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Autonomous Networks and Inter-Vehicle Communication
A S U R V E Y O F PA P E R S B Y:D A R W I N M A C H
F O R :C S 7 8 8 – A U TO N O M I C C O M P U T I N GFA L L 20 17
G E O R G E M A S O N U N I V E R S I T Y
Overview• Background
• VANETs• Autonomic Properties
• Dissemination of Information
• Management
• Architecture Standardization
• Decentralization Challenge
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Background• Networks: all about communication
• Semi-autonomous protocols almost essential to make them easier to use• Most have algorithms that try to detect the network’s state and then optimize
best way to get the data across
• Traditional networks generally static• Nodes and routes don’t change frequently
• Routing protocols built around this
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Autonomic Computing & VANETJ A M E S J . M U LCA HY, S HI HO N G HU A N G , I M A D M A HG O U BF LO R I DA AT L A NT IC U NI VE RS ITY – 20 1 5
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VANETs• Vehicular Ad-Hoc Networks
• Sub-classification of Mobile Ad-Hoc Networks (MANETs)
• Similar to Wireless Sensor Networks (WSNs)• But a more generic use
• Complexity, dynamic nature, resiliency requirements, and needs to self-manage for ease of use
• Node communication• Vehicle to Vehicle (V2V) and Vehicle to Infrastructure (V2I)
• Vehicle = On Board Unit (OBU), Infrastructure = Road Side Units (RSUs)
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VANETs and WSNs
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VANETs: Evolution from WSNs• WSNs have existed since 1950s
• Military used network of sensors to track Solviet submarines• Developed packet radio in 1970s
• WSNs are generally static• Weather stations
• Industrial equipment in factories and utility plants
• What about mobility? (dynamically changing nodes)
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VANETs: Evolution from MANETs• Mobile Ad-Hoc Networks
• Ad-hoc, because the network’s composition can change
• Adapt to changing nodes• Can be as small as 2 nodes up to hundreds
• Mix of mobile peers and infrastructure nodes
• Adapt to changing geographic topologies
• Node movement highly variable but slow
• VANETs: Fast movement
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VANETs: Autonomic Properties• Self-Configuring & Self-Healing
• Vehicles join and leave the network at any time without warning• In/out of range
• Change direction, speed
• Powered on/shut down (ex. start/stop the car)
• Car accident damages the node
• Vehicles may leave network with data that was supposed to be routed• VANET needs to detect this, reconfigure routes, and retransmit lost data
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VANETs: Autonomic Properties• Self-Optimizing
• Can scale from as little as 1 vehicle to hundreds
• Constantly changing topology• Continually needs to calculate optimal communication routes
• Reduce overhead and increase reliability
• Many topology-based routing and geographical-based routing protocols
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VANETs: Autonomic Properties• Self-Protecting
• Anticipate fault or failure• Ex: Route data through interior
nodes
• Must also protect against attacks• Node impersonation or spoofing
• Must detect and block them or false information can create major problems
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Detecting Urban Road Condition and Disseminating Traffic Information by VANETsY U W E I X U, J I A N WA N G , T I N G T I N G L I U, W E N P I N G Y U, J I N G D O N G X UC O L L E G E O F C O M P U T E R A N D C O N T R O L E N G I N E E R I N G , N A N K A I U N I V E R S I T Y – 2 0 1 5
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VANETs: Dissemination of Information• Example: Congestion information used to route traffic
• V2R Scheme vs V2V Scheme
• Use travel time to develop approach of evaluating routes
• Test the approach with both schemes using a simulator (Veins)• Test network performance AND traffic capacity
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VANETs: Dissemination of Information• V2R Scheme
• Vehicles send their state to RSUs by periodic broadcast
• RSUs are wired together and broadcast Traffic State Messages (TSMs) in real time
• Vehicles records time it took to get from RSU1 to RSU2
• RSUs record time each vehicle passes it (broadcast with shortest distancec)
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VANETs: Dissemination of Information
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• V2V Scheme
• Vehicles send periodic broadcasts
• Vehicles also send TSMs• Done immediately when available
VANETs: Dissemination of Information
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• Directional Flooding (DF)• Use received TSM to calculate
distance between sender and receiver
• If it’s closer to destination, update sender position and then re-broadcast
• Cached table of received TSMs
VANETs: Dissemination of Information
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• Restricted Greedy Forwarding (RGF)• Based on GPSR protocol
• Limit hop distance
• Vehicles keep a list of neighbors learned from beacons
• Vehicles calculate distances between each neighbor and destination
• Select neighbor closest to destination within 1 hop
• Repeat process, but stop rebroadcasting once vehicle passes destination
VANETs: Dissemination of Information• Traffic routing
• Shortest Distance First (SDF)
• Shortest Time First (STF)
• Abbreviations• VRS = Valid Route Set
• IRS = Invalid Route Set
• RR = Route Record
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VANETs: Dissemination of Information
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VANETs: Dissemination of Information
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VANETs: Dissemination of Information• Future work…
• Would using a hybrid V2R and V2V be any more effective?
• May be interesting to see varying ratios of V2R and V2V
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Context-aware Trust-based Management of Vehicular Ad-hoc Networks (VANETs)VA N G A L UR A L AG A R , K A I Y U WA NCO N CO R DI A U N I VER SI TY – 20 1 5
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VANETs: Management• VANETs are nice & useful but… how do we manage it securely?
• Several types• Newcomer
• Register as “new” to the VANET; erase negative history
• Sybil• Create/use multiple identities
• Betrayal & Inconsistency• Earn trust, then use it for misbehavior
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VANETs: Management• VANETs are nice & useful but… how do we manage it securely?
• Proposed architecture’s components• Government Transportation Authority (GTA)
• Certification Authority (CA)• May include many Trusted Authorities (TAs)
• Agents (Vehicles & Drivers)
• Enriched RSUs (ERSUs)• Context-aware & reactive• Controller + arbiter
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VANETs: Management• Proposed architecture takes
both context and trust into account
• Context = situation• Valid actions in one context might
be invalid in another
• Used to create traffic control policies (TCPs), stored in databases owned by GTA
• Trust similar to human trust system• “Driving record”
• Positive/beneficial actions increase trust
• Negative/harmful actions decrease trust
• Issued by GTA and CA
• VANET agent identified by (driver, vehicle) tuple
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VANETs: Management
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VANETs: Management• ERSU accesses TCP database from GTA
• Also gets weather & road status info
• Vehicle enters VANET• Credentials: (driver, vehicle) tuple used to obtain ID and trust level from GTA
• ERSU sends profile to agent (identity, coverage zone, trust level in context)
• Agent can accept or reject profile
• ERSU tells everyone not to communicate with agent if it rejects profile, trust levelbelow threshold, or invalid credentials
• ERSU also monitors all communication and punishes otherwise trusted agents who talk to untrusted ones
• Paper says attack by replay of ERSU profile is difficult because its’ trust is dynamic and can change at anytime but that’s really “security by obscurity”
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VANETs: Management• Several types of attacks discussed
• Newcomer• Register as “new” to the VANET; erase negative history• Mitigated by requiring driver & vehicle identification, can’t authenticate without• If driver is required to use Smart Card + biometric, even harder to attack
• Sybil• Create/use multiple identities
• Maybe legitimate: 1 driver multiple cars• Probably illegitimate: multiple driver identities belonging to the same person
• Paper doesn’t offer a proper solution (suggests creating equivalence class, but how would you know beforehand?)
• Betrayal & Inconsistency• Earn trust, then use it for misbehavior• ERSU has to monitor all communications – can have privacy concerns
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VANETs: Management• Other attacks
• Denial of Service
• Signal jamming
• Physical damage & modification
• List goes on…
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Self-corrective Dynamic Networks via Decentralized Reverse ComputationsE VA N G ELO S P O U R NA R A S A ND J OVA N N I KO L I´ CE T H Z U R I CH – 20 17
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Decentralization Challenge• Let’s not rely on central infrastructure
• Because it’s not always available, costly, hard to make ubiquitous, etc
• VANETs share concepts with IoT
• Many nodes on network can work together• Monitoring and providing traffic assistance (as we’ve reviewed)
• How about assisting autonomous (self-driving) vehicles?• Distributed machine learning?
• Challenge: complexity, low computing power (per node), links are dynamic
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Decentralization Challenge• Reverse computations proposed
• Computational “roll-back” when nodes leave the network
• Self-corrective model (no central servers, proxies, checkpoint storage, etc)
• Agent-based (2 of them)• Status: publishes node’s status
information• Corrective: migrates to other nodes
to monitor the parent• If parent fails or goes missing, remote
corrective agent coordinates roll-back
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Decentralization Challenge• Let’s not rely on central infrastructure
• Because it’s not always available, costly, hard to make ubiquitous, etc
• VANETs share concepts with IoT
• Many nodes on network can work together• Monitoring and providing traffic assistance (as we’ve reviewed)
• How about assisting autonomous (self-driving) vehicles?
• Challenge: complexity, low computing power (per node), links are dynamic
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Decentralization Challenge• Test this approach using DIAS
(Dynamic Intelligent Aggregation Service)
• DIAS is a distributedaggregation system• SUMMATION
• AVERAGE
• MAXIMUM
• MINIMUM
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Decentralization Challenge• Experiment: Feed DIAS + self-corrective model with real data from
Electricity Customer Behavior Trial (electricity consumption)
• Each node represents a smart meter that was in the real data• Each have disseminator and aggregator
• Test scenarios• UP DOWN (node leaves), DOWN UP (node rejoins)
• Lightweight (max 20% at a time) and Heavyweight (50% at a time)
• Varying departure speeds and periods
• Measure: Accuracy, total messages, rate of migration success
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Decentralization Challenge• Summarized Results
• Varying departure speeds and periods doesn’t play a big role in accuracy, number of messages, nor rate of migration success
• Corrective network reduces errors in both lightweight and heavyweight scenarios
• Future work• More models? No other ones of this
scale yet• Expand scope to other areas
• Author mentions malware• Could be applied to VANETs!
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