Contention-resolving model predictive control for coupled control systems with shared resources

Priority-based scheduling strategies are often used to resolve contentions in resource constrained control systems. Such scheduling strategies inevitably introduce time delays into controls, which may degrade the performance or sabotage the stability of control systems. Considering the coupling between priority assignment and control, this thesis presents a novel method to co-design priority assignments and control laws for each control system, which aims to minimize the overall performance degradation caused by contentions. The co-design problem is formulated as a mixed integer optimization problem with a very large search space, rendering difficulty in computing the optimal solution. To solve the problem, we develop a contention-resolving model predictive control method to dynamically assign priorities and compute an optimal control. The priority assignment can be generated using a sample-based approach without excessive demand on computing resources, and all possible priority combinations can be presented by a decision tree. We present sufficient and necessary conditions to test the schedulabilty of the generated priorities assignments when constructing the decision tree, which guarantee that the priority assignments in the decision tree always lead to feasible solutions. The optimal controls can then be computed iteratively following the order of the generated feasible priorities. The optimal priority assignment and control design can be determined by searching the lowest cost path in the decision tree. With the fundamental assumptions required in real-time scheduling, the solution computed by the contention-resolving model predictive control is proved to be globally optimal. The effectiveness of the presented method is verified through simulation in three real-world applications, which are networked control systems, traffic intersection management systems, and human-robot collaboration systems. The performance of our method is compared with the well-known and most commonly used scheduling methods in these applications and demonstrate significant improvements using our method.Ph.D

Analysis of Model Predictive Intersection Control for Autonomous Vehicles

Autonomous vehicles are in the main focus for automotive companies and urban traffic engineers as well. As their penetration rate in traffic becomes more and more pronounced due to improvement in sensor technologies and the corresponding infrastructure, new methods for autonomous vehicle controls become a necessity. For instance, autonomous vehicles can improve the performance of urban traffic and prevent the formation of congestions with the usage of Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication based control methods. One of the key area for improvement is centralized intersection control for autonomous vehicles, by which traveling times can be reduced and efficiency of traffic flow can be improved, while safety of passengers can be guaranteed through constraints built in the centralized design. The paper presents the analysis of a Model Predictive Control (MPC) method for the coordination of autonomous vehicles at intersections by comparing it with an offline constraint optimization considering time and energy optimal intervention of vehicles. The analysis has been evaluated in high-fidelity simulation environment CarSim, where the speed trajectories, traveling times and energy consumptions have been compared for the different methods. The simulations show that the proposed time-optimal MPC intersection control method results in similar traveling times of that given by the time-optimal offline constraint optimization, while the energy optimal optimization re-quires significantly more time for the autonomous vehicle to achieve. Due to the possibility of a congestion forming in the latter case, the proposed centralized MPC method is more applicable in real traffic scenarios

Machine Learning-based Orchestration Solutions for Future Slicing-Enabled Mobile Networks

The fifth generation mobile networks (5G) will incorporate novel technologies such as network programmability and virtualization enabled by Software-Defined Networking (SDN) and Network Function Virtualization (NFV) paradigms, which have recently attracted major interest from both academic and industrial stakeholders. Building on these concepts, Network Slicing raised as the main driver of a novel business model where mobile operators may open, i.e., “slice”, their infrastructure to new business players and offer independent, isolated and self-contained sets of network functions and physical/virtual resources tailored to specific services requirements. While Network Slicing has the potential to increase the revenue sources of service providers, it involves a number of technical challenges that must be carefully addressed. End-to-end (E2E) network slices encompass time and spectrum resources in the radio access network (RAN), transport resources on the fronthauling/backhauling links, and computing and storage resources at core and edge data centers. Additionally, the vertical service requirements’ heterogeneity (e.g., high throughput, low latency, high reliability) exacerbates the need for novel orchestration solutions able to manage end-to-end network slice resources across different domains, while satisfying stringent service level agreements and specific traffic requirements. An end-to-end network slicing orchestration solution shall i) admit network slice requests such that the overall system revenues are maximized, ii) provide the required resources across different network domains to fulfill the Service Level Agreements (SLAs) iii) dynamically adapt the resource allocation based on the real-time traffic load, endusers’ mobility and instantaneous wireless channel statistics. Certainly, a mobile network represents a fast-changing scenario characterized by complex spatio-temporal relationship connecting end-users’ traffic demand with social activities and economy. Legacy models that aim at providing dynamic resource allocation based on traditional traffic demand forecasting techniques fail to capture these important aspects. To close this gap, machine learning-aided solutions are quickly arising as promising technologies to sustain, in a scalable manner, the set of operations required by the network slicing context. How to implement such resource allocation schemes among slices, while trying to make the most efficient use of the networking resources composing the mobile infrastructure, are key problems underlying the network slicing paradigm, which will be addressed in this thesis

The Relationship Between Technology Adoption Determinants and the Intention to Use Software-Defined Networking

AbstractThe advent of distributed cloud computing and the exponential growth and demands of the internet of things and big data have strained traditional network technologies\u27 capabilities and have given rise to software-defined networking\u27s (SDN\u27s) revolutionary approach. Some information technology (IT) cloud services leaders who do not intend to adopt SDN technology may be unable to meet increasing performance and flexibility demands and may risk financial loss compared to those who adopt SDN technology. Grounded in the unified theory of acceptance and use of technology (UTAUT), the purpose of this quantitative correlational study was to examine the relationship between IT cloud system integrators\u27 perceptions of performance expectancy, effort expectancy, social influence, facilitating conditions, and their intention to use SDN technology. The participants (n = 167) were cloud system integrators who were at least 18 years old with a minimum of three months\u27 experience and used SDN technology in the United States. Data were collected using the UTAUT authors\u27 validated survey instrument. The multiple regression findings were significant, F(4, 162) = 40.44, p \u3c .001, R2 = .50. In the final model, social influence (ß = .236, t = 2.662, p \u3c .01) and facilitating conditions (ß = .327, t = 5.018, p \u3c .001) were statistically significant; performance expectancy and effort expectancy were not statistically significant. A recommendation is for IT managers to champion SDN adoption by ensuring the availability of support resources and promoting its use in the organization\u27s goals. The implications for positive social change include the potential to enhance cloud security, quality of experience, and improved reliability, strengthening safety control systems

Personality Identification from Social Media Using Deep Learning: A Review

Social media helps in sharing of ideas and information among people scattered around the world and thus helps in creating communities, groups, and virtual networks. Identification of personality is significant in many types of applications such as in detecting the mental state or character of a person, predicting job satisfaction, professional and personal relationship success, in recommendation systems. Personality is also an important factor to determine individual variation in thoughts, feelings, and conduct systems. According to the survey of Global social media research in 2018, approximately 3.196 billion social media users are in worldwide. The numbers are estimated to grow rapidly further with the use of mobile smart devices and advancement in technology. Support vector machine (SVM), Naive Bayes (NB), Multilayer perceptron neural network, and convolutional neural network (CNN) are some of the machine learning techniques used for personality identification in the literature review. This paper presents various studies conducted in identifying the personality of social media users with the help of machine learning approaches and the recent studies that targeted to predict the personality of online social media (OSM) users are reviewed