Data Collection Server Research Articles

PLASMA: Private, Lightweight Aggregated Statistics against Malicious Adversaries

Private heavy-hitters is a data-collection task where multiple clients possess private bit strings, and data-collection servers aim to identify the most popular strings without learning anything about the clients' inputs. In this work, we introduce PLASMA: a private analytics framework in the three-server setting that protects the privacy of honest clients and the correctness of the protocol against a coalition of malicious clients and a malicious server. Our core primitives are a verifiable incremental distributed point function (VIDPF) and a batched consistency check, which are of independent interest. Our VIDPF introduces new methods to validate client inputs based on hashing. Meanwhile, our batched consistency check uses Merkle trees to validate multiple client sessions together in a batch. This drastically reduces server communication across multiple client sessions, resulting in significantly less communication compared to related works. Finally, we compare PLASMA with the recent works of Asharov et al. (CCS'22) and Poplar (S&P'21) and compare in terms of monetary cost for different input sizes.

Построение онтологии для систематизации характеристик сети Интернета вещей

A formalization of the Internet of Things network model designed for monitoring technological premises with tele-communications equipment at the Federal Research Center "Krasnoyarsk Scientific Center SB RAS" is presented. The network includes measuring devices, a telecommunications environment, data collection servers, and application soft-ware. For information interaction, a “publisher-subscriber” scheme and a lightweight protocol with a low load on com-munication channels are used. An ontology has been created that describes the network architecture and the properties of devices that collect, transmit, store, and process data. The ontology contains classes representing the concepts of the subject area, relationships, data properties, ranges of their changes, and critical values that limit the attributes of ontol-ogy elements. Ontology objects have their own digital representation in databases, including measurement results ob-tained by Internet of Things network sensors, precedents of anomalous data, and their statistical and frequency char-acteristics. This formalization made it possible to identify implicit dependencies between objects, connect them with the characteristics of processes observed by Internet of Things network devices, and solve practical tasks. The problem of selecting characteristics that influence changes in information interaction patterns is considered. A survey of experts was carried out, and a Kano model was built to prioritize the characteristics that influence decision-making on the or-ganization of an information interaction scheme in the Internet of Things network.

Development of Solar Panel Monitoring System On Automatic Meter Reading

In dealing with era society 5.0, a gas company uses the Automatic Meter Reading (AMR) system, which is a remote data reading automation technology system for measuring instrument data in the field, which then conveys the information to the center via communication media data. In the Metering/Regulating Station (M/RS) installation, AMR is connected to the Electronic Volume Converter (EVC) which functions to transmit data from measuring instrument readings in the EVC to the data collection server. The modem on the AMR system uses a power supply from the solar panel system during the day and a battery system at night. The system for handling incomplete data disturbances is currently still in corrective action due to the unavailability of a solar panel monitoring system in the AMR system, causing difficulties for the team in conducting root cause analysis due to problems with battery voltage which are unable to cure AMR. To change the operation and maintenance activities of the AMR system, which was previously corrective action into preventive action, the authors will design and build a long-distance integrated solar panel monitoring system at AMR with the following features monitoring temperature, voltage, and current modules of solar panels, monitoring temperature and battery loading, and AMR remote restart function.

Quality of life among patients with the common chronic disease during COVID-19 pandemic in Northwest Ethiopia: A structural equation modelling.

Improving Quality of Life (QoL) for patients with chronic diseases is a critical step in controlling disease progression and preventing complications. The COVID-19 pandemic has hampered chronic disease management, lowering patients' quality of life. Thus, we aimed to assess the quality of life and its determinants in patients with common chronic diseases, in Northwest Ethiopia during the COVID-19 pandemic. A cross-sectional study was conducted among 1815 randomly selected chronic patients with common chronic diseases. A standardized WHOQOL BREF tool was used, and electronic data collection was employed with the kobo toolbox data collection server. Overall QoL and the domains of Health-Related Quality of life (HRQoL) were determined. Structural equation modelling was done to estimate independent variables' direct and indirect effects. Path coefficients with a 95% confidence interval were reported. About one in third, (33.35%) and 11.43% of the study participants had co-morbid conditions and identified complications, respectively. The mean score of QoL was 56.3 ranging from 14.59 and 98.95. The environmental domain was the most affected domain of HRQoL with a mean score of 52.18. Age, psychological, and environmental domains of HRQoL had a direct positive effect on the overall QoL while the physical and social relationships domains had an indirect positive effect. On the other hand, the number of medications taken, the presence of comorbidity, and complications had a direct negative impact on overall QoL. Furthermore, both rural residency and the presence of complications had an indirect negative effect on overall QoL via the mediator variables of environmental and physical health, respectively. The quality of life was compromised in chronic disease patients. During the COVID-19 pandemic, the environmental domain of HRQoL was the most affected. Several socio-demographic and clinical factors had an impact on QoL, either directly or indirectly. These findings highlighted the importance of paying special attention to rural residents, patients with complications, patients taking a higher number of medications, and patients with comorbidity.

Privacy-Preserving Collaborative Data Collection and Analysis with Many Missing Values

Privacy-preserving data mining techniques are useful for analyzing various information, such as Internet of Things data and COVID-19-related patient data. However, collecting a large amount of sensitive personal information is a challenging task. In addition, this information may have missing values, which are not considered in the existing methods for collecting personal information while ensuring data privacy. Failure to account for missing values reduces the accuracy of the data analysis. In this paper, we propose a method for privacy-preserving data collection that considers many missing values. The patient data are anonymized and sent to a data collection server. The data collection server creates a generative model and a contingency table suitable for multi-attribute analysis based on expectation-maximization and Gaussian copula methods. Using differential privacy (the de facto standard) as a privacy metric, we conduct experiments on synthetic and real data, including COVID-19-related data. The results are 50--80\% more accurate than those of existing methods that do not consider missing values.

Assessment of effects of a Cemetery leachate on Groundwater Quality using an Integrated Approach

The dependency of residents in the vicinity of cemeteries on groundwater from hand-dug wells as an alternative source of water supply for their domestic use without cognisance of the quality of such water has been a concern. So, this research assesses the effects of a cemetery on groundwater quality using an integrated approach in Edunabon, South-western Nigeria. Thirty groundwater samples from available hand-dug wells were collected and analysed for various physicochemical parameters. GIS cloud data collection server and Mobile Data Collector (MDC) were used to obtain these hand-dug wells' locations and spatial distributions. The determination in variation in physicochemical concentration parameters was obtained using the curve fitting technique of ARCVIEW GIS software and digital map. Physicochemical results were used to calculate Water Quality Index (WQI) of each sample. Two Schlumberger Vertical Electrical Soundings (VES) of 130.0m long were conducted at 10.0m intervals to the wall and the next VES, on each side of the cemetery. Current spacing (AB/2) and apparent resistivity (ρa) data were plotted on a double logarithm paper, curved matched and iterated using IPI2Win software for qualitative and quantitative interpretation. WQI results ranged from (107.37–173.64), (76.60–93.37), (54.94–71.53), (31.29–48.01), and (10.84–25.31) for fourteen, seven, three, four and two hand-dug wells respectively, an indication that the water from these wells was unfit for drinking, very poor, poor, good, and excellent respectively. VES results revealed that first, second and third layers were mainly clayey topsoil (46.7–96.8Ωm), lateritic soil (169.0–551.3Ωm), and weathered layer (3.21–27.4Ωm), which favourably enhances the migration of leachate from the cemetery to the surrounding soils and groundwater. The lowest resistivity values recorded in the second and third layers indicated leachate presence at these hand-dug wells. The integration of GIS and electrical resistivity methods would be useful tools to assess groundwater quality around cemeteries. Keywords: Groundwater quality, Mobile Data Collection, unfit for drinking, Vertical Electrical Sounding, Water Quality index

Monitoring and analysis of the recovery rate of Covid-19 positive cases to prevent dangerous stage using IoT and sensors

PurposeThis paper has used the well-known machine learning (ML) computational algorithm with Internet of Things (IoT) devices to predict the COVID-19 disease and to analyze the peak rate of the disease in the world. ML is the best tool to analyze and predict the object in reasonable time with great level of accuracy. The Purpose of this paper is to develop a model to predict the coronavirus by considering majorly related symptoms, attributes and also to predict and analyze the peak rate of the disease.Design/methodology/approachCOVID-19 or coronavirus disease threatens the human lives in various ways, which leads to deaths in most of the cases. It affects the respiratory organs slowly and this penetration leads to multiple organ failure, which causes death in some cases having poor immunity system. In recent times, it has drawn the international attention because of the pandemic threat that is harder to control the spreading of infection around the world.FindingsThis proposed model is implemented by support vector machine classifier and Bayesian network algorithm, which yields high accuracy. The K-means algorithm has been applied for clustering the data set models. For data collection, IoT devices and related sensors were used in the identified hotspots. The data sets were collected from the selected hotspots, which are placed on the regions selected by the government agencies. The proposed COVID-19 prediction models improve the accuracy of the prediction and peak accuracy ratio. This model is also tested with best, worst and average cases of data set to achieve the better prediction rate.Originality/valueFrom that hotspots, the IoT devices were fixed and accessed through wireless sensors (802.11) to transfer the data to the authors’ database, which is dedicated in data collection server. The data set and the proposed model yield good results and perform well with expected accuracy rate in the analysis and monitoring of the recovery rate of COVID-19.

Location protection method for mobile crowd sensing based on local differential privacy preference

In view of the location privacy problem of participants in mobile crowd sensing, this paper proposes a method to protect the location of participants based on local differential privacy preference. First of all, the map is discretized and mapped from two-dimensional space to one-dimensional space by means of MHC, which can guarantee the spatial correlation, and the map is segmented based on the density of participants using genetic algorithm; Then, according to the personal privacy needs of current location, two different local differential privacy perturbation methods, RAPPOR and k-RR, are chosen by participants; Next, the chosen local differential privacy is used to perturb the location of each participant in the region after segmentation, and the perturbed location data are sent to the data collection server to protect the participants’ locations. Finally, the simulation experiments are carried out and show that map density segmentation can reduce the privacy cost, and the method proposed in this paper is superior to the method using k-anonymous and differential privacy and the method using Hilbert and differential privacy in terms of running time and average relative error, and prove that the execution time is lower and the data availability is improved.

TPS3: A privacy preserving data collection protocol for smart grids

ABSTRACTSmart grid (SG) allows for two-way communication between the utility and its consumers and hence they are considered as an inevitable future of the traditional grid. Since consumers are the key component of SGs, providing security and privacy to their personal data is a critical problem. In this paper, a security protocol, namely TPS3, is based on Temporal Perturbation and Shamir’s Secret Sharing (SSS) schemes that are proposed to ensure the privacy of SG consumer’s data. Temporal perturbation is employed to provide temporal privacy, while the SSS scheme is used to ensure data confidentiality. Temporal perturbation adds random delays to the data collected by smart meters, whereas the SSS scheme fragments these data before transmitting them to the data collection server. Joint employment of both schemes makes it hard for attackers to obtain consumer data collected in the SG. The proposed protocol TPS3 is evaluated in terms of privacy, reliability, and communication cost using two different SG topologies. The performance evaluation results clearly show that the TPS3 protocol ensures the privacy and reliability of consumer data in SGs. The results also show that the tradeoff between the communication cost and security of TPS3 is negligible.

Implementation of quality coverage map system via wireless communication service and big data analysis

The objective of this study is to realize the big data service quality maps of mobile communication companies, so that the users can objectively compare communication service qualities and make a reasonable choice. A measurement software was installed on smart phones for the purpose of measuring the service qualities of experience by the users. Furthermore, a measurement of five wireless internet services (broadband LTE, LTE-A, LTE, 3G, WiBro, and WiFi) was performed for evaluating the qualities of the service provider networks and the nationwide qualities. The measured data were stored and periodically uploaded to the database. These data were stored in PostGIS spatial DBMS through the connection of DBs, and they were connected to GeoServer for sharing and editing the geospatial data. The measured data were realized into coverage maps by using Google Maps. The coverage range was defined in Google Maps, and the defined range was divided by a specific cell (approximately 100 m x 100 m). The measured GPS ranges of each cell were retrieved from the quality data collection server table, and their average values were used for organizing and updating the coverage maps. These contents were stored in GeoServer, retrieved by database queries, and provided as visible layers in OpenLayers. In addition, statistical coverage maps via Flash maps were provided.

Remote Analysis of Soil Macronutrients Using Optical Sensor for Precision Agriculture

ABSTRACT Continuous cropping without adequate measurement and provisioning of soil nutrient may endanger the sustainability of agriculture. Soil nutrient measurement is greatly required for proper plant growth and effective fertilization. Existing methods of soil testing generally use visual comparison of soil solution colour with the colour-chart and this makes it subjective and error prone and time consuming whereas the spectrophotometer is very expensive and none of the approach suitable for remote analysis of soil macronutrients. On the other hand, the optical sensor could sensitively detect the soil solution colour changes thereby detecting soil nutrients in the sample without delay and subjective error. In this work, a compact optical sensor based on photometric detection of soil nutrients using high precision Photo Diode (PD) and Light Emitting Diode (LED) was developed. Real-time optical sensor using PIC microcontroller was integrated to a remote data collection server for the ease of acquisition and post-processing. The wavelength of LEDs is chosen to fit the absorption band of chemical reagents whose colour develops by reaction with soil nutrients. The sensor was used to detect three soil macronutrients: ammonia nitrogen (NH4-N), nitrate nitrogen (NO3-N), available phosphorus oxide (P2O5) from colour changes caused by addition of chemical reagent in a transparent plastic cell (5.5 mm path length). The resolution of 0.1-20 mg/100g was used as standard solution. The fifteen test samples were taken from different farmlands and ten soil samples were used to calibrate the optical sensor comparing with the result obtained by a colour chart laboratory judgement. The calibration factors obtained were then used to evaluate five unknown soil samples and the results were finally compared with laboratory results, and designed system showed good level of agreement with the laboratory results.

Research on Rolling Force Model Based on Distributed Cooperative Control System

Three-stand mills continues produced of multi-layer composite steel plate, multiple L2 servers together computing to complete the tasks, serviced to many models in whole automated production process. In this paper, multi-embedded systems distributed and cooperative control program takes instead of multi L2 server mode, one L2 data collection and record server was remained. An example of rolling force model is used to discuss the control architecture, Sims simplified formula. The experiment and numerical results show that the control architecture is capable of reliable operation, and the accuracy of the target is no less than 94%.

RETRACTED: A low cost wireless data acquisition system for weather station monitoring

In this paper, the development of wireless data acquisition system (WDAS) for weather station monitoring is described. It is based on the Emitter/Receiver architecture and it does not require the physical connection of the monitored systems to the data collection server. The proposed system consists of a set of sensors for measuring meteorological parameters (solar radiation, temperature, humidity, pressure, wind speed & direction, rain fall, etc.). The collected data are first conditioned using precision electronic circuits and then interfaced to a PC using RS232 connection via wireless unit. The LabVIEW program is used to further process, display and store the collected data in the PC disk. The proposed architecture permits the rapid system development and has the advantage of flexibility and it can be easily extended for controlling the renewable energy systems like photovoltaic system. The measured parameters are available on-line over the internet to any user.

SeismNet Manager: A Web Application to Manage Hardware and Data of a Seismic Network

Modern seismic networks have grown to become increasingly complex infrastructures, composed of hundreds of devices and data streams scattered over wide geographic regions. Among the components of such networks are heterogeneous seismic and environmental sensors, digitizers, data loggers, data collection servers, wired and wireless communication hardware, and other devices and software subsystems charged with different data handling tasks, such as continuous data storage or analysis. In order to be effectively managed, a seismic network therefore needs a tiered software application. This application encompasses tasks that range from the low-level (hardware monitoring for failure detection) to the mid-level (data quality control) to the high-level (managing the final output of the network: recorded events, waveforms, and parametric data). At the same time such an application should provide a centralized and easy-to-use graphical user interface (GUI). Over the past two decades, several institutions and commercial companies have devoted great efforts to the development of software tools to manage and centralize the data acquisition and analysis for regional to global seismic networks. Among the most valuable products worth mentioning are: Earthworm, an open-source real-time seismic management system developed by the U.S. Geological Survey (Johnson et al . 1995); Antelope, a commercial real-time system for environmental data collection, developed by Boulder Real Time Technologies (BRTT 2008); and the more recent SeisComP (Hanka et al . 2000), an open-source tool for real-time data acquisition and analysis developed by the German Research Centre for Geosciences (GFZ-Potsdam). Although well-suited for real-time data collection and analysis, these systems do not currently provide advanced features for managing the infrastructure of a seismic network, such as state-of-health monitoring of the instrumentation or tracking all the network appliances. Trying to fill this gap, Instrumental Software Technologies (ISTI 2008) has recently developed SeisNetWatch (SeisNetWatch 2008), a tool for monitoring and controlling the data …

Development of a data acquisition system for remote monitoring of renewable energy systems

The widespread application of Renewable Energy Sources (RES) requires the use of data acquisition units both for monitoring system operation and control of its operation. In this paper, the development of a data acquisition system for remote monitoring and control of RES plants is presented. It is based on the Client/Server architecture and it does not require the physical connection of the monitored systems to the data collection server. This feature is essential in RES plants since they are usually installed in inaccessible areas. The measured parameters are available on-line over the Internet to any user.