The internet of things (IoT) is essential for the implementation of applications and services that require the ability to sense the surrounding environment through sensors and modify it through actuators. However, IoT devices usually have limited computing capabilities and hence are not always sufficient to directly host resource-intensive services. Fog computing, which extends and complements the cloud, can support the IoT with computing resources and services that are deployed close to where data are sensed and actions need to be performed. Virtualisation is an essential feature in the cloud as in the fog, and containers have been recently getting much popularity to encapsulate fog services. Besides, container migration among fog nodes may enable several emerging use cases in different IoT domains (e.g., smart transportation, smart industry). In this paper, we first report container migration use cases in the fog and discuss containerisation. We then provide a comprehensive overview of the state-of-the-art migration techniques for containers, i.e., cold, pre-copy, post-copy, and hybrid migrations. The main contribution of this work is the extensive performance evaluation of these techniques that we conducted over a real fog computing testbed. The obtained results shed light on container migration within fog computing environments by clarifying, in general, which migration technique might be the most appropriate under certain network and service conditions.

Over the last few years, standardisation efforts are consolidating the role of the Routing Protocol for Low-Power and Lossy Networks (RPL) as the standard routing protocol for IPv6-based Wireless Sensor Networks (WSNs). Although many core functionalities are well defined, others are left implementation dependent. Among them, the definition of an efficient link-quality estimation (LQE) strategy is of paramount importance, as it influences significantly both the quality of the selected network routes and nodesâ\u80\u99 energy consumption. In this paper, we present RL-Probe, a novel strategy for link quality monitoring in RPL, which accurately measures link quality with minimal overhead and energy waste. To achieve this goal, RL-Probe leverages both synchronous and asynchronous monitoring schemes to maintain up-to-date information on link quality and to promptly react to sudden topology changes, e.g. due to mobility. Our solution relies on a reinforcement learning model to drive the monitoring procedures in order to minimise the overhead caused by active probing operations. The performance of the proposed solution is assessed by means of simulations and real experiments. Results demonstrated that RL-Probe helps in effectively improving packet loss rates, allowing nodes to promptly react to link quality variations as well as to link failures due to node mobility

Recent standardization efforts are consolidating the role of RPL as the standard routing protocol for IPv6-based Wireless Sensor and Actuator Networks (WSANs). Investigating possible attacks against RPL is a top priority to improve the security of the future Internet of Things (IoT) systems. In this paper, we present the DIO suppression attack, a novel degradation-of-service attack against RPL. Unlike other attacks in the literature, the DIO suppression attack does not require to steal cryptographic keys from some legitimate node. We show that the attack severely degrades the routing service, and it is far less energy-expensive than a jamming attack

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Urban population is expected to continuously grow in size. The smart city concepts allows to handle the new challenges and issues created by this growth by applying a wide range of technologies that can provide citizens with a better living environment. Smart agriculture will play an important part of smart cities, as a sustainable and high quality food supply chain is crucial to facilitate the grow of human agglomerates. In this context, European laws imposes very strict requirements in the food industry, in order to ensure that food provenance is always guaranteed. Such fine-grained traceability can be only achieved by applying state-of-the-art technologies. In this paper, we present BRUSCHETTA, a blockchain-based application for the traceability and the certification of the Extra Virgin Olive Oil (EVOO) supply chain. EVOO is an emblematic food product for Italy, but it is also one of the most falsified ones. BRUSCHETTA provides a blockchain-based system to enforce the certification of this product by tracing its entire supply chain: from the plantation to the shops. The goal is to enable the final customer to access a tamper-proof history of the product, including the farming, harvesting, production, packaging, conservation, and transportation processes. BRUSCHETTA leverages Internet of Things (IoT) technologies in order to interconnect sensors dedicated to EVOO quality control, and to let them operate on the blockchain. We also provide a support for the correct tailoring of the BRUSCHETTA blockchain system, and we propose a mechanism for its dynamic auto-tuning to optimize it in case of high loads.