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.

Wireless distributed microsensor systems will enable the reliable monitoring of a variety of environments for both civil and military applications. In this paper, we look at communication protocols, which can have significant impact on the overall energy dissipation of these networks. Based on our findings that the conventional protocols of direct transmission, minimum-transmission-energy, multihop routing, and static clustering may not be optimal for sensor networks, we propose LEACH (Low-Energy Adaptive Clustering Hierarchy), a clustering-based protocol that utilizes randomized rotation of local cluster base stations (cluster-heads) to evenly distribute the energy load among the sensors in the network. LEACH uses localized coordination to enable scalability and robustness for dynamic networks, and incorporates data fusion into the routing protocol to reduce the amount of information that must be transmitted to the base station. Simulations show that LEACH can achieve as much as a factor of 8 reduction in energy dissipation compared with conventional routing protocols. In addition, LEACH is able to distribute energy dissipation evenly throughout the sensors, doubling the useful system lifetime for the networks we simulated. 1.

This paper describes the concept of sensor networks which has been made viable by the convergence of microelectro-mechanical systems technology, wireless communications and digital electronics. First, the sensing tasks and the potential sensor networks applications are explored, and a review of factors influencing the design of sensor networks is provided. Then, the communication architecture for sensor networks is outlined, and the algorithms and protocols developed for each layer in the literature are explored. Open research issues for the realization of sensor networks are

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This article describes architectural and algorithmic approaches that designers can use to enhance the energy awareness of wireless sensor networks. The article starts off with an analysis of the power consumption characteristics of typical sensor node architectures and identifies the various factors that affect system lifetime. We then present a suite of techniques that perform aggressive energy optimization while targeting all stages of sensor network design, from individual nodes to the entire network. Maximizing network lifetime requires the use of a well-structured design methodology, which enables energy -aware design and operation of all aspects of the sensor network, from the underlying hardware platform to the application software and network protocols. Adopting such a holistic approach ensures that energy awareness is incorporated not only into individual sensor nodes but also into groups of communicating nodes and the entire sensor network. By following an energy-aware design methodology based on techniques such as in this article, designers can enhance network lifetime by orders of magnitude.

bahl~microsoft, corn ymwang~microsoft, corn rogerwa~microsoft, corn The topology of a wireless multi-hop network can be con-trolled by varying the transmission power at each node. In this paper, we give a detailed analysis of a cone-based dis-tributed topology control algorithm. This algorithm, intro-duced in [16], does not assume that nodes have GPS in-formation available; rather it depends only on directional information. Roughly speaking, the basic idea of the algo-rithm is that a node u transmits with the minimum power P~,,a required to ensure that in every cone of degree a around u, there is some node that u can reach with power Pma- We show that taking a = 57r/6 is a necessary and sufficient con-dition to guarantee that network connectivity is preserved. More precisely, if there is a path from a to t when every node communicates at maximum power then, if a < _ 5~r/6, there is still a path in the smallest symmetric graph Ga con-taining all edges (u, v) such that u can communicate with v using power p~,a. On the other hand, if ~> 51r/6, connec-tivity is not necessarily preserved. We also propose a set of optimizations that further reduce power consumption and prove that they retain network connectivity. Dynamic re-configuration in the presence of failures and mobility is also discussed. Simulation results are presented to demonstrate the effectiveness of the algorithm and the optimizations. 1.

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

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In a sensor network, the infrastructure (in terms of the sensor capabilities, number of sensors, and deployment strategy) plays a significant role in determining the performance of the network. In this paper, we study the effect of infrastructure decisions on the performance of a sensor network. We study the effect of the infrastructure for two types of network delivery models (phenomenon driven and continuous) and different network protocols (DSR, DSDV and AODV). We show the performance both in terms of network efficiency as well as meeting the application accuracy and latency demands. By exploring the criteria for effective infrastructure configurations, we open the door for network optimizations that control the effective topology to better achieve the application requirements.

We can control the topology of a multi-hop wireless network by varying the transmission power at each node. The life-time of such networks depends on battery power at each node. This paper presents a distributed fault-tolerant topology control algorithm for minimum energy consumption in these networks. More precisely, we present algorithms which preserve the connectivity of a network upon failing of, at most, k nodes (k is constant) and simultaneously minimize the transmission power at each node to some extent. In addition, we present simulations to support the effectiveness of our algorithm. We also demonstrate some optimizations to further minimize the power at each node. Finally, we show how our algorithms can be extended to 3-dimensions.