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Figure 2.3. Staged architecture of SensEye.
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In general, object detection requires few resources and it is therefore performed at stage one. The camera and the sensor node are duty-cycled, or woken up periodically, to detect the presence of a new object. In addition, SensEye uses a randomized dutycycling algorithm where different cameras are woken up at different times to further reduce object detection latency. A frame differencing algorithm is used to detect objects. Each camera compares the newly acquired image to a background image obtained when the system is calibrated; the pixel difference is used to indicate the presence of a new object. If a new object is detected in stage one, appropriate stage two nodes are woken up. This involves computing the coordinates of the object and determining the stage two nodes that have cameras pointing at its location; the details of object localization are provided in reference 5. Each stage one node knows the visual range of each stage two node in its vicinity and can therefore use the coordinates of the object to determine the most appropriate stage two nodes. If no appropriate stage two node is identi ed, a stage three camera is woken up, since it can use its pan and tilt capabilities to point to the location of the object. Localization is feasible only when at least two stage one nodes view the object. If only a single stage one node detects the object, then all stage two nodes that have overlapping coverage with it are woken up. The separation of object detection and recognition across stages introduces latency between the execution of tasks. This latency includes the delay in receiving and processing a wake-up packet as well as the delay in waking up a stage two node. The wake-up process begins with the transmission of a wake-up packet to a stage two node similar to wake-on-wireless energy saving strategy of Shih et al. [8]. Upon receiving a wake-up packet, the stage two node transitions from a suspend to an awake state. By running a minimum of device drivers, this transition time is kept small.
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Accurate recognition of an object requires a higher- delity image and signi cantly greater processing and memory resources than are available on a stage one node. As a result, the recognition algorithm is executed using higher- delity webcams and the more capable processors of stage two nodes. As a proof-of-concept, SensEye implements two well-known recognition algorithms from the computer vision literature in the stage two nodes [5]. Object tracking in SensEye involves a combination of detection, localization, and wake-up, in addition to recognition. The current system can track objects moving slowly. An experimental evaluation of SensEye shows that, compared to a at network architecture, an order of magnitude improvement in energy usage is obtained. Despite the energy reduction, similar detection performance (only 6% more missed detections) is obtained. Detection latency and energy usage at the stage one nodes is an order of magnitude less than at the stage two nodes. The mean localization errors indicate that detection can be performed by the lower- delity cameras of stage one while tracking is best done using higher- delity cameras; see Kulkarni et al. [5] for the details of the evaluation. The SensEye heterogeneous camera sensor network testbed has demonstrated successfully the bene ts of a staged architecture over a at architecture with respect to energy usage. Continuing research examines tradeoffs such as system cost and coverage. Design issues that impact performance, such as (a) the number of stages in the architecture and (b) the allocation of tasks to sensors, are also under study. The problem of streaming video (or a sequence of still images) of the object to a monitoring station is not addressed in this work; providing quality-of-service (QoS) support for such highbandwidth, real-time data is a challenging open problem in heterogeneous WSNs. 2.2.2 Detection of Radioactive Sources A team of researchers at Los Alamos National Laboratory have spent the past several years focusing on the development of heterogeneous WSNs for event detection. Typical deployments of sensor networks revolve around biological or environmental monitoring applications where the emphasis is on collecting all of the data from a sensor array to be sent back to a laboratory for detailed analysis. Applications of interest to the Distributed Sensor Networks with Collective Computation (DSN-CC) team have instead focused on the detection, classi cation, and tracking of radiological materials within the sensor network. These goals are very similar to those of the SensEye system, requiring all processing to be performed within the network with no use of base stations. However, this work relies on multiple sensor modalities instead of a single sensor type for event detection. The motivation for this research is to develop systems to guard against attacks from radiological dispersal devices (RDDs) capable of contaminating an area or population with ssile material. A potentially last line of defense for such attacks may reside in systems placed along roadways that are able to detect such material in-transit and alert the appropriate authorities before dispersal occurs [9]. One approach to such a threat employs portal monitoring equipment. Portals provide high delity results; however, they are large, conspicuous, and costly and require
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