Secure and Reliable Resource Allocation and Caching in Aerial-Terrestrial Cloud Networks (ATCNs)
- Authors
- Sharma, Vishal; You, Ilsun; Seo, Jung Taek; Guizani, Mohsen
- Issue Date
- Jan-2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Security; reliability; content caching; ATCNs; STINs; cog-chain
- Citation
- IEEE ACCESS, v.7, pp.13867 - 13881
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 13867
- End Page
- 13881
- URI
- https://scholarworks.bwise.kr/gachon/handle/2020.sw.gachon/80205
- DOI
- 10.1109/ACCESS.2019.2893775
- ISSN
- 2169-3536
- Abstract
- Aerial-terrestrial cloud networks (ATCNs), global integration of air and ground communication systems, pave a way for a large set of applications such as surveillance, on-demand transmissions, data-acquisition, and navigation. However, such networks suffer from crucial challenges of secure and reliable resource allocation and content-caching as the involved entities are highly dynamic and there is no fine-tuned strategy to accommodate their connectivity. To resolve this quandary, cog-chain, a novel paradigm for secure and reliable resource allocation and content-caching in ATCNs, is presented. Various requirements, key concepts, and issues with ATCNs are also presented along with basic concepts to establish a cog-chain in ATCNs. Feed and fetch modes are utilized depending on the involved entities and caching servers. In addition, a cog-chain communication protocol is presented which avails to evaluate the formation of a virtual cog-chain between the nodes and the content-caching servers. The efficacy of the proposed solution is demonstrated through consequential gains observed for signaling overheads, computational time, reliability, and resource allocation growth. The proposed approach operates with the signaling overheads ranging between 30.36 and 303.6 bytes*hops/sec and the formation time between 186 and 195 ms. Furthermore, the overall time consumption is 83.33% lower than the sequential-verification model and the resource allocation growth is 27.17% better than the sequential-verification model.
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