## 1 Introduction
MeshEye [5] is a hybrid resolution smart camera more for applications in distributed intelligence surveillance. MeshEye has a low resolution stereo vision system that continuously determines the position, range, and size of moving objects entering in its field of view. This information triggers a color camera module to acquire a high resolution image of the object. It contains an ARM7 TDMI thumb processor. It is a 32 bit RISC architecture that can operate at up to 47.92 MHz. MeshEye has 64 KB of SRAM and 256 KB of flash memory. It uses a CC2420 radio transceiver and provides an implementation of the IEEE 802.15.4 standard. It uses the Agilent 2700 VGA camera module. In [6], a vision-enabled image processing framework for sensor motes is presented. It uses FireFly motes coupled with CMUCam3 sensor cameras. The main design objective of FireFly Mosaic is to design a low cost, energy efficient, and scalable camera mote compared to the centralized wireless webcam-based solutions. FireFly Mosaic runs Nano-RK, a real time operating system for WSNs and it uses the networking protocol stack provided by Nano-RK. FireFly motes, CMUCam3, and Nano-RK are developed at Carnegie Mellon University. The Fleck hardware platform [7] is designed for low-bandwidth wireless camera networks where image compression is undertaken at each node. Fleck is a robust hardware platform for outdoor use. The current version of FleckTM-3 consists of an ATmega 128 microcontroller running at 8 MHz. It is equipped with a Nordic NRF905 radio transceiver with a bit rate of 76.8 Kbits/s. Another promising feature of Fleck is that it is equipped with solar charging circuitry. It provides an additional DSP board which is a Texas Instruments 32 bit 150 MHz DSP processor (TMS320F2812). It has 1 MB of SRAM and an Omnivision \(640\times 480\) pixel color CCD sensor. Most of the wireless image sensors listed above are intended for general purpose wireless sensor network applications, some of them are designed with powerful processors and a wide set of I/O devices/modules that in practice require considerable power demands, so their autonomy is really constrained. Others are designed for specific applications so they cannot be easily reused for other applications like the one we are interested on. For this reason, we also review some proposals focused on our target application: pest trap monitoring systems based on image processing capabilities. In [8,9] the authors propose an innovative decision support system for _in situ_ early pest detection based on video analysis and scene interpretation to detect the presence of certain insects inside a greenhouse. Several wireless video cameras are suitably installed in the greenhouse capturing HD video at 10 frames per second. The video is recorded through a motion detector sensor that triggers video recording only when there are insects in the camera visual field. The captured video is delivered to a central server where video analysis algorithms combined with a priori knowledge about the visual appearance of insects (e.g., shape, size, color) are applied to the incoming video sequences to detect them. This approach is very ambitious since it was designed to monitoring different kinds of insects, but its cost is too high, requiring also a lot of communication and computing resources. In [10] a monitoring system designed to monitor the fruit fly pest is proposed. The sensor is just a smartphone that includes on-board camera and native cellular communication capabilities. In this work, the sensor nodes periodically capture one image and deliver it to the central server through 3G data service available at trap location. Image processing is performed in the central server to detect the presence and number of fruit flies in each trap.