WISE Abstraction Framework for Wireless Networks
MetadataShow full item record
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.