karp @ eecs.harvard.edu We present Greedy Perimeter Stateless Routing (GPSR), a novel routing protocol for wireless datagram networks that uses the po-sitions of touters and a packer's destination to make packet for-warding decisions. GPSR makes greedy forwarding decisions us-ing only information about a router's immediate neighbors in the network topology. When a packet reaches a region where greedy forwarding is impossible, the algorithm recovers by routing around the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's scalability on densely deployed wireless networks.

Making effective use of the vast amounts of data gathered by largescale sensor networks will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Previous work has identified data-centric routing as one such method. In an associated position paper [23], we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper,

Making effective use of the vast amounts of data gathered by large-scale sensor networks (sensornets) will require scalable, self-organizing, and energy-efficient data dissemination algorithms. For sensornets, where the content of the data is more important than the identity of the node that gathers them, researchers have found it useful to move away from the Internet's point-to-point communication abstraction and instead adopt abstractions that are more data-centric. This approach entails naming the data and using communication abstractions that refer to those names rather than to node network addresses [1,11]. Previous work on data-centric routing has shown it to be an energy-efficient data dissemination method for sensornets [12]. Herein, we argue that a companion method, data-centric storage (DCS), is also a useful approach. Under DCS, sensed data are stored at a node determined by the name associated with the sensed data. In this paper, we first define DCS and predict analytically where it outperforms other data dissemination approaches. We then describe GHT, a Geographic Hash Table system for DCS on sensornets. GHT hashes keys into geographic coordinates, and stores a key-value pair at the sensor node geographically nearest the hash of its key. The system replicates stored data locally to ensure persistence when nodes fail. It uses an efficient consistency protocol to ensure that key-value pairs are stored at the appropriate nodes after topological changes. And it distributes load throughout the network using a geographic hierarchy. We evaluate the performance of GHT as a DCS system in simulation against two other dissemination approaches. Our results demonstrate that GHT is the preferable approach for the application workloads we analytically predict, offers ...

und the perimeter of the region. By keeping state only about the local topology, GPSR scales better in per-router state than shortest-path and ad-hoc routing protocols as the number of network destinations increases. Under mobility's frequent topology changes, GPSR can use local topology information to find correct new routes quickly. We describe the GPSR protocol, and use extensive simulation of mobile wireless networks to compare its performance with that of Dynamic Source Routing. Our simulations demonstrate GPSR's iii scalability on densely deployed wireless networks. iv Contents 1 Introduction 1 1.1 Metrics for Evaluating Routing Scalability . . . . . . . . . . . . . . . . . . 3 1.2 Traditional Shortest-Path Algorithms . . . . . . . . . . . . . . . . . . . . . 4 1.3 Ad-Hoc Routing Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . 5 1.4 Techniques for Routing Scalability . . . . . . . . . . . . . . . . . . . . . . 7 1.5 Applica

Sensornets are large-scale distributed sensing networks comprised of many small sensing devices equipped with memory, processors, and short-range wireless communication. Making effective use of sensornet data will require scalable, self-organizing, and energy-efficient data dissemination algorithms. Recent work has identified data-centric routing as one such method. In this paper we suggest that a companion method, data-centric storage, may also be a useful approach.