Architecture and Scheduling Method of Cloud Video Surveillance System Based on IoT
To realize conveniently deployed video surveillance applications, this paper designs a cloud service system employing ubiquitously available IoT nodes. Considering limited capacity of each IoT node, this paper first describes the system architecture and operation procedure for application requests, and introduces the design of scheduler’s function and typical video processing algorithms. Further, for decreasing transmission conflicts among video/image processor nodes, this paper proposes a scheduling methods based on Genetic Algorithm to rationally utilize the cooperative IoT nodes. Simulation results show that, compared with common methods such as random scheduling and opportunity-balanced scheduling, this method yields much smaller processing delay and transmission delay, together with higher packet delivery ratio.
KeywordsVideo surveillance Internet of things Cloud computing Scheduling Transmission conflicts Video processing
Supported by the National Natural Science Foundation of China (61501337), the Scientific Research Foundation for the Returned Overseas Chinese Scholars from State Education Ministry of China, the Science and Technology Research Project of Education Department from Hubei Province of China (Q20141110, D20151106), Training Programs of Innovation and Entrepreneurship for Undergraduates of Hubei Province, China (201410488046) and College Students’ Renovation Foundation of Wuhan University of Science and Technology, China (14ZRA140).
- 1.Aggarwal, V., Chen, X., Gopalakrishnan, V., Jana, R.: Exploiting virtualization for delivering cloud-based IPTV services. In: Computer Communications Workshops of IEEE INFOCOM, Shanghai, China, pp. 637–641 (2011)Google Scholar
- 2.Zhang, C.W., Chang, E.C.: Processing of mixed-sensitivity video surveillance streams on hybrid clouds. In: IEEE 7th International Conference on Cloud Computing, Anchorage, Alaska, pp. 9–16 (2014)Google Scholar
- 3.Luo, Y.Q., Dai, J., Qi, L.: Fault-tolerant video analysis cloud scheduling mechanism. In: International Conference on Virtual Reality and Visualization, Xi’an, China, pp. 119–126 (2013)Google Scholar
- 4.Rupanagudi, S.R., Ranjani, B.S., Nagaraj, P., et al.: A novel cloud computing based smart farming system for early detection of borer insects in tomatoes. In: International Conference on Communication, Information and Computing Technology, Mumbai, India, pp. 87–94 (2015)Google Scholar
- 5.Ali, A.M.M., Ahmad, N.M., Amin, A.H.M.: Cloudlet-based cyber foraging framework for distributed video surveillance provisioning. In: 4th World Congress on Information and Communication Technologies, Malacca, Malaysia, pp. 199–204 (2014)Google Scholar
- 6.Chien, S.-Y., Chan, W.-K., Tseng, Y.-H., et al.: Distributed computing in IoT: system-on-a-chip for smart cameras as an example. In: 20th Asia and South Pacific Design Automation Conference, Chiba/Tokyo, Japan, pp. 130–135 (2015)Google Scholar
- 7.Zhao, Y.H., Jiang, H., Zhou, K., et al.: Meeting service level agreement cost-effectively for video-on-demand applications in the cloud. In: IEEE INFOCOM, Toronto, Canada, pp. 298–306 (2014)Google Scholar
- 9.Zhao, Z.: Scheduling policy analysis of cloud video service. In: IEEE GLOBECOM, Austin, Texas, pp. 1329–1335 (2014)Google Scholar
- 11.Ashraf, A., Jokhio, F., Deneke, T., et al.: Stream-based admission control and scheduling for video transcoding in cloud computing. In: 13th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing, Delft, Netherlands, pp. 482–489 (2013)Google Scholar
- 12.Bayyapu, K.R., Fischer, P.: Load scheduling in a cloud based massive video-storage environment. In: 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing, Timisoara, Romania, pp. 349–356 (2014)Google Scholar