Santiago Felici-Castell

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.

Evaluation of a Real, Low Cost, Urban WSN Deployment for Accurate Environmental Monitoring

There are many areas where Wireless Sensor Networks (WSN) can offer innovative solutions for environmental monitoring applications and the meteorological community, but due to their complexity in terms of energy constraints and low performance devices, very few real deployments can be found. In this paper we will explain in detail the design, implementation and performance evaluation of a sparse WSN that has been working maintenance-free for over 12 months. The network has been designed for environmental monitoring purposes, and several motes, attached to lampposts, accurately measure the Temperature and Relative Humidity at various locations in a local street. Our contribution is an enhanced data gathering application for environmental applications. We will present the different arguments and tradeoffs to be taken into account when designing a network of this type, such as the type of sensors and their calibration, choice of mote and operating system, power supply, special housing required for the mote, data sampling frequency, requirements for the data sink, the structure of the database at the central site, and different issues relating to the exploitation of the meteorological results. We show that a reliable, low cost WSN deployment for environmental monitoring that takes into account the main properties of these networks, such multi-hop routing, low maintenance, low cost and long mote lifetimes, is achievable.

Using Factor Analysis Techniques to Find Out Objective Video Quality Metrics for Live Video Streaming over Cloud Mobile Media Services

The increased adoption of smartphones, the access to mobile broadband networks and the availability of public Clouds allow new multimedia services, called Cloud Mobile Media Services. Under this new architecture the proliferation of live video streaming applications and the Quality of Experience (QoE) given by the final user are an issue, due to the higher and variable delay, as result of the virtualization methods used in the Clouds. Thus in this paradigm new challenges appear related to keep and estimate a good QoE in terms of a standarized subjective video quality called Mean Opinion Score (MOS). In this paper we analyze different approaches based on Factor Analysis techniques to estimate the subjective MOS both using Full Reference and Non Reference approaches. We compare the performance of the estimated MOS against publicly available video quality algorithms.

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.

Gatherer: an environmental monitoring application based on IPv6 using wireless sensor networks

In this paper, we present an environmental monitoring application based on IPv6 using wireless sensor networks. We show the guidelines to connect this network to internet using the 6LowPAN standard with a real prototype in the evolution of the Internet of Things. We assign a unique IPv6 address to the different nodes motes of the network. Gatherer is based on TinyOS using IPv6 and UDP protocols and allows the data gathering of temperature and relative humidity from the different motes, requesting specific data from individual motes when required, increasing the accuracy of the measures. We compare the performance of our IPv6-based proposed application against others and show the benefits of the client/server architecture defined.

Improving time synchronization in Wireless Sensor Networks using Bayesian Inference

Wireless Sensor Networks are composed of small autonomous devices known as motes. Usually these motes are power-limited and most energy is wasted through the communication process, thus the synchronization is critical. In this paper we improve the accuracy of time synchronization with Bayesian inference over the linear regression model used in synchronization protocols. Synchronization is generally accomplished using packet exchanges, so the goal is to reduce the number of packets while maintaining perfect synchronization. The constraints are the low-cost hardware components of the motes, in particular their clocks and the power consumption. Long-term synchronization is achieved using Adaptive Time Window Linear Regression algorithms using Least Squares. The method of Least Squares is distribution free, but we can make some feasible assumptions (experimentally validated) to improve these protocols using Bayesian Inference to achieve an improvement of 12% compared with the related work. In particular, using 80MHz clock frequency in the motes the mean synchronization error is 147ns. We propose an algorithm to improve the synchronization under these constraints and we test our method in real deployments. HighlightsWe review the state of art of synchronization protocols for Wireless Sensor Networks.We discuss pros and cons of synchronization methods and protocols on these networks.We use Bayesian Inference to enhance Linear Regression coefficients.We compare the proposed method against state of the art with an improvement of 12%.We validate the assumptions and implement the proposal on a real deployment.

Spatio-Temporal Analysis of Urban Acoustic Environments with Binaural Psycho-Acoustical Considerations for IoT-Based Applications

Sound pleasantness or annoyance perceived in urban soundscapes is a major concern in environmental acoustics. Binaural psychoacoustic parameters are helpful to describe generic acoustic environments, as it is stated within the ISO 12913 framework. In this paper, the application of a Wireless Acoustic Sensor Network (WASN) to evaluate the spatial distribution and the evolution of urban acoustic environments is described. Two experiments are presented using an indoor and an outdoor deployment of a WASN with several nodes using an Internet of Things (IoT) environment to collect audio data and calculate meaningful parameters such as the sound pressure level, binaural loudness and binaural sharpness. A chunk of audio is recorded in each node periodically with a microphone array and the binaural rendering is conducted by exploiting the estimated directional characteristics of the incoming sound by means of DOA estimation. Each node computes the parameters in a different location and sends the values to a cloud-based broker structure that allows spatial statistical analysis through Kriging techniques. A cross-validation analysis is also performed to confirm the usefulness of the proposed system.