Research overview
Murat Demirbas
SUNY Buffalo
CSE Dept.
Personal computing ?
• PC processors are only 2% of all processors
• Where do the rest of the processors go?
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Automotive industry, e.g., new car has dozens of microprocessors
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Communications, e.g., cell-phones
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Consumer electronics, e.g., microwaves, washing machines
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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:
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Environmental monitoring
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Precision agriculture
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Asset management
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Military surveillance
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Wireless sensor networks (WSNs)
A sensor node (mote)
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8K RAM, 4Mhz processor
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magnetism, heat, sound, vibration, infrared
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wireless (radio broadcast) communication up to 100 feet
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costs ~$10 (right now costs ~$100)
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Challenges in WSN
• Scalability
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Thousands of nodes collaborate; for achieving scalability distributed &
local algorithms are needed
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Distributed algorithms are notoriously difficult to design
• Fault-tolerance
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Wireless communication is unreliable due to collisions
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Consensus is impossible to achieve
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Nodes fail due to adverse environmental conditions and software bugs
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Maintenance of infrastructures are costly and difficult
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Research statement
Developing distributed, robust, resilient WSN services
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Distributed: decentralized
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Robust: strong, durable
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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. MAC layers for robust single-hop communication
2. Geometric infrastructures for resilient WSN services
3. Programming abstractions for robust computing
4. Real-world deployments of robust WSN
5. Theory of self-stabilization
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1. MAC layers for robust communication
• Coordinated attack problem

Two armies are waiting to attack a city
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They need to attack together to win
 Each army coordinates with a messenger
 Messenger may be captured by the city
• Can generals reach agreement?
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Agreement is impossible in the presence of unreliable channel
• Wireless communication is unreliable due to collisions
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Hidden node problem
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Receiver-side collision detection (RCD)
RCD circumvents the impossibility result
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RCD enables coping with undetectable message loss
• RCD is easily implementable in WSNs
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Receiver side monitoring and notification of collisions
 No info wrt # of lost messages or identities of senders
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Classification of RCDs
 Completeness: Ability to detect collisions
 Accuracy: Ability to avoid false positives
• Synchronized rounds to convey negative feedback
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Collisions of negative feedback imply at least one negative feedback
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Vote-Veto algorithm
• Two phases: vote and veto
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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
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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
• Robcast and BEMA 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: Glance, DQT, PeeR-tree
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O(d) time for querying, where d is the distance to the nearest answer
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Graceful resilience to the face node failures via simplicity of design
• Tracking structures: Stalk, Trail
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O(d) time for querying
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O(m*logm) for update, where m is the distance the evader moved
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Local self-healing via containment wave idea & 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
Transact: A transactional framework for programming WSANs
• Effectively managing concurrent execution is a big challenge
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Concurrency needs to be tamed to prevent unintentional nondeterministic
executions
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Concurrency needs to be boosted for achieving timeliness
• Transactional, optimistic concurrency control framework
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enables understanding of a system execution as a single thread of control,
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while permitting the deployment of actual execution over multiple threads
distributed on several nodes
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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 Sand
• In OSU, we developed a surveillance service for DARPA-NEST
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Detect, track, and classify trespassers as car, soldier, civilian
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LiteS: 100 nodes in 2003,
ExScal: 1000 nodes in Dec 2004
1 km
Thick Entry Line
250 m
AS SET
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4. Real-world deployments of robust WSN…
INSIGHT: 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
Elvis: 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
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Self-stabilization provides a viable method to deal with state corruption
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Case-by-case analysis of faults and recovery is shunned in favor of a
uniform mechanism
• Self-stabilizing systems do not need any initialization
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Self-configuring!
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5. Theory of self-stabilization…
legitimate states
from where safety and liveness
are satisfied
illegitimate states
reached possibly
due 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-stabilization
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Improves over the whitebox and blackbox approaches tried so far
• Compositional reasoning for self-stabilization
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Modular design and verification of self-stabilization
• Syntax-based design of self-stabilization
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Use programming patterns to achieve self-stabilization
• Probabilistic & model-based verification of self-stabilization
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Improves over strictly deterministic design and verification of self-stabilization
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Research group:
iComp
• 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
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Questions ?
1. MAC layers for robust single-hop communication
2. Geometric infrastructures for resilient WSN services
3. Programming abstractions for robust computing
4. Real-world deployments of robust WSN
5. Theory of self-stabilization
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