Topology Formation and
Public PolicyJeff Pang
15-848E
Topology Formation Overview
• Principles and Protocols for Power Control in Ad Hoc Networks, V. Kawadia and P. R. Kumar, IEEE Journal on Selected Areas in Communications.
• Vikas Kawadia and P. R. Kumar, ``A Cautionary Perspective on Cross Layer Design.'' To appear in IEEE Wireless Communication Magazine.
• Roger Wattenhofer, Li Erran Li, Victor Bahl and Yi-Min Wang, Distributed Topology Control for Power Efficient Operation in Multihop Wireless Ad Hoc Networks. Proc. of IEEE INFOCOM, pages 1388-1397, April 2001
• Ning Li and Jennifer C. Hou, Topology control in heterogeneous wireless networks: problems and solutions, in Proc. of IEEE INFOCOM 2004, March, 2004.
Power Control
1 mW
4 mW
1mW
4mW
Power Control: Cross-Layer Design Issues
• Physical Layer– Power control affects quality of signal
• Link Layer– Power control affects number of clients sharing channel
• Network Layer– Power control affects topology/routing
• Transport Layer– Power control changes interference, which causes
congestion
• Application/OS Layer– Power control affects energy consumption
Cross Layer Design Example:Rate Adaptive MAC
• 802.11 MAC adapts rate to minimize errors
• DSDV routes using shortest hop-count paths– Uses lowest rate to
determine links
• “short” paths can have less bandwidth than longer paths!
4Mbps
1Mbps
Cross Layer Design Example:Rate Adaptive MAC
Adaptive
Plain
Cross Layer Design Example:Topology Control
• Goal: choose node degree to maximize end-to-end throughput
• Set transmit power to achieve target-degree– Short time-scale
• Modify target-degree to increase end-to-end throughput– i.e., try to follow gradient to a maxima– Long time-scale
• Problem: can cause oscillations– Topology can oscillate between connected and
disconnected states
Cross Layer Design Example:Topology Control
Topology Control Protocols
• Kawadia and Kumar– COMPOW– CLUSTERPOW– Tunneled CLUSTERPOW– MINPOW
• Wattenhofer, et al.– Angle-based
• Li and Hou– Directed Relative Neighbor Graph (DRNG)– Directed Local Minimum Spanning Tree (DLMST)
COMPOW
• Everyone transmits at same power– Find minimum power s.t. topology remains
connected
• Pros:– Ensures all links bidirectional– Allows higher layers to work properly
• Cons:– Single outlying node causes high-power
CLUSTERPOW
• Run a separate routing protocol at each power level pi
• Route packets using routing table at minimum pi where destination is present
• Pros:– “Clustering” is distributed– Any base routing protocol works– Routing is loop-free (power levels monotonically decrease)
• Cons:– Routing overhead (one per power-level)– Can’t use initial lower-power hops
CLUSTERPOW
Tunneled CLUSTERPOW
• Recursively lookup path to next hop– e.g., if D is reachable through N1, search for min-
power route to N1, etc.
Tunneled CLUSTERPOW• Simple recursion is not loop-free
• Solution: Tunnel packet to intermediate hop
MINPOW• Goal: Route using min-energy route• Energy cost of using a link at power level p:
– PTotal(p) = PTx + PTxRad(p) + PRx
• Topology:– Graph is union of topology at all power levels– Link-cost = minreachable-p(PTotal(p))– Run DSDV (Bellman-Ford) on resulting graph
• Pros:– Globally optimal in terms of energy consumption– Loop-free (just DSDV)
• Cons:– Not optimal for capacity (but close if PTxRad(p) dominates)– Does not take into account interference! (i.e., retransmits)
COMPOW/CLUSTERPOWThroughput vs. Delay
(clustered topology -- mostly 1 hop paths)
COMPOW/CLUSTERPOWRouting Overhead
Cone-based
• Goal: topology with power efficient routes
• Assumptions:– Transmit power dominates energy cost– Nodes can determine angle of reception– Trasmit power p(d) = Ω(dx) for x >= 2
• Basic Idea:– Use min power needed to reach at least
1 node in each cone of 2π/3 around node (π/2 for optimal efficiency)
– Refine by removing unneeded neighbors
X
Cone-based Properties
• Topology is connected– Pf. Intuition: consider disconnected u,v with min d(u,v). For
any neighbor w, > π/3
• Routes are minimum power– Pf. Intuition: multiple short hops cheaper than one long hop
Cone-based Topology
Max Power After Phase 1 Final
Cone-based Results
DLMST Motivation
• Goal: Topology formation for nodes with heterogeneous max power levels
• Problem with Cone-based topology (any MRNG based method):
DLMST Protocol
• Each node broadcasts HELLO at its max power• With knowledge of directed graph in its
neighborhood, construct minimum spanning tree• Pros:
– Connectivity guaranteed– Node degree bounded by constant (limits interference)
• Cons:– Links not necessarily bidirectional (can fix, but may sacrifice
global connectivity)
DLMST Results
Average Radius Average Degree
Topology Control Discussion
• What else besides transmit power affects topology?
• Is power control a problem in infrastructure AP networks?
• How can power control affect fairness?
Public Policy: Spectrum Management
• Spectrum Management Policy Options, Jon Peha, IEEE Communications Surveys, Fourth Quarter 1998, Vol. 1, No. 1.
• Approaches to Spectrum Sharing, Jon M. Peha, Feb. 2005.
• Dynamic Spectrum Policies: Promises and Challenges, Paul J Kolodzy, Jan 2004.
The Bigger Picture
Technology
Society Economy
Government
• 9 million hotspot users in 2003 (30 million in 2004)
• Approx 4.5 million WiFi access points sold in 3Q04
• Sales will triple by 2009• Many more non-802.11 devices
Staggering Market Statistics
US Spectrum Allocation
802.11Bluetooth802.11Bluetooth
The Status Quo
• Government licenses spectrum– By frequency: e.g., for a television channel– By location: e.g., for the Pittsburgh area– Only licensees allowed to transmit
• Licenses are temporary– Allows change in spectrum policy– New spectrum usually auctioned– But 99.9% always renewed
• A small number of unlicensed bands– Industry, Science, and Medicine (prev. slide)– PCS, NII– Anyone can transmit (with limitations)
Governing Spectrum Blocks
• Open access: “Flexible use doctrine”– Let market forces decide applications– => most value, innovation, competition
• Exclusive access:– Government chooses application/transmission standard– => international interoperability, positive “externalities” (e.g., for
police, fire fighters), standardization
Distributing Licenses
• Lotteries– Avoids political favoritism– Does not necessarily maximize value
• Auctions– Tries to maximize value of application– Can be synchronized to allow buyers to get larger chunks
Alternatives to Licensing
• “Property Rights”– Treat spectrum same as land– Allows resale, renting, etc. => opens up secondary
markets for spectrum– But interference (“trespassing”) on region boundaries
unavoidable
• “Commons”– WiFi model: cooperative sharing– Maximize spectrum use if transmission is bursty– Requires some common protocol for cooperation– Requires some altruism
Dynamic Spectrum Management
• Goal: Allocate spectrum more dynamically– For example, without humans in the loop
• Why? Lots of spectrum is wasted!– Time of day (some radio stations turn off at night)– Location (rural areas don’t use all TV frequencies)– Workload (data applications are bursty)
• Enabling Technology– Software Defined Radios– Adaptive Cognitive Radios– Example: Cordless phones vs. Baby monitors -- manual to
automatic freq. adjustment
Enabling Technologies
• Flexibility– Can change waveform on the fly (i.e., modulation protocol)
• Agility– Can change the freq. on the fly (i.e., channel)
• Sensing– Aware of environmental conditions (i.e., interference)
• Networking– Can interact with other radios (i.e., ad hoc nets)
Dynamic Policy Options
• Can policy be varied by:– Transmission duration? (e.g., “TDM”)– RF condition? (e.g., interference sensing)– Short time scales?– Via negotiation between radios?– Impact on environment? (e.g., interference)
• Implementation Options:– High power beacon to all devices?– “P2P” networked radio enforcement?
Implementation Challenges• Quantifying interference
– FCC definition: “unwanted energy”
• Measurement infrastructure– Analog to “pollution monitors”– Dedicated or networked P2P based?
• Liability policies– How to punish policy non-
compliance– Do devices need to be certified?
What about software?
• Identity management– How to identify violators
“Example”
Public Policy Discussion
• How could more dynamic spectrum allocation impact:– WiFi Testbeds?– Community Mesh Networks?– Mixed Networks?– Other topics?
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