Sensor Networks Where Theory Meets Practice Roger Wattenhofer ETH - - PowerPoint PPT Presentation

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Sensor Networks Where Theory Meets Practice Roger Wattenhofer ETH - - PowerPoint PPT Presentation

Oct 06, 2023 261 likes •954 views

Sensor Networks Where Theory Meets Practice Roger Wattenhofer ETH Zurich Distributed Computing www.disco.ethz.ch Theory Meets Practice SenSys OSDI HotNets Multimedia Ubicomp PODC STOC Mobicom FOCS SIGCOMM ICALP SPAA SODA EC

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ETH Zurich – Distributed Computing – www.disco.ethz.ch

Roger Wattenhofer

Sensor Networks

Where Theory Meets Practice

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Theory Meets Practice

PODC SODA STOC FOCS ICALP SPAA EC SenSys OSDI Mobicom Multimedia Ubicomp SIGCOMM HotNets

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Wireless Communication?

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Capacity!

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Interference Range

Protocol Model

Reception Range

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6

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Physical (SINR) Model

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Signal-To-Interference-Plus-Noise Ratio (SINR) Formula

Minimum signal- to-interference ratio Power level

  • f sender u

Path-loss exponent Noise Distance between two nodes Received signal power from sender Received signal power from all other nodes (=interference)

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Example: Protocol vs. Physical Model

1m

Assume a single frequency (and no fancy decoding techniques!)

Let =3, =3, and N=10nW Transmission powers: PB= -15 dBm and PA= 1 dBm SINR of A at D: SINR of B at C: 4m 2m

A B C D

Is spatial reuse possible? NO Protocol Model YES With power control

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This works in practice

… even with very simple hardware Time for transmitting 20‘000 packets: Speed-up is almost a factor 3 u1 u2 u3 u4 u5 u6

[Moscibroda, W, Weber, Hotnets 2006]

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Possible Application – Hotspots in WLAN

X Y Z

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Possible Application – Hotspots in WLAN

X Y Z

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The Capacity of a Network

(How many concurrent wireless transmissions can you have)

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Convergecast Capacity in Wireless Sensor Networks

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Protocol Model Physical Model (power control)

  • Max. rate in arbitrary,

worst-case deployment (1/𝑜) (1/log3 𝑜)

  • Max. rate in random,

uniform deployment (1/log 𝑜) (1/log 𝑜)

Worst-Case Capacity

Topology Model/Power

Classic Capacity

[Giridhar, Kumar, 2005] [Moscibroda, W, 2006]

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Capacity of a Network

Classic Capacity Worst-Case Capacity

How much information can be transmitted in nice networks? How much information can be transmitted in nasty networks? How much information can be transmitted in any network?

Real Capacity

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Core Capacity Problems

Given a set of arbitrary communication links One-Shot Problem Find the maximum size feasible subset of links O(1) approximations for uniform power [Goussevskaia, Halldorsson, W, 2009 & 2014] as well as arbitrary power [Kesselheim, 2011] Scheduling Problem Partition the links into fewest possible slots, to minimize time Open problem: Only 𝑃(log 𝑜) approximation using the one-shot subroutine

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The Capture Effect

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Receiving Different Senders

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Layer 4 Layer 3 Layer 2 Layer 1

  • 64 dBm
  • 70 dBm
  • 75 dBm
  • 81 dBm

“Layer” Abstraction

[König, W, 2016]

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Constructive Interference

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Energy Efficiency?

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Clock Synchronization!

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Clock Synchronization Example: Dozer

  • Multi-hop sensor network with duty cycling
  • 10 years of network life-time, mean energy consumption: 0.066mW
  • High availability, reliability (99.999%)
  • Many different applications use Dozer: TinyNode, PermaSense, etc.

[Burri, von Rickenbach, W, 2007]

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Problem: Physical Reality

t clock rate

1 1 + 𝜁

message delay

1 − 𝜁

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Given a communication network

1. Each node equipped with hardware clock with drift 2. Message delays with jitter Goal: Synchronize Clocks (“Logical Clocks”)

  • Both global and local synchronization!

Clock Synchronization in Theory?

worst-case (but constant)

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  • Time (logical clocks) should not be allowed to stand still or jump

Time Must Behave!

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Local Skew

Tree-based Algorithms Neighborhood Algorithms e.g. FTSP e.g. GTSP

Bad local skew

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Synchronization Algorithms: An Example (“Amax”)

  • Question: How to update the logical clock based on the messages from

the neighbors?

  • Idea: Minimizing the skew to the fastest neighbor

– Set clock to maximum clock value you know, forward new values immediately

  • First all messages are slow (1), then suddenly all messages are fast (0)!

Time is T Time is T

…

Clock value: T Clock value: T-1 Clock value: T-D+1 Clock value: T-D Time is T

skew D

Fastest Hardware Clock

T T

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Dynamic Networks! [Kuhn et al., PODC 2010]

Local Skew: Overview of Results

1 logD √D D …

Everybody‘s expectation, 10 years ago („solved“) Lower bound of logD / loglogD [Fan & Lynch, PODC 2004] All natural algorithms [Locher et al., DISC 2006] Blocking algorithm Kappa algorithm [Lenzen et al., FOCS 2008] Tight lower bound [Lenzen et al., PODC 2009] Dynamic Networks! [Kuhn et al., SPAA 2009] together [JACM 2010]

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Experimental Results for Global Skew

FTSP PulseSync

[Lenzen, Sommer, W, 2014]

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Experimental Results for Global Skew

FTSP PulseSync

[Lenzen, Sommer, W, 2014]

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Network Dynamics?

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Distributed Control!

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Complexity Theory Can a Computer Solve Problem P in Time t?

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Complexity Theory Can a Computer Solve Problem P in Time t? Network Distributed

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Complexity Theory Can a Computer Solve Problem P in Time t? Network Network Distributed

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Distributed (Message-Passing) Algorithms

  • Nodes are agents with unique ID’s that can communicate with neighbors

by sending messages. In each synchronous round, every node can send a (different) message to each neighbor.

69 17 11 10 7

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Distributed (Message-Passing) Algorithms

  • Nodes are agents with unique ID’s that can communicate with neighbors

by sending messages. In each synchronous round, every node can send a (different) message to each neighbor.

  • Distributed (Time) Complexity: How many rounds does problem take?

69 17 11 10 7

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An Example

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How Many Nodes in Network?

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How Many Nodes in Network?

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How Many Nodes in Network?

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How Many Nodes in Network?

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How Many Nodes in Network?

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How Many Nodes in Network?

1 1 1 1 1 1

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How Many Nodes in Network?

2 1 1 2 1 4 1 1 1

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How Many Nodes in Network?

With a simple flooding/echo process, a network can find the number

  • f nodes in time 𝑃(𝐸), where 𝐸 is the diameter (size) of the network.

2 1 1 2 1 4 1 1 1 10

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Diameter of Network?

  • Distance between two nodes = Number of hops of shortest path
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Diameter of Network?

  • Distance between two nodes = Number of hops of shortest path
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Diameter of Network?

  • Distance between two nodes = Number of hops of shortest path
  • Diameter of network = Maximum distance, between any two nodes
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Diameter of Network?

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Diameter of Network?

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Diameter of Network?

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Diameter of Network?

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Diameter of Network?

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Diameter of Network?

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(even if diameter is just a small constant) Pair of rows connected neither left nor right? Communication complexity: Transmit Θ(𝑜2) information over O(𝑜) edges  Ω(𝑜) time! [Frischknecht, Holzer, W, 2012]

Networks Cannot Compute Their Diameter in Sublinear Time!

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e.g., dominating set approximation in planar graphs

Distributed Complexity Classification

1 log∗ 𝑜 polylog 𝑜 𝐸 poly 𝑜

various problems in growth-bounded graphs MIS, approx. of dominating set, vertex cover, ... count, sum, spanning tree, ... diameter, MST, verification of e.g. spanning tree, …

e.g., [Kuhn, Moscibroda, W, 2016]

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Sublinear Algorithms Self- Stabilization Self- Assembly Applications e.g. Multi-Core Dynamic (e.g. Ad Hoc) Networks Distributed Complexity

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Sublinear Algorithms Self- Stabilization Self- Assembly Applications e.g. Multi-Core Dynamic (e.g. Ad Hoc) Networks Distributed Complexity

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Summary

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The Capture Effect

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How many lines of pseudo code Can you implement on a sensor node? The best algorithm is often complex And will not do what one expects. Theory models made lots of progress Reality, however, they still don’t address. My advice: invest your research £££s in ... impossibility results and lower bounds!

Theory for sensor networks, what is it good for?!

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Thank You!

Questions & Comments?

Thanks to my co-authors, mostly Silvio Frischknecht Magnus Halldorsson Stephan Holzer Michael König Christoph Lenzen Thomas Moscibroda Philipp Sommer www.disco.ethz.ch