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dc.contributor.advisorCheffena, Michael
dc.contributor.advisorEkman, Nils Torbjörn
dc.contributor.advisorFontan, Fernando Perez
dc.contributor.authorMohamed, Marshed Kassim
dc.date.accessioned2018-09-21T12:22:43Z
dc.date.available2018-09-21T12:22:43Z
dc.date.issued2018
dc.identifier.isbn978-82-326-3271-8
dc.identifier.issn1503-8181
dc.identifier.urihttp://hdl.handle.net/11250/2563931
dc.description.abstractThe advancements in technology have made it possible to create a network of implant and wearable nodes around the human body known as wireless body area network (WBAN). These communication systems have potentially great applications in areas such as health monitoring, sports activities, and specialized occupations such as paramedics, fire-fighters, and military personals. However, networking of these wearable devices is a challenging task due to the complex propagation mechanisms of radio frequency (RF) signals in the vicinity of the human body. The movement of the body components causes time-varying shadowing and fading effects, and signal reflection/scattering from objects around the human body result in multipath fading effects. Reliable communication in such time-variant channel conditions can only be achieved with a greater understanding of the communication channel. In this Ph.D. project, dynamic channel models for WBANs were developed with focus on on-body, off-body, and body-to-body communication scenarios. We started with physical channel modeling by utilizing a dynamic human walking model, which provides a detailed description of the movement of the different body parts, and the uniform theory of diffraction (UTD) to accurately calculate the timevarying shadowing and scattering effects due to the movements of the body parts. A physical model for the signal affected by moving human bodies in an indoor environment was developed. Further, standard statistical distribution was added to the model to represent the multipath fading effects by the scatterers around the human body, and a physical-statistical model for off-body wireless communications channels was obtained. Both the physical and the physical-statistical models were validated in terms of first- and second-order statistics utilizing measurement data and showed good agreements. For body-to-body communications, a measurement campaign was conducted for different scenarios of running and cycling activities. Among others, the results indicated the presence of good and bad states with each state following a specific distribution for the considered propagation scenarios. The empirical channel model developed here was based on the two-state semi-Markov model. Further, a lognormal mixture shadowing model based on a cluster concept was utilized in the modeling of the first-order statistics of the channels. The mixture model addresses the inaccuracies observed using a single distribution that may not accurately represent the measurement data set. The accuracy of the proposed mixture model was compared to other commonly utilized single distributions showing significant improvement in representing the measurement results. The application of propagation channel fingerprints to improve the security of WBANs was also investigated. More specifically, the usage of received signal strength indicator (RSSI) as a source of gait recognition was proposed. The RSSI approach does not require additional sensors (hardware) or sampling of them but uses the RSSI values already available on all radio devices. Radio channel features were extracted from the unique signature of the RSSI in relation to the corresponding subject. The extracted features were then used together with classification learners to evaluate the method in which an accuracy of up to 98% was achieved. Lastly, the channel characteristics during walking for on-body, off-body, and bodyto- body communication were investigated together in the same conditions so that a complete picture of the overall network could be observed and compared. The finite-difference time-domain (FDTD) was used as it could separate the channel gain into propagation loss and antenna gain, which cannot be achieved through measurement since the body is within the near field of the antenna. Correlation between the channels and the application of multivariate normal distributions in the modeling of WBAN channels was also investigated. The developed channel models and the measurement results in this Ph.D. study can be used for accurate planning and deployment of WBANs in various applications. They can be used for simulating different capacity enhancing techniques, and for exploring new methods for the air interference, multiple access, and architectural approaches.nb_NO
dc.language.isoengnb_NO
dc.publisherNTNUnb_NO
dc.relation.ispartofseriesDoctoral theses at NTNU;2018:240
dc.relation.haspartPaper 1: Mohamed, Marshed Kassim; Cheffena, Michael; Fontán, Fernando Pérez; Moldsvor, Arild. A Dynamic Channel Model for Indoor Wireless Signals: Working Around Interference Caused by Moving Human Bodies. IEEE Antennas & Propagation Magazine 2018 ;Volum 60.(2) s. 82-91 https://doi.org/10.1109/MAP.2018.2796022 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksnb_NO
dc.relation.haspartPaper 2: Mohamed, Marshed Kassim; Cheffena, Michael; Moldsvor, Arild; Fontán, Fernando Pérez. Physical-Statistical Channel Model for Off-body Area Network. IEEE Antennas and Wireless Propagation Letters 2017 ;Volum 16.(1) s. 1516-1519 https://doi.org/10.1109/LAWP.2016.2647323 © 2017 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksnb_NO
dc.relation.haspartPaper 3: Mohamed, Marshed Kassim; Cheffena, Michael; Moldsvor, Arild. Characterization of the Body-to-Body Propagation Channel for Subjects during Sports Activities. Sensors 2018 ;Volum 18.(2) https://doi.org/10.3390/s18020620 printed with Attribution 4.0 International (CC BY 4.0)nb_NO
dc.relation.haspartPaper 4: Cheffena, Michael; Mohamed, Marshed Kassim. The Application of Lognormal Mixture Shadowing Model for Body-to-Body Channels. IEEE Sensors Letters 2018 ;Volum 2.(3) s. 1-4 https://doi.org/10.1109/LSENS.2018.2848296 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksnb_NO
dc.relation.haspartPaper 5: Mohamed, Marshed Kassim; Cheffena, Michael. Received Signal Strength Based Gait Authentication. IEEE Sensors Journal 2018 ;Volum 18.(16) s. 6727-6734 http://doi.org/10.1109/JSEN.2018.2850908 © 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other worksnb_NO
dc.relation.haspartPaper 6: M. Mohamed,W. Joseph, G. Vermeeren, E. Tanghe, and M. Cheffena, "Characterization of dynamic wireless body area network channels during walkingnb_NO
dc.titleDynamic Channel Models for Wireless Body Area Networksnb_NO
dc.typeDoctoral thesisnb_NO
dc.subject.nsiVDP::Technology: 500::Electrotechnical disciplines: 540::Electronics: 541nb_NO


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