## 5 Experimental Results
### Part II--Results of Multi-Scenario and Multi-Objective Optimization  
The main goal of this test case is to verify the capability of the iMASKO framework under the conditions of multi-scenario and multi-objective optimization. The case applied in this experiment is the typical wireless body area networks (WBANs) [44,45], which are widely used in medical and health care applications and it is shown in Figure 7. This typical on-body sensor network consists of six different functional sensor nodes for physiological signals monitoring and measurement, which are ECG (electrocardiogram, 125 Hz sampling rate) sensor node for measuring the rate and regularity of heartbeats, Arterial (125 Hz) and Glucose (50 Hz) nodes for pressure measurements, Motion node (100 Hz) for movement strength recording, Respiration node (25 Hz) for the test of respiration frequency, and Temperature node (10 Hz) for the real-time temperature monitoring of human body. In the experiments, both Telos node and iHop@Node [40] based networks are under investigation, the typical payload length in the packet is set to 2 bytes, and each simulation is set to 2 s. Fixed seed values are also adopted to mitigate the influence of the starting time of data sensing. Further, note that iHop@Node consists of a low power microcontroller PIC16F88 [46] and a high data rate transceiver nRF24L01+ [47] which supports the rate of 2 Mbps, 1 Mbps, and 250 kbps. Since that Microchip's non-beacon based (unslotted CSMA/CA algorithm) MiWi [48] protocol stack is designed for PIC microcontroller families from PIC16 to dsPIC33, so the simulation of unslotted CSMA/CA algorithm is implemented in the PIC16F88 microcontroller of iHop@Node and related results will be shown (detailed use of unslotted CSMA/CA algorithm on iHop@Node is described in [1]). For such a network, each sensor node represents a specific scenario, and the use of different configurations of the unslotted CSMA/CA algorithm under this multi-scenario case could cause different performances at each sensor node. Note that in this experiment during the algorithm tuning process, the same algorithm parameter combination is used to configure all sensor nodes. As mentioned previously, performance optimization problems always involve multiple objectives to be met simultaneously in each specific scenario. These objectives are usually conflicting such as achieving maximum network reliability and at the same time minimizing the energy consumption. However, this kind of situation cannot happen most of the time, because with channel competition packet loss can be prevented if more channel access attempts, retransmission attempts, or longer backoff wait time are used, and hence more energy will be consumed in these processes. Therefore, there is no single solution to a multi-objective optimization problem such as this one, in addition to the use of the Pareto front method to find a set of mathematically equal solutions (Section 4.4). In the test, two objectives (energy consumption and packet loss rate) are subjected to optimization within the framework to achieve the solution trade-offs. In the multi-scenario situation of the network, the fitness function of each individual is composed of two pairs of energy consumption and packet loss rate for two different sensor nodes. The detailed fitness function is given as:  
\[\min_{(x)}\textbf{p}=[\textit{EnergyNode}1,\textit{PktLossProNode}],\textit{EnergyNode2},\textit{PktLossProNode2}] \tag{8}\]  
Figure 7: Typical WBANs for medical and health care. where \(X\) is the parameter space of the unslotted algorithm (four parameters). Node1 and Node2 are different nodes that are selected from ECG, Arterial, Motion, Glucose, Respiration, and Temperature sensor nodes. The multi-objective genetic algorithm in MATLAB R2012a optimization toolbox (ver. 6.2) was integrated into the iMASKO framework and was used to generate the Pareto front for energy consumption and packet loss probability.