Threat-modeling-guided Trust-based Task Offloading for Resource-constrained Internet of Things

• Main Authors: Bradbury, M. (Author), Burton, J. (Author), Butler, M. (Author), Flores, D. (Author), Jhumka, A. (Author), Watson, T. (Author)
• Format: Article
• Language: English
• Published: Association for Computing Machinery 2022
• Subjects: computation offloading; Computation offloading; Computer architecture; edge computing; Edge computing; Edge nodes; Internet of things; Internet of Things; Network architecture; resource constrained; Resource constrained; Resource Constraint; Systems architecture; Task offloading; Threat modeling; threat modelling; Trust; Trust models; Trusted computing
• Online Access: View Fulltext in Publisher

 There is an increasing demand for Internet of Things (IoT) networks consisting of resource-constrained devices executing increasingly complex applications. Due to these resource constraints, IoT devices will not be able to execute expensive tasks. One solution is to offload expensive tasks to resource-rich edge nodes, which requires a framework that facilitates the selection of suitable edge nodes to perform task offloading. Therefore, in this article, we present a novel trust-model-driven system architecture, based on behavioral evidence, that is suitable for resource-constrained IoT devices and supports computation offloading. We demonstrate the viability of the proposed architecture with an example deployment of the Beta Reputation System trust model on real hardware to capture node behaviors. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behavior. Hence, we perform a threat modeling to identify such threats. The proposed system architecture includes threat handling mechanisms that provide security properties such as confidentiality, authentication, and non-repudiation of messages in required scenarios and operate within the resource constraints. We evaluate the efficacy of the threat handling mechanisms and identify future work for the standards used. © 2022 Copyright held by the owner/author(s).