Quantifying wave measurement differences in historical and present wave buoy systems

An intra-measurement evaluation was undertaken, deploying a NOMAD buoy equipped with three National Data Buoy Center and two Environment and Climate Change Canada-AXYS sensor/payload packages off Monterey, California; a Datawell Directional Waverider buoy was deployed within 19 km of the NOMAD site. The six independent wave measurement systems reported hourly estimates of the frequency spectra, and when applicable, the four Fourier directional components. The integral wave parameters showed general agreement among the five sensors compared to the neighboring Datawell Directional Waverider, with the Inclinometer and the Watchman performing similarly to the more sophisticated 3DMG, HIPPY, and Triaxys sensor packages. As the Hm0 increased, all but the Inclinometer were biased low; however, even the Watchman reported reasonable wave measurements up to about 6–7 m, after which the Hm0 becomes negatively biased up to about a meter, comparable to previous studies. The parabolic fit peak spectral wave period, Tpp, results showed a large scatter, resulting from the complex nature of multiple swell wave systems compounded by local wind-sea development, exacerbated by a variable that can be considered as temporally unstable. The three directional sensors demonstrated that NOMAD buoys are capable of measuring directional wave properties along the western US coast, with biases of about 6 to 9 deg, and rms errors of approximately 30 deg. Frequency spectral evaluations found similarities in the shape, but a significant under estimation in the high frequency range. The results from slope analyses also revealed a positive bias in the rear face of the spectra, and a lack of invariance in frequency as suggested by theory.

A Sustainable Scheme for Minimizing Energy in Visual Sensor Network using Disjoint Set Cover Approach

Directional sensors in wireless visual sensor networks attract growing attention as a promising tool for monitoring the real world; directional sensors consume energy for two main tasks: sensing and communication. Since a VSN contains a number of configurable visual sensors with changeable spherical sectors of restricted angle known as a field of view that is intended to monitor a number of targets located in a random manner over a given area. Therefore maximizing the network lifetime through minimizing power consumption while covering the targets remains a challenge. In this paper, the problem of obtaining a disjoint set cover includes a minimum number of camera sensors is solved. The problem is known to be NP-complete. The sustainable design is improving an existing Iterative Target Oriented Algorithm (ITOA) to cover moving targets move randomly over a given area of deployment starting from entry points reaching to exit ones in a realistic simulation. To evaluate the performance of the modified algorithm, a comparison is provided with three existing algorithms (Iterative centralized Greedy Algorithm (ICGA), Iterative Centralized Forced-directed Algorithm ICFA, and Iterative Target Oriented Algorithm ITOA). Simulation results revealed that the sustainable scheme can find a disjoint set with a minimum number of sensors covers the maximum number of moving targets in an energy-efficient way and extended network lifetime.

A learning automata-based algorithm to solve imbalanced k-coverage in visual sensor networks

One of the most important problems in directional sensor networks is k-coverage in which the orientation of a minimum number of directional sensors is determined in such a way that each target can be monitored at least k times. This problem has been already considered in two different environments: over provisioned where the number of sensors is enough to cover all targets, and under provisioned where there are not enough sensors to do the coverage task (known as imbalanced k-coverage problem). Due to the significance of solving the imbalanced k-coverage problem, this paper proposes a learning automata (LA)-based algorithm capable of selecting a minimum number of sensors in a way to provide k-coverage for all targets in a balanced way. To evaluate the efficiency of the proposed algorithm performance, several experiments were conducted and the obtained results were compared to those of two greedy-based algorithms. The results confirmed the efficiency of the proposed algorithm in terms of solving the problem.

Design and Implementation of Directional Sensors for Privacy-Ensured Device-Free Target Localization in Indoor Environment

This paper presents two radio frequency (RF) sensors with different directivities designed and tested for device-free localization (DFL) in an indoor environment. Mostly, in smart homes and smart offices, peoples may be irritated by wearing the device on them all the time. As compared with device-based localization, the proposed sensors can localize both cooperative and non-cooperative targets (intruders and guests etc.) without privacy leakages. Both sensors are tested to detect the change in received signal strength (ΔRSS) due to the presence of an obstacle. RF sensors, i.e., antennas are designed to operate in the ISM band of 2.4–2.5 GHz. Experimental results show that the sensor with higher directivity provides better ΔRSS that helps in improved accuracy to detect a device-free target.

A Comparison of Optimal SONAR Array Amplitude Shading Coefficients

This paper compares two different approaches to deriving shading coefficients (weights) for optimal first order and second order directional sensors (that is; sonobuoys, vectors and dyadic sensors). The first approach is an analytical or a physics-based derivation, involving computations with gradients and linearized momentum; the second is an adaptive minimum variance distortionless response (MVDR) derivation, which finds weights that minimize the cross spectral density (CSD) matrix. The two approaches are shown to be equivalent. In other words, the adaptive MVDR processing procedure does indeed converge to a physics-based solution, without any pre-existing physical knowledge of the behavior of the acoustic field. This suggests that adaptive algorithms innately seek physics-based solutions when these solutions are optimum. The intent of this short communication is not to advocate for one type of adaptive processing method over another. The observation that is presented here is important though, it confirms that at least in an idealized noise field, adaptive processing converges on an optimal set of shading coefficients, similarly derived based on well-established physical acoustics.