ROUTING TOPOLOGY RECOVERY FOR WIRELESS SENSOR NETWORKS

Liu, Rui Ph.D., Purdue University, December 2014. Routing Topology Recovery for Wireless Sensor Networks. Major Professor: Yao Liang

Intelligent Sensor Networks

In the last decade, wireless or wired sensor networks have attracted much attention. However, most designs target general sensor network issues including protocol stack (routing, MAC, etc.) and security issues. This book focuses on the close integration of sensing, networking, and smart signal processing via machine learning. Based on their world-class research, the authors present the fundamentals of intelligent sensor networks. They cover sensing and sampling, distributed signal processing, and intelligent signal learning. In addition, they present cutting-edge research results from leading experts

Adaptive sampling for spatial prediction in environmental monitoring using wireless sensor networks: A review

© 2018 IEEE. The paper presents a review of the spatial prediction problem in the environmental monitoring applications by utilizing stationary and mobile robotic wireless sensor networks. First, the problem of selecting the best subset of stationary wireless sensors monitoring environmental phenomena in terms of sensing quality is surveyed. Then, predictive inference approaches and sampling algorithms for mobile sensing agents to optimally observe spatially physical processes in the existing works are analysed

Data Collection Algorithms in Wireless Sensor Networks Employing Compressive Sensing

This dissertation proposes new algorithms that exploit the integration between Compressive Sensing (CS) and the traditional data collection methods in Wireless Sensor Networks (WSNs).Generally, a WSN with monitoring applications needs to collect all data from all sensors deployed in a sensing area to be sent to a base-station (BS) or a data processing center. Since all the sensors operate on low power with pre-charged batteries and may not easily be accessed by people, the power required for transmitting all data to the BS usually may quickly deplete the sensors and impact network lifetime resulting in network disconnection. In order to prolong the network lifetime, the sensors can be improved or the methods of collecting data can be improved.CS provides a novel technique that offers to reconstruct data from all sensors in the network using undersampled measurements. In the dissertation, four efficient algorithms based on the CS technique have been proposed. Only a certain number of CS measurements is created from the network to be forwarded to the BS for signal reconstruction resulting in reduced data communication and increased network lifetime. Expressions for power consumption for all data transmission in the networks are formulated and analyzed. The networks significantly reduce power consumption while collecting data. Some optimal cases are suggested and analyzed for such networks to consume the least power.Electrical Engineerin

Sensing and Compression Techniques for Environmental and Human Sensing Applications

In this doctoral thesis, we devise and evaluate a variety of lossy compression schemes for Internet of Things (IoT) devices such as those utilized in environmental wireless sensor networks (WSNs) and Body Sensor Networks (BSNs). We are especially concerned with the efficient acquisition of the data sensed by these systems and to this end we advocate the use of joint (lossy) compression and transmission techniques. Environmental WSNs are considered first. For these, we present an original compressive sensing (CS) approach for the spatio-temporal compression of data. In detail, we consider temporal compression schemes based on linear approximations as well as Fourier transforms, whereas spatial and/or temporal dynamics are exploited through compression algorithms based on distributed source coding (DSC) and several algorithms based on compressive sensing (CS). To the best of our knowledge, this is the first work presenting a systematic performance evaluation of these (different) lossy compression approaches. The selected algorithms are framed within the same system model, and a comparative performance assessment is carried out, evaluating their energy consumption vs the attainable compression ratio. Hence, as a further main contribution of this thesis, we design and validate a novel CS-based compression scheme, termed covariogram-based compressive sensing (CB-CS), which combines a new sampling mechanism along with an original covariogram-based approach for the online estimation of the covariance structure of the signal. As a second main research topic, we focus on modern wearable IoT devices which enable the monitoring of vital parameters such as heart or respiratory rates (RESP), electrocardiography (ECG), and photo-plethysmographic (PPG) signals within e-health applications. These devices are battery operated and communicate the vital signs they gather through a wireless communication interface. A common issue of this technology is that signal transmission is often power-demanding and this poses serious limitations to the continuous monitoring of biometric signals. To ameliorate this, we advocate the use of lossy signal compression at the source: this considerably reduces the size of the data that has to be sent to the acquisition point by, in turn, boosting the battery life of the wearables and allowing for fine-grained and long-term monitoring. Considering one dimensional biosignals such as ECG, RESP and PPG, which are often available from commercial wearable devices, we first provide a throughout review of existing compression algorithms. Hence, we present novel approaches based on online dictionaries, elucidating their operating principles and providing a quantitative assessment of compression, reconstruction and energy consumption performance of all schemes. As part of this first investigation, dictionaries are built using a suboptimal but lightweight, online and best effort algorithm. Surprisingly, the obtained compression scheme is found to be very effective both in terms of compression efficiencies and reconstruction accuracy at the receiver. This approach is however not yet amenable to its practical implementation as its memory usage is rather high. Also, our systematic performance assessment reveals that the most efficient compression algorithms allow reductions in the signal size of up to 100 times, which entail similar reductions in the energy demand, by still keeping the reconstruction error within 4 % of the peak-to-peak signal amplitude. Based on what we have learned from this first comparison, we finally propose a new subject-specific compression technique called SURF Subject-adpative Unsupervised ecg compressor for weaRable Fitness monitors. In SURF, dictionaries are learned and maintained using suitable neural network structures. Specifically, learning is achieve through the use of neural maps such as self organizing maps and growing neural gas networks, in a totally unsupervised manner and adapting the dictionaries to the signal statistics of the wearer. As our results show, SURF: i) reaches high compression efficiencies (reduction in the signal size of up to 96 times), ii) allows for reconstruction errors well below 4 % (peak-to-peak RMSE, errors of 2 % are generally achievable), iii) gracefully adapts to changing signal statistics due to switching to a new subject or changing their activity, iv) has low memory requirements (lower than 50 kbytes) and v) allows for further reduction in the total energy consumption (processing plus transmission). These facts makes SURF a very promising algorithm, delivering the best performance among all the solutions proposed so far

Spectrum Sensing Algorithms for Cognitive Radio Applications

Future wireless communications systems are expected to be extremely dynamic, smart and capable to interact with the surrounding radio environment. To implement such advanced devices, cognitive radio (CR) is a promising paradigm, focusing on strategies for acquiring information and learning. The first task of a cognitive systems is spectrum sensing, that has been mainly studied in the context of opportunistic spectrum access, in which cognitive nodes must implement signal detection techniques to identify unused bands for transmission. In the present work, we study different spectrum sensing algorithms, focusing on their statistical description and evaluation of the detection performance. Moving from traditional sensing approaches we consider the presence of practical impairments, and analyze algorithm design. Far from the ambition of cover the broad spectrum of spectrum sensing, we aim at providing contributions to the main classes of sensing techniques. In particular, in the context of energy detection we studied the practical design of the test, considering the case in which the noise power is estimated at the receiver. This analysis allows to deepen the phenomenon of the SNR wall, providing the conditions for its existence and showing that presence of the SNR wall is determined by the accuracy of the noise power estimation process. In the context of the eigenvalue based detectors, that can be adopted by multiple sensors systems, we studied the practical situation in presence of unbalances in the noise power at the receivers. Then, we shift the focus from single band detectors to wideband sensing, proposing a new approach based on information theoretic criteria. This technique is blind and, requiring no threshold setting, can be adopted even if the statistical distribution of the observed data in not known exactly. In the last part of the thesis we analyze some simple cooperative localization techniques based on weighted centroid strategies

Agrégation de données dans les réseaux de capteurs sans fil

Wireless Sensor Networks (WSNs) have been regarded as an emerging and promis- ing field in both academia and industry. Currently, such networks are deployed due to their unique properties, such as self-organization and ease of deployment. How- ever, there are still some technical challenges needed to be addressed, such as energy and network capacity constraints. Data aggregation, as a fundamental solution, pro- cesses information at sensor level as a useful digest, and only transmits the digest to the sink. The energy and capacity consumptions are reduced due to less data packets transmission. As a key category of data aggregation, aggregation function, solving how to aggregate information at sensor level, is investigated in this thesis.We make four main contributions: firstly, we propose two new networking-oriented metrics to evaluate the performance of aggregation function: aggregation ratio and packet size coefficient. Aggregation ratio is used to measure the energy saving by data aggregation, and packet size coefficient allows to evaluate the network capac- ity change due to data aggregation. Using these metrics, we confirm that data ag- gregation saves energy and capacity whatever the routing or MAC protocol is used. Secondly, to reduce the impact of sensitive raw data, we propose a data-independent aggregation method which benefits from similar data evolution and achieves better re- covered fidelity. Thirdly, a property-independent aggregation function is proposed to adapt the dynamic data variations. Comparing to other functions, our proposal can fit the latest raw data better and achieve real adaptability without assumption about the application and the network topology. Finally, considering a given application, a tar- get accuracy, we classify the forecasting aggregation functions by their performances. The networking-oriented metrics are used to measure the function performance, and a Markov Decision Process is used to compute them. Dataset characterization and classification framework are also presented to guide researcher and engineer to select an appropriate functions under specific requirements.Depuis plusieurs années, les réseaux de capteurs sans fil sont considérés comme un domaine émergent et prometteur tant dans le milieu universitaire que dans l’industrie. De tels réseaux ont déjà été largement déployés en raison de leurs propriétés clés, telles que l’auto-organisation et leur autonomie en énergie. Cependant, il reste de nombreux défis scientifiques telles que la réduction de la consommation d’énergie sur des capteurs de plus en plus petits et la capacité du réseau tenant compte de liens à bande passante réduite. Selon nous, l’agrégation de données apparaît comme une so- lution pour ces deux défis, car au lieu d’envoyer une donnée, l’agrégation va traiter les informations collectées au niveau du capteur et produire une donnée agrégée qui sera effectivement transmise au puits. L’énergie et la capacité du réseau seront donc économisées car il y aura moins de transmissions de données. Le travail de cette thèse s’intéresse principalement aux fonctions d’agrégationNous faisons quatre contributions principales. Tout d’abord, nous proposons deux nouvelles métriques pour évaluer les performances des fonctions d’agrégations vue au niveau réseau : le taux d’agrégation et le facteur d’accroissement de la taille des paquets. Le taux d’agrégation est utilisé pour mesurer le gain de paquets non trans- mis grâce à l’agrégation tandis que le facteur d’accroissement de la taille des pa- quets permet d’évaluer la variation de la taille des paquets en fonction des politiques d’agrégation. Ces métriques permettent de quantifier l’apport de l’agrégation dans l’économie d’énergie et de la capacité utilisée en fonction du protocole de routage con- sidéré et de la couche MAC retenue. Deuxièmement, pour réduire l’impact des don- nées brutes collectées par les capteurs, nous proposons une méthode d’agrégation de données indépendante de la mesure physique et basée sur les tendances d’évolution des données. Nous montrons que cette méthode permet de faire une agrégation spa- tiale efficace tout en améliorant la fidélité des données agrégées. En troisième lieu, et parce que dans la plupart des travaux de la littérature, une hypothèse sur le com- portement de l’application et/ou la topologie du réseau est toujours sous-entendue, nous proposons une nouvelle fonction d’agrégation agnostique de l’application et des données devant être collectées. Cette fonction est capable de s’adapter aux données mesurées et à leurs évolutions dynamiques. Enfin, nous nous intéressons aux outilspour proposer une classification des fonctions d’agrégation. Autrement dit, consid- érant une application donnée et une précision cible, comment choisir les meilleures fonctions d’agrégations en termes de performances. Les métriques, que nous avons proposé, sont utilisées pour mesurer la performance de la fonction, et un processus de décision markovien est utilisé pour les mesurer. Comment caractériser un ensem- ble de données est également discuté. Une classification est proposée dans un cadre précis

Collaborative Sensing and Communication Schemes for Cooperative Wireless Sensor Networks

Energy conservation is considered to be one of the key design challenges within resource constrained wireless sensor networks (WSNs) that leads the researchers to investigate energy efficient protocols with some application specific challenges. Dynamic clustering scheme within the deployed sensor nodes is generally considered as one of the energy conservation techniques. However, unbalanced distribution of cluster heads, highly variable number of sensor nodes in the clusters and high number of sensor nodes involved in event reporting tend to drain out the network energy quickly, resulting in unplanned decrease in network lifetime. Performing power aware signal processing, defining communication methods that can provide progressive accuracy and, optimising processing and communication for signal transmission are the challenging tasks. In this thesis, energy efficient solutions are proposed for collaborative sensing and cooperative communication within resource constrained WSNs. A dynamic and cooperative clustering as well as neighbourhood formation scheme is proposed that is expected to evenly distribute the energy demand from the cluster heads and optimise the number of sensor nodes involved in event reporting. The distributive and dynamic behaviour of the proposed framework provides an energy efficient self-organising solution for WSNs that results in an improved network lifetime. The proposed framework is independent of the nature of the sensing type to support applications that require either time-driven sensing, event-driven sensing or hybrid of both sensing types. A cooperative resource selection and transmission scheme is also proposed to improve the performance of collaborative WSNs in terms of maintaining link reliability. As a part of the proposed cooperative nature of transmission, the transmitreceive antennae selection scheme and lattice reduction algorithm have also been considered. It is assumed that the channel state information is estimated at the ii receiver and there is a feedback link between the wireless sensing nodes and the fusion centre receiver. For the ease of system design engineer to achieve a predefined capacity or quality of service, a set of analytical frameworks that provide tighter error performance lower bound for zero forcing (ZF), minimum mean square error (MMSE) and maximum likelihood (ML) detection schemes are also presented. The dynamic behaviour has been adopted within the framework with a proposed index derived from the received measure of the channel quality, which has been attained through the feedback channel from the fusion centre. The dynamic property of the proposed framework makes it robust against time-varying behaviour of the propagation environment. Finally, a unified framework of collaborative sensing and communication schemes for cooperative WSNs is proposed to provide energy efficient solutions within resource constrained environments. The proposed unified framework is fully decentralised which reduces the amount of information required to be broadcasted. Such distributive capability accelerates the decision-making process and enhances the energy conservation. Furthermore, it is validated by simulation results that the proposed unified framework provides a trade-off between network lifetime and transmission reliability while maintaining required quality of service

Source Coding Optimization for Distributed Average Consensus

Consensus is a common method for computing a function of the data distributed among the nodes of a network. Of particular interest is distributed average consensus, whereby the nodes iteratively compute the sample average of the data stored at all the nodes of the network using only near-neighbor communications. In real-world scenarios, these communications must undergo quantization, which introduces distortion to the internode messages. In this thesis, a model for the evolution of the network state statistics at each iteration is developed under the assumptions of Gaussian data and additive quantization error. It is shown that minimization of the communication load in terms of aggregate source coding rate can be posed as a generalized geometric program, for which an equivalent convex optimization can efficiently solve for the global minimum. Optimization procedures are developed for rate-distortion-optimal vector quantization, uniform entropy-coded scalar quantization, and fixed-rate uniform quantization. Numerical results demonstrate the performance of these approaches. For small numbers of iterations, the fixed-rate optimizations are verified using exhaustive search. Comparison to the prior art suggests competitive performance under certain circumstances but strongly motivates the incorporation of more sophisticated coding strategies, such as differential, predictive, or Wyner-Ziv coding.Comment: Master's Thesis, Electrical Engineering, North Carolina State Universit