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  • 1.
    Huang, Xin
    et al.
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Chen, Bangdao
    Department of Computer Science, University of Oxford.
    Markham, Andrew
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Wang, Qinghua
    Högskolan Kristianstad, Sektionen för hälsa och samhälle, Avdelningen för Design och datavetenskap. Department of Communications and Networking, Aalto University, Espoo.
    Zheng, Yan
    Department of Communications and Networking, Aalto University, Espoo.
    Roscoe, Andrew William
    Department of Computer Science, Institute for the Future of Computing/Oxford Martin School, University of Oxford.
    Human interactive secure key and ID exchange protocols in body sensor networks2013Inngår i: IET Information Security, ISSN 1751-8709, E-ISSN 1751-8717, Vol. 7, nr 1, 30-38 s.Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    A body sensor network (BSN) is typically a wearable wireless sensor network. Security protection is critical to BSNs, since they collect sensitive personal information. Generally speaking, security protection of BSN relies on identity (ID) and key distribution protocols. Most existing protocols are designed to run in general wireless sensor networks, and are not suitable for BSNs. After carefully examining the characteristics of BSNs, the authors propose human interactive empirical channel-based security protocols, which include an elliptic curve Diffie–Hellman version of symmetric hash commitment before knowledge protocol and an elliptic curve Diffie–Hellman version of hash commitment before knowledge protocol. Using these protocols, dynamically distributing keys and IDs become possible. As opposite to present solutions, these protocols do not need any pre-deployment of keys or secrets. Therefore compromised and expired keys or IDs can be easily changed. These protocols exploit human users as temporary trusted third parties. The authors, thus, show that the human interactive channels can help them to design secure BSNs.

  • 2.
    Jin, Shasha
    et al.
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Gaoding, Ningcheng
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Signal processing using the wavelet transform and the Karhunen-Loeve transform2012Independent thesis Advanced level (degree of Master (One Year)), 10 poäng / 15 hpOppgave
    Abstract [en]

    This degree project deals with Wavelet transform and Karhunen-Loeve transform. Through the mathematic description to understand and simulation to investigate the denoise ability of WT and the de-correlation ability of KLT. Mainly prove that the new algorithm which is the joint of these two algorithms is feasible.

  • 3.
    Shala, Ubejd
    et al.
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Rodriguez, Angel
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Indoor Positioning using Sensor-fusion in Android Devices2011Independent thesis Advanced level (degree of Master (One Year)), 10 poäng / 15 hpOppgave
    Abstract [en]

    This project examines the level of accuracy that can be achieved in precision positioning by using built-in sensors in an Android smartphone. The project is focused in estimating the position of the phone inside a building where the GPS signal is bad or unavailable. The approach is sensor-fusion: by using data from the device’s different sensors, such as accelerometer, gyroscope and wireless adapter, the position is determined. The results show that the technique is promising for future handheld indoor navigation systems that can be used in malls, museums, large office buildings, hospitals, etc.

  • 4.
    Song, Qunying
    et al.
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Xu, Jingjing
    Högskolan Kristianstad, Sektionen för hälsa och samhälle.
    Study and Implementation of Patient Data Collection and Presentation for an eHealth Application2013Independent thesis Advanced level (degree of Master (One Year)), 10 poäng / 15 hpOppgave
    Abstract [en]

    This degree project is a part of information and communication technology supported self-care system for the diabetes, mainly in diabetes data collection and visualization. The report is organized in four main sections: investigation and internet search, literature review, application design and implementation, system test and evaluation. Existed applications and research studies has been compared and, a responsive web application is developed aiming at providing relevant functionalities and services regarding diabetes self-management.

  • 5.
    Wang, Qinghua
    et al.
    Högskolan Kristianstad, Sektionen för hälsa och samhälle, Avdelningen för Design och datavetenskap.
    Yigitler, Huseyin
    Finland.
    Huang, Xin
    Kina.
    Jäntti, Riku
    Finland.
    Localizing multiple objects using radio tomographic imaging technology2016Inngår i: IEEE Transactions on Vehicular Technology, ISSN 0018-9545, E-ISSN 1939-9359, Vol. 65, nr 5, 3641-3656 s.Artikkel i tidsskrift (Fagfellevurdert)
    Abstract [en]

    Low data rate wireless networks can be deployed for physical intrusion detection and localization purposes. The intrusion of a physical object (or human) will disrupt the radio frequency magnetic field, and can be detected by observing the change of radio attenuation. This gives the basis for the radio tomographic imaging technology which has been recently developed for passively monitoring and tracking objects. Due to noise and the lack of knowledge about the number and the sizes of intruding objects, multi-object intrusion detection and localization is a challenging issue. This article proposes an extended VB-GMM (i.e. variational Bayesian Gaussian mixture model) algorithm in treating this problem. The extended VBGMM algorithm applies a Gaussian mixture model to model the changed radio attenuation in a monitored field due to the intrusion of an unknown number of objects, and uses a modified version of the variational Bayesian approach for model estimation. Real world data from both outdoor and indoor experiments (using the radio tomographic imaging technology) have been used to verify the high accuracy and the robustness of the proposed multi-object localization algorithm.

  • 6.
    Wang, Qinghua
    et al.
    Högskolan Kristianstad, Sektionen för hälsa och samhälle, Avdelningen för Design och datavetenskap. Aalto University, Finland.
    Yigitler, Huseyin
    Aalto University, Finland.
    Jäntti, Riku
    Aalto University, Finland.
    System monitoring and fault diagnosis in wireless sensor systems2012Inngår i: Aalto University Workshop on Wireless Sensor Systems, WoWSS 2012, 2012Konferansepaper (Fagfellevurdert)
    Abstract [en]

    In this paper, we discuss fault diagnosis for wireless sensor systems. Fault diagnosis is only possible if comprehensive system monitoring is in place. We thus also present architecture for in-depth system monitoring.

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