Blockchain-Based Security Mechanisms for IoMT Edge Networks in IoMT-Based Healthcare Monitoring Systems.

Despite the significant benefits that the rise of Internet of Medical Things (IoMT) can bring into citizens' quality of life by enabling IoMT-based healthcare monitoring systems, there is an urgent need for novel security mechanisms to address the pressing security challenges of IoMT edge networks in an effective and efficient manner before they gain the trust of all involved stakeholders and reach their full potential in the market of next generation IoMT-based healthcare monitoring systems. In this context, blockchain technology has been foreseen by the industry and research community as a disruptive technology that can be integrated into novel security solutions for IoMT edge networks, as it can play a significant role in securing IoMT devices and resisting unauthorized access during data transmission (i.e., tamper-proof transmission of medical data). However, despite the fact that several blockchain-based security mechanisms have already been proposed in the literature for different types of IoT edge networks, there is a lack of blockchain-based security mechanisms for IoMT edge networks, and thus more effort is required to be put on the design and development of security mechanisms relying on blockchain technology for such networks. Towards this direction, the first step is the comprehensive understanding of the following two types of blockchain-based security mechanisms: (a) the very few existing ones specifically designed for IoMT edge networks, and (b) those designed for other types of IoT networks but could be possibly adopted in IoMT edge networks due to similar capabilities and technical characteristics. Therefore, in this paper, we review the state-of-the-art of the above two types of blockchain-based security mechanisms in order to provide a foundation for organizing research efforts towards the design and development of reliable blockchain-based countermeasures, addressing the pressing security challenges of IoMT edge networks in an effective and efficient manner.

Generating Datasets for Anomaly-Based Intrusion Detection Systems in IoT and Industrial IoT Networks.

Over the past few years, we have witnessed the emergence of Internet of Things (IoT) and Industrial IoT networks that bring significant benefits to citizens, society, and industry. However, their heterogeneous and resource-constrained nature makes them vulnerable to a wide range of threats. Therefore, there is an urgent need for novel security mechanisms such as accurate and efficient anomaly-based intrusion detection systems (AIDSs) to be developed before these networks reach their full potential. Nevertheless, there is a lack of up-to-date, representative, and well-structured IoT/IIoT-specific datasets which are publicly available and constitute benchmark datasets for training and evaluating machine learning models used in AIDSs for IoT/IIoT networks. Contribution to filling this research gap is the main target of our recent research work and thus, we focus on the generation of new labelled IoT/IIoT-specific datasets by utilising the Cooja simulator. To the best of our knowledge, this is the first time that the Cooja simulator is used, in a systematic way, to generate comprehensive IoT/IIoT datasets. In this paper, we present the approach that we followed to generate an initial set of benign and malicious IoT/IIoT datasets. The generated IIoT-specific information was captured from the Contiki plugin "powertrace" and the Cooja tool "Radio messages".

Let the Children Listen: A First Approximation to the Sound Environment Assessment of Children through a Soundwalk Approach.

The urban sound environment is one of the layers that characterizes a city, and several methodologies are used for its assessment, including the soundwalk approach. However, this approach has been tested mainly with adults. In the work presented here, the aim is to investigate a soundwalk methodology for children, analyzing the sound environment of five different sites of Gothenburg, Sweden, from children's view-point, giving them the opportunity to take action as an active part of society. Both individual assessment of the sound environment and acoustic data were collected. The findings suggested that among significant results, children tended to rank the sound environment as slightly better when lower levels of background noise were present ( L A 90 ). Moreover, traffic dominance ratings appeared as the best predictor among the studied sound sources: when traffic dominated as a sound source, the children rated the sound environment as less good. Additionally, traffic volume appeared as a plausible predictor for sound environment quality judgments, since the higher the traffic volume, the lower the quality of the sound environment. The incorporation of children into urban sound environment research may be able to generate new results in terms of children's understanding of their sound environment. Moreover, sound environment policies can be developed from and for children.