VLSI implementation of an energy-aware wake-up detector for an acoustic surveillance sensor network

We present a low-power VLSI wake-up detector for a sensor network that uses acoustic signals to localize ground-base vehicles. The detection criterion is the degree of low-frequency periodicity in the acoustic signal, and the periodicity is computed from the "bumpiness" of the autocorrelation of a one-bit version of the signal. We then describe a CMOS ASIC that implements the periodicity estimation algorithm. The ASIC is functional and its core consumes 835 nanowatts. It was integrated into an acoustic enclosure and deployed in field tests with synthesized sounds and ground-based vehicles.Fil: Goldberg, David H.. Johns Hopkins University; Estados UnidosFil: Andreou, Andreas. Johns Hopkins University; Estados UnidosFil: Julian, Pedro Marcelo. Consejo Nacional de Investigaciones Científicas y Técnicas; Argentina. Universidad Nacional del Sur. Departamento de Ingeniería Eléctrica y de Computadoras; ArgentinaFil: Pouliquen, Philippe O.. Johns Hopkins University; Estados UnidosFil: Riddle, Laurence. Signal Systems Corporation; Estados UnidosFil: Rosasco, Rich. Signal Systems Corporation; Estados Unido

Bias estimation in sensor networks

This paper investigates the problem of estimating biases affecting relative state measurements in a sensor network. Each sensor measures the relative states of its neighbors and this measurement is corrupted by a constant bias. We analyse under what conditions on the network topology and the maximum number of biased sensors the biases can be correctly estimated. We show that for non-bipartite graphs the biases can always be determined even when all the sensors are corrupted, while for bipartite graphs more than half of the sensors should be unbiased to ensure the correctness of the bias estimation. If the biases are heterogeneous, then the number of unbiased sensors can be reduced to two. Based on these conditions, we propose some algorithms to estimate the biases.Comment: 12 pages, 8 figure

Measurements, Models, Systems and Design

531 s.

Topology Management in Wireless Sensor Networks: Multi-State Algorithms

In order to maximize the network’s lifetime and ensure the connectivity among the nodes, most topology management practices use a subgroup of nodes for routing. This paper provides an in-depth look at existing topology management control algorithms in Multi-state structure. We suggest a new algorithm based on Geographical Adaptive Fidelity (GAF) and Adaptive Self-Configuring Sensor Networks Topology (ASCENT). The new proposed algorithm outperforms both GAF and ASCENT algorithms

Fast, Autonomous Flight in GPS-Denied and Cluttered Environments

One of the most challenging tasks for a flying robot is to autonomously navigate between target locations quickly and reliably while avoiding obstacles in its path, and with little to no a-priori knowledge of the operating environment. This challenge is addressed in the present paper. We describe the system design and software architecture of our proposed solution, and showcase how all the distinct components can be integrated to enable smooth robot operation. We provide critical insight on hardware and software component selection and development, and present results from extensive experimental testing in real-world warehouse environments. Experimental testing reveals that our proposed solution can deliver fast and robust aerial robot autonomous navigation in cluttered, GPS-denied environments.Comment: Pre-peer reviewed version of the article accepted in Journal of Field Robotic

Algorithms for the selection of the active sensors in distributed tracking: Comparison between Frisbee and GNS methods

This paper compares two different approaches for sensor selection for distributed tracking: 1) The Frisbee method, and 2) Global Node Selection (GNS). The Frisbee method is based on the proximity of the nodes to the predicted location of the target; GNS is based on minimizing the unbiased Cramer Rao lower bound (CRLB). Both theoretical and experimental results indicate that the Frisbee method is as effective as GNS. Furthermore, the Frisbee method is attractive due to its very light computational load