Low-cost Data Research Articles

Marginal Effect-aware Multiple-Vehicle Scheduling for Road Data Collection: A Near-optimal Result

Vehicles equipped with abundant sensors offer a promising way for large-scale, low-cost road data collection. To realize this potential, a well-designed vehicle scheduling scheme is essential for deploying the recruited drivers efficiently. Nevertheless, existing works fail to consider the marginal effect among drivers’ collections. Different from them, this article introduces a, to the best of our knowledge, new multiple-vehicle scheduling problem that jointly optimizes task allocation and vehicle trajectory planning to maximize the overall collection utility by accounting for the marginal effect in drivers’ data collections. However, solving this problem is non-trivial due to its involvement with multiple coupled NP-hard problems. To this end, we propose MeSched, a marginal effect-aware multiple-vehicle scheduling scheme designed for road data collection. Specifically, we first present a greedy-based auxiliary graph construction method to disentangle the initial problem into multiple independent single-vehicle scheduling subproblems. Furthermore, we build an approximate surrogate function that transforms each subproblem into a tractable form involving only a single variable. The theoretical analysis proves that MeSched can achieve a 1-(1/ e ) ¼ -approximation ratio in polynomial time. Comprehensive evaluations based on a real-world trajectory dataset of 12,493 vehicles demonstrate that MeSched can significantly improve the collection utility by 104.5% on average compared with four baselines.

Leveraging transcriptome sequence read archives for virus detection in wild and colony populations of triatomines (Hemiptera: Reduviidae: Triatominae)

Triatomines are infamous as vectors of the parasite Trypanosoma cruzi, the causative agent of Chagas disease. However, climate-driven range expansion and urbanization adaptation of triatomine populations, coupled with their highly diverse feeding strategies (vertebrate haematophagy, kleptohaematophagy, and coprophagy), and has elevated interest in triatomines as potential arboviral vectors. Information on the triatomine virome is scant, with prior records including only eight insect-specific viruses: Triatoma virus (TrV) and Rhodnius prolixus viruses 1–7. Here, we leverage publicly available transcriptome datasets to assess viral diversity in 122 wild and colony kissing bugs representing eight species from six countries. In total, six viruses were detected (including Rhodnius prolixus viruses 4–6), and TrV was detected in almost half of all screened triatomines. This is the first report of TrV in Triatoma brasiliensis and in members of the genus Mepraia (M. gajardoi, M. spinolai, and M. parapatrica), and this effort has vastly expanded the publicly available genomic resources of TrV, adding 39 genome sequences to the single genome sequence currently available in the GenBank database. Furthermore, two additional viruses—Meccus longipennis virus 1 and Drosophila melanogaster Nora virus—are herein reported for the first time from kissing bugs. Meccus longipennis virus 1 was detected in Triatoma infestans from Argentina, Brazil, Chile, and Peru, and Drosophila melanogaster Nora virus was found in T. infestans from Argentina. Our results illustrate the advantage and utility of low-cost transcriptome data mining for the discovery of known and novel arboviruses in triatomines and other potential insect vectors.

Integrating low-cost GNSS and MEMS accelerometer for precise dynamic displacement monitoring

We assess the performance of the self-developed receiver coupling dual-frequency low-cost GNSS chipset and IMU MEMS sensor. The system also comprises software dedicated to vibration monitoring based on relative kinematic positioning in a post-processing mode. The observation assessment reveals high accuracy of low-cost GNSS and MEMS accelerometer data. The Septentrio Mosaic X5 chipset exhibits competitive to high-grade receivers’ phase observation noise, outperformance in code noise level and observation auto-correlation at a level that may be neglected even in high-rate applications. The evaluation of the MEMS accelerometer data proves that the sensor accuracy is adequate for vibration monitoring. With the shake table experiment, we examine the performance of vibration recovery. An advantage of the coupled solution over a GNSS-only one is documented with a 15 % reduction of the amplitude error. The backward smoothing of coupled solutions drives the next 20 % error drop. Finally, the high performance of the coupled solution based on low-cost GNSS & accelerometer is confirmed by a mean amplitude error of 0.4 mm. The frequency analyses indicate that both coupled and single-sensor solutions precisely detect the vibration frequencies; however, they also confirm the outperformance of the former over the latter. The power spectra of the recovered displacement time series accentuate the benefits of GNSS + accelerometer fusion and backward filtering for reducing displacement noise.

Multiple channels, low-cost, and dual data storage data logger for building a soil temperature network

Temperature measurement is critical in many areas of research, particularly in agriculture, where it can have a significant impact on crop health and yield. Experiments such as seed germination often require numerous temperature sensors to collect extensive data. Typically, data loggers are used to store information, but market options are expensive and offer limited, non-customizable inputs (channels), creating challenges for comprehensive soil temperature monitoring. This study aims to develop a network of open-source, low-cost data loggers with multiple customizable channels for local and remote temperature data storage. The hardware includes Arduino, temperature sensors, a Real Time Clock, and a LoRa module to transmit data to a LILYGO TTGO board, which sends it to a remote MongoDB database while also storing it locally on a microSD card. In addition, a digital tool was developed to retrieve and display both current and historical readings from the MongoDB database. The total cost of this hardware is approximately US$ 72 (based on current prices) for the simplest network, which is approximately 18 % of the commercial cost. The system achieved a root mean square error (RMSE) of 1.6 °C compared to a manual sampling probe thermometer, proving it to be a reliable measurement source. The hardware developed in this study surpasses commercial options by allowing the integration of multiple sensors and emitters, creating a network of data loggers at a lower cost. In addition to the hardware, an open-source digital tool was developed to visualize historical data at no additional cost.

Unlocking the potential of unlabeled data: Self-supervised machine learning for battery aging diagnosis with real-world field data

Accurate aging diagnosis is crucial for the health and safety management of lithium-ion batteries in electric vehicles. Despite significant advancements achieved by data-driven methods, diagnosis accuracy remains constrained by the high costs of check-up tests and the scarcity of labeled data. This paper presents a framework utilizing self-supervised machine learning to harness the potential of unlabeled data for diagnosing battery aging in electric vehicles during field operations. We validate our method using battery degradation datasets collected over more than two years from twenty real-world electric vehicles. Our analysis comprehensively addresses cell inconsistencies, physical interpretations, and charging uncertainties in real-world applications. This is achieved through self-supervised feature extraction using random short charging sequences in the main peak of incremental capacity curves. By leveraging inexpensive unlabeled data in a self-supervised approach, our method demonstrates improvements in average root mean square errors of 74.54% and 60.50% in the best and worst cases, respectively, compared to the supervised benchmark. This work underscores the potential of employing low-cost unlabeled data with self-supervised machine learning for effective battery health and safety management in real-world scenarios.

Development of a Low-Cost Data Acquisition System for Analyzing the Health of a Photovoltaic System

In recent years, fault diagnosis has become a major concern for ensuring the sustainability of photovoltaic systems. This article aims to develop a low-cost data acquisition device based on the Arduino card to collect data using sensors on two photovoltaic modules. Four defects were intentionally created on a photovoltaic panel and analyzed individually based on the evolution of current and voltage parameters over time. The results indicate that in the event of a fault, the current drops suddenly with a slight decrease in voltage. Under normal conditions, the maximum observed current was 3.8 A for an irradiation of 904 W/m2, compared to 2.2 A for 909 W/m2 and 32.78°C in the event of partial shading. This value further decreases to a maximum of 1.8 A for 867 W/m2 in an open circuit. The observation reveals that the partial shading fault with a disconnected bypass diode and open circuit has similar characteristics to the open circuit fault. However, it is important to note that this cannot be generalized as the fault only occurs in a configuration of two PV modules in parallel.

Influence of Fix Schedule on the Location Accuracy of a Low-Cost GPS Data Logger on Cattle

ABSTRACT Global positioning system (GPS) data loggers are commonly used to track the movements and distribution of both wild and domestic animals. However, the expense often poses a challenge for researchers. Recently, there has been a rise in the utilization of affordable and user-friendly GPS data loggers for tracking animal movements, albeit with compromised accuracy. We aimed to identify factors influencing the accuracy of a low-cost GPS data logger (I-gotU GT-600) and to enhance its location accuracy. Initial investigations revealed that recording intervals impacted the location error of the GPS data logger. To elucidate the relationship between recording intervals and location accuracy, we conducted stationary and motion tests. Our findings indicated that recording intervals of less than 15 sec substantially enhances the location accuracy of the low-cost GPS data logger. Our results highlight the relationship between the fix schedule and location accuracy for these GPS data loggers. Our study provides information that enhances the quality of data for researchers using low-cost GPS data loggers for short-term studies in various settings, such as zoos and livestock facilities.

Optimisation of the adaptive neuro-fuzzy inference system for adjusting low-cost sensors PM concentrations

Driven by the urgent necessity for accurate environmental data in urban settings, this research leverages the Adaptive Neuro-Fuzzy Inference System (ANFIS) as a machine learning-based approach to refine SPS30 low-cost sensor data influenced by hygroscopicity in Turin, Italy. Employing ANFIS offers several advantages: it enhances clarity regarding the correspondence between output and input values and rules, improves system interpretability, and facilitates the representation of linguistic variables and rules, thereby encouraging domain experts' involvement in enhancing the system's performance as needed. This paper illustrates the utility of ANFIS in adjusting the detected particulate matter (PM) concentration and compares its effectiveness with other established machine-learning techniques, including linear regression, decision trees, random forest, SVR and a multilayer perceptron (MLP). These methods are chosen as benchmarks owing to their established effectiveness in calibration procedures.We propose certain preprocessing steps for detecting and rectifying anomalies, alongside introducing two distinct data-splitting methodologies. Additionally, a discussion about feature selection is presented to elucidate the impact of specific features on performance enhancement. The efficacy of ANFIS in refining PM data is demonstrated through a comparative assessment, where it outperforms all the established machine-learning techniques. Notably, incorporating only PM2.5, relative humidity and temperature as features yields optimal performance while mitigating overfitting issues. The paper also explores various ANFIS configurations, including two distinct optimization algorithms, and investigates the impact of the number and type of membership functions on the fuzzy system's performance. Our study highlights the potential of the Adaptive Neuro-Fuzzy Inference System as a versatile and effective tool for addressing real-world challenges in environmental sensing.

Integrating policy, data and technology in pursuing effective management of ecosystem services

With the growing concerns about the protection of ecosystem functions and services, governments have developed public policies and organizations have produced an awesome volume of digital data freely available through their websites. On the other hand, advances in data acquisition through remote sensed sources and processing through geographic information systems (GIS) and statistical tools, allowed an unprecedent capacity to manage ecosystems efficiently. However, the real-world scenario in that regard remains paradoxically challenging. The reasons can be many and diverse, but a strong candidate relates with the limited engagement among the interest parties that hampers bringing all these assets into action. The aim of the study is to demonstrate that management of ecosystem services can be significantly improved by integrating existing environmental policies with environmental big data and low-cost GIS and data processing tools. Using the Upper Rio das Velhas hydrographic basin located in the state of Minas Gerais (Brazil) as example, the study demonstrated how Principal Components Analysis based on a diversity of environmental variables assembled sub-basins into urban, agriculture, mining and heterogeneous profiles, directing management of ecosystem services to the most appropriate officially established conservation plans. The use of GIS tools, on the other hand, allowed narrowing the implementation of each plan to specific sub-basins. This optimized allocation of preferential management plans to priority areas was discussed for a number of conservation plans. A paradigmatic example was the so-called Conservation Use Potential (CUP) devoted to the protection of aquifer recharge (provision service) and control of water erosion (regulation service), as well as to the allocation of uses as function of soil capability (support service). In all cases, the efficiency gains in readiness for plans’ implementation and economy of resources were prognosed as noteworthy.

A novel BP algorithm‐based UWB MIMO ground‐penetrating imaging system

AbstractUltra‐wideband (UWB) multiple input multiple output (MIMO) radar has been widely used in detection systems due to its advantages, such as low cost and high data acquisition capability. The detection system is applied in various ground‐penetrating applications, including mine detection and earthquake rescue. In this paper, a novel UWB MIMO radar system based on step‐frequency continuous wave is designed for underground target detection. To improve the penetration depth and high‐range resolution, a new miniature Vivaldi antenna with a bandwidth of 1–3 GHz is designed and integrated into the ground‐penetrating imaging system. Furthermore, a multiscale weighted time‐domain back projection (BP) algorithm is proposed to suppress artifacts and reduce the computational complexity of the BP algorithm. The designed system is tested by detecting stationary targets. The experimental results agree well with the simulation. The reconstructed images show that the designed MIMO radar system can successfully image underground targets.

Design of a low-cost IoT-based data logger for monitoring the palm sugar drying system using ESP32 and Modbus/MQTT conversion

Design of a low-cost IoT-based data logger for monitoring the palm sugar drying system using ESP32 and Modbus/MQTT conversion

Derivation of Predictive Layers Using Regional Till Geochemistry Data for Mineral Potential Mapping of the REE Line of Bergslagen, Central Sweden

With the increasing need for rare-earth elements (REEs) to reach the goals of the ongoing green energy transition, new and innovative methods are needed to identify new primary resources of these critical metals. This study explores the potential to use a non-biased, uniform till dataset to generate evidentiary layers that describe these critical factors and geochemical anomalies to aid mineral potential mapping (MPM) for REEs using machine-assisted methods. The till samples used in this study were collected from the “REE Line”, a sub-region within the Bergslagen lithotectonic province, Sweden, where numerous REE mineralizations occur. Multiple approaches were used in this study to isolate geochemical anomalies using multivariate methods, namely principal component analysis (PCA) and K-means clustering. Additional factors for classifying till samples were also tested, including alteration indices. Using known REE occurrences in Bergslagen as validation points, the results demonstrated the usefulness of multivariate methods applied to till geochemistry for predictive bedrock mapping, and to identify potential areas of REE mineralization within the REE line. The results of the alteration indices showed that the till geochemistry demonstrated similar levels of alteration when compared to the underlying bedrock, allowing for a regional alteration map to be generated. These results show that regional-scale till sampling can provide low-cost data for mineral exploration at the regional scale and generate usable evidentiary layers for GIS-based MPM.

Assessing low-cost sensor for characterizing temporal variation of PM2.5 in Bandung, Indonesia

Fine particulate matter (PM2.5) is a concern due to its health effects, necessitating critical monitoring for both quantity and variability. Utilizing low-cost sensors to track PM2.5 is essential to augment other monitoring instruments, which are effective in generating temporal and spatial data. Therefore, in this study, we employed the low-cost PurpleAir (low-cost PA-II) to characterize the seasonal and diurnal variation of PM2.5 in Bandung, Indonesia, representing the densely populated metropolitan area of Bandung. During the sampling period from June 2022 to May 2023, co-location sampling with the filter-based Super Speciation Air Sampling System (SuperSASS) was employed to assess the low-cost PA-II. The PM2.5 data collected by the low-cost PA-II were compared to the SuperSASS data. The annual average mass concentration of PM2.5 measured by SuperSASS and the low-cost sensor was 31.51 ± 15.53 μg/m3 and 39.04 ± 15.16 μg/m3, respectively, surpassing the Indonesian government's regulation limit for an annual average of 15 μg/m3. The comparative results of the two methods were obtained with R2 = 0.96, and low-cost PA-II data was 1.24 higher than SuperSASS. The difference may be attributed to several factors, including differences in sensor technology, calibration, location, and data processing. The seasonal variation indicated an increase in concentration during the dry season and a decrease during the wet season. The diurnal pattern indicates the morning peak between 06:00 and 08:00 during the rush hour, as well as the evening peak between 18:00 and 23:00, attributed to low temperatures and stagnant conditions. The diurnal pattern of PM2.5, which often exhibits the lowest peak at midday, is influenced by a combination of meteorological, atmospheric, and human activity factors. The findings suggested that the utilization of low-cost PA-II for a broader spatial scope is promising for real-time monitoring of PM2.5, to increase air pollution awareness in society.

HEROIC: a platform for remote collection of electroencephalographic data using consumer-grade brain wearables

The growing number of portable consumer-grade electroencephalography (EEG) wearables offers potential to track brain activity and neurological disease in real-world environments. However, accompanying open software tools to standardize custom recordings and help guide independent operation by users is lacking. To address this gap, we developed HEROIC, an open-source software that allows participants to remotely collect advanced EEG data without the aid of an expert technician. The aim of HEROIC is to provide an open software platform that can be coupled with consumer grade wearables to record EEG data during customized neurocognitive tasks outside of traditional research environments. This article contains a description of HEROIC’s implementation, how it can be used by researchers and a proof-of-concept demonstration highlighting the potential for HEROIC to be used as a scalable and low-cost EEG data collection tool. Specifically, we used HEROIC to guide healthy participants through standardized neurocognitive tasks and captured complex brain data including event-related potentials (ERPs) and powerband changes in participants’ homes. Our results demonstrate HEROIC’s capability to generate data precisely synchronized to presented stimuli, using a low-cost, remote protocol without reliance on an expert operator to administer sessions. Together, our software and its capabilities provide the first democratized and scalable platform for large-scale remote and longitudinal analysis of brain health and disease.

A low-cost and open-hardware portable 3-electrode sleep monitoring device

To continue sleep research activities during the lockdown resulting from the COVID-19 pandemic, experiments that were previously conducted in laboratories were shifted to the homes of volunteers. Furthermore, for extensive data collection, it is necessary to use a large number of portable devices. Hence, to achieve these objectives, we developed a low-cost and open-source portable monitor (PM) device capable of acquiring electroencephalographic (EEG) signals using the popular ESP32 microcontroller. The device operates based on instrumentation amplifiers. It also has a connectivity microcontroller with Wi-Fi and Bluetooth that can be used to stream EEG signals. This portable single-channel 3-electrode EEG device allowed us to record short naps and score different sleep stages, such as wakefulness, non rapid eye movement sleep (NREM), stage 1 (S1), stage 2 (S2), stage 3 (S3) and stage 4 (S4). We validated the device by comparing the obtained signals to those generated by a research-grade counterpart. The results showed a high level of accurate similarity between both devices, demonstrating the feasibility of using this approach for extensive and low-cost data collection of EEG sleep recordings.

CPR++: Object Localization via Single Coarse Point Supervision.

Point-based object localization (POL), which pursues high-performance object sensing under low-cost data annotation, has attracted increased attention. However, the point annotation mode inevitably introduces semantic variance due to the inconsistency of annotated points. Existing POL heavily rely on strict annotation rules, which are difficult to define and apply, to handle the problem. In this study, we propose coarse point refinement (CPR), which to our best knowledge is the first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces the semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. Furthermore, We design a sampling region estimation module to dynamically compute a sampling region for each object and use a cascaded structure to achieve end-to-end optimization. We further integrate a variance regularization into the structure to concentrate the predicted scores, yielding CPR++. We observe that CPR++ can obtain scale information and further reduce the semantic variance in a global region, thus guaranteeing high-performance object localization. Extensive experiments on four challenging datasets validate the effectiveness of both CPR and CPR++. We hope our work can inspire more research on designing algorithms rather than annotation rules to address the semantic variance problem in POL.

Data Evaluation of a Low-Cost Sensor Network for Atmospheric Particulate Matter Monitoring in 15 Municipalities in Serbia.

Conventional air quality monitoring networks typically tend to be sparse over areas of interest. Because of the high cost of establishing such monitoring systems, some areas are often completely left out of regulatory monitoring networks. Recently, a new paradigm in monitoring has emerged that utilizes low-cost air pollution sensors, thus making it possible to reduce the knowledge gap in air pollution levels for areas not covered by regulatory monitoring networks and increase the spatial resolution of monitoring in others. The benefits of such networks for the community are almost self-evident since information about the level of air pollution can be transmitted in real time and the data can be analysed immediately over the wider area. However, the accuracy and reliability of newly produced data must also be taken into account in order to be able to correctly interpret the results. In this study, we analyse particulate matter pollution data from a large network of low-cost particulate matter monitors that was deployed and placed in outdoor spaces in schools in central and western Serbia under the Schools for Better Air Quality UNICEF pilot initiative in the period from April 2022 to June 2023. The network consisted of 129 devices in 15 municipalities, with 11 of the municipalities having such extensive real-time measurements of particulate matter concentration for the first time. The analysis showed that the maximum concentrations of PM2.5 and PM10 were in the winter months (heating season), while during the summer months (non-heating season), the concentrations were several times lower. Also, in some municipalities, the maximum values and number of daily exceedances of PM10 (50 μg/m3) were much higher than in the others because of diversity and differences in the low-cost sensor sampling sites. The particulate matter mass daily concentrations obtained by low-cost sensors were analysed and also classified according to the European AQI (air quality index) applied to low-cost sensor data. This study confirmed that the large network of low-cost air pollution sensors can be useful in providing real-time information and warnings about higher pollution days and episodes, particularly in situations where there is a lack of local or national regulatory monitoring stations in the area.

Combining Low-Cost UAV Imagery with Machine Learning Classifiers for Accurate Land Use/Land Cover Mapping

Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. LULC mappings has been recently revolutionized by the use of high-resolution imagery from unmanned aerial vehicles (UAVs). The present study proposes an innovative approach for obtaining LULC maps using consumer-grade UAV imagery combined with two machine learning classification techniques, namely RF and SVM. The methodology presented herein is tested at a Mediterranean agricultural site located in Greece. The emphasis has been placed on the use of a commercially available, low-cost RGB camera which is a typical consumer’s option available today almost worldwide. The results evidenced the capability of the SVM when combined with low-cost UAV data in obtaining LULC maps at very high spatial resolution. Such information can be of practical value to both farmers and decision-makers in reaching the most appropriate decisions in this regard.

Monitoring the power flow of a low voltage distribution network using public lighting infrastructure

The production of renewable energy sources can change rapidly in a short period of time, making monitoring the power quality of Low Voltage Distribution Networks (LVDNs) one of the biggest challenges for Distribution System Operators (DSOs). An LVDN can overload some equipment if it does not monitors voltage profiles, voltage unbalance and reverse apparent power flow. Therefore, we propose a monitoring system that uses the Public Lighting Infrastructure (PLI), which is fed directly by the LVDN, to monitor the LVDN. It is based on remote management relays installed in the PLI, and replaces the installation of a power quality analyzer (PQ). The system has been empirically validated and the results show that the proposed system can identify reverse apparent power flow, profiles, and voltage unbalance with an average absolute error of 0.096% compared to a professional PQ analyzer. The proposed system can be a relevant contribution to the challenge of power quality monitoring, with cost savings; for example, DSOs can use the proposed system to access real-time information in the LVDN through a low-cost data acquisition model, without the need to send a team to install a PQ analyzer in the LVDN and then obtain the data.

Challenges and Opportunities in Calibrating Low-Cost Environmental Sensors.

The use of low-cost environmental sensors has gained significant attention due to their affordability and potential to intensify environmental monitoring networks. These sensors enable real-time monitoring of various environmental parameters, which can help identify pollution hotspots and inform targeted mitigation strategies. Low-cost sensors also facilitate citizen science projects, providing more localized and granular data, and making environmental monitoring more accessible to communities. However, the accuracy and reliability of data generated by these sensors can be a concern, particularly without proper calibration. Calibration is challenging for low-cost sensors due to the variability in sensing materials, transducer designs, and environmental conditions. Therefore, standardized calibration protocols are necessary to ensure the accuracy and reliability of low-cost sensor data. This review article addresses four critical questions related to the calibration and accuracy of low-cost sensors. Firstly, it discusses why low-cost sensors are increasingly being used as an alternative to high-cost sensors. In addition, it discusses self-calibration techniques and how they outperform traditional techniques. Secondly, the review highlights the importance of selectivity and sensitivity of low-cost sensors in generating accurate data. Thirdly, it examines the impact of calibration functions on improved accuracies. Lastly, the review discusses various approaches that can be adopted to improve the accuracy of low-cost sensors, such as incorporating advanced data analysis techniques and enhancing the sensing material and transducer design. The use of reference-grade sensors for calibration and validation can also help improve the accuracy and reliability of low-cost sensor data. In conclusion, low-cost environmental sensors have the potential to revolutionize environmental monitoring, particularly in areas where traditional monitoring methods are not feasible. However, the accuracy and reliability of data generated by these sensors are critical for their successful implementation. Therefore, standardized calibration protocols and innovative approaches to enhance the sensing material and transducer design are necessary to ensure the accuracy and reliability of low-cost sensor data.