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CamNets: Coverage, Networking and Storage Problems in Multimedia Sensor Networks

Nael B. Abu-Ghazaleh

State University of New York at Binghamton and

Carnegie Mellon University, Qatar

nael@cs.binghamton.edu

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Talk Outline

• Introduction• Overview of past work• Current Active Research

– Camera Networks • Camera coverage• Networking for data delivery and coordination• Storage and Indexing

• Future directions

Wireless Networks

Mesh networksWireless Local Area Networks

Sensor networks

Sensor Networks

• What is a sensor network?– Sensing– Microsensors– Constraints, Problems, and Design Goals

Applications

Applications

• Interface between Physical and Digital Worlds – Many applications

• Military– Target tracking/Reconnaissance– Weather prediction for operational planning– Battlefield monitoring

• Industry: industrial monitoring, fault-detection…• Civilian: traffic, medical…• Scientific: eco-monitoring, seismic sensors, plume

tracking…

Microsensors for in-situ sensing

• Small

• Limited resources– Battery powered

– Embedded processor, e.g., 8bit processor

– Memory: KB—MB range

– Radio: Kbps – Mbps, tens of meters

– Storage (none to a few Mbits)

Mica2 Mote

128KB Instruction EEPROM

4KB Data RAM

Atmega 128Lmicroprocessor7.3827MHz

ChipcornCC1000Radio TranscieverMax 38Kbps- Lossy transmission

FlashMemory

128KB – 512KB

UART

51 pin expansionconnector

UART, ADC

Properties

• Wireless– Easy to deploy: ad hoc deployment– Most power-consuming: transmiting 1 bit ≈ executing 1000

instructions• Distributed, multi-hop

– Closer to phenomena– Improved opportunity for LOS– radio signal is proportional to 1/r4

– Centralized apporach do not scale– Spatial multiplexing

• Collaborative– Each sensor has a limited view in terms of location and sensor type– Sensors are battery powered– In-network processing to reduce power consumption and data

redundancy

Typical Scenario

DeployWake/Diagnosis

Self-Organize Disseminate

Sensor Network Systems

Ghost of Research Past

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Design Space and Infrastructure Tradeoffs

• We defined the design space for sensor networks

• Studied infrastructure and deployment alternatives– Identified congestion and its impact on sensor

networks• New congestion management solutions

• …including non-uniform information dissemination

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Routing

• Real-Time Routing based on Just-in-Time-Scheduling

• Stateless Routing Protocols– Explain Anomalies in Virtual Coordinate Systems– Developed solutions that addressed them

• Aligned Virtual Coordinates

• Delivery guaranteed routing

• Hybrid geographical/virtual routing protocols

Sensor Network Storage

• Collaborative storage to reduce space and load balance

• Resolution Ordered Storage for space reclamation

• Interval summaries for indexing and coordination

• RESTORE testbed

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Localization and Security

• Securing Localization Systems

• Localization for Mobile Nodes: the self-tracking problem

• Trusted routing

• Defeating Timing and Space/Time Analysis attacks

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Applications and Programmability

• Testbed for chemical/biological attack monitoring

• Camera Networks Testbed

• Filesystem abstraction for sensor networks

• Virtualizing sensor networks19

Ghost of Research Present

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General Areas of Interest

ModelingSimulation

Network testbedRobotic testbed

ApplicationsCharacterization

PerformanceSecurity

Wireless Interference

• Nodes interfere with each other

• Effects• Lower throughput, Longer delays• Application performance

• Our work• Understand and characterize interference• Design interference-mitigating protocols

A B C

Example 1: Two-flow problems

• Only 2 links

• What are different ways in which they interact?

• How often do they occur?

• How does it affect throughput and delay?

A B C D A B C D A B CD

Example 2: Application of interactions

Interaction Engineering

• Goal: Avoid harmful interactions

• Approach:– Detect interactions dynamically– Adapt parameters to overcome harmful

interactions

A B C D A B C D

Routing

• Transmit packets from source to destinationo Link quality, scheduling and application-specific traffic.

• Our work: Study the optimal routing problem and heuristic protocols.

Congested!!

Example 2: Interference-aware routing

Goal: Find routes that are aware of interference.

Approaches:• Multi-objective optimization• Network-flow problems• Approximate heuristic

protocols.

Testbeds

State-of-the-art wireless devices• Soekris boards, Software-Defined

Radios

Current research projects:• Real-time models

o Scheduling, routing• Efficient protocol development

o Power control, rate-control, routing• Robotic projects

o Camera-Netso Localization

Example 3: Mesh Network Monitoring tool

Distributed measurement protocols• Network Topology, Link

Quality, ...• Detect interactions

Framework to build higher level protocols.

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Introduction

• A smart camera network is a network of autonomous and cooperative camera nodes.

• Traditional Camera Networks:

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Why are they interesting?

• Many applications– Military: sensitive areas

– Homeland security: suspicious activities, aftermath

– Disaster recovery: help rescue operations

– Habitat monitoring: capture scientific information such as behavioral/migration patterns of animals

– Road traffic monitoring: detect and report traffic violations

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Motivation

• Problems with traditional networks:– Simple capture-and-stream nature:

• needs human to monitor and control cameras.– Fixed and costly infrastructure:

• high-end cameras, wired connectivity.

• An expectation from a smart camera network:– autonomously capture most useful information

from the deployment region.

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Major Problems in Camera Networks

• Computer vision related problems– Camera calibration– Target detection and identification– Event classification and clustering

• Systems related problems– Camera Coverage– Network: Quality of Service for data delivery– Network: Coordination– Storage and indexing

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Coverage Maximization Problem

How to configure cameras’ FoVs to maximize the total number of targets covered?– Assuming all targets are equally important.

• Camera Configuration Parameters– Pan: horizontal adjustment– Tilt: vertical adjustment– Zoom: coverage range adjustment

• Camera Field-of-View (FoV):– Represented by angle and depth of view

R

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Coverage Maximization Problem

– Assumptions• Discrete pans• Boolean coverage model• No occlusions

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Solution Approach

Why not a greedy approach?

C1 C2 C3

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Contributions

• Integer Linear Programming based formulation

• Centralized heuristic

• Semi-centralized approach for scalability

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ILP Formulation

Subject to:

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Centralized Approach for Solving ILP

• Each camera sends state information to a central node

– State information: <Camera Id, Target Id, Target location>

• Central node computes optimal orientations (pans) for each camera and sends them back.

• The optimization problem is NP-hard!

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Centralized Force-directed Approach (CFA)

Approach: Iteratively choose camera-pan pair with highest force (Fik)

F=1 F=0.5

F=0.5

M: set of targetsN: set of camerasP: set of pans

Approach: Iteratively choose camera-pan pair with highest force (Fik)

Example:

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Centralized Force-directed Approach (CFA)

Algorithm:

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Centralized Force-directed Approach (CFA)

Counter Example:

C3

C1

C4

C2

P1P2 P2 P1

P2 P1

P2 P1

Camera P1 P2

C1 0.25 0.75

C2 0.67 0.33

C3 0.67 0.33

C4 0.67 0.33

Force Matrix

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Scalable Semi-centralized Approach

• Centralized approaches are not scalable– Exponential computations for optimal solution– Large response delay

• Hierarchical Approach– Address scalability by spatially decomposing

camera nodes into multiple partitions.– Key Idea:

• take advantage of physical separation among cameras, at a possible expense of coverage gain

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Spatial Partitioning Approach

• Single Linkage Approach (SLA)– Bottom-up clustering approach

– Start by treating each camera as a cluster

– Merge two clusters if the smallest distance (d) between any two nodes is smaller than threshold.

– Keep increasing the threshold to merge more clusters, forming a hierarchy.

• Modifications in SLA:– Termination condition for merging: d > 2*Rsensing

– Maximum cluster size (Smax) R R

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Performance Evaluation

• Simulations using QualNet network simulator• Parameters:

– FoV Rmax = 100 meters; Rmin = 0 meters

– FoV Angle = 45°

– Terrain 1000x1000 meters

• Benchmarks:– Centralized Greedy Approach (CGA) [Abouzeid’06]

– Distributed Greedy Approach (DGA) [Abouzeid’06]

– Pure Greedy Approach (Greedy)

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Study of varying number of targets

# Cameras = 50

Random Clustered

Percent Coverage: Ratio of covered to coverable objects

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Study of varying number of cameras

# Targets = 100.

E2E delay: Worst-case delay to receive response.

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Scalable Coverage for Static Targets

Study of impact of Smax

#Cameras=50; #Targets=100; Terrain: 500x500m

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Coverage for Mobile Targets

• Problem:– How to maximize the total mobile targets tracked?

• Approach:– How to compute the camera configurations?

• Optimal, CFA, Hierarchical

– How often to compute the optimal solution?• Locally: local collaboration approach

• Globally: periodic recalibration

• Collaboratively: on-demand recalibration

• Hybrids

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Coverage for Mobile Targets

Comparison of different policies and their combinations

Params: N = 20; Mobility: pedestrian mobility parameters

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Conclusion & Future Work

• Focused on the coverage maximization problem• Proposed three solution approaches:

– ILP based formulation– Centralized heuristic: CFA– Semi-centralized approach: Hierarchical

• Semi-centralized approach can reap benefits of centralized and distributed approaches

• Future Work:– Extend formulation for tilt and zoom– Model obstacles in the formulation– Propose approach for mobile targets case

Ghost of Research Future

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Future Directions

• Immediate Future– Camera Networks– Software Defined Radios– Measurement based protocols

• Getting into– Cyber physical systems –Smart cities– Environmental Observatory Networks

• Augmented with mobile sensing and personal sensing

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Barrier Coverage

• Approach– Model the terrain as a Triangulated Irregular

Network (TIN) [Goodchild95]

– Model FoV by assuming each triangle as a planer tile

– Choose minimum number of ‘connected’ triangles.

Спасибо большое

какие-нибудь вопросы?

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Barrier Coverage

• Approach– Model the terrain as a Triangulated Irregular

Network (TIN) [Goodchild95]

– Model FoV by assuming each triangle as a planer tile

– Choose minimum number of ‘connected’ triangles.