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Sensor Tasking

This document discusses sensor tasking and control to efficiently utilize limited resources like battery and bandwidth. A utility-cost approach balances utility of data collected with resource costs like power and communication bandwidth. Critical issues are which sensors to activate and what data to transmit. The design strategy considers important objects to sense, relevant parameters, sensor-object relations, best sensors for parameters, sensing and communication needs, and level of communicated information. Sensor roles depend on application tasks and available resources, with some sensors sensing and routing, some just sensing, and some just routing or idle. Information-based tasking maximizes information utility while minimizing resource usage by selecting sensors to query based on estimation uncertainty and sensor position/range relative to uncertainty.

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Jai chi
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371 views3 pages

Sensor Tasking

This document discusses sensor tasking and control to efficiently utilize limited resources like battery and bandwidth. A utility-cost approach balances utility of data collected with resource costs like power and communication bandwidth. Critical issues are which sensors to activate and what data to transmit. The design strategy considers important objects to sense, relevant parameters, sensor-object relations, best sensors for parameters, sensing and communication needs, and level of communicated information. Sensor roles depend on application tasks and available resources, with some sensors sensing and routing, some just sensing, and some just routing or idle. Information-based tasking maximizes information utility while minimizing resource usage by selecting sensors to query based on estimation uncertainty and sensor position/range relative to uncertainty.

Uploaded by

Jai chi
Copyright
© © All Rights Reserved
We take content rights seriously. If you suspect this is your content, claim it here.
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Download as DOCX, PDF, TXT or read online on Scribd
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SENSOR TASKING AND CONTROL

To efficiently utilize resources such as limited on-board battery and limited bandwidth in a sensor
network, sensor nodes must carefully tasked and controlled to carry out the required set of tasks. A
utility-cost-based approach to sensor network management is to address the balance between utility
and resource costs.

The definitions for utility and cost are given below –

– Utility: Total utility of the data

– Cost: power supply and the communication bandwidth

• Among the total number of nodes, which sensor nodes are to be activated and what information
to transmit to the network is a critical issue.

• This is because the sense values are not known and the cost of sensing may vary with the data.

Design Strategy for Sensor Tasking & Control


The following are the various steps connected with design strategy for sensor tasking and control –
 The important objects in the environment to be sensed
 The relevant parameters of these objects
 The relations among these objects critical to high level information to be known
 The best sensor to acquire a particular parameter
 The sensing and communication operations needed to accomplish the task
 The co-ordination given by the models of different sensors
 The level of communicate information in a spectrum from a signal to symbol
Roles of Sensor nodes and utilities: A sensor may take on a particular role depending on the
application task requirement and resource availability such as node power levels.
Example:
􀀁 Nodes, denoted by SR, may participate in both sensing and routing.
􀀁 Nodes, denoted by S, may perform sensing only and transmit their data to other
nodes.
􀀁 Nodes, denoted by R, may decide to act only as routing nodes, especially if their
energy reserved is limited. Nodes, denoted by I, may be in idle or sleep mode, to
preserve energy.

Information Based Sensor Tasking

Information-based sensor tasking is to query sensors such that information utility is maximized while
minimizing communication and resource usage. For localization or tracking problem, knowledge
about the target state such as position and velocity is required.
This requirement is represented as a probability distribution over the state space in the probabilistic
framework.

1. Sensor Selection: The estimation uncertainty can be approximated by a Gaussian distribution,


illustrated by uncertainty ellipsoids in the state space.
2. Sensor ‘b’ would provide better information than because sensor ‘b’ lies close to the longer axis
of the uncertainty ellipsoid and its range constraint will intersect this longer axis transversely.
Figure 4.13 shows the sensor selection.

The following conditions are assumed. Figure 4.14 shows localizing a stationary source. – All
sensor nodes can communicate with each others. – Sensor ‘a’ is farther from the leader node than
the sensor ‘b’ – There are four different criteria for choosing the next sensor. • Nearest Neighbor
Data Diffusion • Mahalanobis distance • Maximum likelihood • Best Feasible Region

3. Algorithm for IDSQ


A cluster leader selects optimal sensors to request data from using the information utility
measures. Using the Mahalanobis distance measure, the cluster leader can determine which
node can provide the most useful information while balancing the energy cost, without the need
to have sensor data first. This algorithm is a single belief carrier node active at a time.

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