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:  Radios 
1. Abstract of Cited Paper (BIB001):  INTRODUCTION Surveillance isoneofthepromising applications towhichsmart Distributed smartcameras havereceived increased focus inthe cameramotesforming a vision-enabled network canadd research community overthepastseveral months. Thenotion of increasing levels ofintelligence. We seeahighdegree ofin-node camerascombined withembeddedcomputation powerand processing incombination withdistributed reasoning algorithmsinterconnected through radio links opensupa newrealmof asthekeyenablers forsuchintelligent surveillance systems. To intelligent vision-enabled applications. Real-time image putthesesystems intopractice still requires a considerableprocessing anddistributed reasoning madepossible bysmart amountofresearch ranging frommotearchitectures, pixel- cameras cannotonlyenhance existing applications butalso processing algorithms, uptodistributed reasoning engines. This motivate newapplications. Potential application areas range from paperintroduces MeshEye, anenergy-efficient smartcamera homemonitoring, elderly care, andsmartenvironments to motearchitecture thathasbeendesigned withintelligent security andsurveillance inpublic orcorporate buildings. Critical surveillance asthetarget application inmind.Special attention is issues influencing thesuccess ofsmartcameradeployments for given toMeshEye's unique vision system: alow-resolution stereo suchapplications include reliable androbust operation withas vision system continuously determines position, range, andsize of little maintenance aspossible. movingobjects entering itsfield ofview.Thisinformation triggers acolor cameramodule toacquire ahigh-resolution image ncmpaisonito alar sensors, shsm r e pess sub-array cotinn th obet whic ca.eefiinlhumidity, velocity, andacceleration sensors, vision sensors pub-arocessedainysubseq staeIt offerredced complextly generate muchhigher bandwidth dataduetothetwo-dimensional processed insubsequent stages. Itoffers reduced complexity, naueothipxlary.Tesermutofawda response~~~~~~~~ tie.n oe osmto vrcnetoa nature oftheir pixel array. Thesheeramountofrawdata 
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. 
3. Abstract of Cited Paper (BIB003):  Video is an important medium for the observation of a variety of phenomena in the physical world such as for water conservancy engineering. Video sensor networks (VSN) consists of a large number of sensor nodes with cheap CMOS cameras, therefore, it has the ability of large scale visual monitoring. However, a kind of powerful and scalable platform is urgent for simulation and development of VSN. This paper proposed a novel and scalable video sensor networks platform named as Vision Mesh for water conservancy engineering. Vision Mesh is composed of a mass of image or video sensor nodes - Vision Motes, with which multi-view image or video information of FOV (Field of View) can be acquired simply. Vision Mote is built with Atmel ARM9 CPU and operated on Linux operating system, with TI CC2430 Zigbee module taken as wireless transceiver. Furthermore, OpenCV machine vision lib is migrated to Vision Mesh platform so as to improve video processing ability, therefore, Vision Mesh has the ability of image and video processing and is of strong scalability to extend performance. In this paper, we provide an overview of Vision Mesh architecture and provide an insightful platform for VSN.

Literature Review:
There are four type of radios used in the platforms we investigated: (a) CC1000; (b) CC2420 or CC2430; (c) Bluetooth; and (d) IEEE 802.11. The Mica2 node, which is the host for Cyclops, uses CC1000 radio, which supports a maximum of 38.4 Kbps raw channel rate. MeshEye, XYZ (XYZ-ALOHA), FireFly (FireFly Mosaic), Tmote Sky (CITRIC) and Vision Mote are equipped with IEEE 802.15.4/ZigBee compliant CC2420 or CC2430, which supports 250 Kbps data rate. Although the data rate supported by CC2420 is higher than CC1000, it is still not high enough to transfer reasonable quality images in real time. We note that transmission rate achievable in practice is significantly lower than the theoretical maximum of 250 Kbps BIB004 . A JPEG-compressed 176×144 (QCIF) image requires 7 KB of memory, which is more than 50 128-Byte IEEE 802.15.4 packets BIB001 . Note that at most 4.3 QCIF frames can be transmitted per second with a 250 Kbps channel. MicrelEye node is equipped with a Bluetooth transceiver, which is chosen for its low power consumption. Since Bluetooth devices come with several usage profiles, it is easy to utilize them in embedded applications. Although it is possible to reach data rates up to 704 Kbps over RFComm standard, in MicrelEye 230.4 Kbps data rate is utilized. Nevertheless, the power dissipation is approximately 40% more than CC2420 power dissipation BIB002 BIB003 . Both Panoptes and Meerkats utilize IEEE 802.11 transceivers. Since both platforms are equipped with powerful processing hardware and are capable of generating real-time video, IEEE 802.15.4 or Bluetooth transceivers cannot cope with such a large amount of data. Thus, IEEE 802.11 transceivers are the best alternative (if not only) for real-time video streaming. However, the power dissipation of an IEEE 802.11b transceiver is more than an order of magnitude larger than the power dissipation on an IEEE 802.15.4 transceiver.