Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions

Abstract

The purpose of this research was to improve the performance of a wireless body area sensor network, operating on a person in the seated and standing positions. Optimization-focused on both the on-body transmission channel and off-body link performance. The system consists of three nodes. One node (on the user’s head) is fixed, while the positions of the other two (one on the user’s trunk and the other on one leg) with respect to the body (local coordinates) are design variables. The objective function used in the design process is characterized by two components: the first controls the wireless channel for on-body data transmission between the three sensor nodes, while the second controls the off-body transmission between the nodes and a remote transceiver. The optimal design procedure exploits a low-cost Estra, which is an evolutionary strategy optimization algorithm linked with Remcom XFdtd, a full-wave Finite-Difference Time-Domain (FDTD) electromagnetic field analysis package. The Pareto-like approach applied in this study searches for a non-dominated solution that gives the best compromise between on-body and off-body performance.

Optimal Design of Switchable Wearable Antenna Array for Wireless Sensor Networks

“…The radiation patterns of the wearable antennas obtained with the cylindrical model were in good agreement with the one obtained with the heterogeneous model [21]. The cylindrical model is convenient for application to WBAN optimization when the antenna position is variable, but its distance to the body surface is fixed [22,23]. In this case, the position of the antenna can be controlled easily with two coordinates r a , ψ a in a cylindrical coordinate system presented in Figure 2b.…”

Accurate Design of Microwave Filter Based on Surrogate Model-Assisted Evolutionary Algorithm

“…Take 6 points (d 1 , d 2 , d 3 , d 4 , d 5 , d 6 ) at equal intervals on the intercepted S 11 , take the distance between these 6 points and y = −20 dB, and the sum is the fitness function of PSO. The fitness function is Equation (13), and the goal is to find the minimum value.…”

“…The same parameters and fitness functions are used in PSO. The optimization objective is the minimization of the fitness function, cost in Equation (13), to satisfy the design specifications. Figure 13 presents a contrast between the HFSS simulation results and the predicted results from the AOU-1D-CAE model.…”

“…The design of filters often has complex and multi-objective aspects. Reaching the optimal variable is related to the objective function under multiple objectives [12,13]. However, the shortcoming is the comparatively slow rate of convergence in these algorithms.…”