# Mobile Autonomous Sensing Unit (MASU): A Framework That Supports Distributed Pervasive Data Sensing
## 5 MASU Empirical Evaluation
### Evaluation Results
#### 5.4.3 Resource Consumption for an Increasing Number of Nodes  
The evaluation of the Prototype I, provided various insights about the benefits of collaborative sensing and the involved costs. This evaluation represented a worst case scenario due to the poor  
Figure 16: Evaluation of the cost of AllJoyn in a collaborative sensing activity.  
Figure 17: Comparison of the energy costs of high data-intensive and low data-intensive sharing processes.  
efficiency of the software implementation embedded in the Unity platform, as well as due to the higher costs of AllJoyn as data retrieval method. Therefore, we conducted various tests using the prototype II, which provides a more realistic experimentation scenario with a more efficient implementation. In this case, CoAP was used as data retrieval method having only one Producer and a number of Consumers active. In this scenario, the Producer senses GPS data, creates a CoAP service and sends the GPS coordinates to the Consumers subscribed to the service.  
Figure 18 shows the results of these tests when the Producer sends GPS sensed data to the Consumers every time that its location changes. The figure illustrates the CPU and energy consumption of both Consumers and Producers when the number of Consumers of GPS data increases. It shows that the CPU utilization of the Producer increases with the number of Consumers. This increment is mainly caused by the high number of CoAP messages that the Producer has to send due to the high change rate of the GPS data. Particularly, for each 20-min test, the Producer had to send around 900 CoAP messages to each Consumer.  
We can also observe in this figure that the energy cost of the Producer (GPS sensing and transmitting) is slightly higher than the energy cost of sensing GPS, whereas the energy cost of the Consumer (receiving information) is significantly smaller. This indicates that there is a clear benefit for the Consumers, while the cost for the Producer is relatively low. Nevertheless, there is a slight battery consumption increase when increasing the number of Consumers. This was an expected result because the amount of energy should increase when more devices are added to the system, due to more the energy consumption required for the Wi-Fi subsystem maintenance. Despite this, if we consider the overall battery consumption of the system, we have an important reduction in the energy consumption (295 mWh in total for one Producer and four Consumers) when we use the prototype for sharing the sensed data, instead of sensing data independently. Once again, this shows the advantages of collaborative sensing. In this case, we achieved a 43% of reduction in battery drain, considering the overall group of devices involved. Therefore, both Consumer and Producer applications are energy-efficient and most of the energy cost of the Producer is caused by GPS sensing.  
We also evaluated the CPU utilization when the Consumers call the CoAP service of the GPS Producer periodically, every 60 s (Figure 19). In this case, the Producer does not send GPS data continuously. It only sends every 60 s the last GPS value measured. This means that, although the GPS sensor still active, the Producer only does a GPS reading every 60 s. Thus, the number of CoAP messages sent by the Producer decreases significantly (around 20 messages for each Consumer). As a result, the CPU utilization of the Producer is significantly smaller than when it was sending data continuously (Figure 18), and it increases very slowly with the number of Consumers.  
Figure 18: Producers and Consumers resource utilization when sharing GPS data continuously. (**a**) CPU utilization; (**b**) Battery Consumption.