Implementation and Evaluation of A Low-Cost Intrusion Detection System For Community Wireless Mesh Networks

Rural Community Wireless Mesh Networks (WMN) can be great assets to rural communities, helping them connect to the rest of their region and beyond. However, they can be a liability in terms of security. Due to the ad-hoc nature of a WMN, and the wide variety of applications and systems that can be found in such a heterogeneous environment there are multiple points of intrusion for an attacker. An unsecured WMN can lead to privacy and legal problems for the users of the network. Due to the resource constrained environment, traditional Intrusion Detection Systems (IDS) have not been as successful in defending these wireless network environments, as they were in wired network deployments. This thesis proposes that an IDS made up of low cost, low power devices can be an acceptable base for a Wireless Mesh Network Intrusion Detection System. Because of the device's low power, cost and ease of use, such a device could be easily deployed and maintained in a rural setting such as a Community WMN. The proposed system was compared to a standard IDS solution that would not cover the entire network, but had much more computing power but also a higher capital cost as well as maintenance costs. By comparing the low cost low power IDS to a standard deployment of an open source IDS, based on network coverage and deployment costs, a determination can be made that a low power solution can be feasible in a rural deployment of a WMN

Novel Controls of Photovoltaic (PV) Solar Farms

Solar Farms are absolutely idle in the night and even during daytime operate below capacity in early mornings and late afternoons. Thus, the entire expensive asset of solar farms remains highly unutilized. This thesis presents novel technologies for utilization of PV solar farm inverter in nighttime for providing multiple benefits to power systems, as well as accomplishing the same objectives during the daytime from the inverter capacity left after production of real power. The new technology transforms a solar farm inverter functionally into a dynamic reactive power compensator known as STATCOM, and termed PV-STATCOM. A novel coordinated control of PV-STATCOMs is proposed for loss reduction in a distribution network. The saved energy is substantial and can be used for powering several homes annually. The second novel PV-STATCOM control involves a temporary curtailment of real power production and utilization of the available reactive power capacity to prevent the instability of a critical induction motor load. The third novel PVSTATCOM control is employed to significantly enhance the power transfer limit of a long transmission line both in the nighttime and also during daytime even when the solar farm is producing a large amount of real power. A new technique for short circuit current management is developed for a conventional PV solar farm that can potentially solve the problem due to which several solar farms have been denied connectivity in Ontario. This thesis has contributed to two patent applications and presented first time implementations of another two filed patents. A generalized PV solar system model in EMTDC/PSCAD software has been developed and validated with manufacturer\u27s datasheet. Another contribution of this thesis is the first time harmonics impact study of the largest solar farm in Canada, in the distribution utility network of Bluewater Power, in Sarnia, Ontario. This thesis makes a strong case for relaxing the present grid codes to allow solar farms to exercise these novel controls. This technology can open up new avenues for solar farms to earn revenues apart from the sale of real power. This will require appropriate agreements between the regulators, network utilities, solar farm developers and inverter manufacturers

Spatial combination of sensor data deriving from mobile platforms for precision farming applications

This thesis combines optical sensors on a ground and on an aerial platform for field measurements in wheat, to identify nitrogen (N) levels, estimating biomass (BM) and predicting yield. The Multiplex Research (MP) fluorescence sensor was used for the first time in wheat. The individual objectives were: (i) Evaluation of different available sensors and sensor platforms used in Precision Farming (PF) to quantify the crop nutrition status, (ii) Acquisition of ground and aerial sensor data with two ground spectrometers, an aerial spectrometer and a ground fluorescence sensor, (iii) Development of effective post-processing methods for correction of the sensor data, (iv) Analysis and evaluation of the sensors with regard to the mapping of biomass, yield and nitrogen content in the plant, and (v) Yield simulation as a function of different sensor signals. This thesis contains three papers, published in international peer-reviewed journals. The first publication is a literature review on sensor platforms used in agricultural research. A subdivision of sensors and their applications was done, based on a detailed categorization model. It evaluates strengths and weaknesses, and discusses research results gathered with aerial and ground platforms with different sensors. Also, autonomous robots and swarm technologies suitable for PF tasks were reviewed. The second publication focuses on spectral and fluorescence sensors for BM, yield and N detection. The ground sensors were mounted on the Hohenheim research sensor platform Sensicle. A further spectrometer was installed in a fixed-wing Unmanned Aerial Vehicle (UAV). In this study, the sensors of the Sensicle and the UAV were used to determine plant characteristics and yield of three-year field trials at the research station Ihinger Hof, Renningen (Germany), an institution of the University of Hohenheim, Stuttgart (Germany). Winter wheat (Triticum aestivum L.) was sown on three research fields, with different N levels applied to each field. The measurements in the field were geo-referenced and logged with an absolute GPS accuracy of ±2.5 cm. The GPS data of the UAV was corrected based on the pitch and roll position of the UAV at each measurement. In the first step of the data analysis, raw data obtained from the sensors was post-processed and was converted into indices and ratios relating to plant characteristics. The converted ground sensor data were analysed, and the results of the correlations were interpreted related to the dependent variables (DV) BM weight, wheat yield and available N. The results showed significant positive correlations between the DVs and the Sensicle sensor data. For the third paper, the UAV sensor data was included into the evaluations. The UAV data analysis revealed low significant results for only one field in the year 2011. A multirotor UAV was considered as a more viable aerial platform, that allows for more precision and higher payload. Thereby, the ground sensors showed their strength at a close measuring distance to the plant and a smaller measurement footprint. The results of the two ground spectrometers showed significant positive correlations between yield and the indices from CropSpec, NDVI (Normalised Difference Vegetation Index) and REIP (Red-Edge Inflection Point). Also, FERARI and SFR (Simple Fluorescence Ratio) of the MP fluorescence sensor were chosen for the yield prediction model analysis. With the available N, CropSpec and REIP correlated significantly. The BM weight correlated with REIP even at a very early growing stage (Z 31), and with SAVI (Soil-Adjusted Vegetation Index) at ripening stage (Z 85). REIP, FERARI and SFR showed high correlations to the available N, especially in June and July. The ratios and signals of the MP sensor were highly significant compared to the BM weight above Z 85. Both ground spectrometers are suitable for data comparison and data combination with the active MP fluorescence sensor. Through a combination of fluorescence ratios and spectrometer indices, linear models for the prediction of wheat yield were generated, correlating significantly over the course of the vegetative period for research field Lammwirt (LW) in 2012. The best model for field LW in 2012 was selected for cross-validation with the measurements of the fields Inneres Täle (IT) and Riech (RI) in 2011 and 2012. However, it was not significant. By exchanging only one spectral index with a fluorescence ratio in a similar linear model, it showed significant correlations. This work successfully proves the combination of different sensor ratios and indices for the detection of plant characteristics, offering better and more robust predictions and quantifications of field parameters without employing destructive methods. The MP sensor proved to be universally applicable, showing significant correlations to the investigated characteristics such as BM weight, wheat yield and available N.Diese Arbeit kombiniert optische Sensoren auf einer Sensorplattform (SPF) am Boden und in der Luft bei Messungen in Weizen, um die Stickstoff-(N)-Werte zu identifizieren, während gleichzeitig die Biomasse (BM) geschätzt und der Ertrag vorhergesagt wird. Erstmals wurde hierfür der Fluoreszenzsensor Multiplex Research (MP) in Weizen eingesetzt. Die Ziele dieser Dissertation umfassen: (i) Bewertung verfügbarer Sensoren und SPF, die in der Präzisionslandwirtschaft zur Quantifizierung des Ernährungszustandes von Nutzpflanzen verwendet werden, (ii) Erfassung von Daten mit zwei Spektrometern am Boden, einem Spektrometer auf einem Modellflugzeug (UAV) und einem Fluoreszenzsensor am Boden, (iii) Erstellung effektiver Nachbearbeitungsmethoden für die Datenkorrektur, (iv) Analyse und Evaluation der Sensoren für die Abbildung der BM, des Ertrags und des N-Gehaltes in der Pflanze, und (v) Ertragssimulation als Funktion von Merkmalen unterschiedlicher Sensorsignale. Diese Arbeit enthält drei Artikel, die in international begutachteten Fachzeitschriften publiziert wurden. Die erste Veröffentlichung ist eine Literaturrecherche über SPF in der Agrarforschung. Ein detailliertes Kategorisierungsmodell wird für eine allgemeine Unterteilung der Sensoren und deren Anwendungsgebiete herangenommen, die Stärken und Schwächen bewertet, und die Forschungsergebnisse von Luft- und Bodenplattformen mit unterschiedlicher Sensorik diskutiert. Außerdem werden autonome Roboter und für landwirtschaftliche Aufgaben geeignete Schwarmtechnologien beschrieben. Die zweite Publikation fokussiert sich auf Spektral- und Fluoreszenzsensoren für die Erfassung von BM, Ertrag und N. In der Arbeit wurden die Bodensensoren auf der Hohenheimer Forschungs-SPF Sensicle und der Sensor auf dem UAV in dreijährigen Feldversuchen auf der Versuchsstation Ihinger Hof der Universität Hohenheim in Renningen für die Bestimmung von Pflanzenmerkmalen und des Ertrags eingesetzt. Auf drei Versuchsfeldern wurde Winterweizen ausgesät, und in einem randomisierten Versuchsdesign unterschiedliche N-Düngestufen angelegt. Die Sensormessungen im Feld wurden mit einer absoluten GPS Genauigkeit von ±2,5 cm verortet. Die GPS Daten des UAVs wurden mittels der Nick- und Rollposition lagekorrigiert. Im ersten Schritt der Datenanalyse wurden die Sensorrohdaten nachbearbeitet und in Indizes und Ratios umgerechnet. Die Bodensensordaten wurden analysiert, und die Ergebnisse der Korrelationen in Bezug zu den abhängigen Variablen (DV) BM-Gewicht, Weizenertrag, verfügbarer sowie aufgenommener N dargestellt. Die Ergebnisse zeigen signifikant positive Korrelationen zwischen den DVs und den Sensicle-Sensordaten. Für die dritte Publikation wurden die Sensordaten des UAV in die Auswertungen miteinbezogen. Die Analyse der UAV Daten zeigte niedrige signifikante Ergebnisse für nur ein Feld im Versuchsjahr 2011. Ein Multikopter wird als zuverlässigere Luftplattform erachtet, der mehr Präzision und eine höhere Nutzlast ermöglicht. Die Sensoren auf dem Sensicle zeigten ihren Vorteil bedingt durch einen kürzeren Messabstand zur Pflanze und eine kleinere Messfläche. Die Ergebnisse der beiden Sensicle-Spektrometer zeigten signifikant positive Korrelationen zwischen dem Ertrag und den Indizes von CropSpec, NDVI (Normalised Difference Vegetation Index) und REIP (Red-Edge Inflection Point). Auch FERARI und SFR (Simple Fluorescence Ratio) des MP-Sensors wurden für die Analyse des Ertragsvorhersagemodells ausgewählt. Mit dem verfügbaren N korrelierten CropSpec und REIP hochsignifikant. Das BM-Gewicht korrelierte bereits ab einem sehr frühen Wachstumsstadium (Z31) mit REIP und im Reifestadium (Z85) mit SAVI (Soil-Adjusted Vegetation Index). REIP, FERARI und SFR zeigten hohe Korrelationen mit dem verfügbaren N, insbesondere im Juni und Juli. Die Ratios und Signale des MP Sensors sind vor allem ab Z85 gegenüber dem BM-Gewicht hochsignifikant. Durch eine Kombination von Fluoreszenzwerten und Spektrometerindizes wurden lineare Modelle zur Vorhersage des Weizenertrags erstellt, die im Verlauf der Vegetationsperiode für das Versuchsfeld Lammwirt (LW) im Jahr 2012 signifikant korrelierten. Das beste Modell für das Feld LW im Jahr 2012 wurde für die Kreuzvalidierung mit den Messungen der Versuchsfelder Inneres Täle (IT) und Riech (RI) in den Jahren 2011 und 2012 ausgewählt. Sie waren nicht signifikant, jedoch zeigten sich durch den Austausch nur eines Spektralindexes mit einem Fluoreszenzratio in einem ähnlichen linearen Modell signifikante Korrelationen. Die vorliegende Arbeit zeigt erfolgreich, dass sich die Kombination verschiedener Sensorwerte und Sensorindizes zur Erkennung von Pflanzenmerkmalen gut eignet, und ohne den Einsatz destruktiver Methoden die Möglichkeit für bessere und robustere Vorhersagen bietet. Vor allem der MP-Fluoreszenzsensor erwies sich als universell einsetzbarer Sensor, der signifikante Korrelationen zu den untersuchten Merkmalen BM-Gewicht, Weizenertrag und verfügbarem N aufzeigte

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research

A framework to measure human behaviour whilst reading

The brain is the most complex object in the known universe that gives a sense of being to humans and characterises human behaviour. Building models of brain functions is perhaps the most fascinating scientific challenge in the 21st century. Reading is a significant cognitive process in the human brain that plays a critical role in the vital process of learning and in performing some daily activities. The study of human behaviour during reading has been an area of interest for researchers in different fields of science. This thesis is based upon providing a novel framework, called ARSAT (Assisting Researchers in the Selection of Appropriate Technologies), that measures the behaviour of humans when reading text. The ARSAT framework aims at assisting researchers in the selection and application of appropriate technologies to measure the behaviour of a person who is reading text. The ARSAT framework will assist to researchers who investigate the reading process and find it difficult to select appropriate theories, metrics, data collection methods and data analytics techniques. The ARSAT framework enhances the ability of its users to select appropriate metrics indicating the effective factors on the characterisation of different aspects of human behaviour during the reading process. As will be shown in this research study, human behaviour is characterised by a complicated interplay of action, cognition and emotion. The ARSAT framework also facilitates selecting appropriate sensory technologies that can be used to monitor and collect data for the metrics. Moreover, this research study will introduce BehaveNet, a novel Deep Learning modelling approach, which can be used for training Deep Learning models of human behaviour from the sensory data collected. In this thesis, a comprehensive literature study is presented that was conducted to acquire adequate knowledge for designing the ARSAT framework. In order to identify the contributing factors that affect the reading process, an overview of some existing theories of the reading process is provided. Furthermore, a number of sensory technologies and techniques that can be applied to monitoring the changes in the metrics indicating the factors are also demonstrated. Only, the technologies that are commercially available on the market are recommended by the ARSAT framework. A variety of Machine Learning techniques were also investigated when designing the BehaveNet. The BehaveNet takes advantage of the complementarity of Convolutional Neural Networks, Long Short-Term Memory networks and Deep Neural Networks. The design of a Human Behaviour Monitoring System (HBMS), by utilising the ARSAT framework for recognising three attention-seeking activities of humans, is also presented in this research study. Reading printed text, as well as speaking out loudly and watching a programme on TV were proposed as activities that a person unintentionally may shift his/her attention from reading into distractions. Between sensory devices recommended by the ARSAT framework, the Muse headband which is an Electroencephalography (EEG) and head motion-sensing wearable device, was selected to track the forehead EEG and a person’s head movements. The EEG and 3-axes accelerometer data were recorded from eight participants when they read printed text, as well as the time they performed two other activities. An imbalanced dataset consisting over 1.2 million rows of noisy data was created and used to build a model of the activities (60% training and 20% validating data) and evaluating the model (20% of the data). The efficiency of the framework is demonstrated by comparing the performance of the models built by utilising the BehaveNet, with the models built by utilising a number of competing Deep Learning models for raw EEG and accelerometer data, that have attained state-of-the-art performance. The classification results are evaluated by some metrics including the classification accuracy, F1 score, confusion matrix, Receiver Operating Characteristic curve, and Area under Curve (AUC) score. By considering the results, the BehaveNet contributed to the body of knowledge as an approach for measuring human behaviour by using sensory devices. In comparison with the performance of the other models, the models built by utilising the BehaveNet, attained better performance when classifying data of two EEG channels (Accuracy = 95%; AUC=0.99; F1 = 0.95), data of a single EEG channel (Accuracy = 85%; AUC=0.96; F1 = 0.83), accelerometer data (Accuracy = 81%; AUC = 0.9; F1 = 0.76) and all of the data in the dataset (Accuracy = 97%; AUC = 0.99; F1 = 0.96). The dataset and the source code of this project are also published on the Internet to help the science community. The Muse headband is also shown to be an economical and standard wearable device that can be successfully used in behavioural research

Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license

Security of Ubiquitous Computing Systems

The chapters in this open access book arise out of the EU Cost Action project Cryptacus, the objective of which was to improve and adapt existent cryptanalysis methodologies and tools to the ubiquitous computing framework. The cryptanalysis implemented lies along four axes: cryptographic models, cryptanalysis of building blocks, hardware and software security engineering, and security assessment of real-world systems. The authors are top-class researchers in security and cryptography, and the contributions are of value to researchers and practitioners in these domains. This book is open access under a CC BY license