Contribution: This longitudinal study modeled student leadership growth in a course sequence supporting long-term, large-scale, multidisciplinary projects embedded in faculty research. Students (half from computer science, computational media, electrical engineering, and computer engineering) participated for 1-4 semesters.Background: PBL is used widely in higher education. It is used in industry for leadership development, but leadership development in project-based learning (PBL) has not been explored in higher education. A preliminary analysis implied leadership growth through the third semester of participation, but the design did not control for attrition.Research Questions: At the student level, how do leadership role ratings change over multiple semesters of participation? Do first (and second) semester ratings differ by number of semesters students eventually participate?Methodology: The study involved two peer evaluation questions on 1) the degree to which students coordinated the team’s work and 2) served as technical/content area leaders. Analysis employed analysis of variance to examine attrition by initial ratings (N $=$ 1045) and multilevel growth modeling to study change over time (N $=$ 585). A strength of using peer evaluations is the large sample size, but a weakness is that the tool was developed for student assessment and not educational research. The study did not control for participation in leadership programs outside the course.Findings: On average, individual leadership role ratings increased each semester through the third semester of participation. Ratings of students who left the program after 1 or 2 semesters did not differ from ratings for those who participated longer.

In this innovative practice work-in-progress paper, enrollment data from five institutions was used to examine equity in undergraduate research through Vertically Integrated Projects (VIP) Programs. VIP is a model for undergraduate research in which large student teams are embedded in faculty-driven projects. The American Association of Colleges and Universities recognizes undergraduate research as a high-impact experience, associated with higher graduation rates and greater learning gains in college. Participation in multiple high-impact experiences yields cumulative gains to students from all backgrounds, and compensatory gains for minoritized and marginalized students. Nationally however, minoritized students, first-generation college students, and transfer students participate in undergraduate research at lower rates than their peers. In this study, VIP enrollments at five institutions (N = 6,651 over two semesters) were compared to demographics of the institutions to determine the degree to which programs achieved equity among historically underserved minorities, transfer students, first-generation college students, and by gender. Analysis accounted for demographics and level of participation of the academic units involved, comparing enrollments with what would be expected under equitable enrollment. Analyses were done for each institution and across the pooled sample. By institution, equity across categories varied. Across the pooled sample, results show small effects sizes for status as a historically underserved minority, very small effect sizes for first-generation students and transfer students, and slightly higher participation among women than men. The large-scale nature of VIP teams enables institutions to scale-up their undergraduate research offerings. This paper begins answering the question of whether this scaling increases access for marginalized populations, and the results are encouraging. The paper is a work-in-progress, because data needs to be collected from more VIP institutions for a wider-ranging study. The chisquare test and the importance of using effect sizes in interpreting results will be explained, so others can apply the same method. Results, implications, and next steps are discussed.

Purpose: we aim is to present a learning approach for students to work on a hands-on project that may be applied to different contexts. This experience relates to an initiative to foster entrepreneurial education embedded in economic sustainability based on the best local practices (highest-ranked municipalities) that we find and analyze to solve complex problems in the business environment. Design/Methodology/Approach: our approach is to collaborate with policymakers at the municipal level via a research-based project approach called Vertically Integrated Project (VIP). In our VIP, students work with instructors in a long-term effort to identify challenges and opportunities by working with the community to analyze problems, develop solutions (with different techniques and approaches), and monitor their implementation. Findings: new learning approaches can engage the students in real-world problem, adding value to their formation and giving back to society the investment they made in the Brazilian public university. Originality: this initiative is in tune with the 4th and the 11th sustainable development goals (SDGs) adopted by the United Nations (UN). Practical implications: this study aims to stimulate entrepreneurial education using multidisciplinary and alternative learning approaches. Social implications: this approach can deliver SDG-related impact to local communities by linking research-based teaching with community outreach.

The Level-up workshop will challenge exclusive and exclusionary models for undergraduate research experiences, and it will give participants tools to expand undergraduate research to serve all students. The model and associated tools are adaptable, and they have been implemented in 44 colleges and universities of varying sizes, settings and missions in 12 countries.

This innovative-practice work-in-progress paper explores student leadership development over multiple semesters in team-structured project-based courses. While student growth is expected in a single semester, the study asks if multiple semesters of participation lead to continued leadership growth, and if so, over how many semesters of participation growth continues. The study examined peer evaluation ratings in general leadership (coordination of teams’ work) and technical leadership (serving as a technical/content area leader) in a single semester of Georgia Tech’s Vertically Integrated Projects (VIP) Program, a multidisciplinary, multi-semester, team-structured, project-based, and credit-bearing program in which student teams support faculty research. Analysis examined means and distributions on two peer evaluation questions (N = 1,073 and N = 1,047) by student academic rank and number of semesters of participation in the program. Findings indicate that within their teams, students’ leadership increased through the third semester, with students making their greatest leadership contributions in the third semester and beyond; and students of lower academic rank provided as much leadership (including technical leadership) as older students who had comparable experience on the team. Both the VIP model and the operationalization of leadership represent innovative practices, because the VIP model yields measurable gains in student leadership, and the measurement of student leadership is based on peer-evaluations instead of self-assessments. The educational model and research in this paper are aligned with the FIE values of encouraging mentorship and professional growth, appreciating multidisciplinary approaches, valuing new approaches, and generating new knowledge. The paper addresses limitations and next steps for the study.

A football game is an event that offers unique opportunities to research, design, and implement an extensive testbed and applications for the Internet of People and Things (IoPT). We report on the IoPT testbed we developed in the football stadium at Georgia Tech (GT), which gathers information from and about the game from multiple sources and integrates it with information about the structural dynamics of the stadium to provide a unique experience for football fans. Its capabilities are demonstrated with an analysis of the information gathered from a particular football game.

We propose a novel method for monitoring gas distribution networks (GDNs) using intelligent sensor nodes that can be integrated with existing smart gas meters (intelligent meters). The method aims at detecting and locating gas leaks in GDNs in real time. The intelligent meters leverage wireless connectivity in existing smart meters to implement this method collaboratively. The method comprises an active acoustic probing phase and a passive linear imaging phase. In the active acoustic phase, the intelligent meters collaboratively discover the topology of the monitored pipeline network using a novel acoustic pulse reflectometry technique. In the passive linear imaging phase, the intelligent meters use their knowledge of the pipeline network topology to create a linear image of the pipeline using the Time-Exposure Acoustic (TEA) algorithm. The resulting image reveals the presence and locations of active gas leaks in the network. We present the theoretical basis of this new method and the results of its application in experimental settings.

In this chapter, we focus exclusively on our work on gathering data from a large number of sources in the stadium. In the theoretical work reported here, we are particularly interested in minimizing the time and energy required to gather data from many different sensor nodes. There is a parallel effort, reported elsewhere, to take the results of our theoretical research and turn them into real systems that we deploy in the stadium and study when they are used during the sporting events paces. Our current efforts include: Gathering video and data from the game to share with fans in the stands via web applications that enable on-demand access to multimedia content, including video-clips of plays, visualization of game events, and game/player stats. Developing wireless sensor networks to monitor structural vibrations of the stadium and audio of the crowd. An extreme emitter density test bed for RF spectrum sensing and cross-layer localization of wireless devices. Analytical models and associated algorithms for collecting, processing, and communicating very large amounts of data for detection, estimation, and other tasks.

PurposeThis paper aims to share the University of Strathclyde’s experience of embedding research-based education for sustainable development (RBESD) within its undergraduate curricula through the use of an innovative pedagogy called Vertically Integrated Projects (VIP), originated at Georgia Institute of Technology.Design/methodology/approachThis paper discusses how aligning VIP with the SDG framework presents a powerful means of combining both research-based education (RBE) and education for sustainable development (ESD), and in effect embedding RBESD in undergraduate curricula.FindingsThe paper reports on the University of Strathclyde’s practice and experience of establishing their VIP for Sustainable Development programme and presents a reflective account of the challenges faced in the programme implementation and those envisaged as the programme scales up across a higher education institution (HEI).Research limitations/implicationsThe paper is a reflective account of the specific challenges encountered at Strathclyde to date after a successful pilot, which was limited in its scale. While it is anticipated these challenges may resonate with other HEIs, there will also be some bespoke challenges that may not be discussed here.Practical implicationsThis paper offers a practical and scalable method of integrating SDG research and research-based education within undergraduate curricula.Social implicationsThe paper has the potential to deliver SDG-related impact in target communities by linking research-based teaching and learning with community outreach.Originality/valueThe alignment of VIP with the SDG research area is novel, with no other FE institutions currently using this approach to embed SDG research-based teaching within their curricula. Furthermore, the interdisciplinary feature of the VIP programme, which is critical for SDG research, is a Strathclyde enhancement of the original model.

Wireless sensor and adhoc networks are hierarchically clustered for energy-efficiency, while gathering and aggregating data at the central clusterhead. Subsequent longrange communications from the clusterheads cause large-scale interference and energy-hole problems around them. It is thus better to have packets forwarded via short-range multi-hop routes between the clusterheads at different levels of the hierarchy. In order to discover the most optimal routes that serve the purpose, paths that minimize the inter-cluster routing delay within latticed clusters are analyzed. Consequently, a low-delay, energy-balancing distributed algorithm for routing across clusters is developed, which outperforms shortest path routing in high throughput networks. A parametric study comprising large-scale network evaluations is performed by developing an NS-3 based simulator.

Localization is especially challenging in extreme RF emitter density (EED) environments (e.g. football stadiums), in part due to ambiguity in associating localization measurements to the correct emitter. One approach is to use other physical layer features for data association, but such techniques may not scale well for many emitters. This paper proposes exploiting the structure provided by the MAC layer for data association. The idea is explored in the context of IEEE 802.11g by using knowledge of the packet exchange sequence (PES), virtual carrier sense, and CSMA/CA to lower the probability of association error (PE) compared to a signal-to-noise ratio (SNR)-based OSI layer 1 strategy. Analytical expressions are derived for the PE on both a per packet detection and per packet exchange sequence basis. The proposed strategy lowers PE over an entire RTS/CTS sequence and scales well asymptotically in the number of emitters. The results are specific to WLANs, but the idea and approach are broadly applicable to any communications protocol with a MAC layer.

Multi-level clustering offers the scalability that is essential to large-scale ad hoc and sensor networks in addition to supporting energy-efficient strategies for gathering data. The optimality of a multi-level network largely depends on two design variables: 1) The number of levels, and 2) The number of nodes operating at each level. We characterize these variables within a multi-hop, multi-level hierarchical network of variable size that gathers and aggregates data at each level. Our network communication cost model (EEHC-VA) is parameterized by the size of the data forwarded at each level, which depends on the application and aggregation strategy in place. We minimize the communication cost to obtain the optimal probabilities of distributed and independent selection of level-(n+1) nodes from level-n nodes. Interestingly, we have identified intervals based on the number of nodes and aggregated data sizes within which single- or two-level hierarchies are optimal. The results have been numerically verified for a wide range of parameters and validated with network simulations.

We discuss the design, development, and deployment of an inexpensive, power-efficient, clustered, and scalable wireless sensor network (WSN) testbed. The testbed operates in a harsh environment in which neither GPS nor Internet connectivity are available. We use this testbed to collect real-time data during football games and other major events at Bobby Dodd stadium at Georgia Tech. The sensing devices in the testbed are synchronized without GPS or beacons, yet achieve sufficient accuracy to support modal analysis and detect if the stands are experiencing torsion. We have also developed a cognitive radio backhaul link to establish communication between the WSN in the stadium and a server in our lab. We present in detail the architecture, hardware components, and embedded software of the structural health monitoring platform. We also provide data collected during recent football games to verify the accuracy of the new synchronization algorithm and demonstrate that crowd behavior, such as rhythmic stomping, can be detected during a game.

As the RF spectrum becomes increasingly congested, localization algorithms which are tolerant of high levels of interference become necessary. A unique opportunity exists to study these issues during any event in a large venue, such as a football game in a large stadium. We report on the development of a RF sensor localization field deployment, LOC-EED, in the football stadium at Georgia Tech as well as a simplified laboratory testbed for controlled experimentation. During football games, cellphones, stadium personnel radios, media organization radios and wireless controlled devices, game official wireless headsets, etc. create an Extreme Emitter Density (EED) background that is a challenge to any algorithm attempting to identify and localize a single emitter. The laboratory testbed and field deployment to study this problem consists of RF sensor nodes (RFSN) using wideband RF digitizers and general purpose processors to sense the RF environment. We are using software radios as an enabling technology for the development of unique cross-layer localization techniques which are typically not realizable on specialized hardware, such as WiFi APs. This paper reports the details of LOC-EED and offers a preliminary analysis of spectrum captures in the 2.4 GHz band during a live football game. The analysis and a simulation of a simple cross-layer localization technique confirm both the need for, and ability to exploit, cross-layer information for localization.

We consider a distributed composite hypothesis testing problem in which sensor nodes share a collision channel to send their decisions and the fusion center (FC) has a limited time to collect these decisions. When the FC does not have enough time to collect all local decisions successfully, we propose a transmission protocol called sensor censoring random access as the multiple access scheme used by sensor nodes to send their decisions to the FC. By using this protocol, the collection time is divided into frames, where each frame consists of a number of time slots. The sensor nodes whose observations are within a specific range will send their decisions in a specific frame by using slotted ALOHA. Thereafter, we derive a Rao test used by the FC to decide whether the event is happening. Since this Rao test is aware of packet collisions and exploits them to make a global decision, we call it a collision-aware Rao test (CA-Rao test). Its asymptotic performance (the probabilities of detection and false alarm) is determined. The receiver operating characteristics of the CA-Rao test are evaluated and compared to those of a Rao test of distributed detection using parallel access channels.

We consider a single-hop, wireless sensor network (WSN) performing distributed estimation where the fusion center (FC) will collect local binary estimates within a limited collection time over a single transmission channel. We propose a transmission protocol called sensor-censoring random access (SCRA), in which the collection time is divided into frames and only local binary estimates of the observations in a specific range will be sent in a specific frame by using slotted ALOHA. Since we study a WSN with a single transmission channel, during the collection time, the FC will observe idle time slots, successful time slots, and collision time slots. As a result, we derive a collision-aware maximum likelihood estimator and collision-aware type-based estimators at the FC such that the global estimate is computed from not only the successfully received estimates but also idle time slots and collision time slots. The Cramer-Rao lower bound and mean square error of these estimators are evaluated.

Software-Defined Radios (SDR), which digitize RF spectrum and perform traditional receiver tasks in software, are becoming increasingly viable as an enabling technology for mobile networks and sensor networks. The concurrent rise in commercially available small form-factor, low-power, x86-based processors creates the possibility of incorporating General Purpose Processor (GPP) software radios into existing sensor networks. The eStadium VIP project is considering the addition of such nodes to sense digitized RF spectrum data in Bobby Dodd football stadium. The flexibility inherent in GPP software radio provides rapid algorithm testing; however, the hardware is often large, heavy, and power intensive. Due to the limited resources and practical considerations in the stadium, the trade-offs between size, weight, area, and power (SWAP) requirements and SDR capabilities must be studied prior to deployment. A performance analysis across four PC form factors, including one suitable for embedded use, running realistic SDR applications is presented. Case studies include FM radio with the BPSK modulated Radio Broadcast Data Service (RBDS), FM analog video, and distributed processing of digital video with QPSK modulation. Such studies provide valuable insight into SDR testbeds.

We consider a distributed detection problem in a large, single-hop, wireless sensor network. Because of limited collection time and bandwidth, the fusion center (FC) is not able to collect the local observations from all sensor nodes. A distributed detection scheme with a selection strategy and a capability to operate in a finite bandwidth is required. We propose an ordered sequential detection scheme which jointly integrates a reliability-based splitting algorithm, an ordered-transmission strategy, and a sequential probability ratio test (SPRT). The proposed scheme allows the FC to collect the local observations in descending order of their reliabilities by using a reliability-based splitting algorithm. As it receives successfully transmitted observations, the FC sequentially decides whether to make a global decision or to continue collecting more local observations. The numerical results show that the proposed scheme significantly outperforms a conventional SPRT scheme. The improvement increases as the number of sensor nodes in the network increases.

We consider a large, single-hop, wireless sensor network performing distributed detection under time and bandwidth constraints. The fusion center (FC) has a limited time to collect binary local decisions, which are sent through a shared transmission channel. Assume that the allocated collection time is not large enough to collect the local decisions from all sensor nodes. The considered distributed detection applies a sensor selection strategy called the reliability-based splitting algorithm to collect the local decisions. By using this strategy, the sensor nodes are divided into groups according to their observation reliabilities. The sensor nodes in the same group will compete for packet transmissions with each other by exploiting slotted ALOHA. In addition, we propose a modified algorithm called the two-level reliability-based splitting algorithm, where the sensor nodes are divided into groups based on both observation reliabilities and local decisions. Unlike other distributed detection with random access protocols, where the packet collisions are treated as errors and ignored, we derive collision-aware fusion rules, where the numbers of successful and collision time slots are also exploited in making a global decision. The numerical results show that both algorithms outperform TDMA-based distributed detection.