Satisfaction-Aware Data Offloading in Surveillance Systems

In this thesis, exploiting Fully Autonomous Aerial Systems\u27 (FAAS) and Mobile Edge Computing (MEC) servers\u27 computing capabilities to introduce a novel data offloading framework to support the energy and time-efficient video processing in surveillance systems based on satisfaction games. A surveillance system is introduced consisting of Areas of Interest (AoIs), where a MEC server is associated with each AoI, and a FAAS is flying above the AoIs to support the IP cameras\u27 computing demands. Each IP camera adopts a utility function capturing its Quality of Service (QoS) considering the experienced time and energy overhead to offload and process remotely or locally the data. A non-cooperative game among the cameras is formulated to determine the amount of offloading data to the MEC server and/or the FAAS, and the novel concept of Satisfaction Equilibrium (SE) is introduced where the IP cameras satisfy their minimum QoS prerequisites instead of maximizing their performance by consuming additional system resources. A distributed learning algorithm determines the IP cameras\u27 stable data offloading. Also, a reinforcement learning algorithm indicates the FAAS\u27s movement among the AoIs exploiting the accuracy, timeliness, and certainty of the collected data by the IP cameras per AoI. Detailed numerical and comparative results are presented to show the operation and efficiency of the proposed framework

Distributed optimisation framework for in-network data processing

In an information network consisting of different types of communication devices equipped with various types of sensors, it is inevitable that a huge amount of data will be generated. Considering the practical network constraints such as bandwidth and energy limitations, storing, processing and transmitting this very large volume of data is very challenging, if not impossible. However, In-Network Processing (INP) has opened a new door to possible solutions for optimising the utilisation of network resources. INP methods primarily aim to aggregate (e.g., compression, fusion and averaging) data from different sources with the objective of reducing the data volume for further transfer, thus, reducing energy consumption, and increasing the network lifetime. However, processing data often results in an imprecise outcome such as irrelevancy, incompleteness, etc. Therefore, besides characterising the Quality of Information (QoI) in these systems, which is important, it is also crucial to consider the effect of further data processing on the measured QoI associated with each specific piece of information. Typically, the greater the degree of data aggregation, the higher the computation energy cost that is incurred. However, as the volume of data is reduced after aggregation, less energy is needed for subsequent data transmission and reception. Furthermore, aggregation of data can cause deterioration of QoI. Therefore, there is a trade-off among the QoI requirement and energy consumption by computation and communication. We define the optimal data reduction rate parameter as the degree to which data can be efficiently reduced while guaranteeing the required QoI for the end user. Using wireless sensor networks for illustration, we concentrate on designing a distributed framework to facilitate controlling of INP process at each node while satisfying the end user’s QoI requirements. We formulate the INP problem as a non-linear optimisation problem with the objective of minimising the total energy consumption through the network subject to a given QoI requirement for the end user. The proposed problem is intrinsically a non-convex, and, in general, hard to solve. Given the non-convexity and hardness of the problem, we propose a novel approach that can reduce the computation complexity of the problem. Specifically, we prove that under the assumption of uniform parameters’ settings, the complexity of the proposed problem can be reduced significantly, which may be feasible for each node with limited energy supply to carry out the problem computation. Moreover, we propose an optimal solution by transforming the original problem to an equivalent one. Using the theory of duality optimisation, we prove that under a set of reasonable cost and topology assumptions, the optimal solution can be efficiently, obtained despite the non-convexity of the problem. Furthermore, we propose an effective and efficient distributed, iterative algorithm that can converge to the optimal solution. We evaluate our proposed complexity reduction framework under different parameter settings, and show that the problem with N variables can be reduced to the problem with logN variables presenting a significant reduction in the complexity of the problem. The validity and performance of the proposed distributed optimisation framework has been evaluated through extensive simulation. We show that the proposed distributed algorithm can converge to the optimal solution very fast. The behaviour of the proposed framework has been examined under different parameters’ setting, and checked against the optimal solution obtained via an exhaustive search algorithm. The results show the quick and efficient convergences for the proposed algorithm.Open Acces

Federated Learning in Intelligent Transportation Systems: Recent Applications and Open Problems

Intelligent transportation systems (ITSs) have been fueled by the rapid development of communication technologies, sensor technologies, and the Internet of Things (IoT). Nonetheless, due to the dynamic characteristics of the vehicle networks, it is rather challenging to make timely and accurate decisions of vehicle behaviors. Moreover, in the presence of mobile wireless communications, the privacy and security of vehicle information are at constant risk. In this context, a new paradigm is urgently needed for various applications in dynamic vehicle environments. As a distributed machine learning technology, federated learning (FL) has received extensive attention due to its outstanding privacy protection properties and easy scalability. We conduct a comprehensive survey of the latest developments in FL for ITS. Specifically, we initially research the prevalent challenges in ITS and elucidate the motivations for applying FL from various perspectives. Subsequently, we review existing deployments of FL in ITS across various scenarios, and discuss specific potential issues in object recognition, traffic management, and service providing scenarios. Furthermore, we conduct a further analysis of the new challenges introduced by FL deployment and the inherent limitations that FL alone cannot fully address, including uneven data distribution, limited storage and computing power, and potential privacy and security concerns. We then examine the existing collaborative technologies that can help mitigate these challenges. Lastly, we discuss the open challenges that remain to be addressed in applying FL in ITS and propose several future research directions

From serendipity to sustainable Green IoT: technical, industrial and political perspective

Recently, Internet of Things (IoT) has become one of the largest electronics market for hardware production due to its fast evolving application space. However, one of the key challenges for IoT hardware is the energy efficiency as most of IoT devices/objects are expected to run on batteries for months/years without a battery replacement or on harvested energy sources. Widespread use of IoT has also led to a largescale rise in the carbon footprint. In this regard, academia, industry and policy-makers are constantly working towards new energy-efficient hardware and software solutions paving the way for an emerging area referred to as green-IoT. With the direct integration and the evolution of smart communication between physical world and computer-based systems, IoT devices are also expected to reduce the total amount of energy consumption for the Information and Communication Technologies (ICT) sector. However, in order to increase its chance of success and to help at reducing the overall energy consumption and carbon emissions a comprehensive investigation into how to achieve green-IoT is required. In this context, this paper surveys the green perspective of the IoT paradigm and aims to contribute at establishing a global approach for green-IoT environments. A comprehensive approach is presented that focuses not only on the specific solutions but also on the interaction among them, and highlights the precautions/decisions the policy makers need to take. On one side, the ongoing European projects and standardization efforts as well as industry and academia based solutions are presented and on the other side, the challenges, open issues, lessons learned and the role of policymakers towards green-IoT are discussed. The survey shows that due to many existing open issues (e.g., technical considerations, lack of standardization, security and privacy, governance and legislation, etc.) that still need to be addressed, a realistic implementation of a sustainable green-IoT environment that could be universally accepted and deployed, is still missing

Provendo robustez a escalonadores de workflows sensíveis às incertezas da largura de banda disponível

Orientadores: Edmundo Roberto Mauro Madeira, Luiz Fernando BittencourtTese (doutorado) - Universidade Estadual de Campinas, Instituto de ComputaçãoResumo: Para que escalonadores de aplicações científicas modeladas como workflows derivem escalonamentos eficientes em nuvens híbridas, é necessário que se forneçam, além da descrição da demanda computacional desses aplicativos, as informações sobre o poder de computação dos recursos disponíveis, especialmente aqueles dados relacionados com a largura de banda disponível. Entretanto, a imprecisão das ferramentas de medição fazem com que as informações da largura de banda disponível fornecida aos escalonadores difiram dos valores reais que deveriam ser considerados para se obter escalonamentos quase ótimos. Escalonadores especialmente projetados para nuvens híbridas simplesmente ignoram a existência de tais imprecisões e terminam produzindo escalonamentos enganosos e de baixo desempenho, o que os tornam sensíveis às informações incertas. A presente Tese introduz um procedimento pró-ativo para fornecer um certo nível de robustez a escalonamentos derivados de escalonadores não projetados para serem robustos frente às incertezas decorrentes do uso de informações imprecisas dadas por ferramentas de medições de rede. Para tornar os escalonamentos sensíveis às incertezas em escalonamentos robustos às essas imprecisões, o procedimento propõe um refinamento (uma deflação) das estimativas da largura de banda antes de serem utilizadas pelo escalonador não robusto. Ao propor o uso de estimativas refinadas da largura de banda disponível, escalonadores inicialmente sensíveis às incertezas passaram a produzir escalonamentos com um certo nível de robustez às essas imprecisões. A eficácia e a eficiência do procedimento proposto são avaliadas através de simulação. Comparam-se, portanto, os escalonamentos gerados por escalonadores que passaram a usar o procedimento proposto com aqueles produzidos pelos mesmos escalonadores mas sem aplicar esse procedimento. Os resultados das simulações mostram que o procedimento proposto é capaz de prover robustez às incertezas da informação da largura de banda a escalonamentos derivados de escalonardes não robustos às tais incertezas. Adicionalmente, esta Tese também propõe um escalonador de aplicações científicas especialmente compostas por um conjunto de workflows. A novidade desse escalonador é que ele é flexível, ou seja, permite o uso de diferentes categorias de funções objetivos. Embora a flexibilidade proposta seja uma novidade no estado da arte, esse escalonador também é sensível às imprecisões da largura de banda. Entretanto, o procedimento mostrou-se capaz de provê-lo de robustez frente às tais incertezas. É mostrado nesta Tese que o procedimento proposto aumentou a eficácia e a eficiência de escalonadores de workflows não robustos projetados para nuvens híbridas, já que eles passaram a produzir escalonamentos com um certo nível de robustez na presença de estimativas incertas da largura de banda disponível. Dessa forma, o procedimento proposto nesta Tese é uma importante ferramenta para aprimorar os escalonadores sensíveis às estimativas incertas da banda disponível especialmente projetados para um ambiente computacional onde esses valores são imprecisos por natureza. Portanto, esta Tese propõe um procedimento que promove melhorias nas execuções de aplicações científicas em nuvens híbridasAbstract: To derive efficient schedules for the tasks of scientific applications modelled as workflows, schedulers need information on the application demands as well as on the resource availability, especially those regarding the available bandwidth. However, the lack of precision of bandwidth estimates provided by monitoring/measurement tools should be considered by the scheduler to achieve near-optimal schedules. Uncertainties of available bandwidth can be a result of imprecise measurement and monitoring network tools and/or their incapacity of estimating in advance the real value of the available bandwidth expected for the application during the scheduling step of the application. Schedulers specially designed for hybrid clouds simply ignore the inaccuracies of the given estimates and end up producing non-robust, low-performance schedules, which makes them sensitive to the uncertainties stemming from using these networking tools. This thesis introduces a proactive procedure to provide a certain level of robustness for schedules derived from schedulers that were not designed to be robust in the face of uncertainties of bandwidth estimates stemming from using unreliable networking tools. To make non-robust schedulers into robust schedulers, the procedure applies a deflation on imprecise bandwidth estimates before being used as input to non-robust schedulers. By proposing the use of refined (deflated) estimates of the available bandwidth, non-robust schedulers initially sensitive to these uncertainties started to produce robust schedules that are insensitive to these inaccuracies. The effectiveness and efficiency of the procedure in providing robustness to non-robust schedulers are evaluated through simulation. Schedules generated by induced-robustness schedulers through the use of the procedure is compared to that of produced by sensitive schedulers. In addition, this thesis also introduces a flexible scheduler for a special case of scientific applications modelled as a set of workflows grouped into ensembles. Although the novelty of this scheduler is the replacement of objective functions according to the user's needs, it is still a non-robust scheduler. However, the procedure was able to provide the necessary robustness for this flexible scheduler be able to produce robust schedules under uncertain bandwidth estimates. It is shown in this thesis that the proposed procedure enhanced the robustness of workflow schedulers designed especially for hybrid clouds as they started to produce robust schedules in the presence of uncertainties stemming from using networking tools. The proposed procedure is an important tool to furnish robustness to non-robust schedulers that are originally designed to work in a computational environment where bandwidth estimates are very likely to vary and cannot be estimated precisely in advance, bringing, therefore, improvements to the executions of scientific applications in hybrid cloudsDoutoradoCiência da ComputaçãoDoutor em Ciência da Computação2012/02778-6FAPES

Approachable Error Bounded Lossy Compression

Compression is commonly used in HPC applications to move and store data. Traditional lossless compression, however, does not provide adequate compression of floating point data often found in scientific codes. Recently, researchers and scientists have turned to lossy compression techniques that approximate the original data rather than reproduce it in order to achieve desired levels of compression. Typical lossy compressors do not bound the errors introduced into the data, leading to the development of error bounded lossy compressors (EBLC). These tools provide the desired levels of compression as mathematical guarantees on the errors introduced. However, the current state of EBLC leaves much to be desired. The existing EBLC all have different interfaces requiring codes to be changed to adopt new techniques; EBLC have many more configuration options than their predecessors, making them more difficult to use; and EBLC typically bound quantities like point wise errors rather than higher level metrics such as spectra, p-values, or test statistics that scientists typically use. My dissertation aims to provide a uniform interface to compression and to develop tools to allow application scientists to understand and apply EBLC. This dissertation proposal presents three groups of work: LibPressio, a standard interface for compression and analysis; FRaZ/LibPressio-Opt frameworks for the automated configuration of compressors using LibPressio; and work on tools for analyzing errors in particular domains

Sofie: Smart Operating System For Internet Of Everything

The proliferation of Internet of Things and the success of rich cloud services have pushed the horizon of a new computing paradigm, Edge computing, which calls for processing the data at the edge of the network. Applications such as cloud offloading, smart home, and smart city are idea area for Edge computing to achieve better performance than cloud computing. Edge computing has the potential to address the concerns of response time requirement, battery life constraint, bandwidth cost saving, as well as data safety and privacy. However, there are still some challenges for applying Edge computing in our daily life. The missing of the specialized operating system for Edge computing is holding back the flourish of Edge computing applications. Service management, device management, component selection as well as data privacy and security is also not well supported yet in the current computing structure. To address the challenges for Edge computing systems and applications in these aspects, we have planned a series of empirical and theoretical research. We propose SOFIE: Smart Operating System For Internet Of Everything. SOFIE is the operating system specialized for Edge computing running on the Edge gateway. SOFIE could establish and maintain a reliable connection between cloud and Edge device to handle the data transportation between gateway and Edge devices; to provide service management and data management for Edge applications; to protect data privacy and security for Edge users; to guarantee the wellness of the Edge devices. Moreover, SOFIE also provide a naming mechanism to connect Edge device more efficiently. To solve the component selection problem in Edge computing paradigm, SOFIE also include our previous work, SURF, as a model to optimize the performance of the system. Finally, we deployed the design of SOFIE on an IoT/M2M system and support semantics with access control

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing

Proceedings of the 22nd Conference on Formal Methods in Computer-Aided Design – FMCAD 2022

The Conference on Formal Methods in Computer-Aided Design (FMCAD) is an annual conference on the theory and applications of formal methods in hardware and system verification. FMCAD provides a leading forum to researchers in academia and industry for presenting and discussing groundbreaking methods, technologies, theoretical results, and tools for reasoning formally about computing systems. FMCAD covers formal aspects of computer-aided system design including verification, specification, synthesis, and testing