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:  Applications 
1. Abstract of Cited Paper (BIB001):  With the advent of CMOS cameras, it is now possible to make compact, cheap and low-power image sensors capable of on-board image processing. These embedded vision sensors provide a rich new sensing modality enabling new classes of wireless sensor networking applications. In order to build these applications, system designers need to overcome challenges associated with limited bandwidth, limited power, group coordination and fusing of multiple camera views with various other sensory inputs. Real-time properties must be upheld if multiple vision sensors are to process data, communicate with each other and make a group decision before the measured environmental feature changes. In this paper, we present FireFly Mosaic, a wireless sensor network image processing framework with operating system, networking and image processing primitives that assist in the development of distributed vision-sensing tasks. Each FireFly Mosaic wireless camera consists of a FireFly (Rowe et al., 2006) node coupled with a CMUcam3 (Rowe et al., 2007) embedded vision processor. The FireFly nodes run the nano-RK (Eswaran et al., 2005) real-time operating system and communicate using the RT-link (Rowe et al., 2006) collision-free TDMA link protocol. Using FireFly Mosaic, we demonstrate an assisted living application capable of fusing multiple cameras with overlapping views to discover and monitor daily activities in a home. Using this application, we show how an integrated platform with support for time synchronization, a collision-free TDMA link layer, an underlying RTOS and an interface to an embedded vision sensor provides a stable framework for distributed real-time vision processing. To the best of our knowledge, this is the first wireless sensor networking system to integrate multiple coordinating cameras performing local processing. 
2. Abstract of Cited Paper (BIB002):  Wireless sensor networks are an important research area that has attracted considerable attention. Most of this attention, however, has been concentrated on WSNs that collect scalar data such as temperature and vibration. Scalar data can be insufficient for many applications such as automated surveillance and traffic monitoring. In contrast, camera sensors collect visual data, which are rich in information and hence offer tremendous potential when used in WSNs. However, they raise new challenges such as the transmission of visual data with high computational and bandwidth requirements in mainly low-power visual sensor networks. In this article we highlight the challenges and opportunities of VSNs. We discuss major research issues of VSNs, specifically camera coverage optimization, network architecture, and low-power visual data processing and communication, and identify enabling approaches in this area. 
3. Abstract of Cited Paper (BIB003):  Visual sensor networks have emerged as an important ::: class of sensor-based distributed intelligent systems, ::: with unique performance, complexity, and quality of service ::: challenges. Consisting of a large number of low-power camera nodes, ::: visual sensor networks support a great number of novel ::: vision-based applications. The camera nodes provide information ::: from a monitored site, performing distributed and collaborative ::: processing of their collected data. Using multiple cameras in the ::: network provides different views of the scene, which enhances ::: the reliability of the captured events. However, the large amount ::: of image data produced by the cameras combined with the ::: network's resource constraints require exploring new means ::: for data processing, communication, and sensor management. ::: Meeting these challenges of visual sensor networks requires ::: interdisciplinary approaches, utilizing vision processing, communications ::: and networking, and embedded processing. In this ::: paper, we provide an overview of the current state-of-the-art in ::: the field of visual sensor networks, by exploring several relevant ::: research directions. Our goal is to provide a better understanding ::: of current research problems in the different research fields of ::: visual sensor networks, and to show how these different research ::: fields should interact to solve the many challenges of visual sensor ::: networks. 
4. Abstract of Cited Paper (BIB004):  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 many applications envisioned for VSN platforms (Table 3 lists only the applications specifically targeted by each platform). Application scenarios include vehicle tracking applications, environmental observation systems BIB001 , security and surveillance applications BIB001 , emergency response systems BIB001 , assisted living applications and smart homes BIB003 BIB002 BIB004 , water conservatory engineering BIB005 , virtual reality and telepresence systems BIB003 BIB004 and smart meeting rooms BIB004 . By taking a holistic view, we can see that all these applications can be realized by implementing three sets of tasks: (a) object and event detection and localization; (b) recognition of these objects and events; (c) tracking, monitoring and real-time video delivery. It is of no surprise that these tasks match with those already mentioned when we introduce the concept of hierarchical network organization in previous subsection. Panoptes and Meerkats platforms are the best matches for use in the highest tier (tier3) and for real-time video surveillance and object tracking purposes due to their relatively high bandwidth radios (i.e., IEEE 802.11) and ample processing and storage capabilities. MeshEye, FireFly Mosaic, MicrelEye, CITRIC, and Vision Mote platforms can be used as tier2 platforms and for object and event recognition. MeshEye has the lowest energy dissipation among these platforms. With its multiple types of cameras on the same platform, it can also be used as a tier-1 platform (low resolution cameras for object detection and high resolution camera for high resolution image acquisition for object recognition). Cyclops and XYZ-ALOHA are two platforms most suitable as tier1 platforms for object and event detection and object localization applications. In a multi-tiered architecture, sensor nodes which do not have the ability to capture visual information can also be used for detection purposes. For instance, upon sensing an event with a PIR sensor a low-cost sensor node can trigger a tier2 platform which takes high resolution pictures and performs complex object recognition processing. PIR sensors are up to two orders of magnitude more energy-efficient than cameras BIB002 .