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.

Real-time object detection is currently used to automate various tasks in industrial environments. One of the most important tasks is to improve the safety of workers by monitoring the correct use of Personal Protective Equipment (PPE) in dangerous areas. In this context, usually, a monitoring system analyzes the stream of videos from surveillance cameras to assess PPE usage in real time. When a worker not wearing the appropriate PPE is detected, an acoustic or visual alarm is triggered automatically to raise attention and awareness. The solutions proposed so far are mostly cloud-based systems: images from the site are continuously offloaded to the cloud for analysis. This centralized architecture requires significant network bandwidth to transmit the video feeds through an internet connection that must be reliable, as a network outage would disrupt the service. In this work, we propose a system for real-time PPE detection based on video streaming analysis and Deep Neural Network (DNN). We adopt the edge computing model in which the application for image analysis and classification is deployed on an embedded system installed in proximity of the camera and directly connected to it. The system does not require continuous image transmission towards a cloud system, thus ensuring bandwidth efficiency, reliability, and workers’ privacy. A prototype of the proposed system is developed exploiting a low-cost commercial embedded system, i.e. a Raspberry PI, equipped with an Intel Neural Compute Stick 2. We tested the system with five different pre-trained convolutional neural networks (CNNs), fine-tuned to detect different PPEs, namely helmets, vests, and gloves. In our experimental evaluation, we first compared the five CNNs in terms of classification performance and inference latency. Then, we deployed each CNN on the real system and evaluated the system’s throughput regarding the number of video frames analyzed each second.

Recent standardization efforts are consolidating the role of routing protocol for low-power and lossy networks (RPL) as the standard routing protocol for IPv6-based wireless sensor and actuator networks. Investigating possible attacks against RPL is a top priority to improve the security of the future Internet of Things systems. In this letter, 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.

The industrial Internet of Things (IIoT) is expected to revolutionize the current industry. The capillary introduction of sensors and actuators for real-time monitoring and remote control and their seamless integration into existing information systems will represent a technological breakthrough. The definition of wireless communication standards will play a crucial role in reducing deployment costs and minimizing the time for installation. The new IPv6 over the TSCH mode of IEEE802.15.4e communication stack, 6TiSCH, represents the current leading standardization effort that aims at achieving both reliable and timed wireless communication and integration within IPv6 communication networks for industrial systems. In this paper, the network formation dynamics of 6TiSCH networks are assessed, considering the current guidelines for the so-called minimal configuration, a static initial configuration to guarantee control communication during network bootstrap. It is shown that the minimal configuration might lead to long network formation and suboptimal performance of the routing algorithm which may result into a disconnected network. In order to overcome this issue, a dynamic resource management algorithm to be executed during network bootstrap is proposed. Simulation and experimental results show that the proposed solution allows to minimize the network formation time and also helps in optimizing routing operations leading to the discovery of better routes.

Massive diffusion of constrained devices communicating through low-power wireless technologies in the future Internet of Things will require in many scenarios the deployment of IoT gateways to allow applications to discover and access IoT resources. In this context, Fog/Edge Computing will represent an opportunity to deploy IoT gateways in proximity of IoT domains, meeting the requirements of applications needing low-latency interactions with devices. In this work, we present an Edge-centric distributed architecture to provide resource discovery and access services to IoT applications. The proposed approach leverages the CoRE Resource Directory interface and the CoAP protocol to expose a standard interface for global discovery and access. Multiple IoT gateways are federated through a P2P overlay implemented by a Distributed Hash Table (DHT), which is exploited to share the information on IoT resources available across multiple domains. The proposed solution is validated by means of a small-scale prototype, and extensively evaluated through emulation in large scale deployment in comparison to a centralized Cloud-based approach. Experimental results demonstrated that the proposed approach guarantees lower latencies than a centralized solution and can ensure scalability for small to medium sized deployments.

The Internet of Things (IoT) vision foresees billions of devices seamlessly integrated into information systems. In this context, the Constrained Application Protocol (CoAP) has been defined as a technology enabler to allow applications to interact with physical objects. In this work we present CoAPthon, an open-source Python-based CoAP library, which aims at simplifying the development of CoAP-enabled IoT applications. The library offers software developers a simple and easy-to-use programming interface to exploit CoAP as a communication protocol for rapid prototyping and deployment of IoT systems. The CoAPthon library is fully compliant with the CoAP RFC and implements in addition popular extensions such as the block-wise transfer and resource observing.