Edge computing and Federated Learning (FL) can work in tandem to address issues related to privacy and collaborative distributed learning in untrusted IoT environments. However, deployment of FL in resource-constrained IoT devices faces challenges including asynchronous participation of such devices in training, and the need to prevent malicious devices from participating. To address these challenges we present CoLearn, which build on the open-source Manufacturer Usage Description (MUD) implementation osMUD and the FL framework PySyft. We deploy CoLearn on resourceconstrained devices in a lab environment to demonstrate (i) an asynchronous participation mechanism for IoT devices in machine learning model training using a publish/subscribe architecture, (ii) a mechanism for reducing the attack surface in FL architecture by allowing only IoT MUD-compliant devices to participate in the training phases, and (iii) a trade-of between communication bandwidth usage, training time and device temperature (thermal fatigue). CCS CONCEPTS • Networks → Network algorithms; Network experimentation; Network privacy and anonymity; • Computer systems organization → Embedded and cyber-physical systems; • Computing methodologies → Neural networks; Machine learning.

The popularity of the Internet of Things (IoT) devices makes it increasingly important to be able to fingerprint them, for example in order to detect if there are misbehaving or even malicious IoT devices in one's network. However, there are many challenges faced in the task of fingerprinting IoT devices, mainly due to the huge variety of the devices involved. At the same time, the task can potentially be improved by applying machine learning techniques for better accuracy and efficiency. The aim of this paper is to provide a systematic categorisation of machine learning augmented techniques that can be used for fingerprinting IoT devices. This can serve as a baseline for comparing various IoT fingerprinting mechanisms, so that network administrators can choose one or more mechanisms that are appropriate for monitoring and maintaining their network. We carried out an extensive literature review of existing papers on fingerprinting IoT devices-paying close attention to those with machine learning features. This is followed by an extraction of important and comparable features among the mechanisms outlined in those papers. As a result, we came up with a key set of terminologies that are relevant both in the fingerprinting context and in the IoT domain. This enabled us to construct a framework called IDWork, which can be used for categorising existing IoT fingerprinting mechanisms in a way that will facilitate a coherent and fair comparison of these mechanisms. We found that the majority of the IoT fingerprinting mechanisms take a passive approach-mainly through network sniffing-instead of being intrusive and interactive with the device of interest. Additionally, a significant number of the surveyed mechanisms employ both static and dynamic approaches, in order to benefit from complementary features that can be more robust against certain attacks such as spoofing and replay attacks. CCS CONCEPTS • Security and privacy → Biometrics; • Networks → Layering; • Computer systems organization → Sensor networks; • Computing methodologies → Machine learning approaches; Modeling methodologies; • General and reference → Measurement.

IoT and edge computing are profoundly changing the information era, bringing a hyper-connected and contextaware computing environment to reality. Connected vehicles are a critical outcome of this synergy, allowing for the seamless interconnection of autonomous mobile/fixed objects, giving rise to a decentralized vehicle-to-everything (V2X) paradigm. On this front, the European Telecommunications Standards Institute (ETSI) proposed the Multi-Access Edge Computing (MEC) standard, addressing the execution of cloud-like services at the very edge of the infrastructure, thus facilitating the support of low-latency services at the far-edge. In this article, we go a step further and propose a novel ETSI MEC-compliant architecture that fully exploits the synergies between the edge and far-edge, extending the pool of virtualized resources available at MEC nodes with vehicular ones found in the vicinity. In particular, our approach allows vehicle entities to access and partake in a negotiation process embodying a rewarding scheme, while addressing resource volatility as vehicles join and leave the resource pool. To demonstrate the viability and flexibility of our proposed approach, we have built an ETSI MEC-compliant simulation model, which could be tailored to distribute application requests based on the availability of both local and remote resources, managing their transparent migration and execution. In addition, the paper reports on the experimental validation of our proposal in a 5G network setting, contrasting different service delivery modes, by highlighting the potential of the dynamic exploitation of far-edge vehicular resources.

With the strong development of the Internet of Things (IoT), the definition of IoT devices' intended behavior is key for an effective detection of potential cybersecurity attacks and threats in an increasingly connected environment. In 2019, the Manufacturer Usage Description (MUD) was standardized within the IETF as a data model and architecture for defining, obtaining and deploying MUD files, which describe the network behavioral profiles of IoT devices. While it has attracted a strong interest from academia, industry, and Standards Developing Organizations (SDOs), MUD is not yet widely deployed in real-world scenarios. In this work, we analyze the current research landscape around this standard, and describe some of the main challenges to be considered in the coming years to foster its adoption and deployment. Based on the literature analysis and our own experience in this area, we further describe potential research directions exploiting the MUD standard to encourage the development of secure IoTenabled scenarios.

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