Location estimation in sensor networks

Location estimation in wireless sensor networks is the problem of estimating the location of an object from a set of noisy measurements.

These measurements are acquired in a distributed manner by a set of sensors.

Many civilian and military applications require monitoring that can identify objects in a specific area, such as monitoring the front entrance of a private house by a single camera.

Monitored areas that are large relative to objects of interest often require multiple sensors (e.g., infra-red detectors) at multiple locations.

A centralized observer or computer application monitors the sensors.

The communication to power and bandwidth requirements call for efficient design of the sensor, transmission, and processing.

The CodeBlue system[1] of Harvard University is an example where a vast number of sensors distributed among hospital facilities allow staff to locate a patient in distress.

In addition, the sensor array enables online recording of medical information while allowing the patient to move around.

Military applications (e.g. locating an intruder into a secured area) are also good candidates for setting a wireless sensor network.

owing some known or unknown probability density function (PDF).

The sensors transmit measurements to a central processor.

The application processing the data applies a pre-defined estimation rule

The next sections suggest alternative designs when the sensors are bandwidth constrained to 1 bit transmission, that is

is a parameter leveraging our prior knowledge of the approximate location of

The processing center averages the received bits to form an estimate

is a major disadvantage of this method since our model does not assume prior knowledge about the approximated location of

A system design with arbitrary (but known) noise PDF can be found in.

The estimator of [3] also reaches an MSE which is a constant factor times

The processing center estimation rule is generated as follows: As before, prior knowledge is necessary to set values for

to have an MSE with a reasonable factor of the unconstrained MLE variance.

The system design of [3] for the case that the structure of the noise PDF is unknown.

The following model is considered for this scenario: In addition, the message functions are limited to have the form where each

sensors would encode the second bit by setting their decision interval to

In fact, this intuitive design of the decision intervals is also optimal in the following sense.

-unbiased property while theoretical arguments show that an optimal (and a more complex) design of the decision intervals would require

, then this design requires a factor of 4 in the number of sensors to achieve the same variance of the MLE in the unconstrained bandwidth settings.

The design of the sensor array requires optimizing the power allocation as well as minimizing the communication traffic of the entire system.

The design suggested in [5] incorporates probabilistic quantization in sensors and a simple optimization program that is solved in the fusion center only once.

The fusion center then broadcasts a set of parameters to the sensors that allows them to finalize their design of messaging functions

Another work employs a similar approach to address distributed detection in wireless sensor arrays.