The increase of mental illness cases around the world can be described as an urgent and serious global health threat. Around 500 million people suffer from mental disorders, among which depression, schizophrenia, and dementia are the most prevalent. Revolutionary technological paradigms such as the Internet of Things (IoT) provide us with new capabilities to detect, assess, and care for patients early. This paper comprehensively survey works done at the intersection between IoT and mental health disorders. We evaluate multiple computational platforms, methods and devices, as well as study results and potential open issues for the effective use of IoT systems in mental health. We particularly elaborate on relevant open challenges in the use of existing IoT solutions for mental health care, which can be relevant given the potential impairments in some mental health patients such as data acquisition issues, lack of self-organization of devices and service level agreement, and security, privacy and consent issues, among others. We aim at opening the conversation for future research in this rather emerging area by outlining possible new paths based on the results and conclusions of this work.

The growth of Internet use due to the emergence of new paradigms including social media and the Internet of Things (IoT) has presented several challenges. Within the IoT paradigm, there are several domain-specific challenges, among which security is crucial. Billions of devices in the IoT ecosystem have the responsibility of generating, processing, and analyzing large volumes of data. This data may connect with organizations, services, billions of people and other devices. This high level of interconnectivity creates a complex and heterogeneous network which is challenging to adequately secure the IoT system. IoT devices are smart, diverse, portable, interoperable, often autonomous and deployed in distributed topology.Properly managing the identities of these IoT devices plays a critical role in achieving the security of the overall IoT ecosystem. Notably, existing identity management systems fail to satisfy the requirements of identity management for IoT devices. We present a novel solution for IoT devices identity management based on self-sovereign identity and underpinned by proven security offered by distributed ledger technology. This novel approach provides a secure, portable, decentralized, persistent, unique, interoperable, self-owned and self-controlled identity for IoT devices. A Device’s identity with all its relationships in the IoT system are securely managed throughout its entire lifecycle.

The Internet of Things (IoT) is a network of billions of interconnected devices embedded with sensors, software, and communication technologies. Wi-Fi is one of the main wireless communication technologies essential for establishing connections and facilitating communication in IoT environments. However, IoT networks are facing major security challenges due to various vulnerabilities, including de-authentication and disassociation DoS attacks that exploit IoT Wi-Fi network vulnerabilities. Traditional intrusion detection systems (IDSs) improved their cyberattack detection capabilities by adapting machine learning approaches, especially deep learning (DL). However, DL-based IDSs still need improvements in their accuracy, efficiency, and scalability to properly address the security challenges including de-authentication and disassociation DoS attacks tailored to suit IoT environments. The main purpose of this work was to overcome these limitations by designing a transfer learning (TL) and convolutional neural network (CNN)-based IDS for de-authentication and disassociation DoS attack detection with better overall accuracy compared to various current solutions. The distinctive contributions include a novel data pre-processing, and de-authentication/disassociation attack detection model accompanied by effective real-time data collection and parsing, analysis, and visualization to generate our own dataset, namely, the Wi-Fi Association_Disassociation Dataset. To that end, a complete experimental setup and extensive research were carried out with performance evaluation through multiple metrics and the results reveal that the suggested model is more efficient and exhibits improved performance with an overall accuracy of 99.360% and a low false negative rate of 0.002. The findings from the intensive training and evaluation of the proposed model, and comparative analysis with existing models, show that this work allows improved early detection and prevention of de-authentication and disassociation attacks, resulting in an overall improved network security posture for all Wi-Fi-enabled real-world IoT infrastructures.