Wireless LAN MAC protocols Murat Demirbas SUNY Buffalo CSE Dept.
Research overview Murat Demirbas SUNY Buffalo CSE Dept.
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Transcript of Research overview Murat Demirbas SUNY Buffalo CSE Dept.
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Personal computing ?
• PC processors are only 2% of all processors
• Where do the rest of the processors go?
Automotive industry, e.g., new car has dozens of microprocessors
Communications, e.g., cell-phones Consumer electronics, e.g., microwaves, washing machines Industrial equipment, e.g., factory floor robots
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Ubiquitous computing !
• Instead of us interacting with the computers in the virtual world, the computers should interact with us in our physical world
• Technology is now available via MEMS, CMOS, CMOS radios
• Real-world deployments have already started:
Environmental monitoring Precision agriculture Asset management Military surveillance
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Wireless sensor networks (WSNs)
A sensor node (mote)
8K RAM, 4Mhz processor magnetism, heat, sound, vibration, infrared wireless (radio broadcast) communication up to 100 feet costs ~$10 (right now costs ~$100)
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Challenges in WSN
• Scalability
Thousands of nodes collaborate; for achieving scalability distributed & local algorithms are needed
Distributed algorithms are notoriously difficult to design
• Fault-tolerance
Wireless communication is unreliable due to collisions Consensus is impossible to achieve
Nodes fail due to adverse environmental conditions and software bugs Maintenance of infrastructures are costly and difficult
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Research statement
Developing distributed, robust,Developing distributed, robust, resilientresilient WSN servicesWSN services
Distributed: decentralized Robust: strong, durable Resilient: able to adapt and recover from hazards
This requires work on several layers of the WSN protocol stack
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Research overview
1.1. MAC layers for MAC layers for robustrobust single-hop communication single-hop communication
2.2. Geometric infrastructures for Geometric infrastructures for resilientresilient WSN services WSN services
3.3. Programming abstractions for Programming abstractions for robustrobust computing computing
4.4. Real-world deployments of Real-world deployments of robustrobust WSN WSN
5.5. Theory of Theory of self-stabilizationself-stabilization
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1. MAC layers for robust communication
• Coordinated attack problem
Two armies are waiting to attack a city They need to attack together to win
Each army coordinates with a messenger
Messenger may be captured by the city
• Can generals reach agreement?
Agreement is impossible in the presence of unreliable channel
• Wireless communication is unreliable due to collisions
Hidden node problem
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Receiver-side collision detection (RCDRCD)
RCD circumvents the impossibility result
RCD enables coping with undetectable message loss
• RCD is easily implementable in WSNs
Receiver side monitoring and notification of collisionsReceiver side monitoring and notification of collisions
No info wrt # of lost messages or identities of senders
Classification of RCDsClassification of RCDs
Completeness: Ability to detect collisions
Accuracy: Ability to avoid false positives
• Synchronized rounds to convey negative feedbackSynchronized rounds to convey negative feedback
Collisions of negative feedback imply at least one negative feedback
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Vote-VetoVote-Veto algorithm
• Two phases: vote and veto
Vote phase:
Every active node sends out its vote
If a node hears no collision, the node updates its vote to min of received votes
If a node hears collision or different votes, it decides to veto Veto phase:
If no veto messages are received or collisions detected, then a node can decide, else nodes continue to next round
Intuition: By having a dedicated veto phase, effects of collision is detectable
• RobcastRobcast and BEMABEMA MAC protocols for robust broadcast
They eliminate the hidden terminal problem and improve throughput
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2. Geometric infrastructures for resilient WSN services
For scalability, local operations are needed over global structures
By exploiting the geometry of WSNs, we can design efficient, minimal, and resilient infrastructures
• Querying structures: GlanceGlance, DQTDQT, PeeR-treePeeR-tree
O(d) time for querying, where d is the distance to the nearest answer Graceful resilience to the face node failures via simplicity of design
• Tracking structures: StalkStalk, TrailTrail
O(d) time for querying O(m*logm) for update, where m is the distance the evader moved Local self-healing via containment wavecontainment wave idea & stretch-factor stretch-factor idea
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Geometric infrastructures for mobile WSN
Mobility improves coverage and, hence, resilience
• Mobile base-station for efficient data aggregation– Relocating the base-station in
response to varying data rates
• Deployment and relocation of mobile WSN– Sensor nodes relocate to
provide dynamic coverage by following the interest gradient
– Even though neighbors can change for each node, the network should stay connected
– What are local rules to maintain such a mobile WSN ?
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3. Programming abstractions for robust computing
TransactTransact: A transactional framework for programming WSANs
• Effectively managing concurrent execution is a big challenge
Concurrency needs to be tamed to prevent unintentional nondeterministic executions
Concurrency needs to be boosted for achieving timeliness
• Transactional, optimistic concurrency control framework
enables understanding of a system execution as a single thread of control,
while permitting the deployment of actual execution over multiple threads distributed on several nodes
By exploiting the properties of wireless broadcast communication, we provide a distributed and local conflict detection and serializability
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4. Real-world deployments of robust WSN
Line In The SandLine In The Sand
• In OSU, we developed a surveillance service for DARPA-NEST
Detect, track, and classify trespassers as car, soldier, civilian LiteS: 100 nodes in 2003, ExScal: 1000 nodes in Dec 2004
Thick Entry Line
A S S E T
1 km
250 m
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4. Real-world deployments of robust WSN…
INSIGHTINSIGHT: INternet Sensor InteGration for HabitaT monitoring
– Single-hop network
– Basestation serves webpage
– To circumvent firewall a replica
is established via XML query
– http://insight.podzone.net
ElvisElvis: In-building personnel localization
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5. Theory of self-stabilization
• Self-stabilization is the ability of a system to recover within bounded steps from arbitrary states to states from where the system exhibits desired behavior
• Arbitrary state corruption provides a clean abstraction of how many systems are perturbed by their diverse environments
Self-stabilization provides a viable method to deal with state corruption
Case-by-case analysis of faults and recovery is shunned in favor of a uniform mechanism
• Self-stabilizing systems do not need any initialization
Self-configuring!
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5. Theory of self-stabilization…
legitimate states from where safety and livenessare satisfied
illegitimate states reached possiblydue to faults
•Closure: Set of legitimate states is closed under system execution
•Convergence: Starting from any system state, every system
computation eventually reaches a legitimate state
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5. Theory of self-stabilization…
• Graybox self-stabilizationGraybox self-stabilization
Improves over the whitebox and blackbox approaches tried so far
• Compositional reasoning for self-stabilizationCompositional reasoning for self-stabilization
Modular design and verification of self-stabilization
• Syntax-based design of self-stabilization
Use programming patterns to achieve self-stabilization
• Probabilistic & model-based verification of self-stabilization
Improves over strictly deterministic design and verification of self-stabilization
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Research group:
• Current PhD students
Muzammil Hussain Xuming Lu Dola Saha Onur Soysal
• Several MS students are involved (via CSE 646)
• Closely related research groups
Chunming Qiao : networking Jan Chomicki, Michalis Petropoulos : database management
iComp
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Questions ?
1.1. MAC layers for MAC layers for robustrobust single-hop communication single-hop communication
2.2. Geometric infrastructures for Geometric infrastructures for resilientresilient WSN services WSN services
3.3. Programming abstractions for Programming abstractions for robustrobust computing computing
4.4. Real-world deployments of Real-world deployments of robustrobust WSN WSN
5.5. Theory of Theory of self-stabilizationself-stabilization