Fixed-Priority Scheduling Algorithms with Multiple Objectives in Hard Real-Time Systems

In the context ofFixed-Priority Scheduling in Real-Time Systems, we investigate scheduling mechanisms for supporting systems where, in addition to timing constraints, their performance with respect to additional QoS requirements must be improved. This'type of situation may occur when the worst-case res~urce requirements of all or some running tasks cannot be simultaneously met due to task contention. . Solutions to these problems have been proposed in the context of both fixed-priority and dynamic-priority scheduling. In fixed-priority scheduling, the typical approach is to artificially modify the attributes or structure of tasks, and/or usually require non-standard run-time support. In dynamic-priority scheduling approaches, utility functions are employed to make scheduling decisions with the objective of maximising the utility. The main difficulties with these approaches are the inability to formulate and model appropriately utility functions for each task, and the inability to guarantee hard deadlines without executing computationally costly algorithms. In this thesis we propose a different approach. Firstly, we introduce the concept of relative importance among tasks as a new metric for expressing QoS requirements. The meaning of this importance relationship is to express that in a schedule it i~ desirable to run a task in preference to other ones. This model is more intuitive and less restrictive than traditional utility-based app~oaches. Secondly, we formulate a scheduling problem in terms of finding a feasible assignment of fixed priorities that maximises the new QoS metric, and propose the DI and DI+ algorithms that find optimal solutions. By extensive simulation, we show that the new QoS metric combined with the DI algorithm outperforms the rate monotonic priority algorithm in several practical problems such as minimising jitter, minimising the number of preemptions or minimising the latency. In addition, our approach outperforms EDF in several scenarios

Incentive Mechanisms for Participatory Sensing: Survey and Research Challenges

Participatory sensing is a powerful paradigm which takes advantage of smartphones to collect and analyze data beyond the scale of what was previously possible. Given that participatory sensing systems rely completely on the users' willingness to submit up-to-date and accurate information, it is paramount to effectively incentivize users' active and reliable participation. In this paper, we survey existing literature on incentive mechanisms for participatory sensing systems. In particular, we present a taxonomy of existing incentive mechanisms for participatory sensing systems, which are subsequently discussed in depth by comparing and contrasting different approaches. Finally, we discuss an agenda of open research challenges in incentivizing users in participatory sensing.Comment: Updated version, 4/25/201

Dependable Embedded Systems

This Open Access book introduces readers to many new techniques for enhancing and optimizing reliability in embedded systems, which have emerged particularly within the last five years. This book introduces the most prominent reliability concerns from today’s points of view and roughly recapitulates the progress in the community so far. Unlike other books that focus on a single abstraction level such circuit level or system level alone, the focus of this book is to deal with the different reliability challenges across different levels starting from the physical level all the way to the system level (cross-layer approaches). The book aims at demonstrating how new hardware/software co-design solution can be proposed to ef-fectively mitigate reliability degradation such as transistor aging, processor variation, temperature effects, soft errors, etc. Provides readers with latest insights into novel, cross-layer methods and models with respect to dependability of embedded systems; Describes cross-layer approaches that can leverage reliability through techniques that are pro-actively designed with respect to techniques at other layers; Explains run-time adaptation and concepts/means of self-organization, in order to achieve error resiliency in complex, future many core systems

Dynamic scheduling techniques for adaptive applications on real-time embedded systems

Ph.DDOCTOR OF PHILOSOPH

Machine Learning

Machine Learning can be defined in various ways related to a scientific domain concerned with the design and development of theoretical and implementation tools that allow building systems with some Human Like intelligent behavior. Machine learning addresses more specifically the ability to improve automatically through experience

A Reinforcement Learning Quality of Service Negotiation Framework For IoT Middleware

The Internet of Things (IoT) ecosystem is characterised by heterogeneous devices dynamically interacting with each other to perform a specific task, often without human intervention. This interaction typically occurs in a service-oriented manner and is facilitated by an IoT middleware. The service provision paradigm enables the functionalities of IoT devices to be provided as IoT services to perform actuation tasks in critical-safety systems such as autonomous, connected vehicle system and industrial control systems. As IoT systems are increasingly deployed into an environment characterised by continuous changes and uncertainties, there have been growing concerns on how to resolve the Quality of Service (QoS) contentions between heterogeneous devices with conflicting preferences to guarantee the execution of mission-critical actuation tasks. With IoT devices with different QoS constraints as IoT service providers spontaneously interacts with IoT service consumers with varied QoS requirements, it becomes essential to find the best way to establish and manage the QoS agreement in the middleware as a compromise in the QoS could lead to negative consequences. This thesis presents a QoS negotiation framework, IoTQoSystem, for IoT service-oriented middleware. The QoS framework is underpinned by a negotiation process that is modelled as a Markov Decision Process (MDP). A model-based Reinforcement Learning negotiation strategy is proposed for generating an acceptable QoS solution in a dynamic, multilateral and multi-parameter scenarios. A microservice-oriented negotiation architecture is developed that combines negotiation, monitoring and forecasting to provide a self-managing mechanism for ensuring the successful execution of actuation tasks in an IoT environment. Using a case study, the developed QoS negotiation framework was evaluated using real-world data sets with different negotiation scenarios to illustrate its scalability, reliability and performance

Using multi-attribute tradespace exploration for the architecting and design of transportation systems

Thesis (S.M.)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.Cataloged from PDF version of thesis.Includes bibliographical references (p. 241-249).The field of Engineering Systems maintains that fundamental engineering principles exist, which apply across different domains of complex socio-technical systems. In this thesis, a state-of-the art decision and design evaluation method developed using aerospace cases, Multi-Attribute Tradespace Exploration (MATE), is applied for the first time to a transportation design problem. Through the application process across domains, differences between the aerospace and transportation domain are characterized: (1) a "mission objective" has not emerged as a welldefined, integral concept for transportation project planning in the same way it did in the military and space communities; (2) a simple stakeholder structure for the purpose of the analysis is not a reasonable assumption, (3) inheritance (legacy structures and legacy expectations) in transportation planning brings with it the stickiness of the status quo and people's attachment to things they possess; (4) several cost types exist in addition to monetary costs, e.g. harmful effects to life and spending of scarce resources (time, money); (5) decisions about the welfare of stakeholders in transportation planning are inextricably linked to technical decisions. It follows that fundamental engineering systems design principles need to be general enough to encompass these domain differences. Decisions about the welfare of stakeholders (public, future generations, environment) by a legitimized representative decision maker raise the question about the desirability of prescriptive guiding principles for decision making, in order to ensure consideration for the represented constituency when their interests need to be traded off with personal and organizational interests of the decision maker. Decision makers themselves seek such guidance to help them in trading off and justifying decisions about multiple competing goals in complex situations. One established method to provide such guidance is Cost-Benefit Analysis (CBA). CBA is a central, established, prescriptive evaluation method used in several domains, including transportation. In order to compare insights gained through the emerging method MATE and the established method CBA, two case studies, a Chicago Airport Express and a High-Speed Rail link between Portugal and Spain, are evaluated using those two methods. CBA assumes a broad view over all affected stakeholders, decision making or not, and seeks to ensure that net benefits to society outweigh net costs. MATE seeks to best meet decision makers' expectations for a system. Attributes (tangible and intangible) that are valuable to individual stakeholders, but not to society as a whole, are captured in the value-based approach in MATE. They are purposefully excluded in CBA. A challenge that the value-based approach in MATE brings about are framing issues that can arise when utility theory is applied to decision making stakeholders who have mandates to represent other stakeholders. For both aerospace and transportation domains, political vision and technical understanding of properties of different designs are important for decision making. A real feedback cycle between goal capture and low-fidelity technical modeling of different design options as suggested in MATE does not seem to exist in transportation planning. MATE seems useful as a tool to support improved communication about system expectations and technical options. Future research will need to address how value-based attribute capture can be performed in the typical complex stakeholder structure of transportation systems. Recognizing that problems of equity and value judgments are an inherent part of (some) technical decisions, the question of how to support a decision maker in expressing those attributes (even if difficult and controversial) and understanding different design concepts' impact on technical properties becomes part of the design engineer's job.by Julia Nickel.S.M