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1
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
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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
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Wireless Networks
Mesh networksWireless Local Area Networks
Sensor networks
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Sensor Networks
• What is a sensor network?– Sensing– Microsensors– Constraints, Problems, and Design Goals
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Applications
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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…
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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)
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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
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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
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Typical Scenario
DeployWake/Diagnosis
Self-Organize Disseminate
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Sensor Network Systems
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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
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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
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Ghost of Research Present
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General Areas of Interest
ModelingSimulation
Network testbedRobotic testbed
ApplicationsCharacterization
PerformanceSecurity
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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
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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
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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
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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!!
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Example 2: Interference-aware routing
Goal: Find routes that are aware of interference.
Approaches:• Multi-objective optimization• Network-flow problems• Approximate heuristic
protocols.
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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
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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
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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.
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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.