# Multi-Objective Optimization of a Wireless Body Area Network for Varying Body Positions
## 1 Introduction  
Wireless Body Area Networks (WBANs) are a lively field of research and development because they promise the long-awaited progress towards the widely-available monitoring of physiological parameters--vital signs--in healthcare, health and safety, military applications, and elsewhere [1]. This potential has been recognized by both research and engineering communities, resulting in efforts to standardize this class of wireless networks [2]. The standards establish a common framework for the development of WBAN applications, but many issues remain open for research and development, including the use of recognized optimization methods. Some research has been conducted into the improvement of WBANs, but mainly in the higher layers of the Open System Interconnection (OSI) model. For instance, in Reference [3] parameters such as payload length and the number of transmission retries were optimized and in Reference [4] the packet size was optimized. Research has also been conducted into the optimization of wearable antennas (e.g., Reference [5]). Nevertheless, one of the major and intrinsic difficulties with WBANs follows from the complex and unfavorable characteristics of the radio channel for both on-body and-off body links. There may be relatively high and variable path loss between some nodes, due in part to the presence of a human body: its posture and position relative to environmental elements. This problem is exacerbated during the outdoor operation of WBANs, when multipath propagation is weak and does not compensate for the high path loss of the direct node-to-node radio channel [6]. Much effort has been devoted to investigating path loss for various body positions, such as standing and sitting [7]. However, these havefocused on certain predefined and fixed postures. In this paper, we formulate and solve a somewhat different, multi-objective multi-parameter optimization problem, i.e., the automatic optimization of node position on the user's body. Our objective is twofold: to reduce the on-body path losses between nodes and to improve the systemic off-body channel loss (considering the directivity of the wearable antenna system). The paper presents a different and more advanced approach in comparison to our previous work [8], in which we imposed a number of limiting design assumptions--for example, fixed positions for the head and arm nodes. The only degree of freedom was the placement of the chest node. The results were encouraging: a considerable decrease in the path loss between nodes using a single objective automated optimization procedure was achieved, based on evolutionary computing. The current paper has a more ambitious aim. Minimizing a multi-objective function, which simultaneously depends on a set of design variables, is a vitally important problem in many fields of engineering. It is part of many optimization routines which are used widely to search for possible improvements in already existing engineering designs, often developed by heuristic methods or inaccurate design formulas. This also refers to radio communication, where antenna design or wireless system performance can be improved--for instance, by reducing transmit power and battery energy consumption [9]. The design of electromagnetic devices and systems typically requires controlling several aspects of their performance, resulting in multiple design criteria that give rise to multi-objective design problems [10; 11; 12; 13; 14; 15; 16]. To cope with a vector of design criteria, a fully Pareto-like formulation or a preference-function approach can be used. In the former case, the set of best compromise solutions (also known as non-dominated solutions) has to be identified. In contrast, in the latter case, the single-objective algorithms can be exploited, especially those belonging to the evolutionary class, which have proven to be cost-effective and global-optimum oriented. In the research presented in this paper, we used a Pareto optimization algorithm.