| Title |
Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities |
| ID_Doc |
39602 |
| Authors |
Nasiri, H; Nasehi, S; Goudarzi, M |
| Title |
Evaluation of distributed stream processing frameworks for IoT applications in Smart Cities |
| Year |
2019 |
| Published |
Journal Of Big Data, 6, 1 |
| DOI |
10.1186/s40537-019-0215-2 |
| Abstract |
The widespread growth of Big Data and the evolution of Internet of Things (IoT) technologies enable cities to obtain valuable intelligence from a large amount of real-time produced data. In a Smart City, various IoT devices generate streams of data continuously which need to be analyzed within a short period of time; using some Big Data technique. Distributed stream processing frameworks (DSPFs) have the capacity to handle real-time data processing for Smart Cities. In this paper, we examine the applicability of employing distributed stream processing frameworks at the data processing layer of Smart City and appraising the current state of their adoption and maturity among the IoT applications. Our experiments focus on evaluating the performance of three DSPFs, namely Apache Storm, Apache Spark Streaming, and Apache Flink. According to our obtained results, choosing a proper framework at the data analytics layer of a Smart City requires enough knowledge about the characteristics of target applications. Finally, we conclude each of the frameworks studied here have their advantages and disadvantages. Our experiments show Storm and Flink have very similar performance, and Spark Streaming, has much higher latency, while it provides higher throughput. |
| Author Keywords |
Distributed stream processing; Smart City; IoT applications; Latency; Throughput |
| Index Keywords |
Index Keywords |
| Document Type |
Other |
| Open Access |
Open Access |
| Source |
Science Citation Index Expanded (SCI-EXPANDED) |
| EID |
WOS:000599135100001 |
| WoS Category |
Computer Science, Theory & Methods |
| Research Area |
Computer Science |
| PDF |
https://journalofbigdata.springeropen.com/track/pdf/10.1186/s40537-019-0215-2
|