The routing protocol for low-power and lossy networks (RPL) is a specialized routing protocol designed for optimized data routing, specifically for resource-constrained Internet of Things (IoT) networks with unreliable links and high packet loss. However, RPL is highly vulnerable to significant security challenges, particularly the decrease rank attack (DRA), in which malicious nodes attract child nodes by falsely advertising lower ranks, leading to routing inefficiencies, unnecessary retransmissions, and increased energy consumption. To address this problem, we propose a novel intent-based networking-driven centralized real-time reinforced detection scheme (CRRDS), which translates high-level security intents into policy-driven automated control strategies for DRA detection. In the proposed CRRDS, a resource-rich root node acts as a deep reinforcement learning agent that collects critical information from the child nodes, including the node ID, end-to-end delay, received signal strength indicator, and hop count, to detect suspicious behavior accurately and intelligently. Initially, we implemented a deep Q-network (DQN)-assisted CRRDS in detecting DRA. Subsequently, we utilized double DQN (DDQN) and dueling DDQN due to their enhanced capabilities in value estimation and policy learning. The dueling DDQN performed optimally because of its deeper architecture. Simulation results demonstrate that the proposed dueling DDQN-assisted CRRDS achieves the highest detection accuracy of 98% with notable gains in true positive and false positive rates, even in complex scenarios with up to 30% malicious nodes.

Inclusive design appears rarely, if at all, in most undergraduate computer science (CS) curricula. As a result, many CS students graduate without knowing how to apply inclusive design to the software they build, and go on to careers that perpetuate the prolif- eration of software that excludes communities of users. Our panel of CS faculty will explain how we have been working to address this problem. For the past several years, we have been integrating bits of inclusive design in multiple courses in CS undergraduate programs, which has had very positive impacts on students' ratings of their instructors, students' ratings of the education climate, and students' retention. The panel's content will be mostly concrete examples of how we are doing this, so that attendees can leave with an in-the-trenches understanding of what this looks like for CS faculty across specialization areas and classes. We also show how it can be used in a department's BPC Plan and point to resources on the CRA's BPCnet Activity Library and on OERcommons, to enable interested faculty to go forward with this approach in their own classes and departments.

The assessment of student learning is essential, with instructors using end-of-course assessments against Course Objectives and Student Learning Objectives (SLOs), typically through assignments and exams against a predetermined benchmark. However, the reliance on outdated paper-based systems can delay crucial information access and lead to errors and inconsistencies in assessments. Therefore, a pilot study is underway to explore the benefits of transitioning from paper-based to electronic-based assessment systems. This shift aims to facilitate easier monitoring and more consistent tracking of course objectives, potentially enhancing the quality of courses and curriculum. All faculty-submitted assessment data is securely stored on a server, which supports the creation of an interactive data visualization tool. This tool is primarily designed to aid faculty in making informed decisions to better engage, motivate, and improve the learning experience for students. Moreover, the electronic system promises significant efficiency improvements by streamlining information retrieval and standardizing annual assessment processes.

Due to the increasing need for accurate location-based services, indoor positioning systems (IPSs) have evolved rapidly. This study reviews the literature published from 2010 to 2024, employing the preferred reporting items for systematic reviews and meta-analyses (PRISMA) methodology for the identification, screening, validation, and inclusion of research literature. By exploring the complexities of IPS methodologies, algorithms, technologies, and challenges, this study offers a comprehensive introduction for researchers, scholars, and specialists. Additionally, the scope of this study expands its focus to encompass mapping techniques, such as crowdsourcing, geographic information systems (GISs), remote sensing, LiDAR, cartography, and augmented reality (AR). The findings of this study will contribute to the increased applicability of these methods and techniques in the field of urban planning and environmental management. Contributing to the expanding pool of knowledge on indoor positioning, this work is a valuable resource for those exploring the ever-changing field of IPS. This work not only contributes to the academic but also bridges the gap between scientific discoveries and practical, real-world applications.

Computer science (CS) students' curricula is heavily focused on technical skills, and CS ethics, usability, equity, and people/society considerations are not well-integrated into the CS curriculum. If these topics are introduced, they are disconnected from the core courses. As a result, students do not learn to incorporate inclusive practices into their software designs. Thus, students create software through the perspective of a computer scientist - when the important perspective is that of the intended users. As a result, students entering the workforce are inclined to design software that is non-inclusive. We propose the integration of inclusive design in the undergraduate curriculum will result in students creating inclusive software. This research, based on the foundations of inclusive design methods, investigates a new approach to teaching CS. Inclusive software design is embedded into computing courses for all four years of the undergraduate CS curriculum. This new approach is "minimally invasive", occupying very little classroom time, instead it is integrated into the course work that is already assigned. With this work, we hope to answer the following questions: (1) Will this new approach improve students' ability to design inclusive software? (2) Will this approach create an inclusive climate among peers? (3) Will it affect student's success or lack thereof? (4) How and to what extent is this embedded inclusive design curriculum feasible to use?

Within computer science education, a growth mindset is encouraged. However, faculty development on the use of growth mindset in the classroom is rare and resources to support the use of a growth mindset are limited. A framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students, has been developed. The objective is to determine if faculty development in growth mindset and active use of the growth mindset cues in the CS0 and CS1 classroom result in superior academic outcomes. Comparative study results are presented for two semesters of virtual classroom environments: one semester without Growth Mindset, and one semester with Growth Mindset. Female students demonstrated the most growth, as measured by academic grades, in CS0, and maintained that growth in CS1. Males demonstrated growth as well, with both males and females converging at the same high point of accomplishment at the end of CS1. Race and ethnicity gaps between students were reduced, improving academic equity.

A growth mindset encourages the development of intelligence, in contrast to a fixed mindset, which considers intelligence to be fixed and unable to be changed. Within computer science education, there is an awareness of a growth mindset, but resources to support the use of a growth mindset in the computer science classroom are limited, and faculty development in the use of growth mindset in the classroom is rare. Researchers have developed a framework for a computer science growth mindset classroom, which includes faculty development, lesson plans, and vocabulary for use with students. In addition, the faculty have formed a community of practice which identifies areas where growth mindset techniques can be used and has implemented these strategies in their classrooms. The framework and materials developed are presented here, with early results from this ongoing work. The objective is to determine if faculty development on growth mindset and active use of the framework for a growth mindset classroom results in superior academic outcomes in CS0 and CS1. Preliminary results are for faculty in both face-to-face and virtual classroom environments.

The current pollution measurement methodology is coarse-grained where the pollution measurements are spatiotemporally few and far in-between. Our vision is to provide broadly accessible, fine-grained pollution information to a variety of end-users, and in turn, allow them to make better informed decisions using a new, more accurate information stream. To this end, this study proposes a new neural network model to estimate Carbon Monoxide (CO) concentrations outside vehicle from crowd-sourced CO measurements inside vehicles measured using mobile devices (dosimeters). End-users can benefit from the fine-grained pollution information generated by this prediction model along with data from direct measurements. A neural network is used to model the dynamic relationship between the CO measurements inside and outside a moving vehicle. The resulting neural network model is then used to predict outside CO concentrations from CO measurements inside vehicles. Mobile CO dosimeters were used inside and outside vehicles to collect measurements used in training a neural network based regression model. For this regression task, a new neural network architecture was designed using Convolutional layers and Gated Recurrent Unit (GRU) layers. The results show that outside CO concentrations can be estimated from inside vehicle CO measurements with high accuracy. The proposed neural network model provides a promising new and novel source of fine-grained pollution information along with direct measurement streams.

Remote driving brings human operators with sophisticated perceptual and cognitive skills into an over-the-network control loop, with the hope of addressing the challenging aspects of vehicular autonomy based exclusively on artificial intelligence (AI). This paper studies the human behavior in a remote driving setup, i.e., how human remote drivers perform and assess their workload under the state-of-the-art network conditions. To explore this, we build a scaled remote driving prototype and conduct a controlled human study with varying network delays based on current commercial LTE network technology. The study demonstrates that remote driving over LTE is not immediately feasible, primarily caused by network delay variability rather than delay magnitude. In addition, our findings indicate that the negative effects of remote driving over LTE can be mitigated by a video frame arrangement strategy that regulates delay magnitude to achieve a smoother display.

“What would have happened, had I taken the alternative route?” This is a question that many drivers often ponder, hoping that their chosen route is the best. This paper proposes DoppelDriver, a system that attempts to answer such questions on non-chosen alternative routes to a given destination by determining actual times of arrival (ATAs) from participatory users on the non-chosen routes. DoppelDriver offers a direct, actual travel time comparison among route choices. With DoppelDriver's collection of actual travel time comparisons, users will be able to make strategic decisions on, and self-assessments of, their route choices. Also, we describe the potential usage and benefits of ex-post feedback (i.e. travel time on non-chosen routes) and how snapshots of travel time comparisons can be used to support strategic decision making on the choice of a route. Using real taxi GPS data, we investigate whether aggregating ATAs for road segments from other users mimics the ATA for the intended origin-to-destination route. Finally, we present our system design for a prototype implemented on the Android platform.

Intra-vehicle wireless sensor network, which is also called networked control system (NCS) is a promising new research area. NCS can not only provide part cost, assembly, maintenance savings, and fuel efficiency through the elimination of the wires, but also enable new sensor technologies to be integrated into vehicles. Ultra wideband (UWB) communication is a competitive candidate for the intra-vehicle NCS. In this paper, the performance of cyclic prefixed single carrier with frequency domain equalization (SC-FDE) transmission is investigated over intra-vehicle NCS propagation environment. The error-rate performance and the implementation complexity are compared among impulse based single carrier UWB (SC-UWB), multicarrier UWB (MC-UWB) employing orthogonal frequency-division-multiplexing (OFDM), and CP-SC under the same transmitting data rate conditions. Simulation results demonstrate conclusive performance advantage of the SC-FDE scheme on the communication of two different intra-vehicle NCS scenarios, especially when minimum mean square error method is taken into account.

In an intermittently connected environment, access points are sparsely distributed throughout an area. As mobile users travel along the roadway, they can opportunistically connect, albeit temporarily, to roadside 802.11 (Wi-Fi) APs for Internet access. Networking characteristics of vehicular opportunistic Internet access in an intermittently connected environment face numerous challenges, such as short periods of connectivity and unpredictable connection times. To meet these challenges, we propose an access point report (APR) protocol where mobile stations opportunistically collaborate by broadcasting an APR to other mobile stations to fully utilize the short-lived connection periods. APR can optimize the use of short connection periods by minimizing the scanning delay and also act as a hint that enables mobile users to predict when connection can be established.