QF-MAC: Adaptive, Local Channel Hopping for Interference Avoidance in Wireless Meshes

The throughput efficiency of a wireless mesh network with potentially malicious external or internal interference can be significantly improved by equipping routers with multi-radio access over multiple channels. For reliably mitigating the effect of interference, frequency diversity (e.g., channel hopping) and time diversity (e.g., carrier sense multiple access) are conventionally leveraged to schedule communication channels. However, multi-radio scheduling over a limited set of channels to minimize the effect of interference and maximize network performance in the presence of concurrent network flows remains a challenging problem. The state-of-the-practice in channel scheduling of multi-radios reveals not only gaps in achieving network capacity but also significant communication overhead. This paper proposes an adaptive channel hopping algorithm for multi-radio communication, QuickFire MAC (QF-MAC), that assigns per-node, per-flow ``local'' channel hopping sequences, using only one-hop neighborhood coordination. QF-MAC achieves a substantial enhancement of throughput and latency with low control overhead. QF-MAC also achieves robustness against network dynamics, i.e., mobility and external interference, and selective jamming attacker where a global channel hopping sequence (e.g., TSCH) fails to sustain the communication performance. Our simulation results quantify the performance gains of QF-MAC in terms of goodput, latency, reliability, communication overhead, and jamming tolerance, both in the presence and absence of mobility, across diverse configurations of network densities, sizes, and concurrent flows

Threat Modelling Guided Trust-based Task Offloading for Resource-constrained Internet of Things

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 paper, we present a novel trust model-driven system architecture, based on behavioural evidence, that is suitable for resource-constrained IoT devices that 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 behaviours. The open environment of edge-based IoT networks means that threats against edge nodes can lead to deviation from expected behaviour. Hence, we perform a threat modelling 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