| Abstract |
The Internet of Things (IoT) is developing rapidly, and it is estimated that by 2025, 22 billion active devices will be in the IoT (Lueth, 2018). Since it is impossible to let the often low-powered IoT devices perform all computing tasks, a common solution is to combine IoT and cloud computing (Sajid et al., 2016). However, cloud computing alone cannot satisfy all the needs from the IoT, and fog computing has been proposed to complement it (Bonomi et al., 2012). To make the most of the fog resources and maximise the efficiency, good resource allocation mechanisms for fog computing are needed. To address these challenges, researchers have proposed many resource allocation mechanisms for fog computing or similar computing paradigms in order to save energy, reduce cost or improve quality of service (Aazam and Huh, 2015; Cardellini et al., 2015; Gu et al., 2018). However, most of these mechanisms were not specifically designed for settings where users act strategically to maximise their utility. To address this problem, some researchers have proposed truthful mechanisms that incentivise users to truthfully reveal their private information (Chawla et al., 2017; Zhu et al., 2018). However, these approaches cannot be applied directly to our model due to subtle but important differences. |