Incentivized Security-Aware Computation Offloading for Large-Scale Internet of Things Applications

With billions of devices already connected to the network's edge, the Internet of Things (IoT) is shaping the future of pervasive computing. Nonetheless, IoT applications still cannot escape the need for the computing resources available at the fog layer. This becomes challenging since the fog nodes are not necessarily secure nor reliable, which widens even further the IoT threat surface. Moreover, the security risk appetite of heterogeneous IoT applications in different domains or deploy-ment contexts should not be assessed similarly. To respond to this challenge, this paper proposes a new approach to optimize the allocation of secure and reliable fog computing resources among IoT applications with varying security risk level. First, the security and reliability levels of fog nodes are quantitatively evaluated, and a security risk assessment methodology is defined for IoT services. Then, an online, incentive-compatible mechanism is designed to allocate secure fog resources to high-risk IoT offloading requests. Compared to the offline Vickrey auction, the proposed mechanism is computationally efficient and yields an acceptable approximation of the social welfare of IoT devices, allowing to attenuate security risk within the edge network.

I. Introduction

The fog transpired as a computing and networking archi-tecture to support the Internet of Things (IoT). Although fog computing decreases the reliance of edge services on the cloud, it inherits the security and trust issues of cloud computing and presents a larger attack vector against data confidentiality, integrity, and availability [1]. It also engenders many additional functional, performance, and deployment requirements that increase the cost of security management. Indeed, adapting cloud security solutions to fit within the fog paradigm may not be sufficient to provide secure access, efficient authentication, and reliable communication to IoT applications [2].