Body Area Network Security using Discrete Wavelet Transform

This project focuses on the key distribution problem in Body Area Networks (BANs), which provide accurate monitoring of the human periphery through the use of biosensors. The functionalities of these biosensors are to effectively and efficiently collect data from vital body parts and share it with an aggregating device that transfers the physiological signals of the corresponding user to a central server. The captured phenomena are highly sensitive to contraventions against privacy. Moreover, they are exchanged among the BAN elements using wireless communication. Therefore, it is essential to build a security mechanism for the protection of the phenomena gathered from the human body, as well as providing secure node-to-host association.  
 
In this regard, assuming that the biosensors capture electrocardiogram (ECG), blood pressure (BP) and oxygen saturation (through photoplethysmogram (PPG)), the project aims at generating similar sequences from these physiological signals using discrete wavelet transform (DWT). By this means, the sequences generated from the above-mentioned physiological signals, called the physiological parameters, can be used as cryptographic keys to secure the communication among the biosensors. Two datasets consisting of simultaneously measured ECG, BP and PPG signals are available for evaluation purposes.
 
This project requires background information on DWT. For that reason, knowledge on signal processing is mandatory. Additionally, Matlab or Python programming is required to process the physiological signals.
 
Below-given references can be examined for further information:

1. Karaoğlan Altop, D. and Levi, A. 2014. "A survey on the development of security mechanisms for body area networks", The Computer Journal, 57(1), 1484-1512.
2. D. Karaoğlan Altop, A. Levi and V. Tuzcu, "Towards using physiological signals as cryptographic keys in body area networks", PervasiveHealth 9th International Conference on Pervasive Computing Technologies for Healthcare, 92-99, 20-23 May 2015, Istanbul, Turkey.
3. D. Karaoğlan Altop, A. Levi and V. Tuzcu, "Deriving cryptographic keys from physiological signals", Pervasive and Mobile Computing, 39, 65-79, August, 2017.
4. D. Karaoğlan Altop, A. Levi and V. Tuzcu, "Feature-level fusion of physiological parameters to be used as cryptographic keys", ICC'17 IEEE International Conference on Communications, 1-6, 21-25 May 2017, Paris, France.
5. D. Karaoğlan Altop, A. Levi and V. Tuzcu, "SU-PhysioDB: A physiological signals database for body area network security", IEEE BlackSeaCom'17 IEEE International Black Sea Conference on Communications and Networking, 5-8 June 2017, Istanbul, Turkey.
6. D. Karaoğlan Altop, B. Seymen and A. Levi, "SKA-PS: Secure key agreement protocol using physiological signals", Ad Hoc Networks, 83, 111-124, February, 2019.