A Data Intensive
Reputation Management
Scheme for Vehicular
Ad Hoc Networks
Anand Patwardhan
Doctoral Candidate
Department of Computer Science and Electrical Engineering
University of Maryland Baltimore County
Anand Patwardhan, Anupam Joshi, Tim Finin, and Yelena Yesha
V2VCOM
2006
Outline
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Data management in VANETs
Security perspective
Trust-based security
Distributed data-intensive reputation management
Algorithm for screening data
Simulation results
GPS satellite
Localized and distributed
Wireless
Access points
Localized Info-Stream Services
Hazard warnings,
Detours,
Inclement weather,
Road conditions,
Traveler info.
Various forms
of connectivity
GSM, GPRS,
EDGE, E-VDO
WiMax
VANET
connectivity
Location
& directions
GPS
Update propagation
Onboard Computer
with various sensors:
•GPS location
•Cameras
•Engine Condition
•Tire pressure etc.
Situation Awareness allows Adaptation
Objectives
• Objectives
• Situation awareness for smart-vehicles
• adapt to current conditions
• optimal utilization of surface transport infrastructure
• Provisioning context sensitive travel information locally and
directly
• a growing need to provide context-sensitive information to mobile handheld
devices and car-computers with travel related information)
• Distributed control and fault tolerance
• ensure continued functioning in face of infrastructure failures arising from
natural calamities or terrorist attacks
• Prevalent Enabling Technologies
• Smart cars with arrays of sensors (GPS, cameras, etc.)
• Multimodal wireless communication (GSM, WiFi etc.)
• Distributed sensor networks embedded in the transport infrastructure
Background
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Highly dynamic conditions
Lack of centralized trust authority
Data and security guarantees
Information processing and decision making
Distributed collaborative processes
Softer security guarantees
Trust based security
Dynamic conditions
• Network
• Mobility of devices
• Arbitrary topologies
• Limited connectivity
• Mobility
• Time frames important (message transmission and
surface velocity)
• Radio ranges, interference, and obstructions
• Environment
• Road conditions, congestion, inclement weather, hazards
etc.
Trust and Risk Management
• Conventional PKI, variants, or Web-of-Trust (PGP)
infeasible
• Limited connectivity
• I&A difficult
• No guarantees of intent
• Security properties
• Confidentiality, integrity – cryptographic methods
• Availability – multiple sources, epidemic updates
• Reliability of source?
• Malicious entities, selfish-interest, non-cooperative nodes?
VANET Security Perspective
• Data
• Authenticity, reliability (quality), and timeliness
• Network
• Reliable routes
• Cooperative and trustworthy peers
• Intrusion and fault resilience
• Identification and Authentication
• Unique persistent identifiers (e.g. SUCVs)
• Decentralized reputation management
Examples of collaborative processes
• Routing
• On demand route setup
• Maintenance
• Data dissemination
• Relay data packets for others
• Caching
• Intrusion detection
• Reputation management
• Service discovery
Stimulating collaboration
• Cost of collaboration
• Storage
• Communication
• Reputation management
• Self-interest
• What is the payoff? (incentives)
• Higher availability (cooperation)
• Improved response times
• Reliability
• Reciprocity (tit-for-tat)
• Avenues for recourse
Data dissemination model
• Anchored sources (trusted) carousel information
updates
• Mobile devices propagate these further via epidemic
updates (collaboration)
• Burden of collecting relevant information and
verifying it is placed on the consumer devices
• Validation of data is achieved either
• Trusted source (trivial case)
• Agreement
• Post-validation by trusted source
Segment validation algorithm
Simulation setup
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Glomosim v. 2.0.3
Transmission range 100m
Simulated area: Dupont Circle, Washington DC
Geographic area of 700m by 900m
802.11
Mobility speeds 15 to 25 m/s
Pause times of 0 to 30 s
38 anchored resources (trusted)
50 to 200 mobile devices (vehicles)
Simulation time: 30 mins
Simulated area
Autonomous and Assisted
36
36
34
34
32
32
30
30
28
26
26
26
5-6
24
24
22
4-5
20
3-4
20
18
2-3
16
1-2
16
0-1
14
16
Anchors
14
22
12
12
10
10
8
8
6
6
4
4
2
2
0
1
3
5
7
9
11 13 15 17 19 21 23
Time (mins)
25 27 29
Trusted sources only
Anchors
0
1
3
5
7
9
11 13 15 16 19 21 23 25 26 29
Time (mins)
Trusted sources and assisted
5-6
4-5
3-4
2-3
1-2
0-1
Validated segments
36
34
32
30
28
26
24
22
20
18 Anchors
16
14
12
10
8
6
4
2
0
1
3
5
7
9 11 13 15 17 19 21 23 25 27 29
Time (mins)
5-6
4-5
3-4
2-3
1-2
0-1
Effect of malicious nodes
1400
1400
1400
1200
1200
1200
1000
1000
1000
800
800
50
800
100
150
600
600
400
400
400
200
200
200
0
600
200
0
TD0
VD0
ID0
0% malicious
TM0
50
50
100
100
150
150
200
200
0
TD30
VD30
ID30
30% malicious
TM30
TD60
VD60
ID60
TM60
60% malicious
Ongoing and Future work
• Distributed data-intensive reputation management
• Trust relationships built using persistent identities for
further trustworthy collaboration:
• Basis for Distributed intrusion detection
• Service discovery
• Reciprocative/adaptive levels of cooperation
• Contention management
• Adaptive radio-ranges to increase throughput
Questions?