You are presented with multiple paper abstracts, alongside the associated literature review and chapter titles. Given the provided information, you are going to do a literature review. Please generate a coherent literature review that synthesizes the content, draws connections between different works, highlights major findings, and identifies any gaps in the existing research.

Literature Review Title: A survey of visual sensor network platforms 
Chapter Title:  Energy dissipation 
1. Abstract of Cited Paper (BIB001):  Wireless networks in combination with image sensors open up a multitude of previously unthinkable sensing applications. Capable tools and testbeds for these wireless image sensor networks can greatly accelerate development of complex, yet efficient algorithms that meet application requirements. In this paper, we introduce WiSNAP, a Matlab-based application development platform intended for wireless image sensor networks. It allows researchers and developers of such networks to investigate, design, and evaluate algorithms and applications using real target hardware. WiSNAP offers standardized and easy-to-use application program interfaces (APIs) to control image sensors and wireless motes, which do not require detailed knowledge of the target hardware. Nonetheless, its open system architecture enables support of virtually any kind of sensor or wireless mote. Application examples are presented to illustrate the usage of WiSNAP as a powerful development tool. 
2. Abstract of Cited Paper (BIB002):  In this paper we propose MicrelEye, a wireless video node for cooperative distributed video processing applications that involve image classification. The node is equipped with a low-cost VGA CMOS image sensor, a reconfigurable processing engine (FPGA, Microcontroller, SRAM) and a Bluetooth 100-m transceiver. It has a size of few cubic centimeters and its typical power consumption is approximately ten times less than that of typical commercial DSP-based solutions. As regards classification, a highly optimized hardware-oriented support vector machine-like (SVM-like) algorithm called ERSVM is proposed and implemented. We describe our hardware and software architecture, its performance and power characteristics. The case study considered in this paper is people detection. The obtained results suggest that the present technology allows for the design of simple intelligent video nodes capable of performing classification tasks locally.

Literature Review:
While the energy consumption of WSN nodes is dominated by communication, this is generally not true in VSN nodes. For example, it was shown that communication energy dissipation constitutes 91% and 62% of the total energy dissipation in Telos and MicaZ scalar sensor nodes, respectively BIB001 . Unlike in scalar sensor nodes, energy dissipation in visual sensor nodes is dominated by the computation energy rather than the communication energy. For example, communication energy dissipation constitutes only 22% of the total energy dissipation of MicrelEye VSN platform BIB003 . Since vision involves handling large chunks of data, visual sensors are typically power hungry and vision algorithms are computationally expensive BIB001 . Therefore, the hardware that is suitable for handling such computational challenges is liable to exhaust the limited energy sources of sensors quickly, unless they are configured appropriately. In sensor network applications, vision can only play a meaningful role if the power consumption of the nodes is not significantly increased BIB001 . The energy dissipation characteristics of VSN platforms described in Section 4 are collected and presented in Table 2 . Note that we present the maximum and minimum (if available) energy dissipation values reported for the platforms investigated. We do not include more detailed energy dissipation characteristics (e.g., energy cost of one picture transmission) since such measurements are not reported for most of the platforms. Cyclops energy dissipation is the lowest, due to the fact that Cyclops is designed specifically for energy efficiency. It has a lightweight 8-bit processor and small amount of memory suitable for low speed processing and low-resolution images. Furthermore, the whole system is designed to support the capability for automatic relaxation of each subsystem to its lower power state adaptively. However, we note that even the most basic image processing application dissipates more energy with lower capability processors than they dissipate with moderate capability processors BIB002 . The XYZ-ALOHA platform's power dissipation is mostly due to the XYZ sensor node's operation, since the ALOHA imaging module is not a conventional imaging system and its power dissipation is extremely low. On the other hand, most of the power dissipation of the FireFly Mosaic platform is due to the imaging subsystem (i.e., CMUCam3), and the combined power dissipation by the radio and the processor running the radio is less than 13% of the total power in active mode. Likewise in CITRIC when the imaging board is in idle mode, less than 20% of the energy dissipation is due to the networking platform (when the imaging board is active energy dissipation of the imaging board constitutes an even larger percentage of the total energy dissipation). Panoptes and Meerkats are designed as high-end platforms with powerful computational capabilities, and their power dissipation figures are approximately an order of magnitude higher than the rest of the platforms. It should be noted that both of these platforms are equipped with IEEE 802.11 radios and capable of transmitting at least an order of magnitude, larger volume of data than the IEEE 802.15.4 radios used in the lower power platforms.