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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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