The advent of the fifth-generation mobile communication technology (5G) era has catalyzed significant advancements in medical diagnosis delivery, primarily driven by the surge in medical data from wearable Internet of Medical Things (IoMT) devices. Nonetheless, the IoMT paradigm grapples with challenges related to data security, privacy, constrained computational capabilities at the edge, and an inadequate architecture for handling traditionally error-prone data. In this context, our research offers: (1) an exhaustive review of large-scale medical data propelled by IoMT, (2) an exploration of the prevailing cloud-edge Artificial Intelligence (AI) framework tailored for IoMT, and (3) an insight into the application of Edge Federated Learning (EFL) in bolstering medical big data analytics to yield secure and superior diagnostic outcomes. We place a particular emphasis on the proliferation of IoMT wearable devices that incessantly stream medical data, either from patients or healthcare institutions, to centralized repositories. Furthermore, we introduce a federated cloud-edge AI blueprint designed to position computational resources proximate to the edge network, facilitating real-time diagnostic feedback to patients. We conclude by delineating prospective research trajectories in enhancing IoMT through AI integration.

This paper aims to propose a system design, working on the basis of the Internet of Things (IoT) LoRa, for tracking and monitoring the patient with mental disorder. The system consists of a LoRa client, which is a tracking device on end devices installed on the patient, and LoRa gateways, installed in hospitals and other public locations. The LoRa gateways are connected to local servers and cloud servers by utilizing both mobile cellular and Wi-Fi networks as the communications media. The feasibility of the system design is developed by employing the results of our previous work on LoRa performance in the Line of Sight (LoS) and Non-Line of Sight (Non-LoS) environments. Discussions are presented concerning the LoRa network performance, battery power and scalability. The future work is to build the proposed the design in a real system scenarios.

The ultimate benefit of the Internet of Things (IoT) is to improve quality of life by supporting the automation and interconnection of various services; this interconnection includes public facilities in platforms such as smart cities. IoT implementation is believed to improve services and yield additional benefits. However, assessment of how the IoT brings such an experience to users is still a challenge that is not yet standardized. Given that little research has discussed this issue, this paper aims to formulate a framework for measuring the quality of experience (QoE) regarding IoT services on the basis of the Absolute Category Rating with Hidden Reference (ACR-HR) scale, which measures the perception of experience by having the IoT users provide scores for their experiences before and after IoT implementation. The proposed framework consists of 5 steps: setting up the focus of the IoT services and the QoE parameters being assessed, defining users of IoT services, conducting a mean opinion score (MOS) survey of the users, calculating the DMOS (differential MOS) on the basis of the ACR-HR quantitative scale, and finally providing the strategic implications to those who implement the service. To test the proposed framework, we have conducted a proof of concept by measuring the QoE for the IoT services in the Jakarta Smart City platform. Six institutional users operate the IoT for their public services, ranging from fire and rescue brigades to public bus transportation. The results imply that the IoT-based services provide a good experience to institutional users when compared to the state of services without IoT. A strategic implication is that some features of Jakarta Smart City must be evaluated and improved to increase the level of satisfaction of users. To conclude, our framework is sufficiently reliable in measuring the QoE for IoT-based services and can be recommended for use for any other IoT-type service.

This paper aims to characterize the profile of the prospective IoT market in Indonesia. The primary data were collected in July 2018 through a comprehensive survey that sampled respondents representing the whole Indonesian population. The questionnaire was developed by extracting the 4 (four) main issues regarding which the potential users of the IoT technology may have concerns, i.e., willingness to use the IoT services, concerns related to rejection and worries about the IoT, the characteristics of the IoT hardware, and perceptions about the role of the IoT within the existing system. The results of the survey were analyzed to capture the profile of the prospective IoT market, and the strategic implications of the findings were considered. Several interesting results were found, ranging from answering the common question of what kinds of the IoT services are most anticipated to answering the delicate question of how the Indonesian people perceive the disruptive force that the IoT technology may exert. The contribution of this research is that it can be used as an initial guide or reference for regulators, the government and the IoT firms that will begin to deploy services in Indonesia.

This paper investigates the performance of battery capacity in the proposed IoT-LoRa device to track and monitor a patient with a mental disorder. Reduction of battery capacity was correlated against the movement speed of users. Three experimental scenarios were conducted for a patient moving at 1-3 km/h, 5-8 km/h, and 1-30 km/h. The real time data were taken from two multinode LoRa units at 10 different locational points with a time interval of 30 minutes. The experiment had 113 data iterations to determine percentage of battery capacity and RSSI level. It was found that the faster a patient is moving, the greater the capacity of the battery reduces.

A long-range wide area network (LoRaWAN) has a weakness in terms of key management: its root key is static, meaning that it never changes. Since all cryptographic keys are derived from the root key, such a weakness endangers LoRaWAN security. This paper proposes a novel secure root key updating scheme for LoRaWAN that involves periodically changing the root key value based on the CTR_AES DRBG 128 algorithm. The scheme consists of two sequential phases: the initialization process that occurs at the end device and the root key update process that occurs at the join server. To validate the proposed scheme, we conduct randomness and communication protocol tests. The results indicate that the proposed scheme has a high degree of randomness, passes all 15 statistical tests in the NIST suite, and has secure communication protocols. The analyses verify that the new scheme has a mechanism to resist replay attacks and protects data integrity. The main advantage of the scheme is that it has a perfect forward secrecy feature that enhances the root key updating scheme with a lightweight computational load for the end device; additionally, root key updating can be performed automatically from a remote distance within the LoRaWAN coverage network. The proposed scheme also supports simultaneous updates for implementation in a large area with many devices. Finally, the overall results demonstrate how our proposed scheme may compensate for the weakness of LoRaWANs in key management and improve their security performance.