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.

[1]  Ee-Chien Chang,et al.  Processing of Mixed-Sensitivity Video Surveillance Streams on Hybrid Clouds , 2014, 2014 IEEE 7th International Conference on Cloud Computing.

[2]  Li Qi,et al.  Fault-Tolerant Video Analysis Cloud Scheduling Mechanism , 2013, 2013 International Conference on Virtual Reality and Visualization.

[3]  Rittwik Jana,et al.  Exploiting virtualization for delivering cloud-based IPTV services , 2011, 2011 IEEE Conference on Computer Communications Workshops (INFOCOM WKSHPS).

[4]  J. Lilius,et al.  Stream-Based Admission Control and Scheduling for Video Transcoding in Cloud Computing , 2013, 2013 13th IEEE/ACM International Symposium on Cluster, Cloud, and Grid Computing.

[5]  Zhen Zhao Scheduling policy analysis of cloud video service , 2014, 2014 IEEE Global Communications Conference.

[6]  Sudhir Rao Rupanagudi,et al.  A novel cloud computing based smart farming system for early detection of borer insects in tomatoes , 2015, 2015 International Conference on Communication, Information & Computing Technology (ICCICT).

[7]  Hong Jiang,et al.  Meeting service level agreement cost-effectively for video-on-demand applications in the cloud , 2014, IEEE INFOCOM 2014 - IEEE Conference on Computer Communications.

[8]  Chia-han Lee,et al.  Distributed computing in IoT: System-on-a-chip for smart cameras as an example , 2015, The 20th Asia and South Pacific Design Automation Conference.

[9]  Ayse Tugba Dosdogru,et al.  Process plan and part routing optimization in a dynamic flexible job shop scheduling environment: an optimization via simulation approach , 2012, Neural Computing and Applications.

[10]  Anang Hudaya Muhamad Amin,et al.  Cloudlet-based cyber foraging framework for distributed video surveillance provisioning , 2014, 2014 4th World Congress on Information and Communication Technologies (WICT 2014).

[11]  Paul Fischer,et al.  Load Scheduling in a Cloud Based Massive Video-Storage Environment , 2014, 2014 16th International Symposium on Symbolic and Numeric Algorithms for Scientific Computing.