Interference is an inherent characteristic of wireless (multihop) communications. Adding interference-awareness to important control functions, e.g., routing, could significantly enhance the overall network performance. Despite some initial efforts, it is not yet clearly understood how to best capture the effects of interference in routing protocol design. Most existing proposals aim at inferring its effect by actively probing the link. However, active probe measurements impose an overhead and may often misrepresent the link quality due to their interaction with other networking functions. Therefore, in this paper we follow a different approach and: 1) propose a simple yet accurate analytical model for the effect of interference on data reception probability, based only on passive measurements and information locally available at the node; 2) use this model to design an efficient interference-aware routing protocol that performs as well as probing-based protocols, yet avoids all pitfalls related to active probe measurements. To validate our proposal, we have performed experiments in a real testbed, setup in our indoor office environment. We show that the analytical predictions of our interference model exhibit good match with both experimental results as well as more complicated analytical models proposed in related literature. Furthermore, we demonstrate that a simple probeless routing protocol based on our model performs at least as good as well-known probe-based routing protocols in a large set of experiments including both intraflow and interflow interference.

Interference is an inherent property of wireless multihop networks. Adding interference-awareness to their control functions can significantly enhance the overall network performance. In this paper we present an analytical model for the probability that a transmission destined to an arbitrary network node is successful in the presence of interference from other nodes in the network. We introduce the concept of interference areas and interference zones to express this probability as a function of the network density, node transmission probability, radio propagation environment, and network card reception sensitivity. Our derivation includes a simpleMAC model, which captures the carrier sense function of many MAC protocols. Contrary to measurementbased models, our derivation only requires information that is locally available to the nodes, avoiding all measurement-related pitfalls. The validation of our model against experiments in a real testbed, set up for this purpose in our indoor office environment, shows good match of the experimental results with the analytical predictions. Interestingly our model predictions follow closely those of more elaborate state-of-the-art analytical models. Finally, to demonstrate the real utility of our model, we have implemented on our testbed a routing metric that explicitly takes interference into account via our derivation. The throughputs of the resulting routes compare favorably with those achieved by a well-known probe-based routing metric.