IoT Sensor Data Research Articles

A New Feature Scoring Method in Keystroke Dynamics-Based User Authentications

In recent years, computing devices have become widely distributed, and the accumulated data from these devices are growing rapidly, especially as they are increasingly equipped with various sensors and RF communication capabilities. Data science, including machine learning technology, has contributed to the better handling the large amounts of data and feature selection techniques have been a useful strategy. As data amounts continue to grow, scaling features will became crucial in data science. In this paper, we propose a novel filter-based feature-selection method in the context of keystroke dynamics authentication. In particular, we propose a new feature-scoring method and apply it to keystroke-dynamics-based authentications. We implement keystroke-dynamics-based authentications multi-factored with PIN-based authentications and collect data from actual users’ testing experiments. Then, we apply our feature-selection method and compare the performance with that when using all of the features and existing feature-selection methods. Our experimental results show that the classification performance by the proposed method is superior to those of the other methods by up to 21.8%. Moreover, our method provides security to other users’ data sets, as the method utilizes only mean values from imposter data. Our feature-selection method contributes to improving the quality of keystroke dynamics authentications without user privacy issues. More generally, our method can also be applied to other data-mining data sets, such as IoT sensor data sets.

Authentication of Remote IoT Users Based on Deeper Gait Analysis of Sensor Data

In IoT based systems, authentication of users and devices is a major challenge where the traditional authentication mechanisms such as login-password are no longer supportable. It requires continuous authentication methods to ensure authenticity of the users and devices specifically for long sessions. The authentication through the IoT-Sensors based data has already gained the attention of a considerable number of researchers as it does not require direct involvement of the users and provide an extra layer of security and user privacy. Due to the instability of IoT-Sensor data, the authentication techniques need to extract a large number of features to produce high-accurate results. Moreover, the limited capabilities of computation, communication, storage, and the small battery power of IoT devices further makes its implementation hard. In this paper, we have introduced a user authentication framework for remote IoT users based on unique walking patterns to extract gait-related features with minimum data samples and cycle length. For an in-depth analysis of the distinctive features of the sensor-based gait profiles, we have applied an ECG signal processing technique. The proposed approach applies to a diverse range of IoT devices such as cellphones, smartwatches, and wearable sensors. The main contribution of the proposed approach is the deeper gait analysis with the least number of features and data for authentication purposes. We have introduced an authentication algorithm for feature comparison. The machine learning models have been applied to the gait based profiles for validation of the proposed approach. The detailed experimental analysis of different data sets has achieved an accuracy of 94% and an equal error rate (EER) of 6% higher than the existing approaches.

Air pollution potential hot zone evaluation model based on environmental sensing IoT

Abstract. PM2.5 is invisible to the eye, but it is a threat to human health. In order to monitor the small-scale environment status, Taiwan's Environmental Protection Administration (EPA) have deployed more than 2,500 environmental IoT Sensor in 13 cities (Figure 1) to monitor air quality. These sensors are mainly deployed in industrial areas, gather minute-byminute environmental data on PM2.5, temperature, and humid, etc., try to evaluate local pollution hot zone in each area.Mainstream air quality sensing relies on high-precision large-scale stations, but it is difficult to collect local air quality information. In fact, Factories, temples or local air pollution emission behaviour can strongly affect the air quality, and some emissions have already exceeded acceptable limits. However, emissions disperse quickly with the wind in all directions, cause the environment changes greatly in a very short time. As a result, it is difficult to determine the source of emissions, which is a serious problem for environmental protection administration.By environmental IoT sensors, this study collects local air quality information at a small scale (currently IoT installed at intervals of 300 meters), which enhance the air pollution monitor capabilities. Based on the detailed environmental information, this study established an air pollution potential hot zone evaluation model, detect the local suspicious pollution area through data variability, and calculate the grid-based area pollution scores, provide relevant information for EPA to strengthen pollution traceability.Air pollution potential hot zone evaluation model estimate pollution scores through environment factors, such as PM2.5 concentration, wind, degree of data variation, we spread virtual circles every 300 meters in the study area, accumulate the pollution sensing value, and integrate time and spatial characteristics of the pollution, establish a fully automatic pollution analysis model, systematically mark the air pollution potential hot zone, and these hot zones information will deliver to EPA.Taking Figure 2 as an example, if there is an abnormally emission event occurs at the potential pollution IoT (purple cross), and the range of influence covers the potential areas A, B, C, and D, will be affected. Accumulate all these patterns, we can infer the potential pollution hot zones automatically, distinguish normal and abnormal circumstances, and estimated as a polluted hot zone.This study also implemented air quality cloud-based GIS system (Figure 3), and the potential hot zone information is built as a data layers, this system also combines the air pollution real-time sensing data, geographic information, time information, wind direction data, wind speed data, etc. The combined data are used to create data visualization, which enables the reconstruction of circumstances of the air pollution severity at a specific time range, which can assist environment officials to trace excessive emissions evidence. By environmental IoT Sensor data, this study proposed an air pollution potential hot zone evaluation model, which help EPA to find the local abnormal pollution areas. Therefore, environmental analysts can reference as policy recommendations, such as conduct environment inspections on certain days of the week, or certain time of the day, increasing the probability of catching serious emissions behaviour, and strengthen the environmental enforcement capacity.

Big Data Analytics and Security in Healthcare Industry

Big data analytics plays a major role in today’s industry which insisted to use big data analytics for the analysis of previous data. Patient record keeping is very much important to track the history of the patient. According to the patient previous records, decision is made. Large volumes of data are created on a daily basis and this data is used in decision making process. But, health care industry has not sensed the potential benefits from big data analytics. To address this need, four big data analytics capabilities were identified. In addition to four, five capabilities were proposed which provides practical insights for administrator. On the other way, data security plays a key role in health care industry. In order to overcome this, a new architecture is proposed for the implementation to IOT and process scalable sensor data for health care systems. This paper focuses on data security so that we can make use of potential capabilities and benefits of big data analytics in a better way.

ARTIK 클라우드를 통한 분산된 원격 IoT 센서 데이터 모니터링 및 제어

ARTIK 클라우드를 통한 분산된 원격 IoT 센서 데이터 모니터링 및 제어

Semantics-based platform for context-aware and personalized robot interaction in the internet of robotic things

Robots are moving from well-controlled lab environments to the real world, where an increasing number of environments has been transformed into smart sensorized IoT spaces. Users will expect these robots to adapt to their preferences and needs, and even more so for social robots that engage in personal interactions. In this paper, we present declarative ontological models and a middleware platform for building services that generate interaction tasks for social robots in smart IoT environments. The platform implements a modular, data-driven workflow that allows developers of interaction services to determine the appropriate time, content and style of human-robot interaction tasks by reasoning on semantically enriched IoT sensor data. The platform also abstracts the complexities of scheduling, planning and execution of these tasks, and can automatically adjust parameters to the personal profile and current context. We present motivational scenarios in three environments: a smart home, a smart office and a smart nursing home, detail the interfaces and executional paths in our platform and present a proof-of-concept implementation.

Evaluating fidelity of lossy compression on spatiotemporal data from an IoT enabled smart farm

As the volume of data collected by various IoT sensors used in smart farm applications increases, the storing and processing of big data for agricultural applications become a huge challenge. The insight of this paper is that lossy compression can unleash the power of compression to IoT because, as compared with its counterpart (a lossless one), it can significantly reduce the data volume when the spatiotemporal characteristics of IoT sensor data are properly exploited. However, lossy compression faces the challenge of compressing too much data thus losing data fidelity, which might affect the quality of the data and potential analytics outcomes. To understand the impact of lossy compression on IoT data management and analytics, we evaluated four classification algorithms with reconstructed agricultural sensor data based on various energy concentration. Specifically, we applied three transformation-based lossy compression mechanisms to five real-world weather datasets collected at different sampling granularities from IoT weather stations. Our experimental results indicate that there is a strong positive correlation between the concentrated energy of the transformed coefficients and the compression ratio as well as the data quality. While we observed a general trend where much higher compression ratios can be achieved at the cost of a decrease in quality, we also observed that the impact on the classification accuracy varies among the data sets and algorithms we evaluated. Lastly, we show that the sampling granularity also influences the data fidelity in terms of the prediction performance and compression ratio.

A Study of Prescriptive Analysis Framework for Human Care Services Based On CKAN Cloud

A number of sensor devices are widely distributed and used today owing to the accelerated development of IoT technology. In particular, this technological advancement has allowed users to carry IoT devices with more convenience and efficiency. Based on the IoT sensor data, studies are being actively carried out to recognize the current situation or to analyze and predict future events. However, research for existing smart healthcare services is focused on analyzing users’ behavior from single sensor data and is also focused on analyzing and diagnosing the current situation of the users. Therefore, a method for effectively managing and integrating a large amount of IoT sensor data has become necessary, and a framework considering data interoperability has become necessary. In addition, an analysis framework is needed not only to provide the analysis of the users’ environment and situation from the integrated data, but also to provide guide information to predict future events and to take appropriate action by users. In this paper, we propose a prescriptive analysis framework using a 5W1H method based on CKAN cloud. Through the CKAN cloud environment, IoT sensor data stored in individual CKANs can be integrated based on common concepts. As a result, it is possible to generate an integrated knowledge graph considering interoperability of data, and the underlying data is used as the base data for prescriptive analysis. In addition, the proposed prescriptive analysis framework can diagnose the situation of the users through analysis of user environment information and supports users’ decision making by recommending the possible behavior according to the coming situation of the users. We have verified the applicability of the 5W1H prescriptive analysis framework based on the use case of collecting and analyzing data obtained from various IoT sensors.

Rule Based Artificial Intelligent System of Cucumber Greenhouse Environment Control with IoT Technology

The method proposed here allows control cucumber greenhouse environment based on IoT technology. IoT sensors are to measure the room and air temperature, relative humidity, CO2 content, water supply, liquid fertilizer, water content. The basic system is rule based system. All the required rules to control the cucumber greenhouse environment are proposed here. Through regressive analysis between IoT sensor data and the harvest cucumber quality, it is found that the proposed rule based system is appropriate to control the cucumber greenhouse environment.