fuzzy angle fuzzy distance + angle ag = 90 dg = 1 annual conference of ita acita 2009 exact and...
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Fuzzy Angle Fuzzy Distance + Angle
AG = 90AG = 90DG = 1
Annual Conference of ITAAnnual Conference of ITAACITA 2009ACITA 2009
Exact and Fuzzy Sensor AssignmentHosam Rowaih1 Matthew P. Johnson2 Diego Pizzocar3 Amotz Bar-Noy2 Lance Kaplan4 Thomas La Porta1 Alun Preece3
1 Pennsylvania State University 2 City University of New York 3 Cardiff University (funded by ITA via IBM UK) 4 US Army Research Lab
AcknowledgementsResearch was sponsored by the U.S. Army Research Laboratory and the U.K. Ministry of Defence and was accomplished under Agreement Number W911NF-06-3-0001. The views and conclusions contained in this document are those of the author(s) and should not be interpreted as representing the official policies, either expressed or implied, of the U.S. Army Research Laboratory, the U.S. Government, the U.K. Ministry of Defence or the U.K. Government. The U.S. and U.K. Governments are authorized to reproduce and distribute reprints for Government purposes notwithstanding any copyright notation hereon.
Introduction
Sensor network performing multiple simultaneous tasks, each requiring multiple sensors
competition for limited sensing resources
Directional sensors can only be assigned to one task at a time
Problem: which sensors should be assigned to which tasks?
Two NP-hard apps with nonlinear utility:
Event Detection
Target Localization
Localization
Task: choose two sensors that minimize location uncertainty:
Network: maximize the sum
of task utilities uj defined as:
Fuzzy location: based on distance and a fuzzy angle
divides the circle into sectors based on an angle granularity (AG)
System Model
Sensor types: imagery and acoustic
Detection use: both imagery and acoustic
Localization use: only acoustic
Dynamic system
tasks arrive and depart over time
Tasks:
different profits
different locations
Utility = function of assigned sensors
Localization and high profit detection tasks can preempt low profit detection
Fuzzy location benefits:
Lower computational cost: fewer assignment choices to consider
Privacy: sensors not disclosing their exact location
Tradeoff between solution quality and computational cost / privacy
Event Detection
Target Localization
Evaluation
Finer granularity leads to better solution
Fuzzy can achieve profits that are within 1% of exact
Localization is affected more by competition
Detection with Exact Location
Task leaders advertise tasks and location requirements to nearby sensors
Sensors propose to tasks
Task: assign n sensors that maximize cumulative detection probability (CDP)
Network: maximize the sum of task utilities (CDPs) weighted by profits:
Tasks competing with arriving task (within distance 2Rs) compete in rounds:
• Sensors (re)calculate how much they can help tasks (marginally) and propose:
• Tasks accept the best proposals
Detection with Fuzzy LocationDetection probability depends on distance
For fuzzy location, distance is discretized
SS22
SS33SS11
x
DG = 1