FLOC-SPANNER: An (1) O ( 1 ) Time, Locally Self-Stabilizing Algorithm for Geometric Spanner Construction in a Wireless Sensor Network

We present a distributed algorithm for creation of geometric spanners in a wireless sensor network. Given any connected network, we show that the algorithm terminates in O(1) role= presentation style= display: inline; line-height: normal; font-size: 16px; word-spacing: normal; word-wrap: normal; white-space: nowrap; float: none; direction: ltr; max-width: none; max-height: none; min-width: 0px; min-height: 0px; border: 0px; padding: 0px 2px 0px 0px; margin: 0px; position: relative; \u3e(1)O(1) time, irrespective of network size. Our algorithm uses an underlying clustering algorithm as a foundation for creating spanners and only relies on the periodic heartbeat messages associated with cluster maintenance for the creation of the spanners. The algorithm is also shown to stabilize locally in the presence of node additions and deletions. The performance of our algorithm is verified using large scale simulations. The average path length ratio for routing along the spanner for large networks is shown to be less than 2

FLOC-SPANNER: An Time, Locally Self-Stabilizing Algorithm for Geometric Spanner Construction in a Wireless Sensor Network

We present a distributed algorithm for creation of geometric spanners in a wireless sensor network. Given any connected network, we show that the algorithm terminates in time, irrespective of network size. Our algorithm uses an underlying clustering algorithm as a foundation for creating spanners and only relies on the periodic heartbeat messages associated with cluster maintenance for the creation of the spanners. The algorithm is also shown to stabilize locally in the presence of node additions and deletions. The performance of our algorithm is verified using large scale simulations. The average path length ratio for routing along the spanner for large networks is shown to be less than 2

FLOC-SPANNER: An Time, Locally Self-Stabilizing Algorithm for Geometric Spanner Construction in a Wireless Sensor Network

We present a distributed algorithm for creation of geometric spanners in a wireless sensor network. Given any connected network, we show that the algorithm terminates in O ( 1 ) time, irrespective of network size. Our algorithm uses an underlying clustering algorithm as a foundation for creating spanners and only relies on the periodic heartbeat messages associated with cluster maintenance for the creation of the spanners. The algorithm is also shown to stabilize locally in the presence of node additions and deletions. The performance of our algorithm is verified using large scale simulations. The average path length ratio for routing along the spanner for large networks is shown to be less than 2

Vcast: Scalable Dissemination Of Vehicular Information With Distance-Sensitive Precision

Real-time information about the state (location, speed, and direction) of other vehicles in the system is critical for both safety and navigation applications in future intelligent transportation systems. However, reliably obtaining this information over multiple hops in a capacity constrained, contention-prone wireless network poses a significant challenge. In this paper, we describe an algorithm VCAST that addresses this challenge by exploiting a notion of distance sensitivity in information propagation, in which information is forwarded at a rate that decreases linearly with distance from the source. By doing so, the required communication overhead per node can be significantly reduced, thereby reducing channel contention, allowing higher information supply rates, and scaling to larger network sizes. VCAST can be used to improve safety against collisions and to enable dynamic routing and navigation techniques by providing aggregate traffic information in an extended neighborhood. The performance of VCAST is validated using extensive ns-3 simulations under different network sizes and densities with an IEEE 802.11b transmission model and the advantages of VCAST in comparison to non-distance-sensitive approaches are highlighted

A line in the sand: a wireless sensor network for target detection, classification, and tracking

Intrusion detection is a surveillance problem of practical import that is well suited to wireless sensor networks. In this paper, we study the application of sensor networks to the intrusion detection problem and the related problems of classifying and tracking targets. Our approach is based on a dense, distributed, wireless network of multi-modal resource-poor sensors combined into loosely coherent sensor arrays that perform in situ detection, estimation, compression, and exfiltration. We ground our study in the context of a security scenario called "A Line in the Sand" and accordingly define the target, system, environment, and fault models. Based on the performance requirements of the scenario and the sensing, communication, energy, and computation ability of the sensor network, we explore the design space of sensors, signal processing algorithms, communications, networking, and middleware services. We introduce the influence field, which can be estimated from a network of binary sensors, as the basis for a novel classifier. A contribution of our work is that we do not assume a reliable network; on the contrary, we quantitatively analyze the effects of network unreliability on application performance. Our work includes multiple experimental deployments of over 90 sensor nodes at MacDill Air Force Base in Tampa, FL, as well as other field experiments of comparable scale. Based on these experiences, we identify a set of key lessons and articulate a few of the challenges facing extreme scaling to tens or hundreds of thousands of sensor nodes.close26039

Project ExScal

Project ExScal (for Extreme Scale) fielded a 1000+ node wireless sensor network and a 200+ node ad hoc network of 802.11 devices in a 1.3km by 300m remote area in Florida during December 2004. In several respects, these networks are likely the largest deployed networks of either type to date. We overview here the key requirements of the project, describe briefly how they were met and experimentally tested, and provide a pointer to our experimental results