Level based sampling techniques for energy conservation in large scale wireless sensor networks

As the size and node density of wireless sensor networks (WSN) increase,the energy conservation problem becomes more critical and the conventional methods become inadequate. This dissertation addresses two different problems in large scale WSNs where all sensors are involved in monitoring,but the traditional practice of periodic transmissions of observations from all sensors would drain excessive amount of energy. In the first problem,monitoring of the spatial distribution of a two dimensional correlated signal is considered using a large scale WSN. It is assumed that sensor observations are heavily affected by noise. We present an approach that is based on detecting contour lines of the signal distribution to estimate the spatial distribution of the signal without involving all sensors in the network. Energy efficient algorithms are proposed for detecting and tracking the temporal variation of the contours. Optimal contour levels that minimize the estimation error and a practical approach for selection of contour levels are explored. Performance of the proposed algorithm is explored with different types of contour levels and detection parameters. In the second problem,a WSN is considered that performs health monitoring of equipment from a power substation. The monitoring applications require transmissions of sensor observations from all sensor nodes on a regular basis to the base station,which is very costly in terms of communication cost. To address this problem,an efficient sampling technique using level-crossings (LCS) is proposed. This technique saves communication cost by suppressing transmissions of data samples that do not convey much information. The performance and cost of LCS for several different level-selection schemes are investigated. The number of required levels and the maximum sampling period for practical implementation of LCS are studied. Finally,in an experimental implementation of LCS with MICAzmote,the performance and cost of LCS for temperature sensing with uniform,logarithmic and a combined version of uniform and logarithmically spaced levels are compared with that using periodic sampling

Data Driven Performance Evaluation of Wireless Sensor Networks

Wireless Sensor Networks are presented as devices for signal sampling and reconstruction. Within this framework, the qualitative and quantitative influence of (i) signal granularity, (ii) spatial distribution of sensors, (iii) sensors clustering, and (iv) signal reconstruction procedure are assessed. This is done by defining an error metric and performing a Monte Carlo experiment. It is shown that all these factors have significant impact on the quality of the reconstructed signal. The extent of such impact is quantitatively assessed

Reconstruction of Multidimensional Signals from Irregular Noisy Samples

We focus on a multidimensional field with uncorrelated spectrum, and study the quality of the reconstructed signal when the field samples are irregularly spaced and affected by independent and identically distributed noise. More specifically, we apply linear reconstruction techniques and take the mean square error (MSE) of the field estimate as a metric to evaluate the signal reconstruction quality. We find that the MSE analysis could be carried out by using the closed-form expression of the eigenvalue distribution of the matrix representing the sampling system. Unfortunately, such distribution is still unknown. Thus, we first derive a closed-form expression of the distribution moments, and we find that the eigenvalue distribution tends to the Marcenko-Pastur distribution as the field dimension goes to infinity. Finally, by using our approach, we derive a tight approximation to the MSE of the reconstructed field.Comment: To appear on IEEE Transactions on Signal Processing, 200

Measured Channel Hardening in an Indoor Multiband Scenario

A study of channel hardening in a large-scale antenna system has been carried out by means of indoor channel measurements over four frequency bands, namely 1.472 GHz, 2.6 GHz, 3.82 GHz and 4.16 GHz. NTNU's Reconfigurable Radio Network Platform has been used to record the channel estimates for 40 single user non-line of sight radio links to a 64 element wide-band antenna array. By examining the rms delay spread and the ratio of the normalized subcarrier to average SISO link power of the radio channel received by a single user after combination, the hardening of this equivalent channel is analyzed for various numbers of nodes. The channel hardening merits show consistent behaviour throughout the frequency bands. By combining 16 antennas the rms delay spread of the equivalent channel is reduced from above 100 ns to below 35 ns with significantly reduced variation in the channel power.Comment: 6 pages, 7 figures, presented at IEEE PIMRC 201

Spectra: Robust Estimation of Distribution Functions in Networks

Distributed aggregation allows the derivation of a given global aggregate property from many individual local values in nodes of an interconnected network system. Simple aggregates such as minima/maxima, counts, sums and averages have been thoroughly studied in the past and are important tools for distributed algorithms and network coordination. Nonetheless, this kind of aggregates may not be comprehensive enough to characterize biased data distributions or when in presence of outliers, making the case for richer estimates of the values on the network. This work presents Spectra, a distributed algorithm for the estimation of distribution functions over large scale networks. The estimate is available at all nodes and the technique depicts important properties, namely: robust when exposed to high levels of message loss, fast convergence speed and fine precision in the estimate. It can also dynamically cope with changes of the sampled local property, not requiring algorithm restarts, and is highly resilient to node churn. The proposed approach is experimentally evaluated and contrasted to a competing state of the art distribution aggregation technique.Comment: Full version of the paper published at 12th IFIP International Conference on Distributed Applications and Interoperable Systems (DAIS), Stockholm (Sweden), June 201

Structural health monitoring of bridges using wireless sensor networks

Structural Health Monitoring, damage detection and localization of bridges using Wireless Sensor Networks (WSN) are studied in this thesis. The continuous monitoring of bridges to detect damage is a very useful tools for preventing unnecessary costly and emergent maintenance. The optimal design aims to maximize the lifetime of the system, the accuracy of the sensed data, and the system reliability, and to minimize the system cost and complexity Finite Element Analysis (FEA) is carried out using LUSAS Bridge Plus software to determine sensor locations and measurement types and effectively minimize the number of sensors, data for transmission, and volume of data for processing. In order to verify the computer simulation outputs and evaluate the proposed optimal design and algorithms, a WSN system mounted on a simple reinforced concrete frame model is employed in the lab. A series of tests are carried out on the reinforced concrete frame mounted on the shaking table in order to simulate the existing extreme loading condition. Experimental methods which are based on modal analysis under ambient vibrational excitation are often employed to detect structural damages of mechanical systems, many of such frequency domain methods as first step use a Fast Fourier Transform estimate of the Power Spectral Density (PSD) associated with the response of the system. In this study it is also shown that higher order statistical estimators such as Spectral Kurtosis (SK) and Sample to Model Ratio (SMR) may be successfully employed to more reliably discriminate the response of the system against the ambient noise and better identify and separate contributions from closely spaced individual modes. Subsequently, the identified modal parameters are used for damage detection and Structural Health Monitoring. To evaluate the preliminary results of the project\u27s prototype and quantify the current bridge response as well as demonstrate the ability of the SHM system to successfully perform on a bridge, the deployment of Wireless Sensor Networks in an existing highway bridge in Qatar is implemented. The proposed technique will eventually be applied to the new stadium that State of Qatar will build in preparation for the 2022 World Cup. This monitoring system will help permanently record the vibration levels reached in all substructures during each event to evaluate the actual health state of the stadiums. This offers the opportunity to detect potentially dangerous situations before they become critical

A Hybrid Global Minimization Scheme for Accurate Source Localization in Sensor Networks

We consider the localization problem of multiple wideband sources in a multi-path environment by coherently taking into account the attenuation characteristics and the time delays in the reception of the signal. Our proposed method leaves the space for unavailability of an accurate signal attenuation model in the environment by considering the model as an unknown function with reasonable prior assumptions about its functional space. Such approach is capable of enhancing the localization performance compared to only utilizing the signal attenuation information or the time delays. In this paper, the localization problem is modeled as a cost function in terms of the source locations, attenuation model parameters and the multi-path parameters. To globally perform the minimization, we propose a hybrid algorithm combining the differential evolution algorithm with the Levenberg-Marquardt algorithm. Besides the proposed combination of optimization schemes, supporting the technical details such as closed forms of cost function sensitivity matrices are provided. Finally, the validity of the proposed method is examined in several localization scenarios, taking into account the noise in the environment, the multi-path phenomenon and considering the sensors not being synchronized