Robust Controller for Delays and Packet Dropout Avoidance in Solar-Power Wireless Network

Solar Wireless Networked Control Systems (SWNCS) are a style of distributed control systems where sensors, actuators, and controllers are interconnected via a wireless communication network. This system setup has the benefit of low cost, flexibility, low weight, no wiring and simplicity of system diagnoses and maintenance. However, it also unavoidably calls some wireless network time delays and packet dropout into the design procedure. Solar lighting system offers a clean environment, therefore able to continue for a long period. SWNCS also offers multi Service infrastructure solution for both developed and undeveloped countries. The system provides wireless controller lighting, wireless communications network (WI-FI/WIMAX), CCTV surveillance, and wireless sensor for weather measurement which are all powered by solar energy

Information fusion architectures for security and resource management in cyber physical systems

Data acquisition through sensors is very crucial in determining the operability of the observed physical entity. Cyber Physical Systems (CPSs) are an example of distributed systems where sensors embedded into the physical system are used in sensing and data acquisition. CPSs are a collaboration between the physical and the computational cyber components. The control decisions sent back to the actuators on the physical components from the computational cyber components closes the feedback loop of the CPS. Since, this feedback is solely based on the data collected through the embedded sensors, information acquisition from the data plays an extremely vital role in determining the operational stability of the CPS. Data collection process may be hindered by disturbances such as system faults, noise and security attacks. Hence, simple data acquisition techniques will not suffice as accurate system representation cannot be obtained. Therefore, more powerful methods of inferring information from collected data such as Information Fusion have to be used. Information fusion is analogous to the cognitive process used by humans to integrate data continuously from their senses to make inferences about their environment. Data from the sensors is combined using techniques drawn from several disciplines such as Adaptive Filtering, Machine Learning and Pattern Recognition. Decisions made from such combination of data form the crux of information fusion and differentiates it from a flat structured data aggregation. In this dissertation, multi-layered information fusion models are used to develop automated decision making architectures to service security and resource management requirements in Cyber Physical Systems --Abstract, page iv

Cognitive networking for next generation of cellular communication systems

This thesis presents a comprehensive study of cognitive networking for cellular networks with contributions that enable them to be more dynamic, agile, and efficient. To achieve this, machine learning (ML) algorithms, a subset of artificial intelligence, are employed to bring such cognition to cellular networks. More specifically, three major branches of ML, namely supervised, unsupervised, and reinforcement learning (RL), are utilised for various purposes: unsupervised learning is used for data clustering, while supervised learning is employed for predictions on future behaviours of networks/users. RL, on the other hand, is utilised for optimisation purposes due to its inherent characteristics of adaptability and requiring minimal knowledge of the environment. Energy optimisation, capacity enhancement, and spectrum access are identified as primary design challenges for cellular networks given that they are envisioned to play crucial roles for 5G and beyond due to the increased demand in the number of connected devices as well as data rates. Each design challenge and its corresponding proposed solution are discussed thoroughly in separate chapters. Regarding energy optimisation, a user-side energy consumption is investigated by considering Internet of things (IoT) networks. An RL based intelligent model, which jointly optimises the wireless connection type and data processing entity, is proposed. In particular, a Q-learning algorithm is developed, through which the energy consumption of an IoT device is minimised while keeping the requirement of the applications--in terms of response time and security--satisfied. The proposed methodology manages to result in 0% normalised joint cost--where all the considered metrics are combined--while the benchmarks performed 54.84% on average. Next, the energy consumption of radio access networks (RANs) is targeted, and a traffic-aware cell switching algorithm is designed to reduce the energy consumption of a RAN without compromising on the user quality-of-service (QoS). The proposed technique employs a SARSA algorithm with value function approximation, since the conventional RL methods struggle with solving problems with huge state spaces. The results reveal that up to 52% gain on the total energy consumption is achieved with the proposed technique, and the gain is observed to reduce when the scenario becomes more realistic. On the other hand, capacity enhancement is studied from two different perspectives, namely mobility management and unmanned aerial vehicle (UAV) assistance. Towards that end, a predictive handover (HO) mechanism is designed for mobility management in cellular networks by identifying two major issues of Markov chains based HO predictions. First, revisits--which are defined as a situation whereby a user visits the same cell more than once within the same day--are diagnosed as causing similar transition probabilities, which in turn increases the likelihood of making incorrect predictions. This problem is addressed with a structural change; i.e., rather than storing 2-D transition matrix, it is proposed to store 3-D one that also includes HO orders. The obtained results show that 3-D transition matrix is capable of reducing the HO signalling cost by up to 25.37%, which is observed to drop with increasing randomness level in the data set. Second, making a HO prediction with insufficient criteria is identified as another issue with the conventional Markov chains based predictors. Thus, a prediction confidence level is derived, such that there should be a lower bound to perform HO predictions, which are not always advantageous owing to the HO signalling cost incurred from incorrect predictions. The outcomes of the simulations confirm that the derived confidence level mechanism helps in improving the prediction accuracy by up to 8.23%. Furthermore, still considering capacity enhancement, a UAV assisted cellular networking is considered, and an unsupervised learning-based UAV positioning algorithm is presented. A comprehensive analysis is conducted on the impacts of the overlapping footprints of multiple UAVs, which are controlled by their altitudes. The developed k-means clustering based UAV positioning approach is shown to reduce the number of users in outage by up to 80.47% when compared to the benchmark symmetric deployment. Lastly, a QoS-aware dynamic spectrum access approach is developed in order to tackle challenges related to spectrum access, wherein all the aforementioned types of ML methods are employed. More specifically, by leveraging future traffic load predictions of radio access technologies (RATs) and Q-learning algorithm, a novel proactive spectrum sensing technique is introduced. As such, two different sensing strategies are developed; the first one focuses solely on sensing latency reduction, while the second one jointly optimises sensing latency and user requirements. In particular, the proposed Q-learning algorithm takes the future load predictions of the RATs and the requirements of secondary users--in terms of mobility and bandwidth--as inputs and directs the users to the spectrum of the optimum RAT to perform sensing. The strategy to be employed can be selected based on the needs of the applications, such that if the latency is the only concern, the first strategy should be selected due to the fact that the second strategy is computationally more demanding. However, by employing the second strategy, sensing latency is reduced while satisfying other user requirements. The simulation results demonstrate that, compared to random sensing, the first strategy decays the sensing latency by 85.25%, while the second strategy enhances the full-satisfaction rate, where both mobility and bandwidth requirements of the user are simultaneously satisfied, by 95.7%. Therefore, as it can be observed, three key design challenges of the next generation of cellular networks are identified and addressed via the concept of cognitive networking, providing a utilitarian tool for mobile network operators to plug into their systems. The proposed solutions can be generalised to various network scenarios owing to the sophisticated ML implementations, which renders the solutions both practical and sustainable

Comprehensive Survey and Taxonomies of False Injection Attacks in Smart Grid: Attack Models, Targets, and Impacts

Smart Grid has rapidly transformed the centrally controlled power system into a massively interconnected cyber-physical system that benefits from the revolutions happening in the communications (e.g. 5G) and the growing proliferation of the Internet of Things devices (such as smart metres and intelligent electronic devices). While the convergence of a significant number of cyber-physical elements has enabled the Smart Grid to be far more efficient and competitive in addressing the growing global energy challenges, it has also introduced a large number of vulnerabilities culminating in violations of data availability, integrity, and confidentiality. Recently, false data injection (FDI) has become one of the most critical cyberattacks, and appears to be a focal point of interest for both research and industry. To this end, this paper presents a comprehensive review in the recent advances of the FDI attacks, with particular emphasis on 1) adversarial models, 2) attack targets, and 3) impacts in the Smart Grid infrastructure. This review paper aims to provide a thorough understanding of the incumbent threats affecting the entire spectrum of the Smart Grid. Related literature are analysed and compared in terms of their theoretical and practical implications to the Smart Grid cybersecurity. In conclusion, a range of technical limitations of existing false data attack research is identified, and a number of future research directions is recommended.Comment: Double-column of 24 pages, prepared based on IEEE Transaction articl

Measuring Information Security Awareness Efforts in Social Networking Sites – A Proactive Approach

For Social Network Sites to determine the effectiveness of their Information Security Awareness (ISA) techniques, many measurement and evaluation techniques are now in place to ensure controls are working as intended. While these techniques are inexpensive, they are all incident- driven as they are based on the occurrence of incident(s). Additionally, they do not present a true reflection of ISA since cyber-incidents are hardly reported. They are therefore adjudged to be post-mortem and risk permissive, the limitations that are inacceptable in industries where incident tolerance level is low. This paper aims at employing a non-incident statistic approach to measure ISA efforts. Using an object- oriented programming approach, PhP is employed as the coding language with MySQL database engine at the back-end to develop sOcialistOnline – a Social Network Sites (SNS) fully secured with multiple ISA techniques. Rather than evaluating the effectiveness of ISA efforts by success of attacks or occurrence of an event, password scanning is implemented to proactively measure the effects of ISA techniques in sOcialistOnline. Thus, measurement of ISA efforts is shifted from detective and corrective to preventive and anticipatory paradigms which are the best forms of information security approach

Probabilistic Tracking and Behavior Identification of Fluorescent Particles

Explicit and tractable characterizations of the dynamical behavior of virus particles are pivotal for a thorough understanding of the infection mechanisms of viruses. This thesis deals with the problem of extracting symbolic representations of the dynamical behavior of fluorescent particles from fluorescence microscopy image sequences. The focus is on the behavior of virus particles such as fusion with the cell membrane. A numerical representation is obtained by tracking the particles in the image sequences. We have investigated probabilistic tracking approaches, including approaches based on the Kalman filter as well as based on particle filters. For reasons of efficiency and robustness, we developed a tracking approach based on the probabilistic data association (PDA) algorithm in combination with an ellipsoidal sampling scheme that exploits effectively the image data via parametric appearance models. To track objects in close proximity, we compute the support that each image position provides to each tracked object relative to the support provided to the object's neighbors. After tracking, the problem of mapping the trajectory information computed by the tracking approaches to symbolic representations of the behavior arises. To compute symbolic representations of behaviors related to the fusion of single virus particles with the cell membrane based on their intensity over time, we developed a layered probabilistic approach based on stochastic hybrid systems as well as hidden Markov models (HMMs). We use a maxbelief strategy to efficiently combine both representations. The layered approach describes the intensity, intensity models, and behaviors of single virus particles. We introduce models for the evolution of the intensity and the behavior. To compute estimates for the intensity, intensity models, and behaviors we use a hybrid particle filter and the Viterbi algorithm. The developed approaches have been applied to synthetic images as well as to real microscopy image sequences displaying human immunodeficiency virus (HIV-1) particles. We have performed an extensive quantitative evaluation of the performance and a comparison with several existing approaches. It turned out that our approaches outperform previous ones, thus yielding more accurate and more reliable information about the behavior of virus particles. Moreover, we have successfully applied our tracking approaches to 3D image sequences displaying herpes simplex virus (HSV) replication compartments. We also applied the tracking approaches to image data displaying microtubule tips and analyzed their motion. In addition, our tracking approaches were successfully applied to the 2D and 3D image data of a Particle Tracking Challenge

Allocation of Communication and Computation Resources in Mobile Networks

Konvergence komunikačních a výpočetních technologií vedlo k vzniku Multi-Access Edge Computing (MEC). MEC poskytuje výpočetní výkon na tzv. hraně mobilních sítí (základnové stanice, jádro mobilní sítě), který lze využít pro optimalizaci mobilních sítí v reálném čase. Optimalizacev reálném čase je umožněna díky nízkému komunikačnímu zpoždění například v porovnání s Mobile Cloud Computing (MCC). Optimalizace mobilních sítí vyžaduje informace o mobilní síti od uživatelských zařízeních, avšak sběr těchto informací využívá komunikační prostředky, které jsou využívány i pro přenos uživatelských dat. Zvyšující se počet uživatelských zařízení, senzorů a taktéž komunikace vozidel tvoří překážku pro sběr informací o mobilních sítích z důvodu omezeného množství komunikačních prostředků. Tudíž je nutné navrhnout řešení, která umožní sběr těchto informací pro potřeby optimalizace mobilních sítí. V této práci je navrženo řešení pro komunikaci vysokého počtu zařízeních, které je postaveno na využití přímé komunikace mezi zařízeními. Pro motivování uživatelů, pro využití přeposílání dat pomocí přímé komunikace mezi uživateli je navrženo přidělování komunikačních prostředků jenž vede na přirozenou spolupráci uživatelů. Dále je provedena analýza spotřeby energie při využití přeposílání dat pomocí přímé komunikace mezi uživateli pro ukázání jejích výhod z pohledu spotřeby energie. Pro další zvýšení počtu komunikujících zařízení je využito mobilních létajících základových stanic (FlyBS). Pro nasazení FlyBS je navržen algoritmus, který hledá pozici FlyBS a asociaci uživatel k FlyBS pro zvýšení spokojenosti uživatelů s poskytovanými datovými propustnostmi. MEC lze využít nejen pro optimalizaci mobilních sítí z pohledu mobilních operátorů, ale taktéž uživateli mobilních sítí. Tito uživatelé mohou využít MEC pro přenost výpočetně náročných úloh z jejich mobilních zařízeních do MEC. Z důvodu mobility uživatel je nutné nalézt vhodně přidělení komunikačních a výpočetních prostředků pro uspokojení uživatelských požadavků. Tudíž je navržen algorithmus pro výběr komunikační cesty mezi uživatelem a MEC, jenž je posléze rozšířen o přidělování výpočetných prostředků společně s komunikačními prostředky. Navržené řešení vede k snížení komunikačního zpoždění o desítky procent.The convergence of communication and computing in the mobile networks has led to an introduction of the Multi-Access Edge Computing (MEC). The MEC combines communication and computing resources at the edge of the mobile network and provides an option to optimize the mobile network in real-time. This is possible due to close proximity of the computation resources in terms of communication delay, in comparison to the Mobile Cloud Computing (MCC). The optimization of the mobile networks requires information about the mobile network and User Equipment (UE). Such information, however, consumes a significant amount of communication resources. The finite communication resources along with the ever increasing number of the UEs and other devices, such as sensors, vehicles pose an obstacle for collecting the required information. Therefore, it is necessary to provide solutions to enable the collection of the required mobile network information from the UEs for the purposes of the mobile network optimization. In this thesis, a solution to enable communication of a large number of devices, exploiting Device-to-Device (D2D) communication for data relaying, is proposed. To motivate the UEs to relay data of other UEs, we propose a resource allocation algorithm that leads to a natural cooperation of the UEs. To show, that the relaying is not only beneficial from the perspective of an increased number of UEs, we provide an analysis of the energy consumed by the D2D communication. To further increase the number of the UEs we exploit a recent concept of the flying base stations (FlyBSs), and we develop a joint algorithm for a positioning of the FlyBS and an association of the UEs to increase the UEs satisfaction with the provided data rates. The MEC can be exploited not only for processing of the collected data to optimize the mobile networks, but also by the mobile users. The mobile users can exploit the MEC for the computation offloading, i.e., transferring the computation from their UEs to the MEC. However, due to the inherent mobility of the UEs, it is necessary to determine communication and computation resource allocation in order to satisfy the UEs requirements. Therefore, we first propose a solution for a selection of the communication path between the UEs and the MEC (communication resource allocation). Then, we also design an algorithm for joint communication and computation resource allocation. The proposed solution then lead to a reduction in the computation offloading delay by tens of percent

Cyber- Physical Robustness Enhancement Strategies for Demand Side Energy Systems

An integrated Cyber-Physical System (CPS) system realizes the two-way communication between end-users and power generation in which customers are able to actively re-shaped their consumption profiles to facilitate the energy efficiency of the grid. However, large-scale implementations of distributed assets and advanced communication infrastructures also increase the risks of grid operation. This thesis aims to enhance the robustness of the entire demand-side system in a cyber-physical environment and develop comprehensive strategies about outage energy management (i.e., community-level scheduling and appliance-level energy management), communications infrastructure development, and cybersecurity controls that encounter virus attacks. All these aspects facilitate the demand-side system’s self-serve capability and operational robustness under extreme conditions and dangerous scenarios. The research that contributes to this thesis is grouped around and builds a general scheme to enhance the robustness of CPS demand-side energy system with outage considerations, communication network layouts, and virus intrusions. Under system outage, there are two layers for maximizing the duration of self-power supply duration in extreme conditions. The study first proposed a resilient energy management system for residential communities (CEMS), by scheduling and coordinating the battery energy storage system and energy consumption of houses/units. Moreover, it also proposed a hierarchical resilient energy management system (EMS) by fully considering the appliance-level local scheduling. The method also takes into account customer satisfaction and lifestyle preferences in order to form the optimal outcome. To further enhance the robustness of the CPS system, a complex multi-hop wireless remote metering network model for communication layout on the CPS demand side was proposed. This decreased the number and locations of data centers on the demand side and reduced the security risk of communication and the infrastructure cost of the smart grid for residential energy management. A novel evolutionary aggregation algorithm (EAA) was proposed to obtain the minimum number and locations of the local data centers required to fulfill the connectivity of the smart meters. Finally, the potential for virus attacks has also been studied as well. A trade-off strategy to confront viruses in the system with numerous network nodes is proposed. The allocation of antivirus programs and schemes are studied to avoid system crashes and achieve the minimum potential damages. A DOWNHILL-TRADE OFF algorithm is proposed to address an appropriate allocation strategy under the time evolution of the expected state of the network. Simulations are conducted using the data from the Smart Grid, Smart City national demonstration project trials

Tecnologias IoT para pastoreio e controlo de postura animal

The unwanted and adverse weeds that are constantly growing in vineyards, force wine producers to repeatedly remove them through the use of mechanical and chemical methods. These methods include machinery such as plows and brushcutters, and chemicals as herbicides to remove and prevent the growth of weeds both in the inter-row and under-vine areas. Nonetheless, such methods are considered very aggressive for vines, and, in the second case, harmful for the public health, since chemicals may remain in the environment and hence contaminate water lines. Moreover, such processes have to be repeated over the year, making it extremely expensive and toilsome. Using animals, usually ovines, is an ancient practice used around the world. Animals, grazing in vineyards, feed from the unwanted weeds and fertilize the soil, in an inexpensive, ecological and sustainable way. However, sheep may be dangerous to vines since they tend to feed on grapes and on the lower branches of the vines, which causes enormous production losses. To overcome that issue, sheep were traditionally used to weed vineyards only before the beginning of the growth cycle of grapevines, thus still requiring the use of mechanical and/or chemical methods during the remainder of the production cycle. To mitigate the problems above, a new technological solution was investigated under the scope of the SheepIT project and developed in the scope of this thesis. The system monitors sheep during grazing periods on vineyards and implements a posture control mechanism to instruct them to feed only from the undesired weeds. This mechanism is based on an IoT architecture, being designed to be compact and energy efficient, allowing it to be carried by sheep while attaining an autonomy of weeks. In this context, the thesis herein sustained states that it is possible to design an IoT-based system capable of monitoring and conditioning sheep’s posture, enabling a safe weeding process in vineyards. Moreover, we support such thesis in three main pillars that match the main contributions of this work and that are duly explored and validated, namely: the IoT architecture design and required communications, a posture control mechanism and the support for a low-cost and low-power localization mechanism. The system architecture is validated mainly in simulation context while the posture control mechanism is validated both in simulations and field experiments. Furthermore, we demonstrate the feasibility of the system and the contribution of this work towards the first commercial version of the system.O constante crescimento de ervas infestantes obriga os produtores a manter um processo contínuo de remoção das mesmas com recurso a mecanismos mecânicos e/ou químicos. Entre os mais populares, destacam-se o uso de arados e roçadores no primeiro grupo, e o uso de herbicidas no segundo grupo. No entanto, estes mecanismos são considerados agressivos para as videiras, assim como no segundo caso perigosos para a saúde pública, visto que os químicos podem permanecer no ambiente, contaminando frutos e linhas de água. Adicionalmente, estes processos são caros e exigem mão de obra que escasseia nos dias de hoje, agravado pela necessidade destes processos necessitarem de serem repetidos mais do que uma vez ao longo do ano. O uso de animais, particularmente ovelhas, para controlar o crescimento de infestantes é uma prática ancestral usada em todo o mundo. As ovelhas, enquanto pastam, controlam o crescimento das ervas infestantes, ao mesmo tempo que fertilizam o solo de forma gratuita, ecológica e sustentável. Não obstante, este método foi sendo abandonado visto que os animais também se alimentam da rama, rebentos e frutos da videira, provocando naturais estragos e prejuízos produtivos. Para mitigar este problema, uma nova solução baseada em tecnologias de Internet das Coisas é proposta no âmbito do projeto SheepIT, cuja espinha dorsal foi construída no âmbito desta tese. O sistema monitoriza as ovelhas enquanto estas pastoreiam nas vinhas, e implementam um mecanismo de controlo de postura que condiciona o seu comportamento de forma a que se alimentem apenas das ervas infestantes. O sistema foi incorporado numa infraestrutura de Internet das Coisas com comunicações sem fios de baixo consumo para recolha de dados e que permite semanas de autonomia, mantendo os dispositivos com um tamanho adequado aos animais. Neste contexto, a tese suportada neste trabalho defende que é possível projetar uma sistema baseado em tecnologias de Internet das Coisas, capaz de monitorizar e condicionar a postura de ovelhas, permitindo que estas pastem em vinhas sem comprometer as videiras e as uvas. A tese é suportada em três pilares fundamentais que se refletem nos principais contributos do trabalho, particularmente: a arquitetura do sistema e respetivo sistema de comunicações; o mecanismo de controlo de postura; e o suporte para implementação de um sistema de localização de baixo custo e baixo consumo energético. A arquitetura é validada em contexto de simulação, e o mecanismo de controlo de postura em contexto de simulação e de experiências em campo. É também demonstrado o funcionamento do sistema e o contributo deste trabalho para a conceção da primeira versão comercial do sistema.Programa Doutoral em Informátic

Pedagogy in performance: An investigation into decision training as a cognitive approach to circus training

This research project represents the first formal research conducted into the potential application of Decision Training in an elite circus arts school environment. The research examines the effects of the introduction of Decision Training—a training model developed for sports applications—into the elite circus arts training program at the National Circus School (NCS), a key circus arts school in one of the world’s most vital circus domains, Montreal, Quebec, Canada. Decision Training, a cognitive-based training model, has been shown through extensive sports-based research to support the development of decision-making ability and self-regulatory learning behaviour, both of which are fundamental for the long-term retention and application of physical skills. A key research aim was to investigate whether Decision Training had the potential to enhance existing teaching practice at the NCS. This research investigates how this cognitive training model—developed for use in the world of competitive sports—functions in a performing arts context in which not only physical and technical skills are trained, but also elements connected with performance, such as aesthetic expression and the creation and development of new performance material. A qualitative action research methodology was employed, consisting of three reflection–action cycles with three case studies of student–teacher pairings. Data collection took place over an extended training period at the NCS from November 2011 to April 2012. Observation, interviews with teachers and students, and group discussions were used to collect data and to provide the impetus for the Decision Training interventions for the three action research cycles. This qualitative study reveals how teachers implemented the three-step Decision Training model and how students responded to these teaching interventions. This was done through an action research process investigating the lived experiences of the participants involved in each case study. The research findings indicate that incorporating a cognitive training method such as Decision Training into circus pedagogy has the potential benefit of giving students the means of acquiring important skills such as effective decision making in performance situations, and self-regulatory behaviour such as the ability to effectively self-assess their performance. Teachers have the potential to benefit by not having to be the sole providers of feedback or motivation, allowing the rapport between student and teacher to become collaborative and creative. The research findings show that the effectiveness of the Decision Training interventions was influenced by the different learning and teaching backgrounds and styles of the student–teacher pairings, and the different ways in which the teachers integrated Decision Training into their existing teaching practices. The research findings led to the proposal of an “integrated” pedagogical approach based on a combination of Decision Training and direct teaching. This “integrated” pedagogy would enable a teacher to use the cognitivist, student-centred learning approach of Decision Training to develop self-regulation and effective decision making in students, but switch to aspects of direct teaching at appropriate times: for instance, when a student needs to be directly aware of safety issues or has little foundational knowledge in a circus discipline; in the lead-up to a performance showing; or during the period in which a student is adjusting to the new cognitivist learning and teaching environment. Recommendations are made for the gradual phasing in of Decision Training into the main training program at the NCS, and implications for future research are discussed