A survey of visual sensor network platforms <s> Platform design for VSNs <s> All emphasize low-cost components operating on shoestring power budgets for years at a time in potentially hostile environments without hope of human intervention. <s> BIB001 </s> A survey of visual sensor network platforms <s> Platform design for VSNs <s> We introduce Meerkats, a wireless network of batteryoperated camera nodes that can be used for monitoring and surveillance of wide areas. One distinguishing feature of Meerkats (when compared, for example, with systems like Cyclops [10]) is that our nodes are equipped with sufficient processing and storage capabilities to be able to run relatively sophisticated vision algorithms (e.g., motion estimation) locally and/or collaboratively. In previous work [9, 8, 7] we analyzed the energy consumption characteristics of the Meerkats nodes under different duty cycles, involving different power states of the system’s components. In this paper we present an analysis of the performance of the surveillance system as a function of the image acquisition rate and of the synchronization between cameras nodes. Our ultimate goal is to optimally balance the trade-off between application-specific performance requirements (e.g., event miss rate) and network lifetime (as a function of the energy consumption characteristics of each node). <s> BIB002
The main differences between WSNs and VSNs are in acquiring, processing, and transferring data. Visual data requires higher bandwidth usage due to the amount of data to be transmitted and higher power consumption due to the complexity of coding and vision processing algorithms and high volume data acquisition. The spectrum of WSN platforms ranges from lightweight platforms (e.g., Mica2) through intermediate platforms (e.g., Yale XYZ) to PDA-class platforms (e.g., Intel Stargates) BIB001 . Table 1 presents the features of several WSN platforms. Lightweight platforms (e.g., Mica2, Mica2Dot, MicaZ, and Telos) are highly resource-constrained; thus, they are only suitable for simple sensing and detection tasks. The Yale XYZ platform, which is a typical example of intermediate platforms, has more memory and processing resources than the lightweight platforms. At the higher performance set, there is PDA-class platforms (e.g., Stargates) which are more powerful than the intermediate platforms but also consume more power. Vision computing techniques can reduce the amount of visual data to be transmitted at the cost of more computation. To illustrate this fact, let us consider an application that controls registration plates of vehicles traveling in an urban area. Such application is useful for the implementation of local administration policies to reduce the amount of pollution, by alternating the circulation of vehicles based on the parity of their number plates (this kind of a policy has been introduced for the Beijing Olympic Games). It is possible to use a VSN for monitoring the vehicle plates in three different ways: (a) capturing real-time video and sending the data to the base station, which requires real-time video compression and large bandwidth; (b) taking still images of the vehicles and sending the images to the base station, which requires image compression and vision computing (i.e., detection of vehicles); (c) extraction of the plate numbers from the captured still images and sending these to the base station, which requires sophisticated vision computing. Direct utilization of WSN platforms for vision applications is not feasible due to several facts itemized as follows: & Lightweight WSN platforms are not capable enough in terms of computational power and memory for video acquisition and vision computing tasks; hence, direct use of these platforms for VSN applications is not feasible. Furthermore, the radios used with lightweight platforms do not have enough bandwidth to support streaming video with reasonable quality. & Intermediate WSN platforms (e.g., XYZ node) have better computational capabilities; however, they are also equipped with low bandwidth radios (e.g., IEEE 802.15.4 compliant CC2420). Thus, real-time video streaming is also not possible with these platforms. The alternative for real-time video streaming relies on the use of IEEE 802.11 radio networks that can support bandwidths of an order of magnitude higher than IEEE 802. 15.4 . & PDA-class platforms equipped with IEEE 802.11 radios are suitable for real-time video streaming due the highly capable processors and large memory available on board. However, the energy dissipations of these devices are more than an order of magnitude higher than the energy dissipations of lightweight platforms (e.g., Meerkats BIB002 ). Furthermore, the energy consumption for communication also increases with the bandwidth (e.g., CC2420 consumes roughly 50 mW and IEEE 802.11b radios consume more than 1 W). Combination of powerful-enough and energy-efficient hardware with in-network processing techniques is mandatory for vision-based applications. Processing costs, given as joules per instruction, are approximately two to three orders of magnitude lower than communication costs that are given as joules per bit, on available embedded platforms, such as Mica2 and Yale XYZ. However, there are numerous processing options to choose from as the vehicle plate monitoring application discussed above exemplified. Next, we 