- Title
- Real-time dissemination of emergency warning messages in 5G enabled selfish vehicular social networks
- Creator
- Ullah, Noor; Kong, Xiangjie; Lin, Limei; Alrashoud, Mubarak; Tolba, Amr; Xia, Feng
- Date
- 2020
- Type
- Text; Journal article
- Identifier
- http://researchonline.federation.edu.au/vital/access/HandleResolver/1959.17/173764
- Identifier
- vital:14745
- Identifier
-
https://doi.org/10.1016/j.comnet.2020.107482
- Identifier
- ISBN:1389-1286 (ISSN)
- Abstract
- This paper addresses the issues of selfishness, limited network resources, and their adverse effects on real-time dissemination of Emergency Warning Messages (EWMs) in modern Autonomous Moving Platforms (AMPs) such as Vehicular Social Networks (VSNs). For this purpose, we propose a social intelligence based identification mechanism to differentiate between a selfish and a cooperative node in the network. Therefore, we devise a crowdsensing based mechanism to calculate a tie-strength value based on several social metrics. Moreover, we design a recursive evolutionary algorithm for each node's reputation calculation and update. Given that, then we estimate each node's state-transition probability to select a super-spreader for rapid dissemination. In order to ensure a seamless and reliable dissemination process, we incorporate 5G network structure instead of conventional short range communication which is used in most vehicular networks at present. Finally, we design a real-time dissemination algorithm for EWMs and evaluate its performance in terms of network parameters such as delivery-ratio, delay, hop-count, and message-overhead for varying values of vehicular density, speed, and selfish nodes’ density based on realistic vehicular mobility traces. In addition, we present a comparative analysis of the performance of the proposed scheme with state-of-the-art dissemination schemes in VSNs. © 2020 Elsevier B.V.
- Publisher
- Elsevier B.V.
- Relation
- Computer Networks Vol. 182, no. (2020), p.
- Rights
- Copyright © 2020 Elsevier B.V. All rights reserved.
- Rights
- This metadata is freely available under a CCO license
- Rights
- Open Access
- Subject
- 08 Information and Computing Sciences; 09 Engineering; 10 Technology; Artificial Intelligence; Autonomous Moving Platform; Emergency Warning Messages; Selfishness; Vehicular Social Networks
- Full Text
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