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Weather Ontology System in IoT Middleware
Yujin Kim, Soobin Jeon, Inbum Jung
http://doi.org/10.5626/JOK.2019.46.1.97
With the growing importance of weather information, the number of weather information application systems has been increased. Unfortunately, the weather application systems that currently exist neither efficiently store nor manage the vast amount of weather data obtained from various weather sensors. Additionally, they do not utilize the properties contained in the weather data making it difficult to perform intelligent searches using the semantic information present in the weather data. In this paper as a solution to the challenges mentioned, an ontology system for weather information management is constructed using IoT middleware MinT. Based on weather ontology, it is possible to efficiently and easily manage large amounts of weather sensing data by applying ontology in the Internet of Things middleware. Moreover, since inference engine and rule-base information are used, semantic properties are applied to the sensing data collected. The implemented weather ontology uses sensing data and easily provides search results to users through a UI. The usability to search results is selected as the metric for performance evaluation. In the experiments, the proposed weather ontology system exhibited high usability to search results.
Cluster Property based Data Transfer for Efficient Energy Consumption in IoT
Chungsan Lee, Soobin Jeon, Inbum Jung
http://doi.org/10.5626/JOK.2017.44.9.966
In Internet of Things (IoT), the aim of the nodes (called ‘Things’) is to exchange information with each other, whereby they gather and share information with each other through self decision-making. Therefore, we cannot apply existing aggregation algorithms of Wireless sensor networks that aim to transmit information to only a sink node or a central server, directly to the IoT environment. In addition, since existing algorithms aggregate information from all sensor nodes, problems can arise including an increasing number of transmissions and increasing transmission delay and energy consumption. In this paper, we propose the clustering and property based data exchange method for energy efficient information sharing. First, the proposed method assigns the properties of each node, including the sensing data and unique resource. The property determines whether the node can respond to the query requested from the other node. Second, a cluster network is constructed considering the location and energy consumption. Finally, the nodes communicate with each other efficiently using the properties. For the performance evaluation, TOSSIM was used to measure the network lifetime and average energy consumption.
Analysis of Energy Consumption and Processing Delay of Wireless Sensor Networks according to the Characteristic of Applications
Chong Myung Park, Young Tak Han, Soobin Jeon, Inbum Jung
Wireless sensor networks are used for data collection and processing from the surrounding environment for various applications. Since wireless sensor nodes operate on low computing power, restrictive battery capacity, and low network bandwidth, their architecture model has greatly affected the performance of applications. If applications have high computation complexity or require the real-time processing, the centralized architecture in wireless sensor networks have a delay in data processing. Otherwise, if applications only performed simple data collection for long period, the distributed architecture wasted battery energy in wireless sensors. In this paper, the energy consumption and processing delay were analyzed in centralized and distributed sensor networks. In addition, we proposed a new hybrid architecture for wireless sensor networks. According to the characteristic of applications, the proposed method had the optimal number of wireless sensors in wireless sensor networks.
Distributed Computing Models for Wireless Sensor Networks
Chongmyung Park, Chungsan Lee, Youngtae Jo, Inbum Jung
Wireless sensor networks offer a distributed processing environment. Many sensor nodes are deployed in fields that have limited resources such as computing power, network bandwidth, and electric power. The sensor nodes construct their own networks automatically, and the collected data are sent to the sink node. In these traditional wireless sensor networks, network congestion due to packet flooding through the networks shortens the network life time. Clustering or in-network technologies help reduce packet flooding in the networks. Many studies have been focused on saving energy in the sensor nodes because the limited available power leads to an important problem of extending the operation of sensor networks as long as possible. However, we focus on the execution time because clustering and local distributed processing already contribute to saving energy by local decision-making. In this paper, we present a cooperative processing model based on the processing timeline. Our processing model includes validation of the processing, prediction of the total execution time, and determination of the optimal number of processing nodes for distributed processing in wireless sensor networks. The experiments demonstrate the accuracy of the proposed model, and a case study shows that our model can be used for the distributed application.
Modeling and Analysis of Vehicle Detection Using Roadside Ultrasonic Sensors in Wireless Sensor Networks
To address the problems of existing traffic information acquisition systems such as high cost and low scalability, wireless sensor networks (WSN) based traffic information acquisition systems have been studied. WSN based systems have many benefits including high scalability and low maintenance cost. Recently, various sensors are studied for traffic surveillance based on WSN, such as magnetic, acoustic, and accelerometer sensors. However, ultrasonic sensor based systems have not been studied. There are many issues for WSN based systems, such as battery driven operation and low computing power. Thus, power saving methods and specific algorithms with low complexity are necessary. In this paper, we introduce optimal methodologies for power saving of ultrasonic sensors based on the modeling and analysis in detail. Moreover, a new vehicle detection algorithm for low complexity using ultrasonic data is presented. The proposed methodologies are implemented in a tiny microprocessor. The evaluation results show that our algorithm has high detection accuracy.
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