## 4 Related Works and Contemporary VSN Projects
### Sensor Network Virtualization Projects
#### 4.1.7 SensEye  
SensEye is a VSN related project that deals with multi-tier, multi-modal camera sensor network [50, 51, 52]. The speed of technological growth has led to the emergence of a variety of sensors, actuators, and networked sensor platforms. Nowadays networked sensors cover the spectrum of cost, form-factor, resolution, and functionality. For example, we can consider camera sensors, where available products range from expensive pan-tilt-zoom cameras to high-resolution digital cameras, and from inexpensive web-cams and cell-phone-class cameras to tiny cameras such as Cyclops. A similar set of options are becoming available for sensor platforms, with choices ranging from embedded PCs to PDA-class stargates, and from low-power motes to even lower power systems-on-a-chip. Multi-tier multi-modal networks provide an interesting balance of cost, coverage, functionality, and reliability. For instance, the lower tier of such a system can employ cheap, untethered elements that can provide dense coverage with low reliability. However, reliability concerns can be mitigated by seeding such a network with a few expensive, more reliable sensors at a higher tier to compensate for the variability in  
Figure 2: The PRESTO data management architecture. the lower tier. Similarly, a mix of low-fidelity, low-cost sensors and high-fidelity high-cost sensor can be used to achieve a balance between cost and functionality. Application performance can also be improved by exploiting alternate sensing modalities that may reduce energy requirements without sacrificing system reliability. This project assumes a camera sensor network comprising multiple tiers which is depicted in Figure 3. A sensor node within each tier is assumed to be equipped with a camera sensor, a micro-controller, and a radio as well as on-board RAM and flash memory. Nodes are assumed to be battery-powered, and consequently, the overall constraint for each tier is energy. Within each tier, nodes are assumed to be homogeneous, while different tiers are assumed to be heterogeneous with respect to their capabilities. In general, it is assumed that the processing, networking, and imaging capabilities improve as proceed from a lower tier to a higher tier, at the expense of increased power consumption. Consequently, to maximize application lifetime, the overall application should use tier-specific resources judiciously and should execute its tasks on the most energy-efficient tier that has sufficient resource to meet the needs of that task. Thus, different tasks will execute on different tiers and various tiers of camera sensor network will need to interact and coordinate to achieve application goals. Given these intra- and inter-tier interactions, application design becomes more complex--the application designer needs to carefully map various tasks to different tiers and carefully design the various interactions between tasks. One of the goals of SensEye is to illustrate these tradeoffs while demonstrating the overall benefits of the multi-tier approach. To do so, SensEye assumes three-tier architecture. The lowest tier in SensEye comprises more nodes equipped with 900 MHz radios and low-fidelity Cyclops or CMUcam camera sensors. The second SensEye tier comprises storage nodes equipped with web-cams. Each storage is equipped with an embedded 400 MHz XScale processor that runs Linux and a web-cam that can capture higher fidelity images than Tier 1 cameras. Each Tier 2 node also consists of two radios-an 802.11 radio that is used by storage nodes to communicate with each other, and a 900 MHz radio that  
Figure 3: Software architecture of SensEye. is used to communicate with motes in Tier 1. The third tier of SensEye contains a sparse deployment of high-resolution pan-tilt-zoom cameras connected to embed PCs. The camera sensors at this tier are retargetable and can be utilized to fill small gaps in coverage provided by Tier 2 and to provide additional redundancy for tasks such as localization.