A Survey of Applications of Wireless Sensors and Wireless Sensor Networks <s> 2) Outdoor Monitoring -Application to Ecology <s> We investigate task decomposition and collaboration in a two-tiered sensor network for habitat monitoring. The system recognizes and localizes a specified type of birdcalls. The system has a few powerful macronodes in the first tier, and many less powerful micronodes in the second tier. Each macronode combines data collected by multiple micronodes for target classification and localization. We describe two types of lightweight preprocessing which significantly reduce data transmission from micronodes to macronodes. Micronodes classify events according to their cross-zero rates and discard irrelevant events. Data about events of interest is reduced and compressed before being transmitted to macronodes for target localization. Preliminary experiments illustrate the effectiveness of event filtering and data reduction at micronodes. <s> BIB001 </s> A Survey of Applications of Wireless Sensors and Wireless Sensor Networks <s> 2) Outdoor Monitoring -Application to Ecology <s> We are developing an acoustic habitat-monitoring sensor network that recognizes and locates specific animal calls in real time. We investigate the system requirements of such a real-time acoustic monitoring network. We propose a system architecture and a set of lightweight collaborative signal processing algorithms that achieve real-time behavior while minimizing inter-node communication to extend the system lifetime. In particular, the target classification is based on spectrogram pattern matching while the target localization is based on beamforming using time difference of arrival (TDOA). We describe our preliminary implementation on a commercial off the shelf (COTS) testbed and present its performance based on testbed measurements. <s> BIB002
Outdoor monitoring is another vast area for applications of sensors networks. One of the most representative examples is the deployment of a sensor network consisting of 32 nodes on Great Duck Island (GDI),  . This network was used for habitat monitoring. The sensors that were used were able to sense temperature, barometric pressure, and humidity. In addition, passive infrared sensors and photoresistors were used. The aim was to monitor the natural environment of a bird (storm petrel) and its behaviour according to climatic changes. For that reason, some motes were installed inside birds' burrows, to detect the bird's presence, while the rest were deployed in the surrounding area. Data are aggregated via sensor nodes and are passed through to a gateway. The role of the gateway is to transmit data using a higher-level network to a local base station (database). The database is accessible through the internet and is replicated to another remote location for safety. This application provides an example for monitoring using a heterogeneous, multi-level network. The University of Hawaii has also been occupied with the study of the environment  . They have focused the research in Volcano National Park, trying to discover why some species can only live in a specific region. The outcome of this study would be very useful when trying to prevent endangered species from extinction. Each unit (called POD) is equipped with environmental sensors and, in some cases, with a high-resolution digital camera. These platforms were based on "Tephranet" developed by MIT's Media Lab research team. Each sensor unit collects it own data and also forwards data coming from other units toward an internet node. A characteristic of the topology of this deployment is that motes are separated into two subnetworks, one dense, to monitor the selected territory, and another sparse, to serve primarily as a link between the first group and an internet gateway (but also collect data in the vicinity of its nodes). In related work, North Carolina State University is conducting a study of the red wolf, another endangered species [17] . The plan is to attach a node to each animal, in order to record information about its condition and behaviour. The inability of motes to transmit over large distances and their energy constraints implies that it is unfeasible to keep all the motes on-line at all times. To overcome this problem, the network contains two types of nodes: mobile and static. The concept is that a moving mote collects and stores information until the animal runs across a static mote. The static mote triggers the communication capabilities of the moving mote and the latter establishes a connection and uploads the stored data. Research, regarding habitat monitoring, has also been conducted by Wang et al. BIB002 , BIB001 . The nodes that have been used during these experiments are not size optimised (PDAs) and they manipulate acoustic signals. Here the emphasis is on exploiting the computing capabilities of a mote, comparing the input with a reference signal. In that way a mote uses more of its resources. Other applications pertaining to outdoor monitoring that have been implemented concern environmental observations and forecasting weather phenomena. Following their successful implementation on GDI, Mainwaring et al. have deployed another sensor network at the James San Jacinto Mountains Reserve in Idyllwild, California. The aim is environmental observations. In contrast to the GDI network, in this application the time scales are much slower (climatic changes are expected to be sluggish) and the area covered is vast (29 acres). Instrumentation of natural spaces has also been studied by the UCLA Center for Embedded Networked Sensing (CENS) [20] . CENS has developed Networked Infomechanical Systems (NIMS) and has applied them to the Wind River Canopy Crane Research Facility in the Wind River Experimental Forest, Washington. NIMS comprise a heterogeneous network. That is a mixture of fixed and mobile motes. The aim is for fixed motes to support mobile ones (energy distribution, sensor calibration). The particular deployment at the Wind River is to probe the interaction between the atmosphere and the forest canopy. Efforts to save environmentally sensitive lands are now supported by a robust ally: motes. A much earlier implementation of sensor networks was ALERT (Automated Local Evaluation in Real Time), [21], a system developed by National Weather Service in 1970's. ALERT is a precursor of modern sensor networks and it is used in monitoring of rainfalls and flooding prediction in California and Arizona. For that reason, the system is equipped with temperature, water level and wind sensors. Despite the fact that this application uses sensors nodes, these cannot be regarded as motes strictly speaking, because they lack processing capabilities. Raw data are collected and accessed through GUIs. However, the application is currently operational and could be upgraded to use motes. Finally, CORIE [22] is another system that was deployed on the California River by the OGI School of Science & Engineering of the Oregon Heath & Science University. CORIE is another example of a heterogeneous system. Some nodes are installed and connected (hard-wired) along the estuary and a wireless one drifts on the river. The mobile node sends data to the fixed nodes. Sensors measure parameters which describe water or atmospheric conditions. It should be noted that these nodes are large compared to standard mote platforms. The aims of surveillance vary and can be both ecological (e.g. observation of salmon habitat) and economical (hydropower management, navigation improvements). To sum up, wireless sensor networks are ideal for remote monitoring and event detection in geographically large regions or inhospitable areas.