• Title/Summary/Keyword: IoT Data

Search Result 50, Processing Time 0.293 seconds

A Framework for Internet of Things (IoT) Data Management

  • Kim, Kyung-Chang
    • Journal of the Korea Society of Computer and Information
    • /
    • v.24 no.3
    • /
    • pp.159-166
    • /
    • 2019
  • The collection and manipulation of Internet of Things (IoT) data is increasing at a fast pace and its importance is recognized in every sector of our society. For efficient utilization of IoT data, the vast and varied IoT data needs to be reliable and meaningful. In this paper, we propose an IoT framework to realize this need. The IoT framework is based on a four layer IoT architecture onto which context aware computing technology is applied. If the collected IoT data is unreliable it cannot be used for its intended purpose and the whole service using the data must be abandoned. In this paper, we include techniques to remove uncertainty in the early stage of IoT data capture and collection resulting in reliable data. Since the data coming out of the various IoT devices have different formats, it is important to convert them into a standard format before further processing, We propose the RDF format to be the standard format for all IoT data. In addition, it is not feasible to process all captured Iot data from the sensor devices. In order to decide which data to process and understand, we propose to use contexts and reasoning based on these contexts. For reasoning, we propose to use standard AI and statistical techniques. We also propose an experiment environment that can be used to develop an IoT application to realize the IoT framework.

OLAP-based Big Table Generation for Efficient Analysis of Large-sized IoT Data (대용량 IoT 데이터의 빠른 분석을 위한 OLAP 기반의 빅테이블 생성 방안)

  • Lee, Dohoon;Jo, Chanyoung;On, Byung-Won
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.2-5
    • /
    • 2021
  • With the recent development of the Internet of Things (IoT) technology, various terminals are being connected to the Internet. As a result, the amount of IoT data is also increasing, and an index key that can efficient analyze the large-scale IoT data is proposed. Existing index keys have only time and space information, so if data stored in index tables and instance tables were queried using repetition or join operation, IoT data was embedded in the index key of the proposal to create OLAP-based big tables to minimize the number of repetitions or join times.

  • PDF

Design of Context Aware IoT Data Management System based on Ontology (온톨로지 기반 상황인지 IoT 데이터 관리 시스템 설계)

  • Choi, Kang-im;Choi, Young-keun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.331-332
    • /
    • 2016
  • In the IoT environment, The mass IoT data can be collected in a variety of IoT devices. Due to mass IoT data, the services for data processing and providing are needed that fit user requirements. So we propose the design of IoT data management system based on ontology using context information.

  • PDF

An Unified Spatial Index and Visualization Method for the Trajectory and Grid Queries in Internet of Things

  • Han, Jinju;Na, Chul-Won;Lee, Dahee;Lee, Do-Hoon;On, Byung-Won;Lee, Ryong;Park, Min-Woo;Lee, Sang-Hwan
    • Journal of the Korea Society of Computer and Information
    • /
    • v.24 no.9
    • /
    • pp.83-95
    • /
    • 2019
  • Recently, a variety of IoT data is collected by attaching geosensors to many vehicles that are on the road. IoT data basically has time and space information and is composed of various data such as temperature, humidity, fine dust, Co2, etc. Although a certain sensor data can be retrieved using time, latitude and longitude, which are keys to the IoT data, advanced search engines for IoT data to handle high-level user queries are still limited. There is also a problem with searching large amounts of IoT data without generating indexes, which wastes a great deal of time through sequential scans. In this paper, we propose a unified spatial index model that handles both grid and trajectory queries using a cell-based space-filling curve method. also it presents a visualization method that helps user grasp intuitively. The Trajectory query is to aggregate the traffic of the trajectory cells passed by taxi on the road searched by the user. The grid query is to find the cells on the road searched by the user and to aggregate the fine dust. Based on the generated spatial index, the user interface quickly summarizes the trajectory and grid queries for specific road and all roads, and proposes a Web-based prototype system that can be analyzed intuitively through road and heat map visualization.

A Graph Model of Heterogeneous IoT Data Representation : A Case Study from Smart Campus Management (이종 IoT 데이터 표현을 위한 그래프 모델: 스마트 캠퍼스 관리 사례 연구)

  • Nguyen, Van-Quyet;Nguyen, Huu-Duy;Nguyen, Giang-Truong;Kim, Kyungbaek
    • Proceedings of the Korea Information Processing Society Conference
    • /
    • /
    • pp.984-987
    • /
    • 2018
  • In an Internet of Thing (IoT) environment, entities with different attributes and capacities are going to be connected in a highly connected fashion. Specifically, not only the mechanical and electronic devices but also other entities such as people, locations and applications are connected to each other. Understanding and managing these connections play an important role for businesses, which identify opportunities for new IoT services. Traditional approach for storing and querying IoT data is used of a relational database management system (RDMS) such as MySQL or MSSQL. However, using RDMS is not flexible and sufficient for handling heterogeneous IoT data because these data have deeply complex relationships which require nested queries and complex joins on multiple tables. In this paper, we propose a graph model for constructing a graph database of heterogeneous IoT data. Graph databases are purposely-built to store highly connected data with nodes representing entities and edges representing the relationships between these entities. Our model fuses social graph, spatial graph, and things graph, and incorporates the relationships among them. We then present a case study which applies our model for representing data from a Smart Campus using Neo4J platform. Through the results of querying to answer real questions in Smart Campus management, we show the viability of our model.

Analysis Process based on Modify K-means for Efficiency Improvement of Electric Power Data Pattern Detection (전력데이터 패턴 추출의 효율성 향상을 위한 변형된 K-means 기반의 분석 프로세스)

  • Jung, Se Hoon;Shin, Chang Sun;Cho, Yong Yun;Park, Jang Woo;Park, Myung Hye;Kim, Young Hyun;Lee, Seung Bae;Sim, Chun Bo
    • Journal of Korea Multimedia Society
    • /
    • v.20 no.12
    • /
    • pp.1960-1969
    • /
    • 2017
  • There have been ongoing researches to identify and analyze the patterns of electric power IoT data inside sensor nodes to supplement the stable supply of power and the efficiency of energy consumption. This study set out to propose an analysis process for electric power IoT data with the K-means algorithm, which is an unsupervised learning technique rather than a supervised one. There are a couple of problems with the old K-means algorithm, and one of them is the selection of cluster number K in a heuristic or random method. That approach is proper for the age of standardized data. The investigator proposed an analysis process of selecting an automated cluster number K through principal component analysis and the space division of normal distribution and incorporated it into electric power IoT data. The performance evaluation results show that it recorded a higher level of performance than the old algorithm in the cluster classification and analysis of pitches and rolls included in the communication bodies of utility poles.

Analysis Model Evaluation based on IoT Data and Machine Learning Algorithm for Prediction of Acer Mono Sap Liquid Water

  • Lee, Han Sung;Jung, Se Hoon
    • Journal of Korea Multimedia Society
    • /
    • v.23 no.10
    • /
    • pp.1286-1295
    • /
    • 2020
  • It has been increasingly difficult to predict the amounts of Acer mono sap to be collected due to droughts and cold waves caused by recent climate changes with few studies conducted on the prediction of its collection volume. This study thus set out to propose a Big Data prediction system based on meteorological information for the collection of Acer mono sap. The proposed system would analyze collected data and provide managers with a statistical chart of prediction values regarding climate factors to affect the amounts of Acer mono sap to be collected, thus enabling efficient work. It was designed based on Hadoop for data collection, treatment and analysis. The study also analyzed and proposed an optimal prediction model for climate conditions to influence the volume of Acer mono sap to be collected by applying a multiple regression analysis model based on Hadoop and Mahout.

Efficient LSTM Configuration in IoT Environment (IoT 환경에서의 효율적인 LSTM 구성)

  • Lee, Jongwon;Hwang, Chulhyun;Lee, Sungock;Song, Hyunok;Jung, Hoekyung
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.345-346
    • /
    • 2018
  • Internet of Things (IoT) data is collected in real time and is treated as highly reliable data because of its high precision. However, IoT data is not always highly reliable data. Because, data be often incomplete values for reasons such as sensor aging and failure, poor operating environment, and communication problems. So, we propose the methodology for solve this problem. Our methodology implements multiple LSTM networks to individually process the data collected from the sensors and a single LSTM network that batches the input data into an array. And, we propose an efficient method for constructing LSTM in IoT environment.

  • PDF

Privacy-Preserving IoT Data Collection in Fog-Cloud Computing Environment

  • Lim, Jong-Hyun;Kim, Jong Wook
    • Journal of the Korea Society of Computer and Information
    • /
    • v.24 no.9
    • /
    • pp.43-49
    • /
    • 2019
  • Today, with the development of the internet of things, wearable devices related to personal health care have become widespread. Various global information and communication technology companies are developing various wearable health devices, which can collect personal health information such as heart rate, steps, and calories, using sensors built into the device. However, since individual health data includes sensitive information, the collection of irrelevant health data can lead to personal privacy issue. Therefore, there is a growing need to develop technology for collecting sensitive health data from wearable health devices, while preserving privacy. In recent years, local differential privacy (LDP), which enables sensitive data collection while preserving privacy, has attracted much attention. In this paper, we develop a technology for collecting vast amount of health data from a smartwatch device, which is one of popular wearable health devices, using local difference privacy. Experiment results with real data show that the proposed method is able to effectively collect sensitive health data from smartwatch users, while preserving privacy.

IoT Data Processing Framework Design using Mobile Agent in Private Cloud Environment (Private Cloud 환경 내에서 모바일 에이전트를 이용한 IoT 데이터 처리 프레임워크 설계)

  • Choi, Kang-im;Choi, Young-keun
    • Proceedings of the Korean Institute of Information and Commucation Sciences Conference
    • /
    • /
    • pp.329-330
    • /
    • 2016
  • In the Private Cloud Environment, the data are collected by a variety of IoT devices and Cloud service users be provided with a variety of data collected. To take advantage of this convenient data, we propose the IoT data Processing framework using mobile agent.

  • PDF