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