterns of possible pathologies or alarm signs (Ni et al., 2015; Suryadevara et al., 2013; Ridi et al., 2015; Benmansour et al., 2015).

The need for a robust anomaly detection model is fundamental to predict facts that require immediate attention on the part of the user or specialized personnel to manage the situation.

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Smart Railway Based on the Internet of Things

Qingyong Y. Li , ... Weiwei W. Fang , in Big Data Analytics for Sensor-Network Collected Intelligence, 2017

2.2 Perception and Action Layer

The network gathers information that is perceived by its distributed sensors from different sources, including but not limited to the on-board sensor networks, sensors deployed in infrastructure, environmental sensors, and the detectors that are used to capture information from passengers and other transportation participants.

In a modern train, diverse sensors are mounted to acquire a series of key parameters, such as the bearing temperature, air brake pressure, power-supply voltage, and bogie condition. These parameters are used to determine the condition of the train. Some other information like geographical location, train speed, and acceleration is also collected to reflect the operation of the train. Meanwhile, many sensors such as fire alarms and door-lock alarms are deployed to ensure the security of the passengers as well as the train. In addition, a couple of sensors are employed to report the malfunctioning of the on-board equipment like toilets and air conditioners, in order to guarantee the quality of passenger services.

Apart from the on-board devices, a large amount of sensors are deployed beside rail tracks to monitor the infrastructure and the surrounding environment. For example, several types of sensors including piezometers, inclinometers, displacement sensors, and vibration sensors are used in tunnels, bridges, and mountainous areas to ensure that the infrastructure is in a fit state for the train to pass. Obstacle detectors are used to ensure sure the track is still clear. Meanwhile, weather stations and earthquake detectors are deployed to detect extreme weather and geological disasters in the critical areas.

Sensors serving passengers are also very useful to provide passengers with better experience and service. For example, we can deploy some beaconing devices based on Bluetooth to provide services like indoor localization and information recommendation to passengers with smartphones in a railway station. From the management side, the officers can know the motion characteristics and the services that are of interest to passengers. Meanwhile, the low-cost Radio Frequency Identification (RFID) technique offers a possible alternative or complement to existing paper-based tickets.

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Organizational Support Networks and Relational Resilience After the 2010/11 Earthquakes in Canterbury, New Zealand

Joanne R. Stevenson , David Conradson , in Social Network Analysis of Disaster Response, Recovery, and Adaptation, 2017

Sensors in the Community

The ability to make proactive decisions, innovate, and adapt following the earthquakes differentiated organizations that experienced developmental change from those that experienced degenerative change. In this regard, network connections served as environmental "sensors," which delivered a range of information to the organization. Local sensor networks were composed of embedded interorganizational relationships (ie, relationships reinforced by trust, friendship, and/or reciprocity); affiliations with local business associations, customers, and owners; and staff members' personal networks. The cases in Box 11.1 illustrate how an organization's local networks were able to deliver vital information about the local environment, even when the organization was not actively seeking that information.

Box 11.1

Case Comparison: Failure and a Success in Perception 1

Amherst Retail: Developmental Change

Amherst Retail experienced a high degree of damage and disruption as a direct result of the earthquakes, but was able to implement adaptations that helped them avoid degenerative change in the earthquake aftermath. Amherst Retail had a greater capacity to perceive the need for change in part because of its internal and external "sensors," which included a high proportion of local staff aware of issues concerning local residents, staff attending postearthquake business information and networking meetings, and staff participating in the local promotions campaigns.

Amherst Retail's local networks allowed them to perceive potential disruptions and opportunities for growth and to act early. As a result of this awareness of the local area's current and future situation, the organization was able to fill a gap in the retail market by adding popular products that nonoperational local retailers had previously offered. Additionally, even though Amherst Retail did not lose its building, it opted to relocate to a new site to avoid probable future issues with the earthquake resistance of its prior building.

Kaiapoi Corner Store: Degenerative Change

After losing a significant competitor in the September 2010 earthquake, Kaiapoi Corner Store experienced a substantial increase in business in the final quarter of 2010 and through much of 2011. Yet in 2013 the store experienced falling revenue and reported being "worse off" than before the earthquakes. This downturn reflected a significant loss of local customers from nearby residential red zone areas. The owners had not perceived the potential impact of this change on the organization and had not taken adaptive action. The owners' lack of awareness regarding when and how the red zoning decision would affect their business was caused, in part, by their lack of local networks in the area. Kaiapoi Corner Store was not a member of local business associations or groups. The owner's personal friendship and kin networks were located outside of Kaiapoi, and the owners did not participate in local events, attend information nights, or otherwise engage with the community.

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Heart sound detection, analysis, and assistive technologies for the elderly

Joyanta Kumar Roy , ... Subhas Chandra Mukhopadhyay , in Assistive Technology for the Elderly, 2020

5.16 Smart homes for elderly healthcare: prototypes and commercial solutions

In the above discussion, we have presented different healthcare and monitoring systems reported in the literature. As depicted in Fig. 5.32 , a fully-fledged smart home requires all such policies along with a wide range of physiological and environmental sensors to be integrated into a common platform that poses new challenges in terms of volume of information, uninterrupted connectivity, interoperability, and most importantly, privacy and data security. Many researchers, along with some technology companies around the world, have been working to overcome these technological challenges. In this section [57], we present some prototypes of smart homes reported recently in the literature. We also discuss some commercial smart-home solutions currently available in the market.

Figure 5.32. Smart homes for elderly healthcare [57].

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Positioning and Digital Maps

Rafael Toledo-Moreo , ... Javier Sánchez-Cubillo , in Intelligent Vehicles, 2018

4.5.1.2 Visual Odometry Algorithms Using LIDAR

LIDAR can also be used for solving the SLAM problem. For example, in Zhang and Singh (2014) the Lidar Odometry and Mapping (LOAM) algorithm is described. It is a Visual SLAM application where using only LiDAR data it is possible to estimate, with low drift and real time execution, the 6-dof trajectory of the vehicle and it achieves accuracy in real time at the level of state-of-the-art offline batch methods using the KITTI benchmark. Mainly, the algorithm performs two stages, the first one at a high frequency with low accuracy and then, a second step, slower for a fine matching and registration of the point cloud.

In this case, the SLAM stages are quite similar to the ones performed with computer vision and can be summarized as follows:

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Characteristic features (landmarks) extraction:

This is the first step to be performed by the environmental sensor, in this case the LiDAR. Characteristic points like lines, edges of planes, planes, or changes of orientation in neighboring points, are extracted. Usually, different strategies to increase the density of points are applied such as the ICP algorithm. Then, the algorithms used for this extraction could be the same described for features extraction for obstacles detection or estimating the path boundaries (e.g., RANSAC, geometric fitting, etc.).

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Data Association:

Once, the features extraction has been done, it is necessary to perform a tracking with the features previously identified. A number of observations are stored with the location and mapping of the vehicle and also, the landmarks detected in that time step. The landmarks are seen from different perspectives, that is, in different observations, and the matching relates each set of landmarks estimating the ego-motion of the vehicle. To perform an accurate matching, good quality landmarks must be extracted. It is considered a good quality landmark when the feature has been observed in several time-steps. Some issues that may appear in data association are: not observing the same landmarks as in previous time steps due to the shadows in the LiDAR data; or a bad association because of the bad quality identification when the landmark is further from the LiDAR. A wrong data association results in a divergence issue because the vehicle begins to integrate wrong positions.

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Extended Kalman Filter or Particle Filter:

An extended Kalman Filter or a particle filter is used to upload and integrate the results from the vehicle odometry and the data association. This is the usual solution when using several sensors. When GPS/INS or IMUs are included in the sensor fusion, the SLAM algorithm performance improves and reduces the motion estimation drift (Montemerlo et al., n.d).

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RFID for Sensing: Technology and Applications

Filippo Costa , ... Giuliano Manara , in Reference Module in Biomedical Sciences, 2021

Applications

Radio frequency identification has attracted considerable press attention in recent years, and for good reasons: RFID not only replaces traditional barcode technology, it also provides additional features. In the future, RFID tags will likely be used as environmental sensors on an unprecedented scale ( Want, 2004). Major RFID sensing application domains include monitoring physical parameters, automatic product tamper detection, harmful agent detection, and noninvasive monitoring. Communication modes of RFID systems are very heterogeneous. Some applications require reading passive tags from a distance of a few inches while others need to read active tags at a distance of several hundred meters. An overview of the most relevant fields of applications for RFID sensors is shown in Fig. 8. The entries of the table are discussed in the following bullets list.

Fig. 8

Fig. 8. RFID sensors: application fields and examples of practical applications.
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Healthcare: Fully passive RFID systems for monitoring the state of children, disabled and elderly people in domestic and hospital environment during the night have been demonstrated. The systems can detect the presence or the absence of the user in the bed, jerky movements and motion patterns or accidental falls (Amendola et al., 2014). Moreover, by monitoring the fluctuations of backscattered power from sensor-less passive tags placed over the body, it is possible to collect information about the subject such as standing or moving conditions and eventually to estimate the frequency of periodic movements (Krigslund et al., 2013; Manzari et al., 2012).

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Food: Intelligent labeling of food products to indicate and report their freshness and other conditions is one important application for RFID sensors. Indeed, market demands new sensors for food quality and safety with battery-free operation and minimal sensor cost (Potyrailo et al., 2012; Raju et al., 2020). In these sensors, the electric field generated in the RFID sensor antenna is affected by the ambient environment, providing the opportunity for sensing. Examples of applications include monitoring of milk freshness, fish freshness, and bacterial growth in a solution. As food ripens and spoils, its chemical composition changes, resulting in change in its dielectric properties. This changes the coupled capacitance, and in turn the resonant frequency of the sensor.

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Agriculture: Real time information acquired by RFID sensors provides helpful data for farmers to adjust crop strategies at any time by taking into account the environment condition alteration. Among smart agriculture paradigm, the leaf sensing represents a new technology which is used for the detection of the of plants health such us the water status (Daskalakis et al., 2018). A sensor measures the temperature differential between the leaf and the air, which is directly related to the plant water stress. A passive UHF RFID tag for soil moisture and environment temperature sensor for low cost agriculture application was developed in da Fonseca et al. (2017). The soil moisture sensor has been implemented by exploiting the capacitance variation of a passive inter digital capacitive due to the soil permittivity modification, while the environmental temperature was obtained by using a RFID chip with a temperature sensor.

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Fashion/retail: The fashion and apparel retail industry represent another hot topic where the exploitation of RFID sensors is considerably increasing with the purpose of improving the performance of automated checkout, inventory system and theft detection systems (Roussos, 2006). One key component of retail automation is the monitoring of the items on shelves for point-of-sales monitoring and inventory control. In general, retail store application for worldwide rely on low-cost solutions such as passive RFID tags (Škiljo et al., 2020).

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Automotive: An experimental campaign based on RFID technology for univocally identifying and localizing tires moving on conveyor line is illustrated in Caccami et al. (2019). In general, RFID tag sensors on tires can be adopted also to provide information about tire health such as pressure and temperature. However, it is worth observing that, the challenge for a successful implementation lies in dealing with the complex and harsh environment of tire manufacturing. For example, in Grosinger et al. (2009) the detuning effects of tire environment on commercial RFID tags is presented. License plate radio frequency identification (Liang et al., 2017), intelligent parking systems (Mobashsher et al., 2020) as well as the presence detection of a child in vehicles (Lee et al., 2008) represent other examples where RFID sensors are effectively exploited within the automotive market.

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Structural health monitoring: Structural health monitoring (SHM) is nowadays a necessary security measure to provide safe and reliable operation of large-scale structures, e.g., railway, pipelines, dams, bridges, and aircrafts (Zhang et al., 2017). Deploying large-scale sensor networks with cables provides excellent performance for real time acquisition but the cost, installation difficulty, and maintenance rarely justified their use over manual data collection (Cook et al., 2014). Battery-powered sensors have limited battery life, and they are more expensive than RFID ones and this limits the granularity of their deployment. For potential forthcoming applications, spatial granularity is a key issue. This motivates the development of low-cost, wireless, and passive sensors for large-scale infrastructure and big data applications. In order to enable such a vision, radio frequency identification (RFID) technology can play a strategic role, thanks to its low-cost, wireless, and "sensing-friendly" capabilities.

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Space: Sensors RFIDs have also found application on space platforms. Wire harness represents a considerable part of the overall mass therefore eliminating the wiring of the sensors is of the utmost importance for reducing the total mass of the vehicle and thus the fabrication and launch costs. An example of this is represented by the adoption of a RAIN RFID tag chip by NASA (NASA Scientists Use RAIN RFID to Monitor Spacecraft Temperatures, n.d.). The tag mounts a Monza X-8   K Dura chip that can monitor temperature, CO2 and battery levels. A different approach for achieving the sensing function is provided by SAW RF tags (Hines et al., 2013). In this case, two phase-matched SAW RF tags are coupled with a Van Atta antenna array. This configuration exhibits a remarkable read range, it can be employed for monitoring temperature and pressure and it is robust for being deployed in harsh environment.

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Localization and activity monitoring: RFID sensor networks are also a promising approach for indoor activity monitoring (Bandy and Peeters, 2006; Buettner et al., 2009). Many studies have been conducted for track and trace indoors and outdoors. For outdoors, the City of London introduced a system for data collection in its transportation system including buses, subways and trains. The idea was to track users of the transport network through payment and access cards equipped with this communication technology. With the information gathered it was determined the level of use, number of travelers and public transport habits in the city and to know the stations, breakpoints, origin and destination of network travel streams. For indoors, systems to know citizen movement flows have also been implemented, for instance, visits per museum. With the results, visit patterns of the different exhibits and areas were obtained, participants with atypical behaviors were identified and even relationships between visitors' groups were established (Mora-Mora et al., 2015).

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Wearable and implantable RFID sensors: It has been demonstrated that wearable tags with inertial switches are able to detect limb motion in some common sleep disorders like restless legs syndrome and periodic limb movements. Tags applied to the chest may be useful to detect breath. (Occhiuzzi et al., 2010). Embroidery techniques have also been successfully experimented to fully integrate RFID tags inside clothes. Conductive threads, E-textiles and embroidery techniques have been shown to have potential in realizing garment-integrated tags (Kennedy et al., 2009; Moradi et al., 2013). A challenge in designing sensor tags for wearable applications is represented by the design of wearable antennas having high immunity to the human body interaction.

RFID technology has been also demonstrated to be potentially useful to take care of the human health-state from the inside by labeling body prosthesis, sutures, stents or orthopedic fixing. Each item could be monitored in real time or on demand by the IoT infrastructure for the ambitious goal of monitoring biophysical process in evolution, such as tissue regrowth and prosthesis displacement. The major challenge in the design of implantable tags is to establish a convenient communication link by using a reader power that is compliant with local emission regulation. Establishing an RFID link with subcutaneous implants up to 0.1–0.5   m from the reader is nowadays a feasible target (Amendola et al., 2014).

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Environmental Underground Sensing and Monitoring

Tissa H. Illangasekare , ... Anura P. Jayasumana , in Underground Sensing, 2018

4.2 Overview of Conventional and Transitional Environmental Sensors

Use of sensors for monitoring the environment has become more prevalent during the last several decades. Applications include monitoring of environmental phenomena such as weather and storms, volcanoes, air quality, agriculture systems, forests, and ecological systems. Environmental sensors such as biological or chemical sensors have recently become more and more accurate and small, making their use very practical in remote areas where human manual sampling is impossible, and for microscopic applications ( Wolfbeis, 2004). These sensors are widely used for infrastructure and environmental monitoring. Most environmental monitoring applications require sensors that can measure parameters that define air quality and dissolved constituents in water. Sensors for monitoring water quality variables such as pH, conductivity, dissolved oxygen, and turbidity are commercially available (e.g., Global Water Instruments). However, they are generally expensive for large scale distributed applications at a unit cost ranging between $500–1000. In 2003, the EPA reviewed emerging sensor technologies for long-term groundwater monitoring of volatile organic compounds, VOCs (EPA, 2004). The report states that "the process of collecting ground water samples is time consuming and labor intensive, and analysis of the samples is generally conducted in off-site laboratories. Due to the required labor and analysis, ground water monitoring is relatively expensive. In addition, due to the collection, preparation, and transportation of samples from monitoring wells to distant laboratories, ground water monitoring is prone to errors. The large number of samples collected and analyzed each year, the relatively high cost of collecting and analyzing each sample, and the potential for errors warrant research into new technologies for facilitating ground water monitoring." A National Research Council (NRC, 2013) study highlighted the potential of in situ sensor technology for the detection of groundwater contaminants of interest, such as low concentrations of chlorinated solvents. In 2001, the Army conducted a study to validate sensors specifically used for detection of VOCs incorporated into a Penetrometer System (Davis et al., 2001). At the time when that report was written, no rapid on-site method to investigate the extent of groundwater and soil contamination for VOCs was available. The 2003 EPA study identified the key requirements for effective sensor-based long-term groundwater monitoring such as sensitivity, accuracy, precision, reversibility, speed, durability, reliability, simplicity, selectivity, affordability, and acceptability. This report summarized the capabilities and the technologies behind a number of sensors that were able to detect a variety of chlorinated compounds (Table 4.1).

Table 4.1. State of VOC sensors

Sensor description Developer Measurements
Chemoresistors Sandia National Laboratory (Ho, 2003) VOCs, BTEX (benzene, toluene, ethylbenzene, and xylenes)
Quartz Crystal Microbalances Nomadics, Inc. Chlorinated compounds
Ion Mobility Spectrometry Boise State University (Kanu et al., 2008) VOCs and BTEX below the maximum contaminant level (MCL)
Resonance Enhanced Multiphoton Ionization fiber optics Univ. of South Carolina (Chinni et al., 2004) BTEX compounds in the ∼1 μg/L range
Bio-Optoelectronic Sensor System (BOSS) Georgia Tech (EPA, 2004) Detection capabilities in the range of approximately 50 μg/L to 100 μg/L
Mid-Infrared Fiber-optic Sensors Georgia Tech (EPA, 2004) VOCs with detection limits of ∼100 μg/L

EPA (2004) report also provided approximate cost per unit. Summarizing the technology status, the report concluded that the manufacturing cost might be around $100; that does not represent the cost of a fully-functional instrument. A fully developed sensor to measure two or three parameters was estimated to cost around $7,500. An Environmental Security Technology Certification Program (ESTCP) funded project reported the testing of a set of sensors for monitoring VOCs (Lieberman, 2007). The sensors tested included XSD-MIP systems (halogen-specific detector – membrane interface probe) and laser-induced fluorescence, based on the rapid optical screening tool (ROST). The costs of these systems ranged in the thousands of dollars.

Fluorescence-based probes have also been used in groundwater contamination analysis by coupling them to a direct-push method (Lieberman, 1998). In such a system, the sensor can be pushed into the ground while taking continuous measurements, permitting real-time data gathering. In-situ chemical sensing techniques have been developed by Sandia National Laboratories to detect volatile organic contaminants with an electrochemical sensor (Ho, 2003). Other sensors, such as polymer-absorption, metal-oxide-semiconductor, fiber-optic, and mass sensors might also be used for such an application. Further testing and verification remains to be done with these sensors on the field scale to assess their real capabilities and effectiveness.

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