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How Do We Find Our Way Around? We follow the signs! Getting out of lower Manhattan:
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Distributed-In/Distributed-Out: Intelligent Sensor Networks
for Coordination
Constantine (Dean) ChristakosViral Communications
Introduction An everyday problem How sensor networks can be applied The concerns about current designs Our proposed solution Preliminary work Where do we go from here?
How Do We Find Our Way Around? We follow the signs! Getting out of lower Manhattan:
It Doesn’t Always Work Smoothly While following the signs, a driver could
encounter: Congestion A car accident Construction
If the driver is lucky, public works will have time to put up detour signs
Would Sensor Networks Help? Sensors monitor environmental conditions
on remote islands and volcanoes. So why not in Manhattan?
Distributed Input of Sensor Data The sensors are placed all over the
monitored area and record separate pieces of data.
The responsibility for monitoring this wide area is distributed over dozens or even hundreds of tiny sensors.
Distributed Input, Centralized Output Normally, sensor
networks are used to monitor an area and send that data back somewhere to be analyzed
Why can’t observers within the sensor network get data directly?
Base Station/Middleware
Sensor InquirerB
Sensor InquirerA
Sensor InquirerC
sens
or d
ata
Sensor network
Why is this a problem? Centralized Control leads to centralized
failures. New York’s Office of Emergency
Management was located in the World Trade Center.
Re-coordination requires the global control center to be contacted even when the problem itself is localized.
Distributed Output Each sensor acts an output. Instead of acquiring data from a central
point, each sensor node displays relevant local information to the observer.
No Central Control! Localized failures don’t interfere with the
ability to find a solution to global problems. Intelligence moves away from a central
coordinator and out to the individual nodes.
Using Local Information in Problem Solving Using just local information, we can get
closer to our goal. Signs give us the next step to the tunnel or
the bridge. Sensors can tell users the next step to a
needed resource and can warn users of upcoming dangers.
Resource Discovery The sensors direct users towards
resources, based on data they have acquired.
Sensors can keep track of the direction of the nearest route to the exit
Danger Avoidance Nodes that can see a clear path to the
destination guide users towards them Nodes that are informed of nearby danger
guide users away from those areas
The Protocol – Finding the Nearest Exit Each node keeps track of which of its
neighbors is closest to the exit and sends users in that direction.
The next node keeps aware of which of its neighbors is next closest to the exit and sends users in that direction.
And so forth, until the user arrives.
Disaster Recovery In the event of a blocked passageway, the
protocol finds alternates. Only the next step in the process is important.
Example: during building escape, nodes need to direct the users on upper floors to the ground floor. The nodes don’t need to know that users on the ground floor need to use the rear exit, rather than the front exit.
Local Recovery Scales As the network (or building or city) gets
larger, disaster recovery will remain a function of communication within a constant radius. Briefly: the larger the graph, the more paths
that exist
Simulation Using a sensor network simulator, we
have implemented a simple path-finding algorithm and are currently testing.
In this case, nodes trace a path to the center node:
Implementing These Cheaply Recently, the advent of inexpensive
bluetooth devices has allowed us to make wireless multihop networks out of computers throughout the lab:
Future Uses Intelligent decision-making can be pushed
outwards to the agents “in the field.” Examples:
UPS drivers communicate and coordinate locally to makeup lost time due to canceled drop-offs by finding nearby drivers.
“Pullers” on election day can coordinate with others while doing get out-the-vote
All these are done without a central coordinator and by leveraging large numbers of sensors to find what people need.
Questions?