WISE Abstraction Framework for Wireless Networks
WISE Abstraction Framework for Wireless Networks
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Date
2006-08-03
Authors
Lee, Seungjoon
Advisor
Bhattacharjee, Samrat
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Abstract
Current wireless networks commonly consist of nodes with different
capabilities (e.g., laptops and PDAs).
Link quality such as link error rate and data transmit rate can differ widely.
For efficient operation, the design of wireless networks must take into
account such heterogeneity among nodes and wireless links.
We present systematic approaches to overcome problems due to heterogeneous
node capability and link quality in wireless networks.
We first present a general framework called WISE (Wireless Integration
Sublayer Extension) that abstracts specific details of low-level wireless
communication technologies (e.g., modulation or backoff scheme).
WISE provides a set of common primitives, based on which upper-level
protocols can operate efficiently without knowing the underlying details.
We also present a number of protocol extensions that employ the
WISE framework to enhance the performance of specific upper-level
protocols while hiding lower-level heterogeneity (e.g., link error rate).
Our multihop WLAN architecture improves system performance by allowing client
nodes to use multihop paths via other clients to reach an AP.
Our geographic routing extension considers both location and link quality in
the next hop selection, which leads to optimal paths under certain conditions.
To address heterogeneity in node capability, we consider virtual
routing backbone construction in two settings: cooperative and selfish.
In the cooperative setting, we present a protocol extension that
constructs an optimal backbone composed of a small number of
high-capability nodes, which can be generalized to a more resilient
backbone.
For the selfish case, we use game theory and design an incentive-compatible
backbone construction scheme.
We evaluate our work from multiple perspectives.
We use theoretical analysis to prove that our extensions lead to optimal
solutions.
We use simulations to experiment with our schemes in various scenarios
and real-world implementation to understand the performance in practice.
Our experiment results show that our schemes significantly outperform
existing schemes.