Techniques for Improving Opportunistic Sensor Networking Performance Shane Eisenman Nic Lane, Andrew Campbell Columbia UniversityDartmouth College.

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Presentation transcript:

Techniques for Improving Opportunistic Sensor Networking Performance Shane Eisenman Nic Lane, Andrew Campbell Columbia UniversityDartmouth College

Large-scale Sensing requires......a People-centric Approach

Application Query Assignment (i.e., Tasking) Local Tasking

Application Query Assignment Remote Tasking Internet RAN 1 RAN 2 Sensing Query John Q. Public

I have the query. Now what? Query: Sensor type, target location, sensing deadline,... Have sensor --> success No sensor --> fail No sensor --> ?

Improving Success Probability Sensor Sharing Sensor Substitution

Modeling Query Success Probability P(success) = P(reaching tasking point) x P(reaching target) x P(having right sensor)...before the sensing deadline.

N x N grid representation of a neighborhood

Torus Topology (N=10) Move N,E,S,W

Campus Topology (N=10) Move according to real-world connectivity

Results Randomly assign sensors to mobile nodes (20 choose r) Calculate the sensing success probability for various values of number of nodes, number of sensors per node, and sensing deadline

Comparing Campus and Torus The topology of the campus limits node mobility, requiring a longer deadline to reach the same success probability. Fixed num sensor types=3.

Comparing Campus and Torus Qualitative trends are very similar between Torus and Campus, despite the very different mobility patterns. Fixed sensing deadline=50.

Sensor Sharing P(success) = P(reaching tasking point) x P(reaching target) x ( P(having right sensor) + P(sensor sharing) )...before the sensing deadline.

Benefit of Sensor Sharing Sharing improves success probability up to 16%. As the deadline increases the improvement diminishes. Fixed num sensor types=3.

Benefit of Sensor Sharing Sharing improves success probability up to 70%. Increasing number of sensor types decreases possible improvement. Fixed sensing deadline=50.

Sensor Substitution P(success) = P(reaching tasking point) x P(reaching target) x ( P(having right sensor) + P(sensor substitution) )...before the sensing deadline.

Benefit of Sensor Substitution Substitution improves success probability up to 155%. Increasing deadline decreases possible improvement. Fixed num sensor types=3. Fixed num mobile sensors=20.

Benefit of Sensor Substitution Substitution improves success probability up to 36%. Increasing num. sensor types decreases improvement. Fixed sensing deadline=50. Fixed substitution prob=0.02.

Combined Sharing and Substitution P(success) = P(reaching tasking point) x P(reaching target) x ( P(having right sensor) + P(sensor sharing) + P(sensor substitution))...before the sensing deadline.

Combined Sharing and Substitution Up to 270% gains in success probability (sharing-only 16%, substitution-only 160% with same parameterization). Fixed num sensor types=3. Fixed num mobile sensors=20.

Combined Sharing and Substitution Up to 140% gains in success probability (sharing-only 70%, substitution-only 35% with same parameterization). Fixed sensing deadline=50. Fixed substitution prob=0.02.

Summary Opportunistic people-centric sensing is the future for very large-scale sensing. Sensor Sharing and Sensor Substitution are composable techniques to increase the probability of query success. Help to provide a level of abstraction between locally sensing resources and application requirements.

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