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Sensor Networks and Networked Societies of Artifacts

Jose RolimUniversity of Geneva

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Why sensor networks?

Invisible, everywhere computing Invisible computing:

• Tiny• Embedded• Attached …

Everywhere computing:• Wireless• Dynamic Configurable• Remote Access• Adapting …

Sensor Networks seems to be the typical example

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Definition

“Sensor networks are collections of wirelessly interconnected devices with sensing, computing and communication infrastructure that allow us to instrument, observe, and respond to phenomena in the natural environment and physical infrastructure.”

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Some Goals of Pervasive Computing

Invisible technology Integration of virtual and physical

worlds throughout desks, rooms, building, life, society Take the data out of the information,

leaving just an enhanced ability to act

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Situated Networking

Makes use of simple shared context: Space Time Proximity

Participation in the context: Physical Out here with us Many small and large places

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Difficulties of Pervasive Networking

Network explosion Impossibility of a centralized

management Network heterogeneity (not only

sensors but also laptops, servers, etc ..)

The use of adapted standard theoretical models, algorithms and protocols is impossible

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Why the Standard Approaches Fail

At the modeling level (ex: limited energy)

At the networking level (ex: scalability)

At the management level (ex: radically distributed)

At the service level (ex: situated nature)

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Our aim:

A communication system supporting millions of localized services in an networked society consisting of billions of heterogeneous nodes, intermittently connected and invisible

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Issues for sensor networking -1

Technology independent models of sensor networking

Dynamic models of sensor distributed computing

Self-organization in sensor networks Distributed cost sharing mechanisms Energy management, balancing and

complexity formulation Models of collaboration and competition for

resources

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Issues for sensor networking -2

Nature inspired control and adaptation mechanisms

Global impact of the local sensor interactions

Models of ad-hoc deployment Efficient approach to mobility

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Issues for sensor networking -3

Energy aware algorithms and protocols for: Self configuration, reconfiguration and

localization of sensors Long vs short range algorithms Resilience and robustness mechanisms Pervasive communication for routing,

interference and congestion control Achievement of global tasks via local,

event driven interactions

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How to tackle these issues?

Techniques from TCS Techniques from Game Theory Techniques from Natural Computing Techniques from Networking

Information Theory Techniques from Distributed

Computing Most importantly: integration among

those techniques

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Techniques from TCS

Examples:

Extended Random Graphs Models Geometric Approaches Probabilistic Algorithms Markov Chains

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Techniques from Game Theory

Examples: Nash Equilibrium Game Theoretic Methods for Enforcing

Cooperation Security Issues Evolutionary Game Theory Reputation Systems

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Techniques from Natural Computing

Examples: Adaptation by evolution Learning from experience Evolutionary genetics Biological networks Complex ecosystems Swarm intelligence

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Techniques from Distributed Computing

Examples: Approximate counting Randomized methods with noisy

information Manets mechanisms- mobility Obstacle avoidance Power saving schemes

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Example

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Example 2

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Ex

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Energy cost: Square of distance

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The Problem

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A distributed algorithm

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Illustration

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Stability

Markov Chain

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Mix of techniques

Theory Game Natural computing Network information Distributed computing Mix