Graduation Year


Document Type




Degree Name

Doctor of Philosophy (Ph.D.)

Degree Granting Department

Electrical Engineering

Major Professor

Richard D. Gitlin, Sc.D.

Committee Member

Huseyin Arslan, Ph.D.

Committee Member

Nasir Ghani, Ph.D.

Committee Member

Srinivas Katkoori, Ph.D.

Committee Member

Kaiqi Xiong, Ph.D.


In vivo communications, channel modeling, energy efficiency maximization, game theory


This dissertation presents several novel accomplishments in the research area of Wireless Body Area Networks (WBANs), including in vivo channel characterization, optimization and game theoretical approaches for energy efficiency in WBANs.

First, we performed the in vivo path loss simulations with HFSS human body model, built a phenomenological model for the distance and frequency dependent path loss, and also investigated angle dependent path loss of the in vivo wireless channel. Simulation data is produced in the range of 0.4−6 GHz for frequency, a wide range of distance and different angles. Based on the measurements, we produce mathematical models for in body, on body and out of body regions. The results show that our proposed models fit well with the simulated data. Based on our research, a comparison of in vivo and ex vivo channels is summarized.

Next, we proposed two algorithms for energy efficiency optimization in WBANs and evaluated their performance. In the next generation wireless networks, where devices and sensors are heterogeneous and coexist in the same geographical area creating possible collisions and interference to each other, the battery power needs to be efficiently used. The first algorithm, Cross-Layer Optimization for Energy Efficiency (CLOEE), enables us to carry out a cross-layer resource allocation that addresses the rate and reliability trade-off in the PHY, as well as the frame size optimization and transmission efficiency for the MAC layer. The second algorithm, Energy Efficiency Optimization of Channel Access Probabilities (EECAP), studies the case where the nodes access the medium in a probabilistic manner and jointly determines the optimal access probability and payload frame size for each node. These two algorithms address the problem from an optimization perspective and they are both computationally efficient and extensible to 5G/IoT networks.

Finally, in order to switch from a centralized method to a distributed optimization method, we study the energy efficiency optimization problem from a game theoretical point of view. We created a game theoretical model for energy efficiency in WBANs and investigated its best response and Nash Equilibrium of the single stage, non-cooperative game. Our results show that cooperation is necessary for efficiency of the entire system. Then we used two approaches, Correlated Equilibrium and Repeated Game, to improve the overall efficiency and enable some level of cooperation in the game.