## 3 Image Sensor: Power Consumption Analysis
### Image Sensor Autonomy  
With the results obtained before, we may estimate the autonomy of our image sensor equipped with standard battery units, varying the image size (\(80\times 64\), \(160\times 128\), \(320\times 240\), and \(640\times 480\)) and the image capture period (0.5, 1, 3, 6, 12 and 24 h between two consecutive images).  
In Figure 7, we show the autonomy of the image sensor node powered with the proposed battery unit with a nominal capacity of 1,200 mAh (assuming a battery efficiency of 80%) at different image format sizes and image capture periods. As explained before, the image sensor is in standby mode most of the time with ultra-low power consumption (\(\sim\)8 \(\upmu\)A). When the capture cycle starts, the sensor node moves to an active state to capture, compress and send the image before returning to standby mode until next capturing cycle. As it can be observed there is room for usability, since for the largest  
Figure 6: Power consumption profile of the different running mode operations.  
image format size (640 \(\times\) 480 YUV 4:2.1) and the highest capture frequency (1 image every 30 min) the node autonomy is estimated in more than seven months. At the other hand, node autonomy is increased up to more than 25 years (in this case, the sensor node autonomy is limited by the battery operational life time. The proposed batteries are designed to work between 10 to 15 years at optimum operating parameters (temperature, current discharge, _etc._)) using the same image format size but with a longer capture cycle of one image at day.  
Remember that we have made this study assuming a direct communication between sensors and central station (single hop network), and we do not care about other network related issues, as packet error delivery. Although single hop communication approach would be enough for trap deployment in greenhouses or small monitoring areas, the support for network delivery errors should be taken into account. This would not represent any additional complexity in terms of sensor node resources (memory or computing power), but it will increase the power consumption of the image sensor. So, depending on the environmental noise present at the monitoring area, we will suffer a particular packet error rate. This would imply the retransmission of the image slices that were lost with the corresponding power consumption overhead.  
To illustrate the impact of packet transmission errors in power consumption, we show in Figure 8 an approximation of the image sensor autonomy reduction at different packet error rates (0.001, 0.01, 0.05, 0.1 and 0.2) working at the highest capture frequency (one image every 30 min). With the \(640\times 480\) image size, the captured and compressed images are fragmented in an average of 49 slices. So, in order to deliver the whole image to the central station we need to transmit 49 packets. Depending on the actual packet error rate, some of these packets may require retransmission due to  
Figure 7: Autonomy of our image sensor equipped with 1,200 mAh battery (80% efficiency) with different image format sizes and capture frequencies.  
channel errors, increasing the total number of packets that effectively were transmitted to central station. This issue leads to an increase of power required to transmit each captured image in a more realistic network conditions.  
As it can be seen in Figure 8, the autonomy of our image sensor is reduced as the packet error rate grows. However, the autonomy reduction under extreme channel errors conditions (one packet out of five is lost or with errors) is around 8% for the highest image size. Considering the image sensor autonomy shown at Figure 7, we conclude that the impact of packet delivery errors over autonomy is very limited for regular packet error rates.