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The approach balances collection time within each cycle, reducing the probability of load concentration on specific times or nodes. 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Multimedia Communications  \nVolume 12 • Issue 2 • April-June 2021  \nA Sensor Data Stream Collection Scheme Considering Phase Differences for Load Balancing  \nTomoya Kawakami, Graduate School of Engineering, University of Fukui, Japan Tomoki Yoshihisa, Cybermedia Center, Osaka University, Japan  \nYuuichi Teranishi, National Institute of Information and Communications Technology, Japan  \nABSTRACT  \nIn the internet of things (IoT), various devices (things) including sensors generate data and publish them via the internet. The authors define continuous sensor data with difference cycles as a sensor data stream and have proposed methods to collect distributed sensor data streams. In this paper, the authors describe a skip graph-based collection scheme for sensor data streams considering phase differences. In the proposed scheme considering phase differences, the collection time is balanced within each collection cycle by the phase differences, and the probability of load concentration to the specific time or node is decreased. The simulation results show that the proposed scheme can equalize the loads of nodes even if the distribution of collection cycles is not uniform.  \nKeywoRDS  \nCollection Cycle, DAT, Data Aggregation, Distributed Aggregation Tree, Load Distribution, Phase Shifting, Skip Graphs, Structured Overlay Network, TBPS, Topic-Based Pub/Sub  \nINTRoDUCTIoN  \nThe Internet of Things (IoT) (Hodges et al. , 2013) has attracted greater interest and attention with the spread of network-connected small devices such as sensors, smartphones, and wearable devices. In the data science field, stream data generated from IoT devices are analyzed to get various information. A larger amount of data can lead to higher-quality information such a faster stream data collection is one of the main techniques in the data science field and various schemes have been proposed. To enable IoT applications for data collection, pub/sub messaging (Eugster et al. , 2003) is considered to be a promising event delivery method that can achieve the asynchronous dissemination and collection of information in real-time in a loosely-coupled environment. For example, the sensor devices correspond to publishers, and the IoT application corresponds to a subscriber. Topic-Based Pub/Sub (TBPS) protocols are already widely utilized by many IoT applications (Teranishi et al. , 2015; Teranishi et al. , 2017) . These systems have a broker server for managing topics. The broker gathers all the published messages and forwards them to the corresponding subscribers.  \nDOI: 10.4018/IJMCMC.20210401.oa1  \nThis article published as an Open Access article distributed under the terms of the Creative Commons Attribution License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.  \nInternational Journal of Mobile Computing and Multimedia Communications  \nVolume 12 • Issue 2 • April-June 2021  \nIn IoT and Big Data applications, collecting all of the raw (unfiltered) sensor data is important for conducting various forms of analysis (Bessis et al., 2014) . In this case, the larger the number of sensors treated on the application for analysis, the larger the number of messages that need to be received per time unit on the broker and subscribers in TBPS. For example, when the publishers correspond to a certain kind of sensor which publishes sensor data every 10 seconds and the number of target sensors in an application is 10,000, the broker must receive 1,000 messages per second on average. Thus, the number of messages tends to explode on the broker and the subscribers in IoT and Big Data applications. In general, the number of sent/received messages per unit of time affects the network process load because tasks such as adding/r","cbCait3VOnVksHKN","https://ap.wps.com/l/cbCait3VOnVksHKN","pdf",2485273,15,"English","# Abstract\n# Introduction\n## IoT data collection and TBPS background\n## Scalability challenges in brokers and subscribers\n# Sensor data stream definition and related work","[{\"question\":\"How does the paper define a sensor data stream?\",\"answer\":\"It defines continuous sensor data with difference cycles as a sensor data stream.\"},{\"question\":\"What is the main idea of the proposed collection scheme?\",\"answer\":\"It uses a skip graph-based collection scheme that considers phase differences to balance collection time within each cycle and reduce load concentration.\"},{\"question\":\"What do the simulation results show?\",\"answer\":\"The scheme equalizes node loads even when the distribution of collection cycles is not uniform.\"}]","A Sensor Data Stream Collection Scheme Considering Phase Differences for Load Balancing | PDF",38]