# Analyzing the Performance of WBAN Links during Physical Activity Using Real Multi-Band Sensor Nodes
## 1 Introduction  
The continuous development of wireless communications, in addition to the miniaturization and reductions in cost of electronic devices have enabled the advance of Wireless Body Area Networks (WBANs). These networks are a subset of Wireless Sensor Networks (WSNs) and consist of low-power and lightweight sensor nodes placed along the human body to monitor its vital functions and other relevant data [1]. WBANs are primarily applied to healthcare and the biomedical field, but they have potential for use in several other areas, such as enhancing the human experience, sport training or even entertainment. In this latter broader sense, this paradigm has been called the _Human Intranet_[2]. One of the main challenges faced by WBANs is energy efficiency given that sensor nodes are generally autonomous and, thus, battery-powered. Several approaches have been studied to reduce energy consumption in WBANs, in all layers of the communication stack. Perhaps the most researched ones are those at the data link layer, mainly consisting of medium access control (MAC) protocols [3]. These strategies reduce the energy spending by decreasing the duty cycle of sensor nodes, thus, having their radio interfaces turned off for as long as possible, while also minimizing packet retransmissions. At the physical layer, transmission power control (TPC) mechanisms have been widely introduced to the WBAN field, having been a classical research topic of WSNs for years [4]. Very frequently, a WBAN has a star topology with one sink node in the middle section ofthe body and several sensor nodes placed along the limbs or at important body organs [5]. With body movements and posture changes, wireless link conditions between the sensor nodes and the sink change rapidly, creating fading and shadowing effects caused by body parts interrupting the signal path [6]. TPC protocols adapt the transmit power of sending nodes according to the received signal strength indicator (RSSI) recorded at receivers in order to respond to these changes [7]. Along with reducing energy waste these schemes also have the desired consequence of increasing reliability in WBAN links. This is a particularly important challenge in WBANs given the highly dynamic and changing nature of its channels and links. In order to have reliable communications between the sensors and the sink, transmitters must use a high enough transmit power to ensure the RSSI at the desired receiver is above its sensitivity at all times. When this is not achieved, packets are lost and the application quality-of-service (QoS) is dramatically reduced [8]. This is particularly worrying considering the critical nature of some WBAN applications such as prosthesis control, heart monitoring or medication dose management [9]. Research studies generally use the packet reception rate (PRR) or packet delivery rate (PDR) to measure the reliability of WSNs and WBANs. In some cases, authors use the complement of these metrics, referring to the packet loss rate (PLR), packet error rate (PER), packet delivery failure rate (PDFR) or a number of alternative names [7; 8; 10]. As can be inferred, the PRR is closely related to the RSSI measured at receiving nodes. When the RSSI is lower than the receiver sensitivity, packets are lost or some of their bits get corrupted. Some authors even go as far as discarding packets that actually arrive at their destination but with a RSSI lower than a pre-defined threshold [5]. The correlation between the PRR and the RSSI has been widely demonstrated in the literature, both for WSNs and WBANs [4; 11]. However, there are some scenarios where an increase in RSSI is not guaranteed to provide a higher PRR. This happens when interference between links is the main contributor to packet loss, as opposed to simple path loss [7]. Given the specific features of WBANs, i.e., node mobility and radio propagation around the human body, several research studies have attempted to characterize or model their effect on the different wireless links of the network [6; 12].