Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities
نویسندگان
چکیده
منابع مشابه
Benchmarking Distributed Stream Processing Platforms for IoT Applications
Internet of Things (IoT) is a technology paradigm where millions of sensors monitor, and help inform or manage, physical, environmental and human systems in real-time. The inherent closed-loop responsiveness and decision making of IoT applications makes them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSPS) are becoming...
متن کاملProcessing IoT Data with Cloud Computing for Smart Cities
A smart city requires the intelligent management of infrastructure like the Internet of Things (IoT) devices in order to provide smart services that improve the quality of human life. To obtain the information needed to implement smart city services, stream reasoning is used to intelligently process the big data stream constantly generated from IoT devices. However, there are constraints associ...
متن کاملEnabling Social- and Location-Aware IoT Applications in Smart Cities
In the last decade, governments, municipalities, and industries have invested large amounts of funds on research on smart cities with the main goal of developing services to improve people’s quality of life. Many proposals focus on a Cloud-centric network architecture in which all the data collected from a myriad of sensors devices is transferred to the Cloud for processing. However, this appro...
متن کاملRIoTBench: An IoT benchmark for distributed stream processing systems
The Internet of Things (IoT) is an emerging technology paradigm where millions of sensors and actuators help monitor and manage, physical, environmental and human systems in real-time. The inherent closedloop responsiveness and decision making of IoT applications make them ideal candidates for using low latency and scalable stream processing platforms. Distributed Stream Processing Systems (DSP...
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ژورنال
عنوان ژورنال: Journal of Big Data
سال: 2019
ISSN: 2196-1115
DOI: 10.1186/s40537-019-0215-2