IoDAPM: A Reinforcement Learning Approach for Dynamic Assignment of Protection Mechanisms in IoD

Author(s)

Svaigen, Alisson R. ; Boukerche, Azzedine ; Ruiz, Linnyer B. ; Loureiro, Antonio A. F.

• Document type: Journal article
• Source: PROCEEDINGS OF THE INT'L ACM CONFERENCE ON MODELING, ANALYSIS AND SIMULATION OF WIRELESS AND MOBILE SYSTEMS, MSWIM 2023; v. N/A, p. 8-pg., 2023-01-01.

Abstract

Location Privacy Protection Mechanisms (LPPMs) have been designed to enhance privacy in the Internet of Drones (IoD), however, they present suitable privacy levels only in specific network conditions. Also, they can lead to a lack of Quality of Service (QoS) if applied in unfavorable conditions. Thus, the dynamic assignment of the most suitable LPPM, given the IoD conditions, is a significant challenge. Reinforcement Learning (RL) represents a useful concept to handle this problem, given its exploratory characteristics and being able to enhance the knowledge about network dynamics. In this study, we propose IoDAPM, an RL-based approach for the Dynamic Assignment of Protection Mechanisms in IoD. Through simulations, we extensively trained the RL-based model, exploring the possible IoD network conditions. With this model, we carried out a comparative evaluation of existing LPPMs. The results highlighted that IoDAPM outperforms the compared mechanisms considering the QoS, providing enhanced performance regarding location privacy, energy efficiency, and flight delay. (AU)

• FAPESP's process: 15/24494-8 - Communications and processing of big data in cloud and fog computing
• Grantee: Nelson Luis Saldanha da Fonseca
• Support Opportunities: Research Projects - Thematic Grants
• FAPESP's process: 18/23064-8 - Mobility in urban computing: characterization, modeling and applications (MOBILIS)
• Grantee: Antonio Alfredo Ferreira Loureiro
• Support Opportunities: Research Projects - Thematic Grants