Sensors are often used to monitor the status of an environment continuously. The sensor readings are reported to the application for making decisions and answering user queries. For example, a fire alarm system in a building employs temperature sensors to detect any abrupt change in temperature. An aircraft is equipped with sensors to track wind speed, and radars are used to report the aircraft’s location to a military application. These applications usually include a database or server to which the sensor readings are sent. Limited network bandwidth and battery power imply that it is often not practical for the server to record the exact status of an entity it monitors at every time instant. In particular, if the value of an entity (e.g., temperature, location) monitored is constantly evolving, the recorded data value may differ from the actual value. Querying the database can then produce incorrect results. Consider a simple example where a user asks the database: “Which room has a temperature between 10oF and 20oF?” If we represent temperature values of rooms A and B stored in the database by A0 and B0 respectively, we can see from Figure 1(a) that the answer to the user query is “Room B”. In reality, the temperature values of both rooms may have changed to newer values, A1 and B1, as shown in Figure 1(b), where the true query answer should be “Room A”. Unfortunately, because of transmission delay, these newest pieces of information are not propagated in time to the system to supply fresh data to the query, and consequently the query is unable to yield a correct answer.
A statistics-based sensor selection scheme for continuous probabilistic queries in sensor networks