Class Wrapup

1
Class Wrap-up
Richard Yang
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Admin.

• Homework 4
• Projects
• Class feedback

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Summary Three Major Challenges

• Wireless
• Mobility
• Portability

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Class Summary Wireless Networking 15/25

• Physical layer 4 classes
• wireless channel, diversity, spread-spectrum
• ISI, equalization, OFDM
• Link layer 5 classes
• TDMA/FDMA/CDMA/random access, GSM, 802.11
• spatial interference, wireless capacity and
  improvements
• Network layer 4 classes
• mobility management, mobile IP
• mobile routing/routing for lossy links/multiradio
• opportunistic routing and coding/geographical
  routing
• Topology control/Transport layer 1 class
• cone-based topology control/TCP extension
• Cross-layer optimization 1 class

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Mobile/Wireless Applications 8/25

• Localization 2 classes
• Naming and service discovery 1 class
• Mobile applications/programming 2 classes
• CPU and file systems support 1 class
• Security 1 class
• Anonymous mobile computing 1 class

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Wireless Networking
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Challenges in Wireless Networking

• Fading channels
• solutions
• diversity, opportunistic communications
• Low capacity due to spatial interference caused
  by the broadcast nature of wireless
• solutions, e.g.,
• directional antennas (reduce broadcast area)
• network coding to exploit a single broadcast to
  send multiple messages to multiple receivers
• linear coding, e.g., XOR of multiple messages

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Superposition Coding
MIT roofnet
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Superposition Coding Basic Idea

• Non-uniform coding to utilize higher quality
  channels

highsignal strength

• S broadcasts a message reaching both R1 and R2
• R1 cannot distinguish S0 between S1, but R2
  can.

R2
S
lowsignal strength
R1
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Superposition Coding
R2
S
R1

• R1 decodes only the first bit
• R2 first decodes the first bit, and then the
  second bit after subtracting the first bit
• called successive interference cancellation (SIC)

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Superposition Coding Analysis

• Suppose S has backlogto both R1 and R2
• Let received power levels at R1 and R2 be P1
  and P2
• Let background noise be N0
• Let the achieved rates of R1 and R2 be r1 and r2

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Why Successive Interference Cancellation ?
r2
r1
SIC achieves capacity.
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Superposition Coding Routing

• consider only the possib.of piggybacking a
  messageto another neighbor
• - for each neighbor, determineif another
  neighbor has a strong (dashed) channel to use
  superposition coding

http//wireless.ece.ufl.edu/jshea/pubs/jsac_jung0
5.pdf
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Routing at A
- Assume shortest hop-count routing - Keep
shortest hop-count routing with a strong first
hop opportunistic routing look through buffer
to see if a strong can be piggybacked onto a
normal channel
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Superposition Coding

• The preceding design is a strawmans first
  attempt
• As of now, the design of MAC and routing for
  superposition coding is largely open, a good
  research topic.

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Software Radio
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Motivation

• Wireless communicaiton techniques are evolving
  rapidly in the last decade
• Current implementation architecture is not
  flexible
• hardware/software division is typically at the
  MAC layer
• however, many new ideas, e.g., superposition
  coding, need to modify/access physical layer
• Need a flexible radio architecture

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Software Radio

• A collection of software that when combined with
  minimal hardware, allows the construction of
  radios where the actual waveforms transmitted and
  received are computed by software/or highly
  flexible programming techniques such as FPGA.

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Software Radio
softwareradio
minimumhardware
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GNU Universal Software Radio Peripheral (USRP)
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RF Front End and AD/DA

• RF front ends
• shift radio signal from modulation frequency
  (e.g., 2.4 GHz for 802.11) to channel (base)
  frequency range
• AD/DA component
• sample signals

channel
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44
40
48
52
56
60
64
5150
5180
5200
5220
5240
5260
5280
5300
5320
5350
MHz
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Nyquist Sampling Theorem

• In order to reconstruct signal, must sample at
  twice the base frequency range

http//en.wikipedia.org/wiki/NyquistE28093Shann
on_sampling_theorem
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GNU Software Radio Projects

• Military
• Military full connectivity among branches
• A TiVo equivalent for radio, capable of recording
  multiple stations simultaneously.
• Digital Radio Mundial (DRM).
• Radio astronomy.
• A passive radar system that takes advantage of
  broadcast TV for its signal source. For those of
  you with old TVs hooked to antennas, think about
  the flutter you see when airplanes fly over.
• Amateur radio transceivers.
• Time Division Multiple Access (TDMA) waveforms.
• Distributed sensor networks.
• Distributed measurement of spectrum utilization.
• Ad hoc mesh networks.
• RFID detector/reader.
• Multiple input multiple output (MIMO) processing.
  
• TETRA transceiver.
• Software GPS.

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GNU Radio HDTV
http//www.gnu.org/software/gnuradio/hdtv-samples.
html
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Software Radio Pros

• Flexibility
• turning parameters
• center frequency, modulation (dig/analog), symbol
  rate,
• spreading, power, channel coding/decoding,
  interleaving, encryption, MAC-layer techniques
  TDMA, FDMA, CDMA, equalization
• access channel status
• BER, FER (Frame Error Rate), data rate, receive
  signal
• doppler effect, angle of arrival, etc
• DSP can compensate for cheaper RF
• Fast prototyping

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Software Radio Cons

• Software/FPGA speed
• Cost
• Power consumption

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Mobile Computing
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Mobility

• There are several types of mobility in the mobile
  computing context, e.g.,
• node position mobility
• the positions of the network nodes move
• two types of position mobility uncontrolled
  mobility/controlled position mobility
• code mobility
• mobile agents
• process/state mobility
• process/state migration to track user/target
  mobility, e.g., let your X Windows sessions
  follow you, or state tracks a moving target
• a kind of controlled state mobility

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Controlled State Mobility
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Path Selection Ants Foraging

• some ants use pheromone (scent) to create trails
  to food
• the probability that other ants will follow a
  trail is proportional to the density of
  pheromone
• the algorithm is a type of reinforcement learning
  algorithm

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Inspiration Load-Adaptive Routing

• Suppose our objective is to discover low latency
  paths through the network
• the routing algorithms we discussed in class are
  not load-adaptive
• however, the latency of each link depends on load
• why?

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A Distributed Algorithm for Computing
User-Optimal Routing

• A Bellman-Ford like algorithm combined with
  adaptation
• A probabilistic routing scheme
• Each node maintains a forwarding table

Pikj is the routing probability at i to send to
dest. k using neighbor j
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Protocol for Updating the Forwarding Table at
Node i for Destination k
Ljk is the path latency from j to k lij is the
link latency from i to j
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Updating Forwarding Probability The Slow Path

• Update
• where

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Updating Delay Estimation The Fast Path

• Update
• where ?(t) satisfies conditions as ? (t), and

Discussion why the above condition?
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Other Comments on the Algorithm?
Why not set Lik(t1) to the weighted average?
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Responsiveness of the Routing Algorithm
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Target Tracking
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Information-Driven Diffusion

• Detecting model
• zi (t) h(x(t), ?i (t)), where x(t) is
  parameter to be estimated, ?i (t) and zi (t) are
  characteristics and measurement of node i
  respectively
• example for sensors measuring sound amplitude
• zi a / xi - x ?/2 wi ,
  where a is target amplitude, ? is attenuation
  coefficient, wi is Gaussian noise
• State (belief)
• representation of the current a posteriori
  distribution of x given measurement z1, , zN
  p(x z1, , zN)

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Node Selection

• j0 argj?A max ?(p(xzii? U ?zj))
• A 1, , N - U is set of nodes whose
  measurements not incorporated into belief
• ? is information utility function defined on the
  class of all probability distributions of x
• intuitively, select node j for querying such that
  information utility function of updated
  distribution by zj is maximum

Current belief state
Next belief state
Sensor
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Node Position Mobility
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Coverage

• Many formulations, here I first give some
  examples
• Given (uniform) initial node positions, assume
  events happen at different positions
• known to all nodes (by flooding)
• how to move the nodes to match the event
  distribution, i.e., the more likely an event will
  happen at a place, the more likely a node is
  there?

event positions
Initial node positions
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Coverage with Worst Case Guarantee

• Consider a coverage of region ? with N nodes V
  v1, v2, , vN, where vi is the position of
  node i
• For any point p in the region ?, define
  d(p, V) mini distance(vi, p)
• Define the quality of the coverage V as
  d(?, V) maxp d(p, V)
• A good coverage V is one which minimizes
  d(?, V) minV d(?, V)

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Solve the One Node Case

• Where is the best position of the single node?

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Controlled Mobility Rule
- Move towards the furthest vertex - If more than
one vertices, move to the vector with the minimum
norm in the convex hull of the multiple
vertices.
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Controlled Mobility

• How to updatethe moving directionof a group of
  nodes so that they all movealong the same
  direction?
• How to move the nodes without losing
  connectivity to optimize throughput and/or
  minimize energy?

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Wireless and Mobile Computing Summary

• Driven by physics, technology and vision
• wireless communication technology
• global infrastructure
• device miniaturization
• The field faces many challenges
• It is moving fast but far from mature, thus many
  opportunities

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Backup Slides
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Deployment 1-Dimensional Case

• Each node keeps track of event histogram
• Assume the (initial) position of a node is x0
• Partition the range into buckets
• Map node old position to new position
• see right

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Extension

• Application-specific reinforcement-based
  routing/forwarding
• The Directed Diffusion paradigm
• Elements
• Naming
• data is named using attribute-value pairs
• Interests
• a node requests data by sending interests for
  named data
• Gradients
• gradients is set up within the network designed
  to draw events, i.e., data matching the
  interest
• Reinforcement
• sink reinforces particular neighbors to draw
  higher quality ( higher data rate) events

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Naming

• Content based naming
• Tasks are named by a list of ltattribute, valuegt
  pairs
• Task description specifies an interest for data
  matching the attributes
• Animal tracking

Request
Interest ( Task ) Description Type four-legged
animal Interval 20 ms Duration 1
minute Location -100, -100 200, 400
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Interest

• The sink periodically broadcasts interest
  messages to each of its neighbors
• Every node maintains an interest cache
• each item corresponds to a distinct interest
• each entry in the cache has several fields
• timestamp last received matching interest
• several gradients data rate, duration, direction

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Setting Up Gradient
Source
Sink
Interest Query
Gradient Who is interested (data rate,
duration, direction)
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Data Propagation

• When a node receives data
• find a matching interest entry in its cache
• examine the gradient list, send out data by rate
• cache keeps track of recent seen data items (loop
  prevention)
• data message is unicast individually to the
  relevant neighbors

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Reinforcing the Best Path
Source
The neighbor reinforces the neighbor from whom
it first received the latest event (low delay)
Sink
Low rate event
Reinforcement Increased interest
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Evaluation Surveillance

• Five sources are randomly selected within a 70m x
  70m corner in the field
• Five sinks are randomly selected across the
  field
• High data rate is 2 events/sec
• Low data rate is 0.02 events/sec
• Event size 64 bytes
• Interest size 36 bytes

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Average Dissipated Energy
0.018
0.016
Flooding
0.014
0.012
0.01
0.008
(Joules/Node/Received Event)
Average Dissipated Energy
0.006
Diffusion
0.004
0.002
0
0
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100
150
200
250
300
Network Size