Mobile Sensor Research Articles

The Use of Mobile Sensors by Children: A Review of Two Decades of Environmental Education Projects.

Over the past twenty years, the use of electronic mobile sensors by children and youngsters has played a significant role in environmental education projects in Portugal. This paper describes a research synthesis of a set of case studies (environmental education projects) on the use of sensors as epistemic mediators, evidencing the technological, environmental, social, and didactical dimensions of environmental education projects over the last two decades in Portugal. The triggers of the identified changes include: (i) the evolution of sensors, information and communication platforms, and mobile devices; (ii) the increasing relevance of environmental citizenship and participation; (iii) the recognition of the role of multisensory situated information and quantitative information in environmental citizenship; (iv) the cause-effect relation between didactical strategies and environmental education goals; (v) the potential of sensory and epistemic learners' practices in the environment to produce learning outcomes and new knowledge. To support the use of senses and sensors in environmental education projects, the SEAM model was created based on the developed research synthesis.

Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network

Optimization of sensor node location utilizing artificial intelligence for mobile wireless sensor network

Trends in Development of Applied Social and Communication Technol-Ogies in Medical Field: Mobile Applications and Programmes in Healthcare

The purpose of the article is to conduct a comprehensive analysis of the trends in the development of applied social and communication technologies in the medical sector, in particular, to trace dynamics of growth in the number of mobile applications and programmes in healthcare sector. The research methodology is formed by general scientific and special methods of studying the problem, the use of which allowed the authors to achieve the goal. The combination of the methods used helped to clarify the logical sequence and trends in the development of applied social and communication technologies in the modern information space of society. The basic principle was the principle of historicism, which implies consideration of any phenomenon in its self-development under the influence of various factors. The historical and systemic method is aimed to analyse society as a whole and the transformations within it caused by changes in the information and communication space. Quantitative methods are used to process the results of analytical research and to diagnose the phenomena under study. The scientific novelty of the work is to expand the understanding of the trends in the development of applied social and communication technologies in the medical field. The authors' study analysed modern domestic medical information systems, the possibilities of their interaction with eHealth, and identified priority technological areas in the medical sector, such as virtual reality, 3D printing, robotics, artificial intelligence, augmented reality, a global network of medical facilities, wearable devices, smart technologies, and precision (personalised) medicine. The authors state that in recent years, attention has increased to applications, mobile sensors and digital media in order to get help in maintaining one's health. Conclusions. The study has proved the positive dynamics in the growth of mobile applications and programmes in healthcare sector and determined the trends of the further development of information technologies in the medical sector. Key words: healthcare system, information technologies, medical information systems, mobile applications in healthcare.

Clustering Algorithms and Optimization Strategies in WSNs: A State-of-the-Art Review

Wireless Sensor Networks (WSNs) have emerged as a groundbreaking technology for collecting data on various physical phenomena, offering the potential to revolutionize infrastructure systems. The paper provides an exhaustive exploration of the existing literature on clustering algorithms within WSNs and the diverse optimization techniques proposed for cluster head selection. A comprehensive analysis of various routing protocols, mobility-based solutions, and energy conservation strategies is presented, shedding light on their respective strengths and limitations. The examination encompasses a wide spectrum of approaches, including hierarchical cluster-based routing, mobile sensor integration, and energy-efficient techniques inspired by industry standards like ZigBee. The paper further explores ecological monitoring applications, redundancy reduction mechanisms, and power- efficient hierarchical routing protocols, offering valuable insights into their contributions to the field. This review paper aims to consolidate and synthesize the wealth of knowledge in the domain of clustering algorithms and optimization techniques for WSNs. By assessing the strengths and weaknesses of these methodologies, it seeks to provide a comprehensive resource for researchers and practitioners in the field, ultimately contributing to the advancement of energy-efficient and reliable wireless sensor networks. Keywords— APTEEN, EARP, ECCP, GPS, HEED, LEACH, PEGASIS, TEEN, WSN.

Adaptive Techniques for the Online Estimation of Spatial Fields With Mobile Sensors

The regression-based reconstruction of spatial fields by static sensors requires the number of measurements be at least equal to the number of unknown parameters in order to provide a unique solution. Improvements include the use of mobile sensor networks to arrive at higher accuracy estimates. An alternate approach utilizes adaptive techniques to update the parameter estimates online. Such adaptive techniques need persistence of excitation to ensure convergence of the parameter and function estimates. It is shown that the motion of just a single sensor is a necessary condition for persistence of excitation and, subsequently, of function convergence. Having a prescribed sensor guidance is also a sufficient condition for function convergence. First, a Lyapunov-based adaptation and guidance is presented and it is shown that persistence of excitation necessitates the time dependence of the outer product of the regressor vector with itself and which in turn requires the motion of the sensor within the spatial domain. Subsequently, a gradient-based adaptive scheme relaxes the Lyapunov-based sensor guidance to any user-defined guidance as long as the outer product of the associated rank-one regressor vector with itself has a uniformly positive definite integral. The latter adaptation is conducive to the inclusion of platform dynamics resulting in a vehicle control design needed to attain parameter convergence. Extension of the centralized solution to distributed estimation using a network of mobile sensors is also examined. These results are demonstrated with simulations in one and two dimensions.

Using and Testing Camera Sensors with Different Devices at Cosmic Ray Detection

Cosmic Ray (CR) is high-energy charged particles that reach the earth from space. CR detection methods and studies have been progressing rapidly since the beginning of the 20th century. One of these methods is the use of digital cameras with Charge Coupled Device (CCD) and Complementary Metal Oxide Semiconductor (CMOS) sensors. Mobile phone cameras or webcams offer an easily using and economical measurement system for CR measurement. The sensors are exposed to CR during a long exposure. CRs leave traces in the background. Cosmic particle tracks are then separated from the background noise and can be classified. Making the traces of the particles visible is important for understanding the subject. In this context, traces of particles such as electrons, muons, and alphas can be seen with the cloud chamber experiments. Help of sensor technology and cameras have developed in recent years, CR traces can be easily detected so that it can be seen. There are many software and international projects that detect CR using CMOS sensors in cell phone cameras. In this study, related projects, programs and studies were researched; CR traces that we captured with the help of Cosmic-Ray Extremely Distributed Observatory (CREDO) and Cosmic Ray Finder (CRF) software with web cam and a mobile phone cam CMOS sensor are presented. Links have been made about astrophysical events coinciding with previously detected particle images.

Regular Vehicle Spatial Distribution Estimation Based on Machine Learning

For the mixed traffic flow, obtaining the distribution of connected vehicles (CVs) and regular vehicles (RVs) is of great significance for road network analysis and cooperative control in intelligent transportation systems (ITSs). However, whether it is based on fixed sensors or based on CVs and traffic mechanism to estimate the spatial distribution of RVs, the implementation complexity and low estimation accuracy are the points that need to be improved. This paper proposes a regular vehicle spatial distribution estimation method using adjacent connected vehicles as mobile sensors. First, to investigate the hidden relationship between the interaction information of adjacent CVs and the spatial distribution of RVs among CVs, the Gaussian mixture model-hidden Markov model (GMM-HMM) is selected as the identification method. Then, three sets of experiments were designed to study the influence of observed features on the identification capability of the model, generalization capability validation, and comparison with other methods, respectively. Finally, the proposed method is verified by the dataset generated by the car-following model. The experimental results show that selecting the relative position and time headway as observed features can effectively reflect the regular vehicle spatial distribution between adjacent CVs. The average accuracy of the proposed method to identify the regular vehicle spatial distribution is over 93.7%, which can provide valuable suggestions for the Internet of Vehicles application.

An Enhanced Food Digestion Algorithm for Mobile Sensor Localization.

Mobile sensors can extend the range of monitoring and overcome static sensors' limitations and are increasingly used in real-life applications. Since there can be significant errors in mobile sensor localization using the Monte Carlo Localization (MCL), this paper improves the food digestion algorithm (FDA). This paper applies the improved algorithm to the mobile sensor localization problem to reduce localization errors and improve localization accuracy. Firstly, this paper proposes three inter-group communication strategies to speed up the convergence of the algorithm based on the topology that exists between groups. Finally, the improved algorithm is applied to the mobile sensor localization problem, reducing the localization error and achieving good localization results.

Non-Destructive Determination of Plant Pigments Based on Mobile Phone Data

This paper proposes methods and tools for determining plant pigments using data from a mobile phone video sensor. A disadvantage of the known studies in this field is that they are mainly aimed at determining the chlorophyll content. There are few studies related to the determination of pigments such as carotenoids, flavonoids, and betalains, which are also important in terms of determining the condition of plants in their cultivation. Cucumbers were chosen because the long periods of drought in Bulgaria, lead to losses in cultivating these plants. Vectors containing colour and spectral indices were used. These features are obtained through a video sensor on a mobile phone. The kernel method variant of principal components reduces them. Feature vectors are selected using factor analysis, correspondence analysis, and the correlation method. Predictive models have been developed to determine plant pigments. With high accuracy (over 90%), the pigments xanthophyll and chlorophyll A can be predicted.

Monitoring and Control of Particle Matter Concentration’s Traffic-Related Air Pollution Using Low-Cost Mobile Sensors at Six Intersections in Yaoundé, Cameroon

Particle pollution has a major influence on public health in Central African cities. Measuring the levels of pollution to which populations are exposed is difficult because only a few African countries have an air quality monitoring network in place. Yet, given the specific anthropogenic sources prevalent in African countries, as well as the predicted increase in their emissions in the next few years if no laws are implemented, solutions must be developed. The methodology utilised in this study allows diverse research teams to estimate the population's exposure levels to urban particle pollution at a lower cost. In this investigation, we collected data concentrations using Air Master. The spatial mapping of pollutants shows that the Mvog-Mbi crossroad and the Education crossroad are the most polluted points among the six measurement points, with PM2.5 concentrations around 145–170 µg.m-3. Regarding PM10 and PM1, the Mvog-Mbi crossroads is the point where concentrations are highest, fluctuating respectively between 150 and 180 µg.m-3, 41 and 48 µg.m-3. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi Crossroads, Education Crossroads, Poste Centrale, Hilton). These are the high-risk zones for those with respiratory difficulties. At some locations, the particle ratio exceeds the WHO recommended range of 0.5–0.8 (Mvog-Mbi crossroads, education crossroads, Poste Centrale, Hilton). Regardless of particle size, these pollutants in air readings are greater and beyond the World Health Organization's recommended values. Furthermore, this investigation provides critical information on Yaoundé pollution levels, which can be a major source of disease in the city.

Modelling calibration uncertainty in networks of environmental sensors

Abstract Networks of low-cost environmental sensors are becoming ubiquitous, but often suffer from poor accuracies and drift. Regular colocation with reference sensors allows recalibration but is complicated and expensive. Alternatively, the calibration can be transferred using low-cost, mobile sensors. However, inferring the calibration (with uncertainty) becomes difficult. We propose a variational approach to model the calibration across the network. We demonstrate the approach on synthetic and real air pollution data and find it can perform better than the state-of-the-art (multi-hop calibration). In Summary: The method achieves uncertainty-quantified calibration, which has been one of the barriers to low-cost sensor deployment.

Nanobiosensing Based on Electro-Optically Modulated Technology.

At the nanoscale, metals exhibit special electrochemical and optical properties, which play an important role in nanobiosensing. In particular, surface plasmon resonance (SPR) based on precious metal nanoparticles, as a kind of tag-free biosensor technology, has brought high sensitivity, high reliability, and convenient operation to sensor detection. By applying an electrochemical excitation signal to the nanoplasma device, modulating its surface electron density, and realizing electrochemical coupling SPR, it can effectively complete the joint transmission of electrical and optical signals, increase the resonance shift of the spectrum, and further improve the sensitivity of the designed biosensor. In addition, smartphones are playing an increasingly important role in portable mobile sensor detection systems. These systems typically connect sensing devices to smartphones to perceive different types of information, from optical signals to electrochemical signals, providing ideas for the portability and low-cost design of these sensing systems. Among them, electrochemiluminescence (ECL), as a special electrochemically coupled optical technology, has good application prospects in mobile sensing detection due to its strong anti-interference ability, which is not affected by background light. In this review, the SPR is introduced using nanoparticles, and its response process is analyzed theoretically. Then, the mechanism and sensing application of electrochemistry coupled with SPR and ECL are emphatically introduced. Finally, it extends to the relevant research on electrochemically coupled optical sensing on mobile detection platforms.

Intrusion Detection Mechanism for Empowered Intruders Using IDEI

In the past, intrusion detection has been extensively investigated as a means of ensuring the security of wireless sensor networks. Anti-recon technology has made it possible for an attacker to get knowledge about the detecting nodes and plot a route around them in order to evade detection. An "empowered intruder" is one who poses new threats to current intrusion detection technologies. Furthermore, the intended impact of detection may not be obtained in certain subareas owing to gaps in coverage caused by the initial deployment of detection nodes at random. A vehicle collaboration sensing network model is proposed to solve these difficulties, in which mobile sensing cars and static sensor nodes work together to identify intrusions by empowered intruders. An algorithm for mobile sensing vehicles, called Intrusion Detection Mechanism for Empowered Intruders(IDEI), and a sleep-scheduling technique for static nodes form the basis of our proposal. Sophisticated intruders will be tracked by mobile sensors, which will fill in the gaps in coverage, while static nodes follow a sleep schedule and will be woken when the intruder is discovered close. Our solution is compared to current techniques like Kinetic Theory Based Mobile Sensor Network (KMsn)and Mean Time to Attacks (MTTA) in terms of intrusion detection performance, energy usage, and sensor node movement distance. IDEI's parameter sensitivity is also examined via comprehensive simulations. It is clear from the theoretical analysis and simulation findings that our idea is more efficient and available.

Recognition of Human Mental Stress Using Machine Learning: A Case Study on Refugees

This paper introduces a study on stress recognition utilizing mobile EEG and GSR sensors. The research involved collecting samples from a group of 55 refugees who participated in Virtual Reality stress-reduction sessions. The timing of the study coincided with an influx of refugees, prompting the development of software specifically designed to alleviate acute stress among them. The paper focuses on presenting an EEG/GSR signals pipeline for classifying stress levels, emphasizing selecting the most informative features. The classification process employed popular machine learning methods, yielding results of 86.7% for two-stress-level classification and 82.3% and 67.7% for the three- and five-level classifications, respectively. Most importantly, the positive impact of the system has been proven by subjective assessment in alignment with objective features analysis. Such a system has not yet reached the level of autonomy, but it can be a valuable support tool for mental health professionals.

Learning Temporal and Spatial Features Jointly: A Unified Framework for Space-Time Data Prediction in Industrial IoT Networks

Industrial Internet of Things (IIoT) networks, as the application of IoT networks to modern industry, are growing rapidly with the digital transformation accelerates. Mobile sensors are widely adopted in monitoring key information of massive IIoT networks in an ad hoc fashion or retrofitting on to an existing infrastructure. Hence, owing to the characteristics of scalability, unstable locations and highly unstructured, achieving perfect prediction for mobile multi-sensor is a challenging problem. In this paper, a dynamic space-time prediction algorithm combining temporal convolutional network and graph convolutional network (TCN-GCN) is proposed to provide reliable multiple nodes prediction for detection and maintenance in IIoT networks. In particularly, an adaptive learning graph process is exclusively designed according to the dynamic characteristics of mobile sensors. Moreover, the improved dilated time convolution network and dynamic graph convolution network are combined to effectively capture the time dependence and topology information. Furthermore, an advanced loss function is applied to avoid overfitting. Numerical simulations show that the proposed TCN-GCN prediction algorithm exhibits higher effectiveness in accuracy and complexity than convolutional long short-term memory (ConvLSTM), temporal graph convolutional network (T-GCN) and residual graph convolutional long short-term memory (Graph-ResLSTM).

Automatic patient functionality assessment from multimodal data using deep learning techniques – Development and feasibility evaluation

Wearable devices and mobile sensors enable the real-time collection of an abundant source of physiological and behavioural data unobtrusively. Unlike traditional in-person evaluation or ecological momentary assessment (EMA) questionnaire-based approaches, these data sources open many possibilities in remote patient monitoring. However, defining robust models is challenging due to the data's noisy and frequently missing observations.This work proposes an attention-based Long Short-Term Memory (LSTM) neural network-based pipeline for predicting mobility impairment based on WHODAS 2.0 evaluation from such digital biomarkers. Furthermore, we addressed the missing observation problem by utilising hidden Markov models and the possibility of including information from unlabelled samples via transfer learning. We validated our approach using two wearable/mobile sensor data sets collected in the wild and socio-demographic information about the patients.Our results showed that in the WHODAS 2.0 mobility impairment prediction task, the proposed pipeline outperformed a prior baseline while additionally providing interpretability with attention heatmaps. Moreover, using a much smaller cohort via task transfer learning, the same model could learn to predict generalised anxiety severity accurately based on GAD-7 scores.

Evaluation and modeling of a solar photovoltaic power plant: Case of the Sakal plant

This paper proposes an evaluation and modeling of the Sakal solar PV plant. In this work, we have developed and validated a model that takes into account shading effects. The studied solar power plant is mobile on a horizontal north-south axis of east-west tracking. The results showed that the model tracked the system with a correlation coefficient of 0.993. In the following, we considered the power plant fixed to evaluate the gain between the fixed and mobile collector power plants. Subsequently, we considered the plant a two-axis mobile plant. The performance of the different configurations was evaluated. The results showed that the power plant with a two-axis mobile sensor provided a gain of 40.49 % compared to the power plant configured with a fixed sensor, and a gain of 5.378 % compared to the power plant with a single-axis mobile sensor.

Virtual audio design based on mobile sensor network and English MOOC teaching

Virtual audio design based on mobile sensor network and English MOOC teaching

An autonomous rail-road amphibious robotic system for railway maintenance using sensor fusion and mobile manipulator

The current maintenance of railway infrastructure replies heavily on human involvement, requiring possession of the track section during maintenance, resulting in high costs and inefficient execution. This paper proposes an autonomous rail-road amphibious robotic system for railway inspection and maintenance tasks. By virtue of its road and rail-autonomous mobility, it is able to execute the complete maintenance execution flow in multiple phases. The system provides flexible track job location access, low-cost maintenance execution, and reduced track network possession. The payload mobile manipulator and sensor fusion enhance the system's capabilities for multiple types of inspection and repair. The design of a command and control system was guided by a rule-based expert system strategy to enable remote operation of the whole system. The developed demonstrator of a track wheel accompanied unmanned ground vehicle was integrated and demonstrated in both operational and realistic track environments with multiple testing activities of remote operation, navigation, accurate job detection, inspection, and repair, confirming effective job completion and logical human interaction. The proposed method produces an outstanding hardware-software integrated robotic inspection and repair system with a high level of technological readiness for autonomous railway maintenance and intelligent railway asset management.

Robust Control for Semilinear Parabolic Distributed Parameter Systems With External Disturbances via Mobile Actuators and Sensors.

This article presents a robust feedback compensator design approach for semilinear parabolic distributed parameter systems (DPSs) with external disturbances via mobile actuators and sensors. An performance constraint is introduced to deal with the external disturbances from the model and measurement noise. Two types of feedback compensators are designed in terms of the collocated and noncollocated mobile actuators and sensors. By the Lyapunov direct technique, some sufficient conditions based on LMI constraints are proposed for the exponential stability under performance constraints in the L²-norm. Moreover, the open-loop and closed-loop well-posedness of the semilinear DPSs with external disturbances are analyzed via the C0 -semigroup theory approach. Finally, extensive numerical simulation results for semilinear DPSs with external disturbances via collocated and noncollocated mobile actuators and sensors are shown to verify the effectiveness of the proposed method.