Name
Title | ||
|---|---|---|
Artificial Intelligence Aware And Security-Enhanced Traceback Technique In Mobile Edge Computing |
Abstract | ||
|---|---|---|
Sensor network, as one component of mobile edge computing (MEC), is a promising platform to provide services for users. With the development of artificial intelligence (AI) applications, the integration of mobile edge computing and AI unlocks unlimited possibilities in people's daily lives. However, AI techniques and mechanisms specifically designed for the devices and servers operating in the mobile edge computing environment face secure challenge. To improve the security of wireless network, a security-enhanced traceback (SET) scheme is proposed. Firstly, the network is divided into three areas, nodes in different areas adopt different marking probability. Nodes in the area far from the sink adopt higher marking probability, nodes in the area nearest to the sink adopt lower marking probability to save energy. Secondly, the marking tuple of data packets is not only stored in nodes, but also is migrated to nodes far from the sink to balance the storage space of nodes. The results of both theoretical analysis and extensive experimental simulations indicate that the network performance of SET scheme is better than the existing traceback scheme. |
Year | DOI | Venue |
|---|---|---|
2020 | 10.1016/j.comcom.2020.08.006 | COMPUTER COMMUNICATIONS |
Keywords | DocType | Volume |
Artificial intelligence, Mobile computing, Traceback, Probability marking and migrating, Lifetime | Journal | 161 |
ISSN | Citations | PageRank |
0140-3664 | 0 | 0.34 |
References | Authors | |
0 | 5 | |