Transmitter Optimization in Multiuser Wireless Systems with Quality of Service Constraints

In this dissertation, transmitter adaptation for optimal resource allocation in wireless communication systems are investigated. First, a multiple access channel model is considered where many transmitters communicate with a single receiver. This scenario is a basic component of a. wireless network in which multiple users simultaneously access the resources of a wireless service provider. Adaptive algorithms for transmitter optimization to meet Quality-of-Service (QoS) requirements in a distributed manner are studied. Second, an interference channel model is considered where multiple interfering transmitter-receiver pairs co-exist such that a given transmitter communicates with its intended receiver in the presence of interference from other transmitters. This scenario models a wireless network in which several wireless service providers share the spectrum to offer their services by using dynamic spectrum access and cognitive radio (CR) technologies. The primary objective of dynamic spectrum access in the CR approach is to enable use of the frequency band dynamically and opportunistically without creating harmful interference to licensed incumbent users. Specifically, CR users are envisioned to be able to provide high bandwidth and efficient utilization of the spectrum via dynamic spectrum access in heterogeneous networks. In this scenario, a distributed method is investigated for combined precoder and power adaptation of CR transmitters for dynamic spectrum sharing in cognitive radio systems. Finally, the effect of limited feedback for transmitter optimization is analyzed where precoder adaptation uses the quantized version of interference information or the predictive vector quantization for incremental updates. The performance of the transmitter adaptation algorithms is also studied in the context of fading channels

Enhancing Student Learning through Proactive Feedback Based Adaptive Teaching for Engineering Courses

This paper presents a proactive anonymous feedback based adaptive teaching for enhancing student learning for engineering courses. In conventional university teaching, typically, students come to the class and instructors lecture the material, assign home assignments, take exams, etc. After grading assignments or exams, the instructor provides feedback to students. Most of the time, students are reluctant to ask questions or ask instructor to revisit the topic which was already covered. However, there is no immediate anonymous feedback mechanism for each topic or class to notify the instructor about topics which are not clear to students. There are advantages that enhance students’ learning experience by using a proactive anonymous feedback approach in teaching, learning and assessment. In this paper, we present the immediate impacts of proactive anonymous feedback based adaptive teaching on student learning and assessment. Furthermore, anonymous online based feedback mechanism provides faster feedback than conventional mechanism (where students wait until the first exam or so). Immediate feedback for each topic discussed in the class streamlines the process of reporting and the provision of active studying. The results show that students get better grade and instructors get better student evaluation score since the anonymous feedback provides a mechanism for students to ask questions anonymously and the instructors get an opportunity to answer the questions or concerns in a timely manner. We implemented the proactive anonymous feedback approach in many courses in different semesters and observed similar results. However, as an example, we present one course and instructor to illustrate the effectiveness of the proposed approach

On Using Blockchains for Safety-Critical Systems

Innovation in the world of today is mainly driven by software. Companies need to continuously rejuvenate their product portfolios with new features to stay ahead of their competitors. For example, recent trends explore the application of blockchains to domains other than finance. This paper analyzes the state-of-the-art for safety-critical systems as found in modern vehicles like self-driving cars, smart energy systems, and home automation focusing on specific challenges where key ideas behind blockchains might be applicable. Next, potential benefits unlocked by applying such ideas are presented and discussed for the respective usage scenario. Finally, a research agenda is outlined to summarize remaining challenges for successfully applying blockchains to safety-critical cyber-physical systems

Developing and Applying Smartphone Apps in Online Courses

Online courses provide students flexible access to class at anytime and anywhere. Most online courses currently rely on computer-based delivery. However, computers still burden instructors and students with limited mobility and flexibility. To provide more convenient access to online courses, smartphones have been increasingly adopted as a mobile method to access online courses. In this paper, we share our practical experience in designing and developing a smartphone platform for accessing online courses. The main contributions of this paper include: 1) we present the main technical issues of applying smartphones to online courses; 2) we discuss several key factors of designing, developing and delivering online courses that support smartphone access

On the Security of Information Dissemination in the Internet-of-Vehicles

Internet of Vehicles (IoV) is regarded as an emerging paradigm for connected vehicles to exchange their information with other vehicles using vehicle-to-vehicle (V2V) communications by forming a vehicular ad hoc networks (VANETs), with roadside units using vehicle-to-roadside (V2R) communications. IoV offers several benefits such as road safety, traffic efficiency, and infotainment by forwarding up-to-date traffic information about upcoming traffic. For instance, IoV is regarded as a technology that could help reduce the number of deaths caused by road accidents, and reduce fuel costs and travel time on the road. Vehicles could rapidly learn about the road condition and promptly respond and notify drivers for making informed decisions. However, malicious users in IoV may mislead the whole communications and create chaos on the road. Data falsification attack is one of the main security issues in IoV where vehicles rely on information received from other peers/vehicles. In this paper, we present data falsification attack detection using hashes for enhancing network security and performance by adapting contention window size to forward accurate information to the neighboring vehicles in a timely manner (to improve throughput while reducing end-to-end delay). We also present clustering approach to reduce travel time in case of traffic congestion. Performance of the proposed approach is evaluated using numerical results obtained from simulations. We found that the proposed adaptive approach prevents IoV from data falsification attacks and provides higher throughput with lower delay

Green network protocols and algorithms

Lloret, J.; Ghafoor, KZ.; Rawat, DB.; Nasser, Y. (2015). Green network protocols and algorithms. Journal of Network and Computer Applications. 58:192-193. https://doi.org/10.1016/j.jnca.2015.11.004S1921935