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 scale of Internet of Things (IoT) systems has expanded in recent times and, in tandem with this, IoT solutions have developed symbiotic relationships with technologies, such as edge Computing. IoT has leveraged edge computing capabilities to improve the capabilities of IoT solutions, such as facilitating quick data retrieval, low latency response, and advanced computation, among others. However, in contrast with the benefits offered by edge computing capabilities, there are several detractors, such as centralized data storage, data ownership, privacy, data auditability, and security, which concern the IoT community. This study leveraged blockchain’s inherent capabilities, including distributed storage system, non-repudiation, privacy, security, and immutability, to provide a novel, advanced edge computing architecture for IoT systems. Specifically, this blockchain-based edge computing architecture addressed centralized data storage, data auditability, privacy, data ownership, and security. Following implementation, the performance of this solution was evaluated to quantify performance in terms of response time and resource utilization. The results show the viability of the proposed and implemented architecture, characterized by improved privacy, device data ownership, security, and data auditability while implementing decentralized storage.

Currently, Internet of Things (IoT) devices can integrate into an existing network where they may interact with a myriad of other devices that may host a range of capabilities. Such IoT devices may need to share data that is consumed by other devices or services. This data is generated by the capabilities built into devices within the ecosystem. A typical IoT ecosystem that is heterogeneous in nature should be able to have devices that offer a range of capabilities that could be explored in the event a device breakdown or malfunction. This is to ensure that the system is self-sustaining, and adequately perform during undesirable conditions. Hence, an IoT ecosystem should be able to collaborate, self-organize itself to explore these capabilities towards achieving an overall goal. As such, interoperability of these devices which will improve functionality, availability, and robustness of the IoT ecosystem must be achieved. Also, Several IoT representations today store their data centrally which gives rise to inherent issues such as single point of failure, and other possible vulnerabilities. Addressing these deficiencies alongside proper profiling of IoT device capability and other device details is viewed as the first stage in securing IoT ecosystems and this was explored in this research. This study presents the use of Distributed Ledger Technology which has the inherent property of being secure to profile the capability of IoT devices within self-organized IoT ecosystems. A system overview, data structures, and algorithms are presented.