# Vehicular Alarm System Using mHealth Data and Lightweight Security Algorithms
## Security Algorithms with Lightweight Security
### Algorithm Development for the Authentication Procedure
Apart from providing alarms in advance to the driver, this system aims to facilitate other relevant parties such as healthcare providers and road assistance services to undertake an appropriate action based on the real-time intelligently integrated computational model. This novel proposal has the advantage of a continuous monitoring inference system for mHealth wearable devices and IoT vehicular sensors with a trust-based authentication model to predict and send alarms accurately. The health care providers, road transportation, and other relevant parties could make use of such a safe and intelligent alarm system to support the driver and the vehicle, without causing any inconvenience to the road traffic system. This paper considered the technologies, interfacing standards, privacy and security aspects in the design, and presented future works of the system development. Overall, this work is the first of its kind in proposing a vehicle safety alarm system designed to combine mHealth data of the driver and vehicular sensor data using a lightweight security framework. With this modest step forward, our work generates research interest in this multi-disciplinary problem of integrating mHealth and VIS for improving road safety. ## 8 Future Works

Future work of this ongoing research would involve a prototype development of our proposed design model of the mHealth integrated vehicular alarm system using the trust-based authentication and security framework. Some of the key activities envisioned to be undertaken are described below. _Software and algorithm development--_in addition to the security algorithms proposed, further defining of the thresholds of each personalized health data to determine the abnormality levels is

Figure 10: Message transfer without (**a**) and with (**b**) digital signature. required. These threshold levels will be determined in consultation with the health experts and physicians of the user (driver). For example, the normal resting heart rate of 60 to 100 beats per minute may not be relevant to a particular user due to variables such as medications. In addition, the HR may change in different situations such as the user's activity level, fitness level, environmental factors such as air temperature, body posture (e.g., standing, sitting, lying down), emotional state, body size, and medications [23]. Robust software algorithms require development and verification to handle these personalized data within the inference engine of the integrated alarm system. To secure health data exchange between sensor nodes and the cloud servers, we propose to use trusted time-servers to detect man-in-the-middle attacks [3]. This can be integrated with the lightweight security algorithms this paper proposed for real-time detection of attacks and an emergency alarm system for physiological sensors [6]. _Database collection/creation/management/queries_--when sensed data from IoT devices are sent to the cloud server, data wrangling and transparency of the data should be verified. How much of the data can be trustworthy? What is the validity of the data received and the maximum expiry? When further data are required, how does the system automatically authenticate, perform a query, and collect the data? All these questions need to be addressed before accepting the data from sensor devices within the trust-based security framework including data compliance between mHealth and integrated vehicular networks. _Data analysis/visualization/presentation using machine learning and statistical approaches_--data visualization facilitates quicker inferencing and decision-making for providing timely and accurate alarms to the driver and the relevant parties. Applying and testing appropriate machine learning algorithms is crucial to avoid false alarms in order to enhance adoptability. The evaluation and benchmarking of the system would involve measuring how accurate the alarm notification is triggered and validated in different circumstances.