Issue No. 01 - (preprint vol. )
Muhammad Usman , Department of Computer Science and Software Engineering, Swinburne University of Technology, 3783 Hawthorn, Victoria Australia (e-mail: firstname.lastname@example.org)
Mian Ahmad Jan , Computer Science, Abdul Wali Khan University, 208933 Mardan, Khyber Pukhtoonkhwa (KPK) Pakistan (e-mail: email@example.com)
Xiangjian He , Faculty of Computing and Communications, University of Technology Sydney, Sydney, New South Wales Australia (e-mail: Xiangjian.He@uts.edu.au)
Jinjun Chen , Faculty of Science, Engineering and Technology, Swinburne University of Technology, Australia, Melbourne, Victoria Australia 3122 (e-mail: firstname.lastname@example.org)
Wireless Multimedia Sensor Networks (WMSNs) produce an enormous amount of big multimedia data. Due to large size, Multimedia Sensor Nodes (MSNs) cannot store generated multimedia data for a long time. In this scenario, mobile sinks can be utilized for data collection. However, due to vulnerable nature of wireless networks, there is a need for an efficient security scheme to authenticate both MSNs and mobile sinks. In this paper, we propose a scheme to protect an underlying WMSN during mobile multimedia data collection. The proposed scheme is a two-layer scheme. At the first layer, all MSNs are distributed into small clusters, where each cluster is represented by a single Cluster Head (CH). At the second layer, all CHs verify identities of mobile sinks before sharing multimedia data. Authentication at both layers ensures a secure data exchange. We evaluate the performance of proposed scheme through extensive simulation results. The simulation results prove that the proposed scheme performs better as compared to existing state-of-the-art approaches in terms of resilience and handshake duration. The proposed scheme is also analyzed in terms of authentication rate, data freshness, and packet delivery ratio, and has shown a better performance.
Authentication, Wireless sensor networks, Data collection, Servers, Computational modeling, Data models