1. Deploying Long-Lived and Cost-effective Hybrid Sensor Networks
As any graduate student knows, every research project has chiefs who bark out the orders and one Indian who do the actual work.
I am the chief, so I’ll try to do a bit better than Dilbert’s boss in explaining this work.
Nirupama Bulusu, Portland State University
joint work with Wen Hu, Chun-tung Chou, Sanjay Jha
(University of New South Wales)
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2. Our Sensor Net App Cane Toad Monitoring Requirements
“Cane toads will progressively shell
shock an unsuspecting Kakadu environment,
in particular they will come close to wiping
out native quoll populations, poison large
masses of goannas and disturb the food
supply of many native animals.”
Requirements
Cheap, wide-area sensing coverage
… but sophisticated processing, communication for target localization and vocalization
Cane Toad
Explosion
So, I’ll start this talk by describing a motivating app. Which is monitoring cane-toads.
Cane-toads are not a native species to Australia and were introduced there for pest control. Because they had no natural predators, they are multiplying in rapid numbers and endangering the species.
Our goal is to build a sensor system that can track the population of cane-toads, as well as their location. This is done by running machine learning
Algorithms which analyze the frog chorus.
This is an application which demonstrates the need for both cheap, and
Wide sensing coverage as well as sophisticated processing and communication to run the machine learning algorithms in the network.
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3. Hybrid Sensor Networks
How do you achieve these goals?
By having a mix of both cheap, resource-impoverished devices
Which provide you abundant coverage;
And resource-rich devices such as the Stargates which provide much more
Functionality.
You can think of a two-tier network in which the motes communicate
With the Stargates using Chipcon radios, and Stargates have an additional
Out of band communication in which Stargates can talk with each other.
Platforms
A Hierarchical View
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4. Previous Work: Data Anycast
Deliver data to preferably nearest micro-server, which can
Forward it using out of band, broadband (802.11b, g) communications link
Store/process it
Perform the desired actuation
Build up a reverse-tree at each sensor efficiently
(scalability, adaptability/mobility, little over-head/energy efficient, distributed, simplicity)
Reference:
Wen Hu, Nirupama Bulusu, and Sanjay Jha, “A communication paradigm for hybrid sensor/actuator networks,” in Proceedings of the IEEE International Symposium on Personal, Indoor and Mobile Radio Communications (PIMRC 2004), Barcelona, Spain, October 2004.
An interesting question is how communications and control should be
Structured in such hybrid systems, which sort of require a greater synergy between devices than would be possible in traditional networks.
In our previous work, we have thought of an anycast architecture, where instead of sending all data back to a central root node, the nodes just deliver data to preferably the nearest micro-server, which can take the appropriate action depending on the app.
- For example, forwarding it on a high bandwidth link.
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5. This Paper: Deployment Issues
What is the maximum network lifetime possible for a given number of micro-servers?
What is the optimal micro-server placement of micro-servers to achieve maximum lifetime?
Is the deployment cost-effective? How to achieve optimal cost benefit?
When studying the performance of our communication paradigm, it became clear that any reasonable performance analysis depending on our understanding of how these sensors and micro-servers are deployed, and we will explored those issues in this paper.
Specifically, three questions we asked are:
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6. Cost Model Energy consumption limitation of sensor
To study the problem, we start with the simple assumption that sensors
And micro-servers will be placed in a 2D grid layout.
We proposed a cost-model that accounts for different event rates, communication costs, energy variations between sensors and micro-servers.
And trying to maximize the system lifetime under these cost constraints.
Energy consumption limitation of Micro-server
(assuming sensors, micro-servers in 2D grid layout)
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7. A Tabu Search Algorithm
The model is a huge combinatorial problem.
An approximation tabu-search algorithm
Algorithm benchmark (a 20 grid network)
This is a combinatorially very complex problem, because it requires us
to consider all possible combinations of placing microservers in a huge grid.
So, the major contribution of this paper is a Tabu-search algorithm which
Is an approximation algorithm.
You benchmark the algorithm against Cplex solver for a 20-grid network.
And we find that our results match the optimal results but our computation is significantly faster.
So, how does the Tabu search algorithm work. In a nutshell, basically it prepares a tabu-list of microservers, and iterates at each step, though a process of intensification of the good results and diversification of other microservers.
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8. Network lifetime of a 100 grid network
This graph plots the network lifetime as a function of the number of microservers. Not surprisingly, the network lifetime
Increases as the number of micro-servers increases And you see that the best case lifetime can be nearly 5 times better than
The worst case lifetime. If you just scattered sensors at random, and the spatiotemporal event patterns were fairly uniform
You would expect it to lie somewhere in between these two graphs.
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9. Micro-server Placement
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10. Cost-effectiveness (network size=100, vary k)
We all know that we can arbitrarily increase the lifetime of the network by adding more microservers, but how cost-effective is it?
In order to understand that, we use a normalized lifetime/unit cost metric, which Is basically the network lifetime divided by the cost of the network,
which is the cost of sensors + microservers.
K represnts the ratio of the micro-server cost to the sensor cost.
This graph shows the normalized lifetime as a function of the number of microservers for a 100-grid network, for different values of k.
We see two trends:
There is a clear peak in terms of the micro-servers beyond which you don’t get significant performance benefits.
The lifetime benefits itself fall off as the cost of the microservers increases relative to the sensors, which is what you would expect.
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11. Cost-effectiveness (k=50, vary network size)
This graph keeps the cost ratio constant, and varies the network size.
Not suprisingly, you can see that as the size of the network increases, the cost-benefit of having the same micros-servers increases.
Again, the interesting thing about this graph is the existence of inflection points.
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12. Concluding Thoughts
A methodology for studying hybrid sensor deployment (cost model + tabu search algorithm)
spatiotemporal event rates captured as discrete variables
learn from inference, use to reconfigure micro-server deployment
applicable to storage, computation etc.
Use as engineering guideline, not as a guarantee
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