The wide range of sensors and collective instrumental functionality of WSNs, coupled with the underlying wireless networks, make it possible to provide unprecedented levels of data access and associated intelligence, bringing about a new dimension of application for different industry sectors. WSN applications can be divided into three categories: monitoring space, including objects as part of the space; monitoring operation states of objects; and monitoring interactions between objects and space. The first category represents the most common and basic use of WSNs (dealing with physical environments), whereas the second is mainly concerned with a specific entity rather than its surroundings. The third category encompasses more sophisticated monitoring and control over communications and interactions between objects and between an object and its surroundings. Some pilot projects have explored WSNs for a number of different application scenarios. Many potential applications are being developed to leverage WSNs. Some examples are introduced as follows.
Using a large number of sensor nodes deployed in a target geographic location, it is possible to derive useful patterns and trends based on datasets collected over time. Examples of environmental sensing are light sensing, microclimate monitoring, traffic monitoring, pollution level monitoring, indoor climate control, and habitat monitoring. Very often users are only concerned with independent characteristics of an entity, such as the number of vehicles passing by during a time period or the propagation speed of some contaminant in a river.
Aside from environmental sensing, sensors can be attached to objects and collect data regarding motion, pressure, or any mechanical, electronic, or biological characteristics of the host. Object sensing is predominantly used in industrial control and maintenance. Examples include structural monitoring of buildings, bridges, vehicles, and airplanes; sensing machinery wear in a factory; industrial asset tracking in warehouses and stores; surveillance in parking lot and streets; crop monitoring; and military-related object sensing in battlefields. In particular, RFID, a scaled-down wireless sensing technology, utilizes small tags of very limited local computing power and storage to identify and inventory objects. Section 13 has presented a detailed introduction to RFID.
Sensing with Intelligence
More challenging application scenarios require embedded intelligence that goes beyond raw data sensing, thus requiring the simultaneous sensing of multiple related quantities and in-network processing so as to detect internal interactions between objects. Examples of this category are monitoring wildlife habitats, telemedicine sensing, context-aware pervasive computing using sensors, and disaster management. For instance, researchers at the University of California, Berkeley, and Intel have developed a successful experimental WSN to monitor petrels on an uninhabited island off the coast of Maine. The birds being observed are Leach’s store petrels, a type of tiny reclusive seabird that burrows in sandy soil and emerges only at night. To ornithologists, monitoring and understanding the comings and goings of these birds in a wild area are not simple tasks, as they would have to dig into the birds ’ burrows for more information. The WSN deployed on the island consists of 190 wireless sensor nodes called motes , some of which are located in burrows and others on the ground, and a solar-powered central computer station that collects sensory data from a gateway mote and reports back to a remote site in real-time via satellite links. Sensors on the motes monitor temperature, humidity, barometric pressure, and ambient light. The temperature reading within a burrow can be used to infer if a petrel is present or not. Other data also contribute to our understanding of the behavior of these petrels and their responses to changes in their surroundings.
Source of Information : Elsevier Wireless Networking Complete 2010