Rapid urbanization poses huge challenges to people's daily lives, such as traffic congestion, environmental pollution, and public safety. Mobile Internet of things (MIoT) applications serving smart cities bring the promise of innovative and enhanced public services such as air pollution monitoring, enhanced road safety and city resources metering and management. These applications rely on a number of energy constrained MIoT units (MUs) (e.g., robots and drones) to continuously sense, capture and process data and images from their environments to produce immediate adaptive actions (e.g., triggering alarms, controlling machinery and communicating with citizens). In this thesis, we consider a scenario where a battery constrained MU executes a number of time-sensitive data processing tasks whose arrival times and sizes are stochastic in nature. These tasks can be executed locally on the device, offloaded to one of the nearby edge servers or to a cloud data center within a mobile edge computing (MEC) infrastructure. We first formulate the problem of making optimal offloading decisions that minimize the cost of current and future tasks as a constrained Markov decision process (CMDP) that accounts for the constraints of the MU battery and the limited reserved resources on the MEC infrastructure by the application providers. Then, we relax the CMDP problem into regular Markov decision process (MDP) using Lagrangian primal-dual optimization. We then develop advantage actor-critic (A2C) algorithm, one of the model-free deep reinforcement learning (DRL) method to train the MU to solve the relaxed problem. The training of the MU can be carried-out once to learn optimal offloading policies that are repeatedly employed as long as there are no large changes in the MU environment. Simulation results are presented to show that the proposed algorithm can achieve performance improvement over offloading decisions schemes that aim at optimizing instantaneous costs.

This thesis explores possibilities of using recently introduced ensemble-based component models (represented by the DEECo model) as a management layer in a cloudlet-like environment for mobile computation offloading, which can lead to savings of limited mobile-specific resources such as battery life. As a part of the solution, the goal is analyzed, possible issues and problems are identified and then addressed by designing and implementing a reference architecture for computation offloading of parts of mobile applications managed by a DEECo-based control layer. The implementation is presented in the form of an offloading framework, whose use is demonstrated on two applications for the Android platform. Finally, the framework's performance and utility is evaluated by comparing offloaded and local executions of the same application. Powered by TCPDF (www.tcpdf.org)

This thesis considers mobile computation offloading where task completion times are subject to hard deadline constraints. Hard deadlines are difficult to meet in conventional computation offloading due to the stochastic nature of the wireless channels involved. Rather than using binary offload decisions, we permit concurrent remote and local job execution when it is needed to ensure task completion deadlines. The thesis addresses this problem for homogeneous Markovian wireless channels. Two online energy-optimal computation offloading algorithms, OnOpt and MultiOpt, are proposed. OnOpt uploads the job to the server continuously and MultiOpt uploads the job in separate parts, each of which requires a separate offload initiation decision. The energy optimality of the algorithms is shown by constructing a time-dilated absorbing Markov process and applying dynamic programming. Closed form results are derived for general Markovian channels. The Gilbert-Elliott channel model is used to show how a particular Markov chain structure can be exploited to compute optimal offload initiation times more efficiently. The performance of the proposed algorithms is compared to three others, namely, Immediate Offloading, Channel Threshold, and Local Execution. Performance results show that the proposed algorithms can significantly improve mobile device energy consumption compared to the other approaches while guaranteeing hard task execution deadlines. / Thesis / Master of Applied Science (MASc)

The Internet of Things is the networking of electronic devices, or “Things”, that enables them to collect and share data, as well as interact with their physical surround- ings. Analyzing this collected data allows us to make smarter economic decisions. These interconnected networks are usually driven by low-powered micro-controllers or cheap CPUs that are designed to function optimally with very little hardware. As scale and computational requirements increase, these micro-controllers are unable to grow without being physically replaced. This thesis proposes a system, IoTA, that assists the Internet of Things by pro- viding a shared computational resource for endpoint devices. This solution extends the functionality of endpoint devices without the need of physical replacement. The IoTA system is designed to be easily integrable to any existing IoT network. This system presents a model that allows for seamless processing of jobs submitted by endpoint devices while keeping scalability and flexibility in mind. Additionally, IoTA is built on top of existing IoT protocols. Evaluation shows there is a significant performance benefit in processing computationally heavy algorithms on the IoTA system as compared to processing them locally on the endpoint devices themselves.

Mobile computation offloading (MCO) can alleviate the hardware limitations of mobile devices by migrating heavy computational tasks from mobile devices to more powerful cloud servers. This can lead to better performance and energy savings for the mobile devices. This thesis considers MCO over stochastic wireless channels when task completion times are subject to hard deadline constraints. Hard deadlines, however, are difficult to meet in conventional computation offloading due to the randomness caused by the wireless channels. In the proposed offloading policies, concurrent local execution (CLE) is used to guarantee task execution time constraints. By sometimes allowing simultaneous local and remote execution, CLE ensures that job deadlines are always satisfied in the face of any unexpected wireless channel conditions. The thesis introduces online optimal algorithms that reduce the remote and local execution overlap so that energy wastage is minimized. Markov processes are used to model the communication channels. MCO is addressed for three different job offloading schemes: continuous, multi-part, and preemptive. In the case of continuous offloading, referred to as 1-Part offloading, the mobile device will upload the entire job in one piece without interruption, when the scheduler decides to do so. In multi-part computation offloading, the job is partitioned into a known number (K) of parts, and each part is uploaded separately. In this offloading mechanism, which is referred to as K-Part Offloading, the upload initiation times of each part must be determined dynamically during runtime, and there may be waiting time periods between consecutive upload parts. Preemptive offloading is a generalization of K-Part Offloading where the number of task upload parts is unknown. In this scheme, a decision to either continue offloading or to temporarily interrupt the offload is made at the start of each time slot. Compared to the conventional contiguous computation offloading, interrupted offloading mechanisms (i.e., K-Part and preemptive offloading) allow the system to adapt when channel conditions change and therefore may result in lower mobile device energy consumption. This energy reduction will be obtained at the expense of having higher computational complexity. In this thesis, for each offloading scheme, an online computation offloading algorithm is introduced by constructing a time-dilated absorbing Markov chain (TDAMC) and applying dynamic programming (DP). These algorithms are shown to be energy-optimal while ensuring that the hard task deadline constraints are always satisfied. The optimality of these algorithms is proved using Markovian decision process stopping theory. Since the computational complexity of the proposed online algorithms, especially in the case of preemptive offloading, can be significant, three simpler and computationally efficient approximation methods are introduced: Markovian Compression (MC), Time Compression (TC), and Preemption Using Continuous Offloading (Preemption-CO). MC and TC reduce the state space of the offloading Markovian process by using a novel notion of geometric similarity or by running an optimal online offloading algorithm in periodic time steps. In Preemption-CO, while a task is offloaded preemptively, the offloading decision at every time-slot is based on non-preemptive calculations. These methods are used alone or in combination to construct practical offloading algorithms. A variety of results are presented that show the tradeoffs between complexity and mobile energy-saving performance for the different algorithms. / Thesis / Doctor of Philosophy (PhD)

In this thesis, we compare the energy efficiency of various strategies of mobile computation offloading over stochastic transmission channels where the task completion time is subject to a latency constraint. In the proposed methods, finite-state Markov chains are used to model the wireless channels between the mobile devices and the remote servers. We analyze the mechanisms of efficient mobile computation offloading under both soft and hard latency constraints. For the case of soft latency constraint, the task completion could miss the deadline below a specified probability threshold. On the other hand, under a hard deadline constraint, the task execution result must be available at the mobile device before the deadline. In order to make sure the task completes before the hard deadline, the hard deadline constraint approach requires concurrent execution in both local and cloud in specific circumstances. The closed-form solutions are often obtained using the broad Markov processes. The GE (Gilbert-Elliott) model is a more efficient method for demonstrating how the Markov chain’s structure can be used to compute the best offload initiation (Hekmati et al., 2019a).The effectiveness of the algorithms is studied under various deadline constraints and offloading methods. In this thesis, six algorithms are assessed in various scenarios. 1) Single user optimal (Zhang et al., 2013), 2) LARAC (Lagrangian Relaxation Based Aggregated Cost) (Zhang et al., 2014), 3) OnOpt (Online Optimal) algorithm (Hekmati et al., 2019a), 4) Water-Filling With Equilibrium (WF-Equ), 5) Water-Filling With Exponentiation (WFExp) (Teymoori et al., 2021), 6) MultiOPT (Multi-Decision Online Optimal). The experiment demonstrates that the studied algorithms perform dramatically different in the same setting. The various types of deadline restrictions also affect how efficiently the algorithms use energy. The experiment also highlights the trade-off between computational complexities and mobile energy savings (Teymoori et al., 2021).

This thesis introduces a novel framework for enabling intelligent machines and robots with the fifth-generation (5G) cellular network technology. Autonomous robots, such as Unmanned Aerial Vehicles (UAVs), Autonomous Guided Vehicles (AGVs), and more, can notably benefit from multi-agent collaboration, human supervision, or operation guidance, as well as from external computational units such as cloud edge servers, in all of which a framework to utilize reliable communication infrastructure is needed. Autonomous robots are often employed to alleviate humans by operating demanding missions such as inspection and data collection in harsh environments or time-critical operations in industrial environments - to name a few. For delivering data to other robots to maximize the effectiveness of the considered mission, for executing complex algorithms by offloading them into the edge cloud, or for including a human operator/supervisor into the loop, the 5G network and its advanced Quality of Service (QoS) features can be employed to facilitate the establishment of such a framework. This work focuses on establishing a baseline for integrating various time-critical robotics platforms and applications with a 5G network. These applications include offloading computationally intensive Model Predictive Control (MPC) algorithms for trajectory tracking of UAVs into the edge cloud, adapting data sharing in multi-robot systems based on network conditions, and enhancing network-aware surrounding autonomy components. We have identified a set of key performance indicators (KPIs) crucially affecting the performance of network-dependent robots and applications. We have proposed novel solutions and mechanisms to meet these requirements, which aim to combine traditional robotics techniques to enhance mission reliability with the exploitation of 5G features such as the QoS framework. Ultimately, our goal was to develop solutions that adhere to the essential paradigm of co-designing robotics with networks. We thoroughly evaluated all presented research using real-life platforms and 5G networks.

The demand and interest in serving Computer Vision applications at the Edge, where Edge Devices generate vast quantities of data, clashes with the reality that many Devices are largely unable to process their data in real time. While computational offloading, not to the Cloud but to nearby Edge Nodes, offers convenient acceleration for these applications, such systems are not without their constraints. As Edge networks may be unreliable or wireless, offloading quality is sensitive to communication bottlenecks. Unlike seemingly unlimited Cloud resources, an Edge Node, serving multiple clients, may incur delays due to resource contention. This project describes relevant Computer Vision workloads and how an effective offloading framework must adapt to the constraints that impact the Quality of Service yet have not been effectively nor properly addressed by previous literature. We design an offloading controller, based on closed-loop control theory, that enables Devices to maximize their throughput by appropriately offloading under variable conditions. This approach ensures a Device can utilize the maximum available offloading bandwidth. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques. / Master of Science / Devices like security cameras and some Internet of Things gadgets produce valuable real-time video for AI applications. A field within AI research called Computer Vision aims to use this visual data to compute a variety of useful workloads in a way that mimics the human visual system. However, many workloads, such as classifying objects displayed in a video, have large computational demands, especially when we want to keep up with the frame rate of a real-time video. Unfortunately, these devices, called Edge Devices because they are located far from Cloud datacenters at the edge of the network, are notoriously weak for Computer Vision algorithms, and, if running on a battery, will drain it quickly. In order to keep up, we can offload the computation of these algorithms to nearby servers, but we need to keep in mind that the bandwidth of the network might be variable and that too many clients connected to a single server will overload it. A slow network or an overloaded server will incur delays which slow processing throughput. This project describes relevant Computer Vision workloads and how an effective offloading framework that effectively adapts to these constraints has not yet been addressed by previous literature. We designed an offloading controller that measures feedback from the system and adapts how a Device offloads computation, in order to achieve the best possible throughput despite variable conditions. Finally, we constructed a realistic testbed and conducted measurements to demonstrate the superiority of our offloading controller over previous techniques.

Os dispositivos móveis passaram por grandes transformações na última década e tornaram-se complexos computadores dotados de grande poder de processamento e memória, além de prover aos usuários diversos recursos como sensores e câmeras de alta resolução. O uso de dispositivos móveis para diversas tarefas aumentou consideravelmente, o que levantou uma grande preocupação com o o alto consumo de energia desses dispositivos. Portanto, estudos tem sido realizados no sentido de encontrar soluções para diminuir o custo de energia das aplicações que executam em dispositivos móveis. Uma das alternativas mais utilizadas é o \\textit{computation offloading}, cujo objetivo é transferir a execução de uma tarefa para uma plataforma externa com o intuito de aumentar desempenho e reduzir consumo de recursos, como a bateria, por exemplo. Decidir sobre usar ou não esta técnica implica entender a influência de fatores como a quantidade de dados processados, a quantidade de computação envolvida, e o perfil da rede. Muitos estudos tem sido realizados para estudar a influência de diversas opções de rede wireless, como 3G, 4G e Wifi, mas nenhum estudo investigou a influência das escolhas de comunicação no custo de energia. Portanto, o objetivo deste trabalho é apresentar uma investigação sobre a influência da quantidade de dados, da quantidade de computação e dos protocolos de comunicação ou estilo arquitetural no consumo de energia quando a técnica de \\textit{computation offloading} é utilizada. Neste estudo, foram comparados REST, SOAP, Socket e RPC na execução de algoritmos de ordenação de diferentes complexidades aplicados sobre vetores de diversos tamanhos e tipos de dados. Os resultados mostram que a execução local é mais econômica com algoritmos menos complexos, pequeno tamanho de entrada e tipo de dados menos complexos. Quando se trata de execução remota, o REST é a escolha mais econômica seguida por Socket. Em geral, REST é mais econômico com vetores do tipo Object, independentemente da complexidade do algoritmo e tamanho do vetor, enquanto Socket é mais econômico com entradas maiores e com vetores de tipos primitivos, como Int e Float / Mobile devices have significantly changed in the last decade and they become complex computer machines equipped with large processing power and memory. Moreover, they provide users with several resources such as sensors and high resolution cameras. The usage of mobile devices has significantly increased in the past years, which raised an important concern regarding the high energy consumption. Therefore, several investigations have been conducted aiming at finding solutions to reduce the energy cost of mobile applications. One of the most used strategy is called computation offloading, whose main goal is to transfer the execution of a task to an external platform aiming at increasing performance and reducing resource consumption, including the battery. Deciding towards offloading certain tasks requires to understand the influence of the amount of data, amount of computation, and the network profile. Several studies have investigated the influence of different wireless flavours, such as 3G, 4G and wifi, but no study has investigated the influence of the communication choices on the energy cost. Therefore, the purpose of this research project is to present an investigation on the influence of the amount of data, amount of computation and the communication protocols and architectural style on the energy consumption in the context of the computation offloading technique. In this study, we compare REST, SOAP, Socket and RPC when executing algorithms of different complexities and different input sizes and types. Results show that local execution is more economic with less complex algorithms and small input data. When it comes to remote execution, REST is the most economic choice followed by Socket. In general, REST is the most economic choice when applied on Object type arrays, regardless the complexity and size, while Socket is the most economic choice with large arrays and primitive types such as integers and floats

Para explorar campos de petróleo offshore em águas profundas, o uso de plataformas offshore (FPSO - do inglês Floating Production Storage and Offloading) e navios aliviadores, nas últimas décadas, tornou-se uma alternativa economicamente e tecnicamente viável. A FPSO é um tipo de navio petroleiro transformado para a exploração, e armazenamento petróleo. O escoamento da produção é usualmente realizado por um navio tanque aliviador, conectado em tandem, ou por dutos. Porém o transporte marítimo realizado pelos navios petroleiros está sendo cada vez mais aceito, chegando a ser o principal meio para escoar a produção em águas profundas. Entretanto, como contrapartida desta viabilidade técnica, passou-se a executar operações de transbordo entre unidades flutuantes em ambientes mais agressivos, causando um aumento do risco associado com estas operações. Este trabalho, visando garantir a segurança das operações de transferência em alto mar, apresenta a aplicação de técnicas de análise de risco para a avaliação de operações de offloading entre unidades de produção tipo FPSO e navios aliviadores. É aplicado um método indutivo para a identificação de riscos baseado no princípio de que os acidentes acontecem como consequência do desenvolvimento de um evento de perigo durante a operação, que pode durar cerca de 24 horas. No contexto deste trabalho de pesquisa, a análise de risco é entendida como quatro processos sequenciais: (i) identificação dos cenários de perigos, (ii) estimação da probabilidade de ocorrência de falhas para cada cenário, (iii) avaliação das consequências, e, (iv) tomada de decisão. Para melhorar a avaliação da probabilidade é proposto o uso de técnicas bayesianas. Para fazer uma análise mais abrangente das consequências de falha é proposto utilizar o processo markoviano para modelar a probabilidade de mudanças do sistema FPSO-navio aliviador durante a operação de offloading que podem causar mudanças no perfil de risco. A tomada de decisão é usada para avaliar a possibilidade de desconexão de emergência durante a operação. O método é aplicado para avaliar o risco de uma operação de offloading na Bacia de Campos (Brasil), entre uma plataforma tipo FPSO e um navio aliviador tipo Suezmax. Verifica-se que as condições ambientais e a forma de realização da amarração exercem significativa influencia no perfil de risco. / To explore offshore oil fields in deep water the use of a Floating Production Storage and Offloading (FPSO) unit coupled to a shuttle tanker is economically and technically feasible. The FPSO unit normally consists of a ship shaped hull, with an internal or external turret, and production equipment on the deck. The unit is also equipped for crude oil storage. Oil transportation systems required for supporting this infrastructure are pipelines or shuttle tankers. Shuttle tankers are increasingly being accepted as a preferred transportation method for remote and deepwater offshore developments. The offloading operation is considered one of the most risky operations in offshore environments. This dissertation presents a risk-based analysis method aiming at defining the risk profile associated with an offloading operation. For offloading operations the risk profile is usually evaluated considering that the environmental condition could suffer changes during offloading that has an approximate duration of 24 hours. The method follows four basic steps: identification of hazard (using PHA technique), definition of failure scenarios and their probability of occurrence (using cause-consequence diagram and FTA) and evaluation of failure consequences. To improve the evaluation of failure consequences a more comprehensive analysis is proposed aiming at using the Markovian process to model the probability of changes during offloading operation that could cause changes in the risk profile developed in step two. The decision method is used to evaluate the possibility of emergency disconnection during the operation. The method is applied to evaluate the risk profile of an offloading operation in Campos Basin, Brazil, considering o FPSO. The analysis shows that the environmental conditions and the way that the tanker is moored have great influence on the risk profile.