Research overview

Murat Demirbas

University at Buffalo, SUNY

CSE Dept.

iComp

2

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

3

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 and CMOS radios

• Real-world deployments have already started:

Environmental monitoring Asset management Military surveillance

iComp

4

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)

5

Challenges in WSN

• Scalability

Thousands of nodes collaborate; for achieving scalability distributed & local algorithms are needed

Distributed algorithms are notoriously difficult to design

• Reliability

Wireless communication is unreliable due to collisions

Consensus is hard to achieve

Nodes fail due to adverse environmental conditions and software bugs

Maintenance of infrastructures are costly and difficult

6

Research overview

1.1. WSNs in-network querying and tracking servicesWSNs in-network querying and tracking services

2.2. Wireless Sensor Actor Networks (WSANs)Wireless Sensor Actor Networks (WSANs)

3.3. Internet-integrated large-scale WSN deploymentsInternet-integrated large-scale WSN deployments

4.4. Self-stabilizing systems theorySelf-stabilizing systems theory

7

1. In-network querying & tracking

The objective is to answer contextual & spatial queries in WSNs

• What is the location of the nearest enemy tank?

a soldier may query the WSN (via a palm device) in a battlefield scenario

• What is a safe path to the nearest available exit?

a firefighter may ask in a rescue-evacuation scenario

8

“Line In The Sand” military surveillance WSN

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: pipeline or border to be secured

1 km

250 m

9

Our results...

For scalability, local operations are needed over global structures

By exploiting the geometry of WSNs, we 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, Trail, DQT-mobileTrail, DQT-mobile

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

10

2. Wireless Sensor Actor Networks

The objective is to achieve collaboration and coordination in swarm robotic/vehicular/sensor networks

• Distributed control applications for in-network actuation Active vibration and noise cancellation in factory floors Task and resource allocation in multimedia (video) WSNs Leader election, consensus, in-network information fusion

• Deployment and relocation of mobile WSANs for environmental pollution surveillance and removal Sensors relocate to provide dynamic coverage by following the gradient– Although neighbors can change for nodes, the network should stay

connected– What are local rules and suitable primitives to maintain such mobile

WSANs?

11

Our results... (NSF Career award)

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 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

Pollcast, Countcast, CoordcastPollcast, Countcast, Coordcast: Lightweight singlehop collaboration and coordination primitives for WSANs

OmnitaxisOmnitaxis: A decentralized mobile topology control protocol for robotic surveillance and rescue networks

12

3. Internet-integrated large-scale WSN deployments

INSIGHTINSIGHT: INternet Sensor InteGration for HabitaT monitoring– Single-hop network

– Basestation serves webpage

– Deployed for monitoring the

greenhouse at UB

ElvisElvis: In-building personnel

localization and tracking

Gate monitoring WSNs

HPC monitoring WSNs

Cellphone as a healthcare Cellphone as a healthcare

platform platform : Indirect sensing

applications

Insight Deployment

14

Plant & play parking lot monitoring

14

15

Plant & play parking lot monitoring

16

Plant & play parking lot monitoring

17

Anybody interested in

iphone/ipod programming class?

17

18

4. Self-stabilizing systems theory

• 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!

18