In this paper, we present a new routing paradigm that generalizes opportunistic routing for wireless multihop networks. In multirate anypath routing, each node uses both a set of next-hops and a selected transmission rate to reach a destination. Using this rate, a packet is broadcast to the nodes in the set, and one of them forwards the packet on to the destination. To date, there is no theory capable of jointly optimizing both the set of next-hops and the transmission rate used by each node. We solve this by introducing two polynomial-time routing algorithms and provide the proof of their optimality. The proposed algorithms have roughly the same running time as regular shortest-path algorithms and are therefore suitable for deployment in routing protocols. We conducted measurements in an 802.11b testbed network, and our trace-driven analysis shows that multirate anypath routing is on average 80% better than 11-Mbps anypath routing, with a factor of 6.4 improvement in the best case. If the rate is fixed at 1 Mbps instead, performance improves by a factor of 5.4 on average.

In many networks, it is less costly to transmit a packet to any node in a set of neighbors than to one specific neighbor. This observation was previously exploited by opportunistic routing protocols by using single-path routing metrics to assign to each node a group of candidate relays for a particular destination. This paper addresses the least-cost anypath routing (LCAR) problem: how to assign a set of candidate relays at each node for a given destination such that the expected cost of forwarding a packet to the destination is minimized. The key is the following tradeoff: On one hand, increasing the number of candidate relays decreases the forwarding cost, but on the other, it increases the likelihood of “veering” away from the shortest-path route. Prior proposals based on single-path routing metrics or geographic coordinates do not explicitly consider this tradeoff and, as a result, do not always make optimal choices. The LCAR algorithm and its framework are general and can be applied to a variety of networks and cost models. We show how LCAR can incorporate different aspects of underlying coordination protocols, for example a link-layer protocol that randomly selects which receiving node will forward a packet, or the possibility that multiple nodes mistakenly forward a packet. In either case, the LCAR algorithm finds the optimal choice of candidate relays that takes into account these properties of the link layer. Finally, we apply LCAR to low-power, low-rate wireless communication and introduce a new wireless link-layer technique to decrease energy transmission costs in conjunction with anypath routing. Simulations show significant reductions in transmission cost to opportunistic routing using single-path metrics. Furthermore, LCAR routes are more robust and stable than those based on single-path distances due to the integrative nature of the LCAR's route cost metric.

In this paper, we present a new routing paradigm that generalizes opportunistic routing in wireless mesh networks. In multirate anypath routing, each node uses both a set of next hops and a selected transmission rate to reach a destination. Using this rate, a packet is broadcast to the nodes in the set and one of them forwards the packet on to the destination. To date, there is no theory capable of jointly optimizing both the set of next hops and the transmission rate used by each node. We bridge this gap by introducing a polynomial-time algorithm to this problem and provide the proof of its optimality. The proposed algorithm runs in the same running time as regular shortest-path algorithms and is therefore suitable for deployment in link-state routing protocols. We conducted experiments in a 802.11b testbed network, and our results show that multirate anypath routing performs on average 80% and up to 6.4 times better than anypath routing with a fixed rate of 11 Mbps. If the rate is fixed at 1 Mbps instead, performance improves by up to one order of magnitude.

We introduce the TinyNode platform for wireless sensor networks. Supporting both research and industrial deployments, the platform offers communication ranges that exceed current platforms by a factor of 3 to 5, while consuming similar energy. It comes with a rich, practical set of hardware extensions and full TinyOS support. We describe the design choices of the TinyNode, the accompanying hardware modules, and the MAC layer implementation.

This paper presents the Simple Packet Combining (SPaC) error-correction scheme for wireless sensor networks. Nodes buffer corrupt packets, and when two or more corrupt versions of a packet have been received, a packet combining procedure attempts to recover the original packet from the corrupt copies. Packet combining exploits the broadcast medium and spatial diversity of a multi-hop wireless network by using packets overheard at any node, in addition to the next-hop destination of the packet itself. Unlike point-to-point forward error correction (FEC), packet combining therefore helps multi-node interactions such as multi-hop routing or broadcasting as well as to hop-by-hop communication. Also, SPaC does not transmit redundant overhead on good links and does not require costly probes to estimate channel conditions.We have implemented SPaC as a link-layer extension on sensor nodes; it is transparent to upper layer protocols and has low memory and CPU footprints. We evaluate performance through a combination of analysis, trace-driven simulation, indoor and outdoor testbed micro-benchmarks, and deployment on a live network. The results show significant performance gains, even when accounting for the energy cost of CPU processing. We also present detailed bit-level link measurements and the design and evaluation of a new preamble detection scheme motivated by these measurements.

We propose Voronoi scoping, a distributed algorithm to constrain the dissemination of messages from different sinks. It has the property that a query originated by a given sink is forwarded only to the nodes for which that sink is the closest (under the chosen metric). Thus each query is forwarded to the smallest possible number of nodes, and per-node dissemination overhead does not grow with network size or with number of sinks. The algorithm has a simple distributed implementation and requires only a few bytes of state at each node. Experiments over a network of 54 motes confirm the algorithm's effectiveness.

We propose FResher Encounter SearcH (FRESH), a simple algorithm for efficient route discovery in mobile ad hoc networks. Nodes keep a record of their most recent encounter times with all other nodes. Instead of searching for the destination, the source node searches for any intermediate node that encountered the destination more recently than did the source node itself. The intermediate node then searches for a node that encountered the destination yet more recently, and the procedure iterates until the destination is reached. Therefore, FRESH replaces the single network-wide search of current proposals with a succession of smaller searches, resulting in a cheaper route discovery. Routes obtained are loop-free.The performance of such a scheme will depend on the nodes' mobility processes. Under standard mobility processes our simulations show that route discovery cost can be decreased by an order of magnitude, a significant gain given that route discovery is a major source of routing overhead in ad hoc networks.