A primary motivation of our research is the monitoring of physical, physiological, and biochemical parameters - in any environment and without activity restriction and behaviour modification - through using miniaturised, wireless Body Sensor Networks (BSN). Key research issues that are currently being addressed include novel sensor designs, ultra-low power microprocessor and wireless platforms, energy scavenging, biocompatibility, system integration and miniaturisation, processing-on-node technologies combined with novel ASIC design, autonomic sensor networks and light-weight communication protocols. Our research is aimed at addressing the future needs of life-long health, wellbeing and healthcare, particularly those related to demographic changes associated with an ageing population and patients with chronic illnesses. This research theme is therefore closely aligned with the IGHI’s vision of providing safe, effective and accessible technologies for both developed and developing countries.

Some of our latest works were exhibited at the 2015 Royal Society Summer Science Exhibition.


BibTex format

author = {Sun, Y and Lo, B},
doi = {10.1049/cp.2018.0028},
publisher = {IET},
title = {Random number generation using inertial measurement unit signals for on-body IoT devices},
url = {http://dx.doi.org/10.1049/cp.2018.0028},
year = {2018}

RIS format (EndNote, RefMan)

AB - With increasing popularity of wearable and implantable technologies for medical applications, there is a growing concern on the security and data protection of the on-body Internet-ofThings (IoT) devices. As a solution, cryptographic system is often adopted to encrypt the data, and Random Number Generator (RNG) is of vital importance to such system. This paper proposes a new random number generation method for securing on-body IoT devices based on temporal signal variations of the outputs of the Inertial Measurement Units (IMU) worn by the users while walking. As most new wearable and implantable devices have built-in IMUs and walking gait signals can be extracted from these body sensors, this method can be applied and integrated into the cryptographic systems of these new devices. To generate the random numbers, this method divides IMU signals into gait cycles and generates bits by comparing energy differences between the sensor signals in a gait cycle and the averaged IMU signals in multiple gait cycles. The generated bits are then re-indexed in descending order by the absolute values of the associated energy differences to further randomise the data and generate high-entropy random numbers. Two datasets were used in the studies to generate random numbers, where were rigorously tested and passed four well-known randomness test suites, namely NIST-STS, ENT, Dieharder, and RaBiGeTe.
AU - Sun,Y
AU - Lo,B
DO - 10.1049/cp.2018.0028
PY - 2018///
TI - Random number generation using inertial measurement unit signals for on-body IoT devices
UR - http://dx.doi.org/10.1049/cp.2018.0028
UR - http://hdl.handle.net/10044/1/58327
ER -