Peer-to-peer networks can offer benefits to distributed content-based publish/subscribe data dissemination systems. In particular, since a peer-to-peer network's aggregate resources grows as the number of participants increases, scalability can be achieved without managing or deploying additional infrastructure. This thesis proposes an efficient algorithm for supporting publish/subscribe subscriptions that specify a range of interest. The algorithm is built over the Pastry distributed hash table and is completely decentralized. Load balance is addressed by subscription delegation away from overloaded peers, and a bottom up tree search technique that avoids root hotspots. As well, fault-tolerance is achieved with a light-weight replication scheme that quickly detects and recovers from faults. Simulations support the scalability and fault-tolerance properties of the algorithm.