Feature-based Visual Simultaneous Localization and Mapping (V-SLAM) employs feature descriptors to recognize landmarks in successive frames. For networked (offloaded and/or collaborative) V-SLAM, the feature descriptors typically need to be transmitted over wireless communication networks with limited bitrates. Conventionally, V-SLAM systems have employed general computer vision feature descriptors whose size (in bytes) accounts for most of the data transmission. We develop a probabilistic model of the V-SLAM feature matching and identify three parameters that govern the probability of the correct feature match: the feature set size, the bit error probability between feature descriptors observing the same landmark, and the probability that the correct feature match exists in the feature set. Based on these three parameters, we formulate a research agenda for achieving high V-SLAM performance (i.e., high correct feature match probability) for reduced V-SLAM feature descriptor sizes (and thus reduced network throughput requirements). We address the first item on this research agenda by pursuing the reduction of the feature set size through a novel Orientation Restriction with Frame-to-Frame Projection (ORFFP). We evaluate ORFFP in comparison to the state-of-the-art ORB-SLAM2 approach using three representative V-SLAM test sequences. Our ORFFP approach reduces the size of the feature set to as little as 9.1% of ORB-SLAM2, allowing for the use of smaller feature descriptors. Thereby, ORFFP reduces the overall required throughput for networked V-SLAM down to roughly a third of the current approach, while achieving essentially the same V-SLAM performance. We also find that for learned descriptor-based models (HashSIFT, BAD), the feature descriptor size can be reduced using ORFFP without severely degrading the performance of V-SLAM. We outline future research directions towards comprehensively addressing the formulated research agenda for enabling networked V-SLAM on low-bitrate networks.

Emerging 5G/6G use cases span various industries, necessitating flexible solutions that leverage emerging technologies to meet diverse and stringent application requirements under changing network conditions. The standard 5G RAN packet error handling using retransmission reduces packet loss but can increase transmission delay. Random Linear Network Coding (RLNC) offers an alternative by proactively sending combinations of original packets, thus reducing both delay and packet loss. Previous research typically only simulates the integration of RLNC in 5G but does not demonstrate nor evaluate this integration in real 5G systems. In contrast, we implement and evaluate our approach through measurements with commercially available servers and switches, running the OpenAirInterface (OAI) 5G software stack. We introduce Flexible Network Coding (FlexNC), which enables the flexible fusion of several RLNC protocols. Specifically, FlexNC provides a forwarder that flexibly interfaces with multiple RLNC protocols in a cloud-native (containerized) solution. Network operators can configure FlexNC based on network conditions and application requirements. For boosting network programmability, our Recoder in the Network (RecNet) leverages In-Network Computing (INC) in intermediate network nodes. We have developed an open-source cloud-native (Docker-based) implementation of both FlexNC and RecNet on OAI, including INC for the Recoder on a Cisco Catalyst switch. Measurements for video, haptic, and audio traffic indicate that i.) FlexNC adapts to various application needs in terms of latency and packet loss, and RecNet significantly reduces packet loss for a remote user with minimal increase in delay compared to pure RLNC. To the best of our knowledge, this is the first article to report testbed measurement results for cloud-native network coding in a 5G system, thus creating a baseline for reliable 5G communication.

This innovative practice WIP paper presents our work to increase student motivation and engagement using real-world projects. Specifically, we assigned final projects to students that came from a genuine need from our research program on campus. The team projects were assigned in a networking course offered to undergraduate and graduate students. Anecdotally, the students seemed more motivated to complete the projects because they knew the requested designs were novel and provided local utility. Student performance toward project completion was, anecdotally, exceptional compared to traditional projects. We surveyed students to assess their perceptions of their mo-tivation and subsequent engagement. The survey results suggest that the students perceived what we also perceived: students had more motivation from these types of projects than the typical contrived recycled projects typically assigned. In this paper, we provide the details behind these projects, how we managed the students to complete them, how we attempted to make the process mirror a commercial environment, and analyze the results of the student survey data. We also outline our experimental design to be executed in future course offerings to better understand how student motivation and engagement is affected by these types of projects in comparison with typical contrived recycled projects. Lastly, we outline how we intend to scale up across campuses and to create an experiential learning environment outside of formal course work.

This work in progress research paper describes the findings of an anonymous survey modeled after the persistence in engineering survey instrument that we conducted a decade apart in 2014 and 2024. Significant changes in the mid-2010s to the mid-2020s have altered course delivery and resulting student perceptions, and we find statistically significant differences for program persistence, student self-perceived performance, perceived course value, course enjoyment, self-perceived learning gains, and self-perceived learning material engagement efforts. Interestingly, we did not find significant differences between the two groups with respect to self-reported time spent on different course activities or general student population characteristics, such as self-reported credits, GPA, work hours, or commutes. When considering the averaged changes, we note that students in the more recent blended and virtualized environments exhibit a higher level of persistence than their peers. This showcases the overall increase of blended and decentralized/virtualized instruction having a positive impact on student attitudes based on the feedback collected.

This work in progress innovative practice paper details our open source approach, which directly addresses new challenges to equitable access and privacy originating from increased technology-driven learning. We provide results from the Technology Acceptance Model's anonymous survey approach for a second-year introductory networking and a fourth-year operating systems course that employed the environment with comparable respondent characteristics. Although perceived ease of use and perceived use are in the middle of possible ranges, we find an interesting reversal for the network course, where student attitudes are low despite high perceptions. Future work will be directed at further refinements of the framework and a more in-depth evaluation of the privacy concerns that students might have and whether these could be included in the Technology Accentance Model.

Object recognition tasks are commonplace in industrial and medical applications, but resource-limited end devices common to the Internet of Things (IoT) are limited in capabilities and commonly require offloading of compute-intensive tasks. We evaluate the negative latency concept as a new approach to image detection and object recognition applications in low-bandwidth scenarios without a feedback channel. We employ image quality assessment and risk estimators associated with image quality degradation for progressively transmitted images to realize negative latency. Our comprehensive evaluation on image recognition and object detection tasks shows that image quality assessment over progressively transmitted images can be enabled by using risk estimators associated with image quality degradation during progressive transmission. In turn, our approach provides the framework for service latency and resource use reductions.

This paper describes our universally applicable approach to the gathering of learning process data to support learning analytics with the ultimate goal of generating actionable intelligence for learners and instructors. This support is needed as increasingly, course content is provided in an online environment (independent of a class delivery modality, e.g., face-to-face, hybrid, hyflex, or online) where learners are operating independently and in absence of timely feedback loops. Providing learners with automatically generated feedback can stimulate their self-regulation toward success. Specifically, we describe an approach that generates learning process analytics dashboard data that are driven by clickstream data. Our approach enables the gathering of learning process data outside the Learning Management System (LMS) confines to support the generation of models that can produce actionable intelligence and enable deeper exploration options for the instructor. Our approach thus enables continuous education improvement and its operationalization (EdOps).

Deterministic communications are required for industrial environments, yet their realization is a challenging task. Time-Sensitive Networking (TSN) is intended to enable deterministic communication over inexpensive Ethernet networks. Standardized by the IEEE TSN working group, TSN enables precise control of time synchronization, traffic shaping, reliability enhancements, and network administration to answer the demands of industrial control applications. Subsequently, there is a significant need to enable turnkey research and implementation efforts. However, a current lack of open-sourced testbed implementations to investigate and study the behavior of TSN network devices limits verification to simulation and theoretical models. We introduce a publicly available, flexible, and open-sourced measurement testbed for evaluating TSN in the context of industrial automation applications to address the need to perform real-world measurements. In this contribution, we describe our testbed combining Commercial-Off-The-Shelf (COTS) hardware and existing open-source tools as a platform for in-depth evaluation of TSN devices. Providing detailed TSN backgrounds, we describe an in-depth performance analysis for our implementation. For a common Tactile Internet scenario, we observe an accuracy of close to 5 ns achievable with our publicly available COTS setup.

Future networks relying on models in their applications, e.g., control loops in the Tactile Internet, will exhibit a need to have model parameters available before interactions take place. This anticipatory delivery of model weights effectively results in required negative latencies for the overall services. We provide an exemplary motivation based on advertising to mobile users that is optimized by user-modeled preferences. We employ this example for our discussion of anticipatory networking in this paper, with an overall focus on the overarching concepts.

The emergence of 5G services includes those requiring extremely low-latency for command and control application scenarios in the low millisecond range. With a significant human-machine interaction component, the Tactile Internet will require that the experiences based on the human visual interface can be dynamically adjusted within similar time frames. In this paper, we evaluate the impact that different delays currently attainable with commercial hardware would have on predicting the Quality of Experience (QoE) with immersive images. Specifically, we employ electroencephalography (EEG) data to predict how future subjects would determine the media quality in a Passive Human In-the-Loop (PHIL) scenario. This initial extension of our prior work focuses specifically on the delay in the gathering and processing of data and presents a first foray of bringing the passive human-in-the-loop QoE adjustment approach to the Tactile Internet. We find that there is limited value in increasing the delays of two different approaches to predicting the QoE. We additionally note that current approaches to predicting a user's QoE based on other users' EEG patterns exhibit only limited prediction accuracy.

This Research Work-in-Progress Paper presents an evaluation of feedback gathered from a post-semester survey jointly taken by students in Ireland and the United States. The ability to function in international team-based environments becomes increasingly important and represents a desirable skill for employers of graduating students. Despite incentives, an international course offering in mobile application development resulted in only two cross-institutional teams by student choice, which we investigate here.We encountered that about half of the respondents considered dropping the course for at least one reason at one point, with one third identifying the course difficulty level as a reason, but also indicating agreement with the course being worthwhile. Some of the feedback presented indicates that students have a preference for project-based courses and like working in teams, but are also wary of the potential coordination overheads and dependability on their peers, despite modern communication tools as part of the class offering.

The Tactile Internet is a prominent example for the need to re-evaluate the trade-off between compression and real-time needs for smaller messages from/to sensors and actuators. Considering the ultra-low latency requirements in the millisecond range, we developed a use-case agnostic low-level approach to compress the amount of data that need to be sent. Our approach combines threshold-based delta-coding for individual values (BIN) and bit-plane level flexible compression through precision reduction (BLISS). We demonstrate how realistic data savings can be obtained with low compression losses and overheads using common Tactile Internet data sets through application of our approach. We find that our agnostic and case-independent approach yields data savings over the 50% range for low compression losses for Tactile Internet scenarios.

The classrooms used by Computer Science students come in all shapes and sizes, from large lecture halls to small computer labs to collaborative active learning classrooms. Does our teaching style need to match the space? Are particular classroom environments more effective than others and might the types of artifacts generated by Computer Science students lend themselves better to one space over the other? As many students come to class with their own personal computer, development environment and opinions about effective instruction, does the classroom and supporting technology even matter? What effect does the space have on the instructional choices made? This BOF will provide a platform for a discussion around individual teaching styles and preferences and how they relate to the classroom space. Access and awareness of active learning pedagogy and active learning classrooms can create both tension (e.g., not wanting to teach in an active learning classroom) and challenges (e.g., desire to have access to an active learning classroom despite the costs associated, how working specific techniques into a given space). Shared experiences will help participants better leverage their classroom spaces to their desired pedagogy.

The dominant HTML/HTTP-based web-centric means of networking application implementations have given rise to significant efforts to improve performance, predominantly through caching of web objects. Caching limitations force a common web object to refresh at pre-set time intervals, resulting in latency and data transmission inefficiencies we term Web object Access Setting Timeout Excess (WASTE). We provide an initial definition of the WASTE concept here paired with an example analysis for a small set of web sites where we highlight the relative amount of web objects and their data that are WASTE downloads when content did not change. We subsequently describe a methodology and practical implementation approach to limit WASTE that leans on principles found in Contend Information Centric and Named Data Networking (CCN/ICN/NDN). Specifically, we describe an implementation approach of transparently embedding explicit forward hinting of content details for caching purposes.

We evaluate the Quality of Experience (QoE) for spherical images presented in a multimedia consumer setting with commercial mobile devices. We find that approximations and initial predictions from the non-referential BRISQUE image quality metric is possible within ten percent of the common Likert-type scale ratings. A comparison of results with those obtained in augmented reality settings yields significant differences for the same content, requiring different approaches for approximations. Overall, our findings can readily applied in real world settings by network and content service providers in future offerings.

This Work in Progress in the Research to Practice Category advocates for new evidence-based training opportunities for potential future faculty to increase student access to active learning. Active learning has been used in the classroom for years and demonstrated its effectiveness in terms of enhancing students' performance. In spite of the reported benefits of active learning, a sizable gap exists between faculty that are aware of active learning as a teaching style and those that adopt it for their classrooms. In response, the broader community has developed several successful aids to overcome often cited barriers to adoption. Nevertheless, this gap remains and indicates the need for further interpretation of existing research as well as additional avenues of research and training to advance adoption. These avenues should augment existing initiatives to increase adoption and further understanding of faculty reticence. Based on the current topography of active learning and its adoption, we argue for new training mechanisms to be offered at the graduate level that would have broad appeal and provide a foundation for evidence-based techniques. We also argue for research that is more inclusive and qualitative in nature to specifically explore faculty attitudes, beliefs, and backgrounds as they pertain to instruction.

Active learning has gained significant traction in the past decade and implementations can be found across all institutions of higher learning. While large costs are incurred to modify available spaces to technology-supported active classrooms, economy-based active learning systems that provide similar functionality could be employed instead. In this paper, we provide initial results for a comparison of second-semester programming course student learning outcomes. Students for an economy-based active learning system and a high-cost active learning classroom indicate through pre- and post-test results that there is little difference between delivery modes. This supports the emerging research that the activity, not the space is the dominant factor for active learning gains, which can alleviate some of the pressures for budgetarily challenged institutions of higher learning.

Augmented Reality (AR) devices and phone adaptations are commonly head-worn to overlay context-dependent information into the field of view of the device operators. One particular scenario is the overlay of still images, for which we evaluate the interplay of user ratings as Quality of Experience (QoE) with (i) the non-referential BRISQUE objective image quality metric as QoS and (ii) human subject dry electrode EEG signals gathered with a commercial device. We find strong correlations for subject-specific EEG portfolios, resulting in an approach to the predictability of the QoE. Our overall results can be employed in practical scenarios by mobile content and network service providers to optimize the user experience in augmented reality scenarios with a passive human in-the-loop in the future.

With the emergence of Augmented Reality (AR) services in a broad range of application scenarios, the interplay of (compressed) content as delivered and quality as experienced by the user becomes increasingly important. While significant research efforts have uncovered interplays for traditional (opaque) media consumption scenarios, applications in augmented vision scenarios pose significant challenges. This paper focuses on the Quality of Experience (QoE) for a grounded truth reference still image set. We corroborate previous findings which indicate that there is only limited QoE benefit obtainable from very high image quality levels. The main contribution is the first evaluation of several popular objective image quality metrics and their relationships to the QoE in opaque and vision augmenting presentation formats. For the first time, we provide an assessment of the possibility to predict the QoE based on these metrics. We find that linear regression of a small degree is able to accurately capture the coefficients and fairly accurate prediction can be performed even with a non-referential image quality metric as parameter. We note, however, that prediction accuracy still fluctuates with the image content and subjects removed from the data set. Our overall findings can be employed by AR service providers to perform an optimized delivery of still image content.

The consideration of mobile web page and service access for optimization schemes requires a detailed interpretation that goes beyond the commonplace evaluations of HTTP-delivered objects. Evaluating mobile browser interactions with popular web sites, we note that a non-negligible amount of data is delivered with HTTPS and web site interactions could almost exclusively rely on HTTPS delivery of their content. This increasingly popular approach is currently not widely regarded for performance evaluation and modeling purposes and can result in significant deviations between a model and the real mobile web. We find significant amounts of data are delivered in web site interaction by employing HTTPS and there is an additionally non-significant amount of this data that can be cached. Additionally, we find that the same interactions on the same web site could exhibit fairly large variations over time, which results in considerable measurement bias if individual points in time were used. In turn, our findings indicate that the adequate capturing of the mobile web's characteristics as input for modeling and simulation-driven approaches needs to be reconsidered.

The proliferation of mobile device platforms and operating systems has resulted in an increased challenge for developers and mobile users alike. While developers face fragmentation issues in their efforts, mobile users find an increasing number of online services to access in a variety of ways. The increased adoption of HTML5-based service access methods, however, allows to circumvent some of these problems by employing a browser-based service access, either in form of hybrid applications or by directly employing a mobile web browser installed on a user's device. We present and employ a framework that can be readily utilized to perform repeated evaluations of applications on mobile devices with the Android OS to provide statistically meaningful performance measurements. Specifically, we compare dedicated and browser-based access of online social network services with respect to their corresponding CPU and memory resource utilizations. We find a trade-off between increased CPU usage for browser-based service access and increased memory usage when accessing the services with their dedicated mobile applications. Depending on current system resource utilizations, it might hence be advantageous to choose one over the other.

Active learning has demonstrated value in STEM education and various meta analyses show improvements in student performance when making use of active learning pedagogy and active learning classrooms. One limiting factor to extending the reach of active learning is cost. Many models for state-of-the-art active learning environments make use of tools and infrastructure through which students can connect their own devices and share their digital artifacts in real time with their teammates and the entire class. For computing disciplines, the desire to make use of this type of technology is intuitive as it allows students to see alternative solutions, inspire confidence in student capabilities and can mimic pair programming practices used in industry. Still, retro fitting existing spaces or new construction of state-of-the-art active learning environments and the installation of standard, supporting technology can be cost prohibitive for many institutions, indicating a need for alternative implementations to active learning technology which are applicable to a wider audience. In this work we describe the construction, use and initial evaluation of commodity hardware and open source software to support active learning which we term Practical Active Learning Stations (or PALS).

In this paper, we perform a high-level comparison between different modifications of an entry-level programming course in computer science. The course iterations feature an online tutoring component paired with (i) textbook/traditional lecture/lab, as well as (ii) active learning in a specially designed active learning room, and (iii) active learning in a computer laboratory, both featuring an online textbook. We find that shifting the course format to active learning alone does not necessarily improve learner performance across all commonly graded course activities and might even result in lower performances in certain categories. Simply replacing traditional classroom settings with a laboratory in which to perform hands-on exercises might not be sufficient, as performance overall declined. Interestingly, we do not notice a very pronounced impact of lower performance and increased failing rates on student attitudes, as measured by student opinion scores.

Next generation applications of wireless IP networks face an ever increasing demand of real-time dissemination of sensory and similar data to nearby devices. During situations when the transmission capabilities become scarce due to overused bandwidth, such as vehicular and various IoT use-cases, limiting the number of transmitted bytes over the wireless interfaces could ease the network load considerably. One of the main potential approaches for reducing the traffic in such scenarios is broadcasting, which could be even more efficient when paired with compression approaches, such as header compression. Normally header compression is employed to minimise the overhead of IP-based cellular traffic between two directly connected peers. However, in broadcasting scenarios, the compression has to balance the trade- off between servicing the least reliable channels (i.e., robustness to losses) and performing at peak compression rates (generating smaller packet headers). In order to circumvent these limitations, we propose a constraint on various IP and RTP fields when employing the industry-standard Robust Header Compression version 2 (which was designed for direct links) in a broadcast scenario without modifications of the underlying mechanisms, i.e., with full RFC compliance. With this approach the compression becomes impervious to packet losses up to 50% and for UDP profile compression with IPv6 it is practically unaffected by the link quality. In turn the compression retains at least 50% efficiency but can be as high as 90%.

A significant number of mobile applications employ HTTP as their primary network protocol, as the underlying remote services oftentimes are based on World Wide Web (WWW) technology stacks. Similarly, in recent years, mobile devices have emerged as the primary means to access the WWW. We present cacheConnect, a portable mobile proxy implementation that enables aggregation and content optimization on mobile devices, rather than the commonplace proxy operations performed in the cloud. Based on this additional local caching tier that negotiates between remote data servers (for browsing or otherwise) and local applications, we enable new means of content provisioning and application-level transparent optimizations. Our demonstration highlights the operations of our solution on Android mobile devices.

In this paper, we provide an overview and results of a long-term field study that captured network interface traffic on-device for 30 participating mobile devices. For the involved devices, the majority of applications installed are found to exhibit network activity, resulting in a high traffic variability. Bursts of data are observed for about 20 minute durations, with an overall average volume of 317 kByte in a two-minute time frame, while exhibiting an average inter-arrival of observed bursts of about 18 hours. We discover a significant dominance of the download direction of network traffic, to a majority on wireless LAN interfaces. We attribute this behavior to the common practice of mobile data offloading. We furthermore find a lack of distinct long-range dependence or self-similarity in the analyzed traffic, across interface types as well as directions. Our findings could impact the ongoing efforts to decrease networking battery impacts on mobile devices, such as through anticipatory resource optimization, as little correlation between directions and interfaces were observed in this data set.

With vision augmentation entering consumer application scenarios with a wide range of adaptation possibilities, an understanding of the interplay of Quality of Service (QoS) factors and resulting device operator Quality of Experience (QoE) becomes increasingly significant. We evaluate the effects of image compression as QoS factor on the QoE by describing the difference between traditional opaque and highly transparent vision augmenting display scenarios, which we denote as Visual User Experience Difference (VUED). We find that based on mean opinion scores, higher ratings are attained in augmented settings only for higher qualities, while lower qualities exhibit a reverse trend. Furthermore, we present a quantified relationship between traditional and augmented vision for a set of common images for the first time. The differential mean opinion score in the vision augmenting setting is additionally compared to major objective image quality metrics and embedded into current theories for mapping QoS to QoE, for which we find and describe suitable fits.

In this paper, the impacts that the evolutionary development of an introductory programming course has on student achievements is described. For the introduction of pass/fail grading with student self-empowerment through tokens, a significant increase in performance for programming assignments, quizzes, and total semester scores can be found. Furthermore, increased times of instructor-guided hands-on programming exercises have a positive effect as well. Thirdly, a strong correlation between how well students perform on an automatically scored self-directed online programming training environment and their achievements in programming assignments, quizzes, and the entire semester can be observed. In turn, the inclusion of these types of environments can benefit students significantly while introducing only little overhead for course instructors. The active utilization of these additional hands-on training options represents a significant potential to increase the total semester performance and should be actively communicated to students.

We describe the evolution of an introductory programming course into an active learning format, thereby replacing the traditional textbook and online tutoring system with an integrative version. We find a strong positive correlation between the student achievement scores in the integrated online textbook exercises and quizzes as well as overall course achievement. However, we observe that the comparable performance is not across all learning assessment categories when comparing subsequent course offerings. Particularly, we note slightly lower average performance for programming assignments and quizzes in the flipped classroom offering. For this active learning format offering, we perceive that performing students have a higher average score in the interactive textbook environment and skipped fewer overall assignments than the lower performing learners. We additionally find that students performing well in the online self-guided hands-on programming exercises perform significantly better than their lower performing peers.

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. We employ machine learning approaches for the prediction of Robust Header Compression version 2's (RFC 5225) compression utility for VoIP transmissions, which enables the compression to dynamically adapt to varying channel conditions. We evaluate the prediction models employing R2and mean square error scores next to complexity (number of coefficients) based on an RTP specific training data set and a separately captured live VoIP audio call. We find that the proposed weighted Ridge regression model explains about 70% of the training data and 72% of a separate VoIP transmission's utility. This approach outperforms the Ridge and first-order Bayesian regressions by up to 50% and the second and third order regressions utilising polynomial basis functions by up to 20%, making it well-suited for utility estimation.

Modern cellular networks utilising the long-term evolution (LTE) and the coming 5G set of standards face an ever-increasing demand for low-latency mobile data from connected devices. Header compression is employed to minimise the overhead for IP-based cellular network traffic, thereby decreasing the overall bandwidth usage and, subsequently, transmission delays. Since Robust Header Compression, among others, is primarily designed for the compression of live audio transmissions on endpoint devices, it performs best if certain fields, like the IP ID and RTP Timestamp, stay constant or change at a predetermined rate. Moreover, the compressor expects the uncompressed stream to be loss-free as well, which might not be the case in general scenarios. We employ machine learning approaches for the prediction of Robust Header Compression version 1's and version 2's compression utility under various loss rates and header field dynamics. We analyse how the compressions react to different fluctuations in the headers and choose the compressor configuration which maximises utility. We show that the appropriate choice of compressor repetition configuration increases the overall utility under dynamic channel conditions, such as in the case of remote steering and various IoT applications, and finding the optimal configuration could produce significant benefits, like 1.2 speed-up in a fully utilised network.

With increasing numbers of devices per user, data delivery and annoyance problems arise from sending real-time or near real-time application push notification messages to all these devices. This paper presents a push notification service approach that is based on a user-centric viewpoint to deliver notification messages to the right device at the right time. The approach increases the chances of a user to timely respond to a notification without annoyance. As side benefit, the overload on the server side can be significantly reduced in terms of minimizing unnecessary duplicate notification messages sent to an individual user's multiplicity of devices. We exemplarily discuss a prototypical implementation based on cross-platform web technologies that can be readily implemented.

With the future explosive growth anticipated for wearable devices, this paper provides a first evaluation of the typical network traffic behavior to be expected from the interplay of the Google Glass augmented reality wearable and a connected smartphone in a baseline scenario. We include evaluation results ranging from a second to minutes time scale obtained from an exemplary measurement including both devices in a scenario that encompasses the default applications installed. We find that while overall traffic amounts are relatively small, they exhibit contrasting patterns on different time scales. Similarly, we find that on small time scales, little correlation between the traffic of the two devices exist, while higher correlation can be observed on larger time scales. The baseline results presented in this paper can thus be regarded as starting point to estimate future traffic schemes on a user's primary and secondary mobile devices and connecting wireless networks. The findings can be employed to optimize future mobile data delivery for the increasing multitude of similarly connected wearable devices associated with individual mobile users.

We are experimentally conducting a fine-grain comparative analysis of two different methods of producing pre-recorded lectures versus a traditional live lecture. The two different pre-recorded lecture production methods do not require assistance from university staff members and therefore represent a low-cost approach. The first method is generating narrated slides using PowerPoint during a live lecture and the second method is the recording of a live lecture from the instructor's point of view using a head mounted camera such as Google's Glass. We present some of our preliminary results of this comparative analysis, which indicate that little interactions exist overall between the implemented modus of presenting instructional materials and the observed student learning gains within the study configuration. Thus, as a preliminary indicator within our study confines, instructors are enabled to select the most convenient fashion of lecture presentation. We are presently expanding our set of experiments (different cohorts, different specific subject matter, etc.) to obtain broader conclusions.

Common questions faced by instructors in their course designs are how to incorporate interactive technologies to increase student motivation as well as limiting the costs associated with education, contributed predominantly through course textbooks. We describe two course changes from a baseline scenario for a networking course, which aim at making the course no-cost using public domain content or, alternatively, focus on making the course highly interactive. We find that course cost benefits were offset by shortcomings in the open-source material replacing the textbook, but that the additional course costs of an interactive course are offset by increased course and learning perceptions reported by students. The focus on instructional interactivity, however, left the hands-on components up to students, with a potentially negative impact on the perceived quality of instruction.

As the main web page access modality shifts to mobile devices, evaluations of the impact on underlying networks is required to perform long-term strategic optimizations. In this paper, we characterize individual mobile web page objects by size, cache expiration, and their composition into pages. Employing popular mobile web landing pages, we evaluate their characteristics on an individual and composed page level to derive a model that captures their main facets with respect to object size and caching distributions on a lumped and contextually aggregated page level. We observe that similar distributions can be employed for overall object sizes as well as their composition to web pages, while the cache expiration ages need to be taken into account for different contextual types of web pages. Employing our model, we successfully approximate cache lifetime for the mobile web, demonstrating its use for mobile web performance evaluations. The employed model thus allows network operators to perform broad planning on different time scales.

In this paper, we introduce a caching proxy architecture, which is used to (i) enable seamless device switching in the increasingly connected mobile user space, (ii) create user-based application optimizations, and (iii) realize energy savings in application contexts. We provide an overview of the static long-term gains that can be expected for individual applications using a generic cost approach and present numerical evaluations for the impact of different performance factors. We find that significant optimization potentials exist for cases of negligible computational overheads (i) downstream from the proxy if even small savings around 5% from the proxy downstream can be realized and (ii) upstream from the proxy if data needs to be replicated on the mobile user end in current multiple device scenarios.

We present different video traffic models for H.264 variable bit rate (VBR) videos. We propose our models on top of the recent unified traffic model developed by Dai et al. [1], which presents a frame-level hybrid framework for modeling MPEG-4 and H.264 multi-layer VBR video traffic. We exploit the hierarchical predication structure inherent in H.264 for intra-GoP (group of pictures) analysis. We model the children frames by considering various combinations of the correlation between the parent frames in the prediction structure. Our simulations show that modeling using the hierarchical prediction structure indeed improves capturing the statistical features of the videos and prediction of network performance, without an increase in the complexity as compared to the unified traffic model by Dai et al. [1], which was shown earlier to be better than previous traffic models.

In this work, we present a scheme based on Bluetooth friendly device names to enable power-optimized ad-hoc localization of mobile devices. Eliminating the service discovery and connection (including potential pairing) phases in Bluetooth allows for speedier and more power-efficient conveying of location information using friendly device names. Furthermore, we observe that using the signal strength commonly provided in reference APIs of mobile OSs, client distances can be calculated with high accuracy and without additional power penalties.

In this paper we introduce a system based on commercial off-the-shelf components for augmented reality support in general assisted living scenarios. Our system is comprised of (i) a smartphone with near field communication (NFC) capabilities and (ii) a wearable 3D see-through display. Both components utilize the Android operating system and are connected wirelessly. We demonstrate how such system can be used for aiding interaction of mobile users with their environment, e.g., as patients in assisted living or rehabilitation facilities. In our demonstrated state, the system allows users to gain language-independent contextual hints about their environment by touching tagged objects with their phone, which displays corresponding information on the head-mounted display.

In this Work in Progress, we describe the `my Learning Community' application design principles and theoretical foundation. The application is a learning community support tool, which we implement in a peer mentoring project in the Department of Computing and New Media Technologies at the University of Wisconsin-Stevens Point. Combining different learning frameworks, we implement location-based services utilizing publicly available interfaces to Foursquare/Gowalla, which allow the identification and tracking of student participation in study sessions and identification of experts (“mayors”), which in turn mentor fellow students. As students progress through the curriculum, the identified student participation will allow for a transition of students into informal topic leadership. The student and study group location and additional communication among participants of study sessions will be facilitated by the `my Learning Community' application, which we make available to our students for installation on mobile and desktop devices and are currently implementing our approach.

The scalable video coding extension (SVC) of the H.264/AVC standard is widely considered for IPTV. SVC supports a variety of scalability modes, including temporal, spatial as well as coarse-grain and medium-grain quality scalabilities. In this paper, we first give an overview of coarse-grain quality scalability (CGS). We generate traces of CGS encodings of long CIF resolution video sequences; the traces provide a simple yet effective characterization of CGS encoded video for performance evaluation of video transport systems, including IPTV systems. We conduct a detailed statistical analysis of the CGS video traces. We compare the bit rate-distortion (RD) and the bit rate variability-distortion (VD) performances of scalable CGS encodings with those of non-scalable SVC single layer encodings. We thus quantify the tradeoff between the rate adaptability afforded by CGS encoding and the cost in terms of RD efficiency compared to non-scalable single-layer video.

In pervasive sensor networks with high densities, similar networks might overlap, resulting in different coordinators for end devices to associate with. This can result in several problems, especially for home monitoring and hospital scenarios, where easy, fast, and accurate association of pre-dete

Packet-switched unidirectional and bidirectional ring wavelength division multiplexing (WDM) networks with destination stripping provide an increased capacity due to spatial wavelength reuse. Besides unicast traffic, future destination stripping ring WDM networks also need to support multicast traffic efficiently. This article examines the largest achievable transmitter throughput, receiver throughput, and multicast throughput of both unidirectional and bidirectional ring WDM networks with destination stripping. A probabilistic analysis evaluates both the nominal capacity, which is based on the mean hop distances traveled by the multicast packet copies, and the effective capacity, which is based on the ring segment with the highest utilization probability, for each of the three throughput metrics. The developed analytical methodology accommodates not only multicast traffic with arbitrary multicast fanout but also unicast and broadcast traffic. Numerical investigations compare the nominal transmission, receiver, and multicast capacities with the effective transmission, receiver, and multicast capacities and examine the impact of number of ring nodes and multicast fanout on the effective transmission, reception, and multicast capacity of both types of ring networks for different unicast, multicast, and broadcast traffic scenarios and different mixes of unicast and multicast traffic. The presented analytical methodology enables the evaluation and comparison of future multicast-capable medium access control (MAC) protocols for unidirectional and bidirectional ring WDM networks in terms of transmitter, receiver, and multicast throughput efficiency.

We examine a recently proposed multichannel upgrade of optical single-channel ring networks where a subset of ring nodes is WDM upgraded and interconnected by a single-hop star WDM subnetwork in a pay-as-you-grow fashion. This evolutionary approach not only allows for fast and efficient multiple-failure recovery but also is well suited to efficiently sustain unpredictable changes and shifts in traffic loads. In this paper, we analytically investigate the maximum achievable capacity of the WDM star subnetwork upgrade of optical single-channel networks under a variety of unicast and multicast traffic scenarios and compare it to that of conventional WDM ring networks. In our analysis, we take priority of ring in-transit traffic, destination stripping, and maximum spatial reuse into account. Our findings show that under multicast traffic the configuration of the star subnetwork plays an important role in order to achieve high multicast capacity. Furthermore, under multicast traffic WDM upgrading and interconnecting a subset of ring nodes might be sufficient to achieve a larger multicast capacity than in WDM rings

Abstract — The development of pricing schemes that account for the specific challenges in streaming video to wireless clients is one of the key requirements for making wireless video services economically viable. In this paper we develop a conceptual framework for the pricing of wireless video streaming. Our framework incorporates the quality of the delivered video in the given networking context in an earnings model and captures the costs for the video service in a cost model. We discuss these models in the context of cellular, WLAN, and multi-hop wireless networks. We illustrate the developed pricing framework through numerical experiments with videos of a range of quality levels. Index Terms — wireless, video, multimedia, pricing I.

Currently available video traces for scalable encoded video with more than one layer are a convenient representation of the encoded video for the evaluation of networking mechanisms. The video distortion (RMSE) or quality (PSNR) for individual video frames in these traces, however, only allow for the calculation of the video quality of correctly received video frames; for lossy network transport, only a rough approximation can be made. With the availability of scalable offset distortion traces, which we introduce and evaluate in this paper, networking researchers are enabled to accurately calculate the video quality of scalable encoded video as it is perceived by the receiving client after lossy network transport.

Providing video services to heterogeneous clients in wireless ad hoc networks is particularly challenging as (i) the heterogeneous client processing and display capabilities typically prevent clients from processing and displaying the same encoded video information, and (ii) wireless connections typically suffer from bandwidth variability and transmission errors. We jointly address these two challenges by introducing a novel video coding strategy which combines multiple description coding, in particular temporal descriptors, with layered spatial coding. Our spatial scalable descriptor coding strategy enables heterogeneous clients in wireless multi-hop networks with path diversity to receive preencoded video streams over independent paths and to process only that amount of encoded video information that suits their processing and display capabilities. We evaluate our coding strategy through simulation experiments with a highly dynamic video sequence. We find that our coding strategy with two descriptors improves the quality of the received video by approximately 4 dB and cuts the quality variability approximately in half compared to layered coding.

Video traces containing the sizes and (PSNR) qualities of the individual frames of a video encoding are a convenient video representation for the evaluation of video networking mechanisms. These video traces can be used to find the frame loss probabilities of a lossy networking mechanism, but can not give the PSNR video quality after lossy network transport. To date the video quality after lossy network transport could only be determined through experiments with actual video or by approximating the quality of the frames affected by a loss with some low PSNR quality. In this paper we introduce and evaluate offset distortion traces with which the video quality after lossy network transport can be accurately determined without requiring experiments with actual video. We explain how the offset distortion traces can be used by networking researchers without equipment or experience in video signal processing to accurately evaluate video networking mechanisms in terms of the PSNR video quality.

Packet-switched unidirectional and bidirectional ring wavelength division multiplexing (WDM) networks with destination stripping provide an increased capacity due to spatial wavelength reuse. Besides unicast traffic, future destination stripping ring WDM networks also need to support multicast traffic efficiently. In this paper, we provide a probabilistic analysis of the mean hop distances traveled by multicast packet copies on the wavelength channels, and based on the mean hop distances analyze the nominal transmission capacity, reception capacity, and multicast capacity of both unidirectional and bidirectional ring WDM networks with destination stripping. The developed analytical methodology accommodates not only multicast traffic with arbitrary multicast fanout but also unicast and broadcast traffic. In our numerical investigations we examine the impact of number of ring nodes and multicast fanout on the transmission, reception, and multicast capacity of both types of ring networks for different unicast, multicast, and broadcast traffic scenarios and different mixes of unicast and multicast traffic. Our analytical methodology provides a foundation for extended analyses of the multicast capacity of WDM ring networks and enables the evaluation and comparison of future multicast-capable medium access control (MAC) protocols for unidirectional and bidirectional ring WDM networks in terms of transmitter, receiver, and multicast throughput efficiency.

Video services are expected to account for a large portion of the traffic in future wireless networks. Therefore, realistic traffic sources are needed to investigate the network performance of future communication protocols. Previously, we provided a publicly available library of frame size traces of long MPEG-4 and H.263 encoded videos in the QCIF format resulting in low bandwidth video streams. These traces can be used in 3G network simulations. Some future communication systems, such as WLAN systems, offer high data rates and therefore high quality video can be transmitted over such higher speed networks. We now present an addition to our existing trace library. For this addition we collected over 100 pre-encoded video sequences from the Web, generated the trace files, and conducted a thorough statistical evaluation. Because the pre-encoded video sequences are encoded by different users, their video settings differ in terms of codec, quality, format, and length. The advantage of user diversity for encoding is that it reflects very well the traffic situation in upcoming WLANs. Thus, the new traces are very suitable for the network performance evaluation of future WLANs.

Wavelet-based encoding is now emerging as an efficient way to encode video for streaming over the Internet and for wireless applications. "Wavelet-based video coding has been recently added to the JPEG-2000 video standards. As wavelet encoded video emerges as the next generation video encoding method, it is vital to compare the efficiency of wavelet encoded video against the widely used DCT-based MPEG encoded video. However, due to the lack of long wavelet encoded video streams, most research has so far been based on short video traces. This paper presents a comparison study on MPEG vs wavelet encoded video traces for one hour movie excerpts with rate control. These long video sequences allow for the evaluation of long range dependency and self similarity of the generated video traffic, which has not been studied before in the context of comparing DCT and wavelet-based encoding. We focus on the elementary as well as self-similar traffic characteristics of the encoded video. A hump behavior for the variability of frame sizes is observed for increasing video bit rates for both wavelet and MPEG encoded video. In addition, the quality characteristics of the encoded video is examined and related to the traffic. Our results indicate that the wavelet encoded video results in higher video quality than MPEG encoded video. For the frame size variability we find different characteristics depending on the aggregation level for a given data rate. The results also indicate that the variation of quality resulting from the wavelet encoding is lower than for the MPEG encoded video