# Realistic Simulation for Body Area and Body-To-Body Networks
## 2 Related Works, Simulators and Theirs Limits
### BAN-Specific Frameworks
#### 2.2.1 Channel-Oriented Frameworks  
Several categories of BAN communications can be defined according to the specific node positions. These differences yield to a large variety of channel models that have been proposed in the literature. As our study focuses on BANs' simulation, we will address on-body channels for intra-BAN communications and body-to-body channels for modeling the interference generated by one BAN on another and the inter-BAN communications.  
Investigations on the on-body channel have been carried out for several years. Initially, works attempted to classically model the channel as a function of the distance between the on-body antennas [36]. The same approach was used in the IEEE 802.15.6 reference document for the channel, yielding the channel model (CM3-A) and channel model (CM3-B) [37] (as detailed later in Section 3.2.2). Later, a scenario-based approach [38] has been found more suitable to describe such channels as body shadowing and human mobility. It is found that inter-node distances are not the only factors that have an impact to the greatest extent. Therefore, a scenario can be represented as the specific Tx and Rx antenna location on the body along with the environment and human body mobility condition.  
With respect to the on-body channel, body-to-body channels have been less investigated. In the IEEE 802.15.6 model, CM4 has been proposed for these emerging communications, especially focusing on hospital environments [37]. In the framework of cooperative communications, classical indoor and outdoor environments and different dynamic conditions of the users have to be considered [39; 40]. Body-to-body scenarios have been investigated to characterize interference given by nearby BANs at 2.4 GHz and 60 GHz [41].  
Aiming at the exploitation of the body-to-body cooperative communications, channel measurements were performed at 2.45 GHz in [42]. Different movements were considered for different subjects: forward walk, opposite walk, parallel walk and rotation. The results show that the mean channel gain is strictly related to the specific node emplacement (relative position of the transmitter and the receiver) and to the antenna type considered. Shadowing from the body is a predominant effect. The NLOS condition given by the obstruction from one subject implies an important reduction of channel gain. The direct path being strongly attenuated, all propagation comes from multi-paths on the surrounding environment.  
In [43; 44], the authors propose a simulation platform to study interference between BANs and propose to take advantage of possible inter-body channel correlations to judiciously select the slot in the superframe. This platform is interesting, but it needs a more accurate inter-BAN channel model (scenario-based instead of statistical). In [45; 46], [PERSON] and [PERSON] evaluate the co-channel interference of multiple BANs using a TDMA MAC using a guaranteed time slot with a realistic environment model. Their simulations are based on large sets of empirical everyday activity channel data, but no network simulators are linked to this database.  
Finally, context-aware body area networks (CABAN) [47; 48] introduce channel fading characteristics for an interactive environment. For example, [47] proposed a context-aware measurement system, which is capable of simultaneously characterizing both off-body and on-body channels. It is reported that fading characterization was often arbitrary due to the divergence of the empirical data to that of the fading distributions and that reflects that with context variation and mobility, the channel variations can be significantly different.