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