Title page for ETD etd-07222008-142101

Type of Document Dissertation
Author Komali, Ramakant S
Author's Email Address rskomali@vt.edu
URN etd-07222008-142101
Title Game-Theoretic Analysis of Topology Control
Degree PhD
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
MacKenzie, Allen B. Committee Chair
DaSilva, Luiz A. Committee Member
Gilles, Robert P. Committee Member
Reed, Jeffrey Hugh Committee Member
Shukla, Sandeep K. Committee Member
  • node cooperation
  • cognitive network
  • distributed algorithm
  • cross-layer optimization
  • game theory
  • network design
  • topology control
  • ad hoc network
Date of Defense 2008-07-09
Availability unrestricted
Ad hoc networks are emerging as a cost-effective, yet, powerful tool for communication.

These systems, where networks can emerge and converge on-the-fly, are guided by the

forward-looking goals of providing ubiquitous connectivity and constant access to information.

Due to power and bandwidth constraints, the vulnerability of the wireless medium, and

the multi-hop nature of ad hoc networks, these networks are becoming increasingly complex

dynamic systems. Besides, modern radios are empowered to be reconfigurable, which harbors

the temptation to exploit the system. To understand the implications of these issues, some

of which pose significant challenges to efficient network design, we study topology control

using game theory.

We develop a game-theoretic framework of topology control that broadly captures the radio

parameters, one or more of which can be tuned under the purview of topology control. In

this dissertation, we consider two parameters, viz. transmit power and channel, and study

the impact of controlling these on the emergent topologies.

We first examine the impact of node selfishness on the network connectivity and energy

efficiency under two levels of selfishness: (a) nodes cooperate and forward packets for one

another, but selfishly minimize transmit power levels and; (b) nodes selectively forward

packets and selfishly control transmit powers. In the former case, we characterize all the

Nash Equilibria of the game and evaluate the energy efficiency of the induced topologies.

We develop a better-response-based dynamic that guarantees convergence to the minimal

maximum power topology. We extend our analysis to dynamic networks where nodes have

limited knowledge about network connectivity, and examine the tradeoff between network

performance and the cost of obtaining knowledge. Due to the high cost of maintaining

knowledge in networks that are dynamic, mobility actually helps in information-constrained

networks. In the latter case, nodes selfishly adapt their transmit powers to minimize their

energy consumption, taking into account partial packet forwarding in the network. This

work quantifies the energy efficiency gains obtained by cooperation and corroborates the

need for incentivizing nodes to forward packets in decentralized, energy-limited networks.

We then examine the impact of selfish behavior on spectral efficiency and interference minimization

in multi-channel systems. We develop a distributed channel assignment algorithm

to minimize the spectral footprint of a network while establishing an interference-free connected

network. In spite of selfish channel selections, the network spectrum utilization is

shown to be within 12% of the minimum on average. We then extend the analysis to dynamic

networks where nodes have incomplete network state knowledge, and quantify the price of

ignorance. Under the limitations on the number of available channels and radio interfaces, we

analyze the channel assignment game with respect to interference minimization and network

connectivity goals. By quantifying the interference in multi-channel networks, we illuminate

the interference reduction that can be achieved by utilizing orthogonal channels and by distributing

interference over multiple channels. In spite of the non-cooperative behavior of

nodes, we observe that the selfish channel selection algorithm achieves load balancing.

Distributing the network control to autonomous agents leaves open the possibility that nodes

can act selfishly and the overall system is compromised. We advance the need for considering

selfish behavior from the outset, during protocol design. To overcome the effects of

selfishness, we show that the performance of a non-cooperative network can be enhanced by

appropriately incentivizing selfish nodes.

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