Juan J. Perez-Solano

Narrowband Interference Suppression in Frequency-Hopping Spread Spectrum Using Undecimated Wavelet Packet Transform

Suppression of narrowband interference in frequency-hopping spread spectrum (FH-SS) systems is treated in this paper. First, we analyze the effect of continuous-wave tone interference in noncoherent binary frequency-shift keying (BFSK) signaling with FH-SS. Based on this analysis, a new receiver for FH-SS systems is proposed, including a transform-domain interference suppression module. The algorithm is executed in two steps: 1) interference detection and 2) signal reconstruction. The novelty of this suppression algorithm is the combination of the undecimated wavelet packet transform with frequency shifts of the received signal to center the narrowband interference in a subband. Once the subband that contains the interference has been determined, the algorithm removes this subband and then reconstructs the signal. The performance of this receiver has been evaluated in a variety of situations with different types of narrowband interference. The results obtained show the improvement of the proposed receiver in comparison to the traditional FH-SS receiver.

Low-Cost Alternatives for Urban Noise Nuisance Monitoring Using Wireless Sensor Networks

Noise pollution caused by vehicular traffic is a common problem in urban environments that has been shown to affect peoples health and childrens cognition. In the last decade, several studies have been conducted to assess this noise, by measuring the equivalent noise pressure level (called Leq) to acquire an accurate sound map using wireless networks with acoustic sensors. However, even with similar values of Leq, people can feel the noise differently according to its frequency characteristics. Thus, indexes, which can express peoples feelings by subjective measures, are required. In this paper, we analyze the suitability of using the psychoacoustic metrics given by the Zwickers model, instead of just only considering Leq. The goal is to evaluate the hardware limitations of a low-cost wireless acoustic sensor network that is used to measure the annoyance, using two types of commercial and off-the-shelf sensor nodes, Tmote-Invent nodes and Raspberry Pi platforms. Moreover, to calculate the parameters using these platforms, different simplifications to the Zwickers model based on the specific features of road traffic noise are proposed. To validate the different alternatives, the aforementioned nodes are tested in a traffic congested area of Valencia City in a vertical and horizontal network deployment. Based on the results, it is observed that the Raspberry Pi platforms are a feasible low-cost alternative to increase the spatial-temporal resolution, whereas Tmote-Invent nodes do not confirm their suitability due to their limited memory and calibration issues.

Designing an open source maintenance-free Environmental Monitoring Application for Wireless Sensor Networks

We discuss the analysis and design of an Environmental Monitoring Application.The application is reliable and maintenance-free, runs in multihop wireless network.We analyze the different alternatives and tradeoffs, using open source software.The application is validated in long-term outdoor deployments with good results.Related work does not analyze the software design with open source. We discuss the entire process for the analysis and design of an Environmental Monitoring Application for Wireless Sensor Networks, using existing open source components to create the application. We provide a thorough study of the different alternatives, from the selection of the embedded operating system to the different algorithms and strategies. The application has been designed to gather temperature and relative humidity data following the rules of quality assurance for environmental measurements, suitable for use in both research and industry. The main features of the application are: (a) runs in a multihop low-cost network based on IEEE 802.15.4, (b) improved network reliability and lifetimes, (c) easy management and maintenance-free, (d) ported to different platforms and (e) allows different configurations and network topologies. The application has been tested and validated in several long-term outdoor deployments with very good results and the conclusions are aligned with the experimental evidence.

Cumulative-sum-based localization of sound events in low-cost wireless acoustic sensor networks

Wireless acoustic sensor networks (WASNs) are known for their potential applications in multiple areas, such as audio-based surveillance, binaural hearing aids or advanced acoustic monitoring. The knowledge of the spatial position of a source of interest is usually a requirement for many of these applications. Therefore, source localization is an important problem to be addressed in WASNs. Unfortunately, most localization algorithms need costly signal processing stages that prevent them from being implemented in low-cost sensor networks, requiring additional modules for signal acquisition and processing. This paper presents a low-complexity method for acoustic event detection and localization considering a change detection statistical framework. Two possible implementation approaches based on the efficient cumulative sum (CUSUM) algorithm are presented and discussed. Results from simulations and a real deployment show that the proposed techniques can be easily implemented in low-cost sensor networks, providing good localization accuracy and making good use of the available node resources.

RF-Based Location Using Interpolation Functions to Reduce Fingerprint Mapping.

Indoor RF-based localization using fingerprint mapping requires an initial training step, which represents a time consuming process. This location methodology needs a database conformed with RSSI (Radio Signal Strength Indicator) measures from the communication transceivers taken at specific locations within the localization area. But, the real world localization environment is dynamic and it is necessary to rebuild the fingerprint database when some environmental changes are made. This paper explores the use of different interpolation functions to complete the fingerprint mapping needed to achieve the sought accuracy, thereby reducing the effort in the training step. Also, different distributions of test maps and reference points have been evaluated, showing the validity of this proposal and necessary trade-offs. Results reported show that the same or similar localization accuracy can be achieved even when only 50% of the initial fingerprint reference points are taken.

A Method for Modeling the Battery State of Charge in Wireless Sensor Networks

In this paper, we propose a method for obtaining an analytic model of the battery state-of-charge in wireless sensor nodes. The objective is to find simple models that can be used to estimate accurately the real battery state and consequently the node lifetime. Running the model in the network nodes, we can provide the motes with the required information to implement applications that can be considered as battery-aware. The proposed methodology reduces the computational complexity of the model avoiding complicated electrochemical simulations and treating the battery as an unknown system with an output that can be predicted using simple mathematical models. At a first stage, during a setup period, the method starts with the measurement of several battery parameters under different environmental and operational conditions. After that, the method uses the previous collected data for building mathematical models, considering the linear regression or multilayer perceptron as the most appropriated. Finally, the models are validated experimentally with new measures. Results show the suitability of the method that produces accurate and simple models, capable of being implemented even in low cost and very constrained real motes.

Simultaneous Ranging and Self-Positioning in Unsynchronized Wireless Acoustic Sensor Networks

Automatic ranging and self-positioning is a very desirable property in wireless acoustic sensor networks, where nodes have at least one microphone and one loudspeaker. However, due to environmental noise, interference, and multipath effects, audio-based ranging is a challenging task. This paper presents a fast ranging and positioning strategy that makes use of the correlation properties of pseudonoise sequences for estimating simultaneously relative time-of-arrivals from multiple acoustic nodes. To this end, a proper test signal design adapted to the acoustic node transducers is proposed. In addition, a novel self-interference reduction method and a peak matching algorithm are introduced, allowing for increased accuracy in indoor environments. Synchronization issues are removed by following a BeepBeep strategy, providing range estimates that are converted to absolute node positions by means of multidimensional scaling. The proposed approach is evaluated both with simulated and real experiments under different acoustical conditions. The results using a real network of smartphones and laptops confirm the validity of the proposed approach, reaching an average ranging accuracy below 1 cm.

Narrowband Interference Suppression Using Undecimated Wavelet Packets in Direct- Sequence Spread-Spectrum Receivers

A new algorithm for narrowband interference suppression in direct-sequence spread-spectrum (DS-SS) communications is presented. The algorithm combines the undecimated wavelet packet transform (UWPT) with frequency shifts to confine the interference energy in a subband that is subsequently eliminated. Computer simulation shows a robust performance that appears to be independent of the interference frequency

Adaptive time window linear regression algorithm for accurate time synchronization in wireless sensor networks

In this article we propose a new algorithm for time synchronization in wireless sensor networks. The algorithm is based on linear regression to achieve long-term synchronization between the clocks of different network motes. Since motes are built using low-cost hardware components, usually their internal local clocks are not very accurate. In addition, there are other effects that affect the clock precision, such as: environmental conditions, supply voltage, aging, manufacturing process. Because some of these causes are external and unpredictable, the clock drift between two motes can change in a random way. Due to these changes, the optimum time window used for performing the linear regression varies with time. The proposed time synchronization algorithm adjusts the resynchronization periods and the linear regression window size to these variations, minimizing the synchronization error. Our algorithm has been tested in real multihop network deployments and the results obtained show higher clock accuracy when compared to the related work.

Flaw location from perpendicular NDE ultrasonic transducers using the wavelet packet transform

Ultrasonic inspection of materials from different planes is a technique that produces complementary information of the same piece using different echographic traces. The combination of these traces in order to obtain an improved representation of the inspected area is a problem that can be solved by means of digital signal processing techniques. A new method for the combination of the traces coming from perpendicular transducers is presented in this paper. The method is based on the undecimated wavelet packet transform and it is applied to the localization of isolated flaws in a plastic piece.