Many ad hoc network protocols and applications assume the knowledge of geographic location of nodes. The absolute location of each networked node is an assumed fact by most sensor networks which can then present the sensed information on a geographical map. Finding location without the aid of GPS in each node of an ad hoc network is important in cases where GPS is either not accessible, or not practical to use due to power, form factor or line of sight conditions. Location would

Evolving networks of ad-hoc, wireless sensing nodes rely heavily on the ability to establish position information. The algorithms presented herein rely on range measurements between pairs of nodes and the aprioricoordinates of sparsely located anchor nodes. Clusters of nodes surrounding anchor nodes cooperatively establish confident position estimates through assumptions, checks, and iterative refinements. Once established, these positions are propagated to more distant nodes, allowing the entire network to create an accurate map of itself. Major obstacles include overcoming inaccuracies in range measurements as great as ±50%, as well as the development of initial guesses for node locations in clusters with few or no anchor nodes. Solutions to these problems are presented and discussed, using position error as the primary metric. Algorithms are compared according to position error, scalability, and communication and computational requirements. Early simulations yield average position errors of 5 % in the presence of both range and initial position inaccuracies. 1.

Abstract not found

Two main problems prevent the deployment of geographic forwarding in real systems: geographic forwarding requires that all nodes know their locations, and it has trouble routing around local dead ends. This paper presents practical solutions to each problem. The location proxy technique allows a node that does not know its location to find a nearby location aware node to use as a proxy for geographic forwarding. The technique works well over a large range of densities of location aware nodes, and allows a tradeoff between bandwidth used for routing information and expense of providing location information. The intermediate node forwarding (INF) mechanism is a probabilistic solution for routing around bad geographic topologies via intermediate geographic locations. Existing solutions unrealistically assume that nodes have identical radio propagation; INF works on a restricted set of situations but makes assumptions that better match reality. Experiments using the ns simulator show that location proxies and INF are effective enough to make geographic forwarding practical. We believe geographic forwarding will enable scalable ad hoc networking. 1

We present a distributed algorithm for localization of nodes in a discrete model of a random ad hoc communication network. We compute the expected value of the position estimate, A S , and the probability that A S = 1 cell (the perfect estimate). This leads to bounds of the average complexity of the algorithm at each node. 1

Accurate positioning mechanisms are important in large scale sensor networks to achieve a number of functionalities like location aware routing, efficient coordination of resources and other application specific requirements. This paper proposes a distributed and scalable GPS free positioning algorithm for wireless sensor networks. This approach is an effort in the direction of finding a solution to the positioning problem which minimizes the number of messages exchanged and the coordinate setup time. We use a clustering based approach for the coordinate formation wherein a small subset of the nodes can successfully establish the coordinate system for the whole network. We also compare the performance of this system against existing mechanisms and show that our system scales linearly as the number of nodes in the network increases in contrast to the exponential increase in current mechanisms. Additionally, our mechanism takes considerably lower convergence times. The proposed mechanism is scalable, distributed and able to support the ad hoc deployment of large scale sensor networks quickly and efficiently.

We present a distributed algorithm for environmental monitoring of a scalar field (such as temperature, intensity of light, atmospheric pressure, etc.) using a random sensor network. We derive an error estimate, discuss the average complexity of the algorithm, and present some simulation results.

We define a discrete model for a random wireless network, present a distributed algorithm for localization of its nodes, and provide probabilistic error and complexity bounds.

Contents Preface iv 1.0 Introduction 1 1.1 Smart Dust Scenarios 2 1.1.1 Forest Fire Warning 2 1.1.2 Enemy Troop Monitoring 3 1.2 Smart Dust Capabilities 3 1.2.1 Distributed Sensor Networks and Ad-hoc Networking 4 1.2.2 High Level Interpretation of Spatial Sensor Data 4 1.2.3 Distributed Processing 5 1.2.4 COTS Dust 6 2.0 COTS Dust Architecture 7 2.1 Power 8 2.2 Computation 9 2.2.1 Static vs. Dynamic Current 11 2.2.2 Strong Thumb 11 2.3 Sensors 12 2.3.1 Magnetometer (2/3 Axis) 13 2.3.2 Accelerometers (2/3 Axis) 14 2.3.3 Light Sensor 16 2.3.4 Temperature Sensor 17 2.3.5 Pressure Sensor 17 2.3.6 Humidity Sensor 19 2.4 Communication 19 2.4.1 Acoustic Communication 20 2.4.2 RF Communication 23 2.4.3 Optical Communication 27 2.4.4 Optical Communication vs. RF Communication 32 3.0 COTS Dust Systems 35 3.1 Mouse Collars 35 3.2 Radio Frequency Mote (RF Mote) 39 3.2.1 RF Communica

Abstract: Wireless sensor networks have recently become an incredibly active research area in the networking community. Much attention has been given to the construction of power-conserving protocols and techniques, as battery life is the one factor that prevents successful wide-scale deployment of such networks. These techniques concentrate on the optimization of network behavior, as the wireless transmission of data is the most expensive operation performed by a sensor node. Very little work has been published on the integration of such techniques, and their suitability to various application domains. This paper presents an exhaustive power consumption analysis of network stacks constructed with common algorithms, to determine the interactions between such