With the increase in globalization, the degree of electronic communication increases. Such an increase is also experienced by many sectors including the medical sector, which communicates and generates large amounts of data related to the spread of diseases as observed in the case of the COVID-19 pandemic. This has led to the deployment of Internet of Things (IoT) networks in many medical centers. However, one main challenge is how to maintain the security of the data and devices in the network. In this study, we discuss the cybersecurity risk associated with IoT networks used for medical services and provide a solution for protecting medical data and devices using an agent-based approach. Unlike most conventional cybersecurity models that use agents based on deterministic logic or independent learning agents to detect and prevent cybersecurity attacks, we propose a cybersecurity model using a collaborative network of learning agents, called Collabo, that share both mutual and causal values regarding their actions on a common security target. Our experimental results demonstrate the significance of our model over conventional models.

Automotive driving assistant systems (ADAS) and Automated driving (AD) technologies are widely used in unmanned vehicle control along a trajectory, and just like many other technologies, they present some risks such as the risk of misdirection and collision of the vehicle with an obstacle along its trajectory. To avoid these, many tracking systems such as the radar tracking system are used to detect and track targets and trajectories of ADAS/AD vehicles. However, the performance of the tracking operation is an important factor in determining the reliability of the tracking systems and ADAS/AD technologies, especially at the edge computing level. In this study, we proposed a tracking technique using a collaborative predictive model in time series, called CoPreMo, to improve the reliability of the tracking operation of a radar system. We carried out three experiments with the model on a simulated radar system to track the range of a target with different speeds in three ADAS/AD scenarios and achieve a range tracking error of 0.21m, 0.26m, and 0.32m, better than those of the baseline models.