Wireless Sensor Network
Introduction And Overview (1)
Budi Mulyawan, Wei Sheng Yong, Xuan Thanh, Nirupama Bulusu
Supervisor: Associate Professor Sanjay Jha
Introduction
Imagine a world with smart machines that can self-diagnose
and repair, predict aging components and proactively alert
factories for replacement parts before the machine breaks
down. Smart roads will make travel safer and highways less
congested by noting accidents, potholes, alternate routes
and reporting the information to a car's global positioning
system (GPS). Smart appliances, such as refrigerators, will
understand families' dietary requirements or doctor's orders
and take inventory of refrigerators to relay information to
a shopping list on a personal digital assistant (PDA).
Collaborative sensor networks will help realize this vision.
Sensors are tiny devices capable of capturing physical information,
such as heat, light or motion, about an environment. Rapid
advances in technology have enabled a new generation of tiny,
inexpensive, networked sensors. Embedding millions of sensors
into an environment creates a digital skin or wireless network
of sensors, each sensor capable of capturing physical information
about its immediate space. These massively distributed sensor
networks communicate with one another and summarize the immense
amounts of low-level information to produce data representative
of the overall environment. Collaborative, smart sensor networks
present information in a qualitative, human-interpretable
form, which allows people (or computers) to respond intelligently.
Sensor networks will change the way we work and live.
Although sensor nodes will be equipped with a power supply
(battery) and embedded processor that makes them autonomous
and self-aware, their functionality and capabilities will
be very limited. Therefore, collaboration between nodes is
essential to deliver smart services in a ubiquitous setting.
One of the challenge in sensor network is localization.
Localization refers to the process by which the nodes in a
sensor network discover their geographical location. In a
typical sensor network only a few nodes know their position
a priori. Our research is about implement localization by
measuring the received signal strength (RSSI) of messages
received from each of the sensor devices at the mobile-robot.
Location tracking based on signal strength measurements is
solved by treating it as on-line estimation in a nonlinear
dynamic system. We use Robust Extended Kalman Filter to estimate
the signal strength and to keep refining our estimation so
as to predict the location within certain error bounds.
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