Keeping wireless network theory useful
This presentation is the property of its rightful owner.
Sponsored Links
1 / 23

Keeping Wireless Network Theory Useful PowerPoint PPT Presentation


  • 85 Views
  • Uploaded on
  • Presentation posted in: General

Keeping Wireless Network Theory Useful. Nancy Lynch, MIT EECS, CSAIL WRAWN workshop Montreal, July, 2013. Wireless Network Models. Purely graph-based models Radio Broadcast (protocol) model Dual Graph model. . Wireless Network Models. Purely graph-based models

Download Presentation

Keeping Wireless Network Theory Useful

An Image/Link below is provided (as is) to download presentation

Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author.While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server.


- - - - - - - - - - - - - - - - - - - - - - - - - - E N D - - - - - - - - - - - - - - - - - - - - - - - - - -

Presentation Transcript


Keeping wireless network theory useful

Keeping Wireless Network Theory Useful

Nancy Lynch, MIT EECS, CSAIL

WRAWN workshop

Montreal, July, 2013


Wireless network models

Wireless Network Models

  • Purely graph-based models

    • Radio Broadcast (protocol) model

    • Dual Graph model


Wireless network models1

Wireless Network Models

  • Purely graph-based models

    • Radio Broadcast (protocol) model

    • Dual Graph model

  • Geometry-based models

    • Unit Disk Graph (UDG)

    • Quasi-Unit-Disk Graph

    • Signal-to-Noise Ratio (SiNR)

  • Q: Are these models “realistic”?

  • In many ways, they are quite strong:

    • Graphs derived from geometry in stylized ways.

    • Mostly reliable.

    • Mostly static.

    • Known graphs and geometry (sometimes).


So are these models realistic

So Are These Models Realistic?

  • It depends on the settings and applications we want to consider.

  • Potential wireless network applications:

    • Hazardous waste cleanup

    • Search and rescue

    • Military operations

    • Exploring an unknown terrain

    • Cooperative construction

    • Flash mob dancing


So are these models realistic1

So Are These Models Realistic?

  • It depends on the settings and applications we want to consider.

  • Potential wireless network applications:

    • Hazardous waste cleanup

    • Search and rescue

    • Military operations

    • Exploring an unknown terrain

    • Cooperative construction

    • Flash mob dancing

  • Biological systems:

    • Insect colonies

    • Cells during development

    • Brains


Algorithm characteristics

Algorithm Characteristics

  • Algorithms should be efficient (in terms of time, storage, and communication).

  • Algorithms should be flexible:

    • They should work in many different settings,.

    • Participating nodes should not need to know very much about the setting.

  • Algorithms should be robust to limited amounts of failure and recovery.

  • More generally, algorithms should be adaptive to changes during execution, e.g.:

    • The set of participating nodes may change (join, leave, fail, recover) during execution.

    • Communication is subject to uncertainty, success may vary during execution.

    • Nodes may move, connectivity may change.


Algorithm characteristics1

Algorithm Characteristics

  • Efficient.

  • Flexible, Robust, Adaptive

  • Q: Why should we focus on these kinds of algorithms?

  • A: They correspond to many (most) real wireless settings. 

  • A: They also correspond to biological systems (insect colonies, cells during development, brains), which might provide inspiration for new wireless algorithms.

  • We need new theory for these algorithms:


New theory

New Theory

  • New models that can describe the new platforms and algorithms.

  • New kinds of problem statements.

  • New complexity measures that take change into account.

  • New kinds of algorithms, new analysis methods.

  • New lower bounds that depend on the additional requirements.

  • New concurrency theory foundations.

  • Problem guarantees will typically be approximate and probabilistic, not exact and absolute.

  • Costs of solving the problems will be inherently higher if we include requirements of flexibility and robustness.


New theory1

New Theory

  • New models that can describe the new platforms and algorithms.

  • New kinds of problem statements.

  • New complexity measures that take change into account.

  • New kinds of algorithms, new analysis methods.

  • New lower bounds that depend on the additional requirements.

  • New concurrency theory foundations.

  • Algorithms may be simpler, more “self-organizing” than usual.

  • Foundations based on Probabilistic Timed I/O Automata.


Examples

Examples


Examples1

Examples

  • Low-level wireless communication

  • High-level wireless communication and computation.

  • Social insect colonies

  • Developing organisms


1 low level wireless communication

1. Low-Level Wireless Communication

  • Dual Graph model [Kuhn, Lynch, Newport DISC 09]

    • Collisions result in message loss.

    • Unreliable and reliable edges.

    • Dynamic: Message reach varies over time.

  • Example algorithms using Dual Graphs:

  • Building Dominating Sets, MISs [K,L,N, Oshman, Richa PODC 10]

  • Local and global broadcast [Ghaffari, Haeupler, L,N DISC 12]

  • Reasonably efficient algorithms for local and global broadcast, provided message reach is determined by an oblivious adversary, and some geographical constraints are satisfied [Ghaffari, Lynch, Newport PODC 13]


Low level wireless communication

Low-Level Wireless Communication

  • Algorithms are more costly than for the radio broadcast model.

  • Adaptive to dynamic uncertainty of message reach.

  • Partially flexible: Nodes use partial knowledge of the networks.

  • Not robust.

  • Questions:

    • Consider more dynamic behavior: Failures. Mobility.

    • Can we get good bounds for local/global broadcast in such highly dynamic settings?

    • What are the limits of flexibility? That is, what knowledge of the networks is actually required to solve problems using this model?


2 high level wireless communication and computation

2. High-Level Wireless Communication and Computation

  • Some work on higher-level algorithms in wireless networks assumes completely reliable local broadcast (RLB) communication.

  • Examples:

    • Global broadcast in static graph networks

    • Building network structures

    • Computing in dynamic graph networks

    • Robot coordination

  • Abstract MAC layers [Kuhn, Lynch, Newport 09], mask low-level wireless communication, yield RLB guarantees.

  • But low-level wireless protocols do not guarantee completely reliable local broadcast.

    • They involve probabilistic transmission, random backoff, random coding,…

    • Yield high-probability guarantees only.

  • So we defined a probabilistic abstract MAC layer [Khabbazian, Kowalski, Kuhn, Lynch DIALM-POMC 10].

    • Fast delivery of each message to all neighbors whp.

    • Each receiver receives some message quickly whp.


High level wireless communication and computation

High-Level Wireless Communication and Computation

  • Questions:

    • Design algorithms above a local bcast layer that tolerate occasional exceptions (lost messages).

    • Which currently-existing high-level algorithms, written over a RLB layer, already tolerate such exceptions, or can easily be modified to do so? Which do not?

    • What are inherent limitations?

    • How do we model/verify compositions of high-level probabilistic algorithms and probabilistic implementations of local broadcast?

  • Problems to consider:

    • Communication, building network structures.

    • Robot problems: task allocation, forming geometric patterns, exploration/routing/navigating.

  • Also consider other kinds of failures, mobility.

  • Combine these considerations with Dual Graph issues.


3 social insect colonies

3. Social Insect Colonies

  • Social insects (ants and bees) live in colonies, cooperate to solve complex problems, including:

    • Division of labor (foraging for food, feeding larvae, cleanup, defense,…)

    • Searching/routing/navigating.

    • Agreeing on the site of a new nest.

    • Constructing nests.

  • They use distributed algorithms, based on direct chemical or physical communication, or on leaving chemical signals in the environment (stigmergy).

  • Algorithms are highly flexible, robust, and adaptive.

  • Efficient: Colonies perform their work quickly, with low energy usage.


Social insect colonies

Social Insect Colonies

  • Flexible:

    • Insects don’t know the exact size of the colony, though they may have a rough idea.

    • Insects don’t know all the details of their physical environment.

    • But colonies may have evolved to do better in certain kinds of settings than others.

  • Robust:

    • Death of some insects doesn’t affect the colony much.

    • Destroying the nest leads the insects to find/build another nest.

    • Homeostasis?

  • Adaptive to changes to the colony, to the environment.


Proposed research project

Proposed Research Project

  • Dornhaus(insect colony bio), Lynch (dist. algs.), Nagpal(robotics)

  • Distributed Problem Solving in Dynamic Collectives: Theory, Insects, and Robots

  • Identify/analyze distributed algorithms that may be used by insect colonies. 

  • Define platform models, problems, algorithms.

  • Examples: Division of labor, foraging, nest construction.

  • Contributions to insect colony research:

    • Discover what algorithms insects actually use, and why.

    • Analyze the algorithms based on performance plus adaptivity.

  • Contributions to (wireless) distributed algorithms:

    • New adaptive algorithms, inspired by insect colony behavior.

    • New measures and analysis methods, for adaptive algorithms.

    • New concurrency theory.

  • Contributions to robotics:

    • Adapt insect algorithms for robot swarms.


4 developing organisms

4. Developing organisms

  • Cells in a developing embryo cooperate to solve problems of patterning.

  • Sometimes involves scaling.

  • They use distributed algorithms, based on:

    • Local chemical signaling between cells.

      • Like “beep” communication, as studied in our community.

    • Global morphogen gradients [Turing].

  • Simple local rules.

  • Flexible: Not dependent on exact number of cells, size of organism.

  • Robust: Death of some cells doesn’t matter much; homeostasis.


Developing organisms

Developing organisms

  • Questions: Identify/analyze distributed algorithms that may be used by cells in developing organisms.

  • Define platform models, problems, algorithms.

  • Contributions to developmental biology:

    • Discover what algorithms developing organisms actually use, and why.

    • Analyze algorithms based on performance, robustness to failures

  • Contributions to (wireless) distributed algorithms:

    • New algorithms, inspired by developmental behavior.

    • New measures and analysis methods

    • New concurrency theory.

  • In general, understanding biological algorithms could help us understand how to build simple, efficient, flexible, robust, adaptivewireless network algorithms.


Summary needed work

Summary: Needed Work

  • Research on algorithms for wireless networks that are flexible, robust, and adaptive to changes.

  • New kinds of models, cost metrics

  • New kinds of algorithms

  • New kinds of analysis


Concurrency theory f oundations

Concurrency theory foundations

  • General models based on interacting automata.

  • Must include time, discrete + continuous behavior, motion, probability.

  • Composition, abstraction.

  • Tailor for wireless systems.


Thank you

Thank you!


  • Login