A survey of visual sensor network platforms <s> Hardware <s> Video-based sensor networks can provide important visual information in a number of applications including: environmental monitoring, health care, emergency response, and video security. This paper describes the Panoptes video-based sensor networking architecture, including its design, implementation, and performance. We describe a video sensor platform that can deliver high-quality video over 802.11 networks with a power requirement of approximately 5 watts. In addition, we describe the streaming and prioritization mechanisms that we have designed to allow it to survive long-periods of disconnected operation. Finally, we describe a sample application and bitmapping algorithm that we have implemented to show the usefulness of our platform. Our experiments include an in-depth analysis of the bottlenecks within the system as well as power measurements for the various components of the system. <s> BIB001 </s> A survey of visual sensor network platforms <s> Hardware <s> Despite their increasing sophistication, wireless sensor networks still do not exploit the most powerful of the human senses: vision. Indeed, vision provides humans with unmatched capabilities to distinguish objects and identify their importance. Our work seeks to provide sensor networks with similar capabilities by exploiting emerging, cheap, low-power and small form factor CMOS imaging technology. In fact, we can go beyond the stereo capabilities of human vision, and exploit the large scale of sensor networks to provide multiple, widely different perspectives of the physical phenomena.To this end, we have developed a small camera device called Cyclops that bridges the gap between the computationally constrained wireless sensor nodes such as Motes, and CMOS imagers which, while low power and inexpensive, are nevertheless designed to mate with resource-rich hosts. Cyclops enables development of new class of vision applications that span across wireless sensor network. We describe our hardware and software architecture, its temporal and power characteristics and present some representative applications. <s> BIB002 </s> A survey of visual sensor network platforms <s> Hardware <s> Although imaging is an information-rich sensing modality, the use of cameras in sensor networks is very often prohibited by factors such as power, computation cost, storage, communication bandwidth and privacy. In this paper we consider information selective and privacy-preserving address-event imagers for sensor networks. Instead of providing full images with a high degree of redundancy, our efforts in the design of these imagers specialize on selecting a handful of features from a scene and outputting these features in address-event representation. In this paper we present our initial results in modeling and evaluating address-event sensors in the context of sensor networks. Using three different platforms that we have developed, we illustrate how to model address-event cameras and how to build an emulator using these models. We also present a lightweight classification scheme to illustrate the computational advantages of address-event sensors. The paper concludes with an evaluation of the classification algorithm and a feasibility study of using COTS components to emulate address-event inside a sensor network. <s> BIB003 </s> A survey of visual sensor network platforms <s> Hardware <s> 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. <s> BIB004 </s> A survey of visual sensor network platforms <s> Hardware <s> 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. <s> BIB005
Architecture of Cyclops BIB002 is given in Fig. 3 . Cyclops is designed to operate with Mica2 mote, a WSN platform from Crossbow technology [10] . The imager is a CIF (352×288) resolution camera. The MCU is an 8-bit processor, which is responsible for controlling Cyclops, communicating with Mica2 and performing image inference. In addition to limited amount of internal MCU memory, there is an external SRAM (64 KB) as well as an external FLASH (512 MB). Image capture is performed by the CPLD (Complex Programmable Logic Device).  Hardware architecture of Panoptes BIB001 is illustrated in Fig. 5 . The Panoptes platform is originally developed on an Applied Data Bitsy board, which utilizes a processor with 64 MB of on-board memory. A video camera is utilized for acquisition with resolutions ranging from VGA (640×480) to lower resolutions (160×120). The communication module is an off-theshelf IEEE 802.11 card. The system is later migrated to the Crossbow Stargate platform [10] . Panoptes use USB 1.0 as the primary I/O interconnect with a maximum of 12 Mbps aggregate bandwidth due to the fact that at the time of implementation low-powered embedded devices do not support higher bandwidth (455 Mbps) USB 2.0.  Hardware architecture of MicrelEye BIB004 is presented in Fig. 8 . The processor is an ATMEL FPSLIC SoC, which includes an AT40K MCU, a Field Programmable Gate Array (FPGA) with 40 K gates, and 36 KB of onboard SRAM (16 KB can be used for data and 20 KB is reserved for program storage). The external memory for frame storage is a 1 MB SRAM. Wireless capabilities of the node are provided by the integration of LMX9820A Bluetooth transceiver, which includes a radio, a baseband controller, and a memory block. Hardware architecture of XYZ-ALOHA platform BIB003 is given in Fig. 9 . XYZ nodes use a processor that features an ARM7TDMI 32-bit microcontroller. The processor has 32 KB of internal RAM and 256 KB of FLASH. There is an additional 2 MB external RAM available on the node. The radio transceiver of the platform is a CC2420 radio. Three different platforms are built on top of XYZ sensor nodes with (a) ALOHA image sensor; (b) OV 7649 camera module from OmniVision; (c) software image emulator. The first of these platforms is named XYZ-ALOHA. OV7649 camera module can capture images at VGA (640×480) and QVGA (320×240) resolutions.   Hardware architecture of Vision Mote BIB005 is presented in Fig. 11 . Vision Mote has 128 MB Flash and 64 MB SDRAM memory. An unspecified CMOS camera is used for visual data acquisition.