## 11 Hardware and Testbeds
### Camera Motes
The Aquis Grain Zigbee from ChipCon'CC2420 transceiver implements the wireless communication module. The multimedia processing in this camera sensor mote is divided into three levels: low, intermediate, and high-level image processing. Low-level image processing (pixel level) is manipulated by the SIMD processor and it is associated with typical kernel operations such as convolutions, data dependent operations using neighboring pixels, and initial pixel classification. The intermediate and high-level image processing (object level) are done by the general purpose processor because it has the flexibility to implement complex software tasks, run an operating system, and do networking application. In [102] the authors present a camera mote for behavior recognition in wireless multimedia sensor networks based on biologically inspired address-event imagers and sensory grammars. In Address Event Representation (AER), the camera networks operate on symbolic information rather than images by filtering out all redundant information at the sensor level and outputting only selected handful of features in address-event representation. This leads to minimize power consumption and bandwidth (they only consume a few \(\mu\)W of power in active state and use different computation model that is faster and more lightweight than conventional image processing techniques), and helps to offer privacy concerns as certain features are being transmitted. Then the output of the AER imagers can be connected into the sensing grammar that converts low-level sensor measurements to higher-level behavior interpretation based on probabilistic context free grammars (PCFGs). PCFGs are very similar to the Hidden Markov Models and they are used because of their expressiveness, generative power, and modularity. The authors developed three different platforms to experiment the above techniques where each platform is built on top of the XYZ sensor node [109]. XYZ uses an OKI ML67Q5002 processor based on ARM7 TDMI core running at 58 MHz. The processor has 32 KB of internal RAM and 256 KB of Flash, and there is additional 2 Mbit memory available on-board. The first platform is (XYZ-ALOHA) an XYZ sensor node with ALOHA image sensor that is composed of four quadrants of 32 \(\times\) 32 pixels and it is able to generate 10,000 events in 1.3 sec with a power consumption of 6 \(\mu\) W per quadrant. The ALOHA image sensor uses the simple ALOHA medium access technique to transmit individual events to a receiver. The second platform is (XYZ-OV) an XYZ sensor node with a camera sensor from Omnivision that can capture images at resolution of VGA (640 \(\times\) 480) and QVGA (320x240). Currently, imote2 has been used with Omnivision OV7649 camera as a third platform. The paper shows an example for assisted living application where the prototype network (imote2-OV) was able to distinguish between \"cooking\" from \"cleaning\" actions done by a person in a kitchen. \begin{table}
\begin{tabular}{|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|p{56.9 pt}|} \hline \multirow{2}{*}{Platform} & \multirow{2}{*}{Processor} & \multicolumn{2}{c|}{Memory} & \multicolumn{1}{p{56.9 pt}|}{Camera \&} & \multicolumn{1}{p{56.9 pt}|}{Power} \\  & & RAM & \multicolumn{1}{p{56.9 pt}|}{Flash} & \multicolumn{1}{p{56.9 pt}|}{Resolution} & & \multicolumn{1}{p{56.9 pt}|}{consumption} \\ \hline Cyclops [106] & 8-bit ATHEI ATmega128L MCU +  
An energy-efficient smart camera mote, called MeshEye [110], is proposed for distributed intelligent surveillance application in WMSN. MeshEye mote architecture is designed to support in-node image processing, with sufficient processing power capabilities, for distributed intelligent algorithms in wireless sensor network of two tiers while minimizing component count and power consumption. In the first tier, a low-resolution stereo vision system is used to determine position, range, and size of moving objects in its field of view. The second tier contains high resolution cameras that are triggered in case of detecting objects by the first tier.