Model-Driven Optimization of Opportunistic Routing

Opportunistic routing aims to improve wireless performance by exploiting communication opportunities arising by chance. A key challenge in opportunistic routing is how to achieve good, predictable performance despite the incidental nature of such communication opportunities and the complicated effects of wireless interference in IEEE 802.11 networks. To address the challenge, we develop a model-driven optimization framework to jointly optimize opportunistic routes and rate limits for both unicast and multicast traffic. A distinctive feature of our framework is that the performance derived from optimization can be achieved in a real IEEE 802.11 network. Our framework consists of three key components: 1) a model for capturing the interference among IEEE 802.11 broadcast transmissions; 2) a novel algorithm for accurately optimizing different performance objectives; and 3) effective techniques for mapping the resulting solutions to practical routing configurations. Extensive simulations and testbed experiments show that our approach significantly outperforms state-of-the-art shortest-path routing and opportunistic routing protocols. Moreover, the difference between the achieved performance and our model estimation is typically within 20%. Evaluation in dynamic and uncontrolled environments further shows that our approach is robust against inaccuracy introduced by a dynamic network and it also consistently outperforms the existing schemes. These results clearly demonstrate the effectiveness and accuracy of our approach.