A Scheme to Improve Stream Transaction Rates for Real-Time IoT Applications
Due to the recent proliferation of IoT (Internet of Things) devices, a large amount of stream data such as video data or sensor data is transmitted to remote processing computers. The stream transaction rate is one of the main factors to improve the performance of some IoT applications. For instance, in surveillance systems, the probability to catch a moving person increases as the processing computer analyzes video with a higher transaction rate. To improve stream transaction rates, some schemes reduce communication time between a processing computer and stream data sources. They target periodic stream transactions and assume static transmission intervals. However, the communication and transaction time changes dynamically. Therefore, stream transaction rates can be further improved by changing transmission intervals dynamically depending on it. In this paper, we propose a scheme to improve stream transaction rates by changing transmission intervals dynamically. In our proposed scheme, a processing computer sometimes changes transmission intervals to be the same length as the average transaction time. Moreover, our proposed scheme adopts a progressive quality improvement (PQI) approach to reduce communication and transaction time. Our evaluation results revealed that the proposed scheme can achieve a higher stream transaction rate than a conventional scheme with the PQI approach.
This work was supported in part by JSPS KAKENHI Grant Numbers JP17K00146 and JP18K11316 and by Research Grant of Kayamori Foundation of Informational Science Advancement. Also this work include the result of NICT \(\cdot \) Osaka University joint research “research and development of advanced network platform technology for large scale distributed computing”.
- 1.Yu, S., Tian, T., Zhou, J., Guo, H.: An adaptive packet transmission model for real-time embedded network streaming server. In: Proceedings of the IEEE International Conference on Audio, Language and Image Processing, Shanghai, pp. 848–853 (2008)Google Scholar
- 3.Kanzaki, H., Schubert, K., Bambos, N.: Video streaming schemes for industrial IoT. In: Proceedings of the IEEE International Conference on Computer Communication and Networks (ICCCN), Vancouver, pp. 1–7 (2017)Google Scholar
- 4.Agrawal, U.A., Jani, P.V.: Performance analysis of real time object tracking system based on compressive sensing. In: Proceedings of the IEEE International Conference on Signal Processing, Computing and Control (ISPCC), Solan, pp. 187–193 (2017)Google Scholar
- 5.Ortega, A., Khansari, M.: Rate control for video coding over variable bit rate channels with applications to wireless transmission. In: Proceedings of the IEEE International Conference on Image Processing, Washington, pp. 388–391 (1995)Google Scholar
- 6.Incel, O.D., Krishnamachari, B.: Enhancing the data collection rate of tree-based aggregation in wireless sensor networks. In: Proceedings of the IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks, San Francisco, pp. 569–577 (2008)Google Scholar
- 7.Xhafa, F., Naranjo, V., Caballé, S., Barolli, L.: A software chain approach to big data stream processing and analytics. In: Proceedings of the IEEE International Conference on Complex, Intelligent, and Software Intensive Systems, Blumenauv, pp. 179–186 (2015)Google Scholar
- 8.Papadopoulos, G.Z., Pappas, N., Gallais, A., Noel, T., Angelakis, V.: Distributed adaptive scheme for reliable data collection in fault tolerant WSNs. In: Proceedings of the IEEE 2nd World Forum on Internet of Things (WF-IoT), Milan, pp. 116–121 (2015)Google Scholar
- 9.Beard, J.C., Chamberlain, R.D.: Analysis of a simple approach to modeling performance for streaming data applications. In: Proceedings of the IEEE 21st International Symposium on Modelling, Analysis and Simulation of Computer and Telecommunication Systems, San Francisco, pp. 345–349 (2013)Google Scholar
- 10.Zhu, X., Huang, P., Han, S., Mok, A.K., Chen, D., Nixon, M.: MinMax: a sampling interval control algorithm for process control systems. In: Proceedings of the IEEE International Conference on Embedded and Real-Time Computing Systems and Applications, Seoul, pp. 68–77 (2012)Google Scholar
- 11.Dias, G.M., Nurchis, M., Bellalta, B.: Adapting sampling interval of sensor networks using on-line reinforcement learning. In: Proceedings of the IEEE 3rd World Forum on Internet of Things (WF-IoT), Reston, pp. 460–465 (2016)Google Scholar