Sensorweb Architecture and Dynamic Sensor Tasking in Mobile Sensor Networks

Sanjoy K. Mitter, Massachusetts Institute of TechnologyJoint work with: Maurice Chu (currently at PARC, Palo Alto, CA) and Peter Jones (Lincoln Laboratory)

Phone 617-253-2160, 617-258-8364 (Fax)Email mitter@mit.eduWeb http://web.mit.edu/mitter/www/

MURI Review: SensorWebData Fusion in Large Arrays of MicrosensorsDec 2, 2005

SensorWeb MURI Review Meeting, Dec 2, 2005

Sensorweb Architecture …

Extension of Maurice Chu’s Ph.D Thesis

Current research at PARC (Palo Alto, CA)

S.M. Thesis of Peter Jones (currently at Lincoln Laboratory, MIT)

Distributed Attention for Sensor Networksand the

Beginnings of a Conceptual Framework for Designing Resource Aware Distributed Algorithms

Maurice ChuPARC

Research Vision

Goal Move from specialized information processing systems engineered for specific domains toward general-purpose systems embedded in unstructured, dynamic environments

Framework for managing the complexity in designing information processing algorithms for data interpretation.

Complexity can come from limited resources of the system like processing power,

communication bandwidth, sensing capabilities, energy, link/node failure characteristics

application requirements – latency, scalability, robustness to link/node failures, reliability, scalability

inherent algorithmic complexity to extract information Challenges

Information architecture Mapping to physical system (e.g., distributed implementations) Efficient representations of information Interpretation algorithms (estimation, detection, inference)

Sensor Networks Dense ad hoc network of heterogeneous

sensor nodes (equipped with sensing, processing, communication)

Enormous potential for extracting all kinds of information from data sensed about the environment

Applications: surveillance, intelligent transportation grids, factory monitoring, battlefield situational awareness

processor

Wireless communicationdevice

Sensors

Limited sensing coverage, processing power, and communication bandwidth.

Need collaborative in-network processing capabilities.

PARC IDSQ Tracker in Action

SensIT Experiment (video), 29 Palms, MCAGCC, November 2001

Tracking result (right) from post-processing acoustic amplitude data from 21 Sensoria wireless nodes (yellow dots).

For more info: www.parc.com/ecc

Moving to Complex Environments

A few sensors can relatively easily find and track a vehicle in the desert.

But how do we find and monitor anything amongst all these distractors and clutter?

Large Scale Video Surveillance Goal

Enable video surveillance of large complex environments to monitor and detect multiple potential threats and surprises.

Difficulties Unstructured environment

Distractors, clutter, occlusions Information Overload

Human operators overwhelmed Resource limited

Insufficient sensing, processing, and communication resources to monitor all phenomena over extended periods

Solution: Distributed AttentionInspired by biological focusing mechanism

Challenges Information Processing

How do we efficiently represent and monitor known dynamic phenomena?

Abnormal BehaviorHow do we learn what is abnormal behavior, detect them, and react to them?

Peripheral AwarenessHow do we maintain awareness of newly emerging unboserved phenomena? How do we model the “emergence” of phenomena?

Resource AllocationHow do we share limited sensing resources among multiple competing tasks? How do we evaluate task priorities and what kinds of negotiations must occur to allocate resources optimally?

Distributed ImplementationHow do we implement algorithms in a distributed fashion under bandwidth constraints, energy considerations, processing time, latency constraints, etc.? What is the information exchanged and how does it flow through the network?

AdaptationHow can the system adapt to changes in the environment and failures and additions to the network?

Outline

Problem Statement and Challenges

Distributed Attention ArchitectureConceptual view

Testbed Implementation

Future Work

Outline

Problem Statement and Challenges

Distributed Attention ArchitectureConceptual view

Testbed Implementation

Future Work

Distributed Attention Architecture

Layered Information Processing Architecturedata interpretation unit (detection, estimation, inference)

Peripheral Awareness Moduleenables attention to the unobserved emergence of abnormal phenomena

Resource Allocationallocates tasks to resources

Adaptation - evolve system behavior according to dynamic system characteristics and environment

Layered Information Processing

Architecture Peripheral Awareness

Resource Allocation

Sensing

Adaptation

taskstasks

controls

observations

Layered Information Processing ArchitectureConcept Global view of the transformation from data to information

Multi-layered filtering approach Layers loosely ordered from continuous signal-level representations to discrete

symbolic representations Organize distinct information processing tasks into separate layers (modularity)

Two-way layer interactions Bottom-up triggering Top-down priming

Information flow considerations for distributed implementation Lower layers – little cross node communications, high bit-rate local data Higher layers – cross node communications, low bit-rate global data

Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow detect flow to landmark

sensor observation

Signal level

Knowledge level

Layered Information Processing ArchitectureConcept

Transitioning to a distributed system architecture Vertical cuts of layers Communications across vertical boundaries within

layers

Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow detect flow to landmark

sensor observation

Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow

detect flow to landmark

sensor observation

Node 1Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow

detect flow to landmark

sensor observation

Node 2Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow

detect flow to landmark

sensor observation

Node 4Cognitive

Attentive

Pre-attentive

anomalous flow

optical flow

tracking

groups of tracks

behavior recognition

attacker identities

adjust tracking priority

known track position

ignore anomalous flow

detect flow to landmark

sensor observation

Node 3

Signallevel

Knowledgelevel

Information flow through sensor network

Distributed Attention ArchitectureConceptual View

Layered Information Processing

Architecture Peripheral Awareness

Resource Allocation

Sensing

Adaptation

taskstasks

controls

observations

Peripheral Awareness ModuleConcept

Models the emergence and propagation of abnormal behavior for intelligent focusing of attention on unobserved, emerging events.

Ex. Monte Carlo simulation of a stochastic process(flow of suspicion samples)

Mechanics Sensed areas clear suspicion

samples. Detection of abnormal behavior

handled by the information architecture

Effect Potential emerging targets compete

for resources with known targets

Bernoulli process generates suspicion samples

Propagation model simulates motion of suspicion samples

LSP Progress Review Presentation

Peter JonesMaster’s StudentJune 16, 2005

Completed Thesis Dynamic Sensor Tasking in Heterogeneous,

Mobile Sensor Networks Goal of time-optimal detection/discrimination in

sensor networks Extension of previous work in using conditional

entropy/mutual information for sensor tasking Methods applicable to multi-modal sensors Coordination protocol developed for exploiting

sensor inter-dependencies Accepted May 5, 2005 in fulfillment of the

requirements for EECS Master’s Degree

Background

Information Driven Sensor Query Intended to limit communication (and power usage) in sensor networks Compromises quality of inference (detection, tracking, etc.) for

computational and communication simplicity Primarily applied to static networks of power-limited, homogeneous

sensors Basic principle: choose a new “leader” in the neighborhood of the

current leader to maximize the expected information gain of the next sensory action

)]|()([max )(1 ssNsk zxHxIEsk

−= ∈+

Multi-Modal Sensor Management

Tsitsiklis, Popp, Bailey (MIT/Alphatech) Considered two discrete modes (HRR v. GMTI) Optimized for footprint location (continuous

variable) Kreucher, Kastella, Hero (Veridian/UMich)

Use of information measure (Renyi entropy) Considers only discrete modes Similar to IDSQ (choose mode to maximize

expected entropic change)

Contributions

Minimum Time Formulation Optimization approach to multi-sensor scheduling Definitions of objectives, constraints and actions

Objective: to finish in minimum (expected) time Constraint: solve inference problem within a user-specified

level of uncertainty (entropy) Actions: deploy or query schedules for one or more sensors

with a chosen set of sensor parameters Optimization Equation

γμ ≤∈

)( s.t.

)]([min }{

a

atEAa

Maximum Rate of Information Acquisition

Dynamic analysis leads to dynamic programming solution method

Allows for information feedback (sensor measurements) in decision process

Provably optimal action for stationary and decomposable underlying distribution when entropy is large compared to

])(

),()([1 max

}{ atzxHxI

Eka

Aaa

k

−+ =

∈

γ

Coordinated Scheduling Coordination Protocol

Iterative algorithm with bounded Pareto sub-optimality Uses information theoretic utilities Market-based negotiation Axomiatic bargaining principles enforce “fairness”

1. Each sensor chooses “ideal” joint action from set of possible joint actions, Sk

2. Ratio of “ideal” utilities leads to mixed operating point

3. Set of dominating joint actions identified, Sk+1

4. If Sk+1 empty, end; else repeat process

Coordination Algorithm

Results

Simulation Setup

• Indeterminate number of targets

• Discrete number of possible locations

• Time for measurements increases linearly with measurement area radius

rbast *)( +=

)N(0,;)( 2 σηη ←+= SNRz

MIAR results

Mean Time

StdTime

MinTime

MaxTime

Random 11944 3460 6282 20978All-in 4519 1195 2259 8066MIAR 2519 479 1766 3964

MIAR results II

Mean Time

StdTime

MinTime

MaxTime

Wide Area 6529 1174 4583 10511High Res 5667 759 4280 7258MIAR 5429 775 3957 7870

• Multi-modal Simulation

• Constants a,b now a function of mode

• Results of 50 monte-carlo simulations

Coordination Experiment Setup

Cannonical Problem Definition: Two sensors, three locations, one common between the two. If both sensors attempt to measure the common location, neither receives a good measurement (mutual jamming).

Coordination Results

Advanced Coordination Experiment

• Set of 5 heterogeneous sensors, each with different detection/discrimination characteristics

• 25 possible target locations, 3 different target types

• Entropic threshold set low enough that there were no missed detections or incorrect classifications in any of the 150 trials

Mean Time

Std Time

Min Time

Max Time

Random 874.06 146.76 567 1327

Utilitarian 94.74 6.68 81 115

Negotiated MIAR

124.52 10.96 107 165

Summary

• Information driven sensing helpful in groups of (possibly interacting) multi-modal sensors

• Extension of entropy-based measures to time-optimal scheduling

• Viable coordination protocol• Verification of entropy-based utilities and

coordination via simulation