# Relay-Enabled Task Offloading Management for Wireless Body Area Networks
## 5 Results and Discussion
### Performance Evaluation  
In this section, the performance of the proposed task offloading-based resource management scheme is compared with a non-offloading technique. We focus on the performance of the relays and assume that the energy resources of the implanted sensor and the C-MEC are infinite. The performance is conducted on the basis of the residual network energy, network lifetime, PL, and total number of executed tasks \(C_{i}\). The characteristics of a commercially available WBAN transceiver (CC2420) are employed in the simulations and an initial energy of 0.5 Joule (J) is provided to all relays. The number of relays is limited to five, each with a single antenna. The packet size of \(C_{i}\) is set as 1500 bits since this is the typical payload defined by the IEEE 802.15.6 technical standard [8] and \(D_{i}\) is set as 1000 bits. The simulation parameters are summarized in Table 2. Simulations were conducted in MATLAB and performance results are compared in terms of \(f_{i}^{c}=1500\) CPU cycles, \(f_{i}^{c}=\)2500 CPU cycles, and the scheme without offloading. One should notice that other simulation techniques such as NS2 and OMNET+ are also applicable; detailed information can be found in References [33; 34]. Considering the strict QoS requirements of healthcare applications, and according to the previous work on WBANs, a minimum acceptable BER threshold of \(10^{-3}\) is selected for on-body transmission channel analysis. To guarantee human body safety, the maximum transmission power is set as \(-12\) dBm since this is recommended by the IEEE 802.15.6 technical standard. The maximum data transmission distance is selected as 2.5 m since this is a typical value of commercial IoT WBAN platforms for home and hospital areas [35; 36]. Since the on-body fading channel is time-varying, one should note that the channel capacity of the transmission link is not static. Figures 2 and 3 demonstrate the CDF versus channel capacity per unit and the link quality versus different transmission distances, respectively. It can be seen from Figure 2 that, under the same channel capacity, the higher transmission rate achieves a lower CDF performance. Moreover, from Figure 3 it can be seen that the lower data rate realizes a better link quality performance. As a result, an appropriate data rate \(\mathcal{R}_{ij}(U_{i})=500\) kbps is selected for further investigation. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Type** & **Node ID** & **X-Coordinate** & **Y-Coordinate** \\ \hline \multirow{4}{*}{On-body relay} & 1 & 0.2 & 1.65 \\  & 2 & 0.1 & 1.5 \\  & 3 & 0.65 & 1.5 \\  & 4 & 0.25 & 0.8 \\  & 5 & 0.7 & 1 \\ \hline Implanted \multirow{2}{*}{sensor} & \(-\) & 0.4 & 0.85 \\ \cline{1-1}  & \(-\) & 2.5 & 2.5 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Coordinates of on-body relays, implanted sensor, and coordinator-based mobile edge computer (C-MEC). In this paper, the network lifetime is defined as the time spanning from the network initiation until the energy of all relays is exhausted. The relationship between the residual energy of all relays and the network lifetime is given in Figure 4 under different scenarios. One can see that application requiring higher computation resources leads to a shorter lifetime when employing the offloading strategy. Primarily, the network lifetime is approximately 1200 rounds when \(f_{i}^{c}=2500\) CPU cycles, while it is 1800 rounds when \(f_{i}^{c}=1500\) CPU cycles. The reasons for these results are as follows: relay \(i\) with a higher computation capacity is highly likely to accept the offload task from others according to the proposed task offloading recipient selection algorithm in Section 4.2. Moreover, when considering the non-offloading technique, the relays only forward \(C_{i}\) to the C-MEC, thus significantly reducing the relays' task computing power consumption.