## 5 Results and Evaluation  
We set up the network scenario similar to the application described in the first section. The simulations were performed in the Omnet++ [4] simulation tool with Energy Framework [5] developed at Technical University, Berlin. The network area is square shape with side of 1000 m and there is one data sink in every corner, totaling 4 data sinks. The sensor nodes are randomly placed into the area and are moving by Waypoint Mobility model with pedestrian speeds (uniform distribution 1-3 m/s) and pause time (uniform distribution 1-2 s). We assume using Imote2 wireless sensor node with CC2420 chipset [6]. To mitigate the energy loses from sleep mode scheduler, we assume ideal scheduler. A device is in the idle state only when it waits for message (e.g., for acknowledgments ACK) or when it performs calculation. Otherwise, device is in the sleep mode between communications (Tx or Rx modes). Ideal scheduler is unreal from the point that global knowledge or extra communication is required to make one sensor awake when another sensor node is willing to communicate with it. On the other hand, evaluation of the multi-hop forwarding methods or routing protocols might be affected by scheduler as some protocols might involve more frequent exchange of small messages, some less frequent larger message exchange, some control information exchange is periodical, some is on demand. Therefore, using one of the available schedulers for routing performance comparison could handicap one or other routing method. On the application layer, every sensor generates data messages with mean of two seconds. The simulation run lasts five minutes. When evaluating the lifetime, we run the same settings until the first node dies. Due to time and processing complexity, we set the initial battery capacity to 10 percent of the common 3 V battery capacity and then linearly extrapolated the results for lifetime of the full battery capacity. The summary of the simulation parameters is shown in the Table 1.  
Based on the initial goals and the functionality of the target application, we evaluated the performance of the routing methods from the following aspects:  
* _Lifetime_--time when the first sensor node completely dissipates (dies) in the system. Note, that many works refer to lifetime as the time until the last sensor node dies. As explained in the first section, our goal is to maximize lifetime of every node, not to maximize lifetime of the overall system.  
* _Energetic cost_--the ratio of the total energy dissipated in the system and the number of successfully received data packets. We introduce this novel evaluation method to describe the energetic performance of the routing protocol. More closely, we are interested how much energy was spent by the system in order to deliver certain amount of the useful information (data packets).
* _Routing overhead_--number of control bits per node per second. This evaluation method shows the heaviness of the protocol and its scalability. It is also directly related to energy effectiveness as redundant control information exchange cause unnecessary dissipation of nodes. On the other hand, if there is lack of information about the network, the performance and reliability can be affected.
* _Packet delivery ratio_--the ratio of successfully delivered data packets to any data sink, and all sent data by sensor nodes. This evaluation is important as it shows how reliable is the routing method in the mobile environment.
* _End-to-end delay_--the delay from the generation of data packet in the sensor to the successful detection of the packet at the destination (data sink) at network layer. Our application assumes health related data, therefore it is relatively critical for delay. On the other hand, delay sensitivity is not of the level of multimedia transfer, therefore we believe that several seconds would not affect the overall system performance.