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Emotions play a vital role in almost all the activities we perform, including learning. In fact, the success of any learning system is largely dependent upon its ability to deliver the course content in such a form so as to meet the learning requirements of the target audience. Learning Systems can be tailored to effectively utilize the feedback from learners to improve the course content, and thus the feedback can prove to be a valuable asset. There is an increased demand for focusing on a learner-centric approach to content delivery. In this study we attempt at detecting different learning-relevant emotions from the feedback for a course so as to enable course designers to incorporate the type of content that matches a learners requirements. Rather than taking into account six basic emotions (sadness, happiness, fear, anger, surprise and disgust) we consider interest, engagement, confusion, frustration, disappointment, boredom, hopefulness and satisfaction emotions for the purpose of our study since they are more relevant in a learning setup. We employed a supervised algorithm, Support Vector Machine, for affect detection from the textual feedback in our experiments. [ABSTRACT FROM AUTHOR]

Content dissemination in Vehicular Ad hoc Networks (VANET) is a challenging topic due to the high mobility of nodes, resulting in the difficulty of keeping routing tables updated. State-of-the-art proposals overcome this problem by avoiding the management of routing tables but resort to the so-called table of neighbors (NT) from which a next-hop is selected. However, NTs also require updating. For this purpose, some solutions resort to broadcasting beacons. We propose a Context- and Mobility-Aware Forwarding (CMAF) strategy that resorts to a Short-Term Mobility Prediction—STMP—algorithm, for keeping the NT updated. CMAF is based in Named Data Networking (NDN) and works in two modes, Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I). V2V CMAF leverages the overheard packets to extract mobility information used to manage NT and feed the STMP algorithm. V2I CMAF also uses a controlled and less frequent beaconing, initially from the Road-Side Units (RSUs), for a further refinement of the predictions from STMP. Results from extensive simulations show that CMAF presents superior performance when compared to the state of the art. In both modes, V2V and V2I (with one beacon broadcast every 10 s) present 5–10% higher Interest Satisfaction Ratio (ISR) than those of CCLF for the same overhead, at a cost of 1 s of increased Interest Satisfaction Delay (ISD). Moreover, the number of retransmissions of CMAF is also comparatively low for relatively the same number of hops. Compared to VNDN and Multicast, CMAF presents fewer retransmissions and 10 % to 45 % higher ISR with an increased overhead of about 20 % . [ABSTRACT FROM AUTHOR]

In peer-to-peer(P2P) networks, content delivery is very critical, but there are not many relevant research results in content delivery technology. In this paper, in order to simulate content delivery in P2P mode, the number of online players is abstracted into the number of servers that can provide services, the peers write content to buffer is abstracted into the arrival of customers, and the players read content from buffer is abstracted into the service process of the servers. Based on the consideration of the effect of the number of P2P online players on energy consumption, an M/M/c queueing model with random variation in the number of servers is developed, and negative customers and preemptive priority policies are introduced. The matrix–geometric solution method and Gauss-Seidel iterative method are used to derive the performance measures of the system at steady state for two types of contents. And some numerical examples are given using Matlab for analyzing the trend of performance measures in P2P networks with parameters. The benefit function is established to obtain the parameter values that make the social benefit optimal by Nash equilibrium and social optimal strategy, and provide the theoretical basis for the scheduling of P2P peers. [ABSTRACT FROM AUTHOR]

Copyright of Frontiers of Information Technology & Electronic Engineering is the property of Springer Nature and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper presents a solution using Bluetooth Low Energy (BLE) beacon technology to enhance visitor experiences in museums, both indoors and outdoors. Organizations using the developed mobile service can associate text, images, video, and audio content to specific exhibits, providing a dynamic and engaging encounter for visitors. This innovative approach extends applicability to a wide range of organizations, not just museum environments.

The rapid development of 5G/6G and AI enables an environment of Internet of Everything (IoE) which can support millions of connected mobile devices and applications to operate smoothly at high speed and low delay. However, these massive devices will lead to explosive traffic growth, which in turn cause great burden for the data transmission and content delivery. This challenge can be eased by sinking some critical content from cloud to edge. In this case, how to determine the critical content, where to sink and how to access the content correctly and efficiently become new challenges. This work focuses on establishing a highly efficient content delivery framework in the IoE environment. In particular, the IoE environment is re-constructed as an end-edge-cloud collaborative system, in which the concept of digital twin is applied to promote the collaboration. Based on the digital asset obtained by digital twin from end users, a content popularity prediction scheme is firstly proposed to decide the critical content by using the Temporal Pattern Attention (TPA) enabled Long Short-Term Memory (LSTM) model. Then, the prediction results are input for the proposed caching scheme to decide where to sink the critical content by using the Reinforce Learning (RL) technology. Finally, a collaborative routing scheme is proposed to determine the way to access the content with the objective of minimizing overhead. The experimental results indicate that the proposed schemes outperform the state-of-the-art benchmarks in terms of the caching hit rate, the average throughput, the successful content delivery rate and the average routing overhead.

Determining the overall level of satisfaction among students who are being educated via distance learning methods and how instructional approach (synchronous versus asynchronous) varies, in terms of both knowledge outcome and level of satisfaction, is the hallmark of this study. It also considers student perception of online learning compared to face-to-face learning, and whether or not students can learn as much online as in person. Further, students can, in some cases, be more satisfied with online learning. Student success levels and satisfaction are, however, related to the level of engagement they experience and the cultural adaptability of the course work, as it relates to the online teaching methods used and the teacher's approach to online learning. Thus, the study concludes that distance learning is an appropriate approach for students but more research is needed regarding best practices for collaboration, content delivery, and adaptability. [ABSTRACT FROM AUTHOR]

Fog computing (FC) is a concept that encompasses cloud paradigm-like amenities to the edge of the network. The fog layer that complements the cloud-based concept aids in strengthening the standards of services (QoS) in time-sensitive activities. One such application of FC arises in the deployment of content delivery networks for reducing the latency in transferring content to customers and enhancing the user experience. This paper presents a fog-based system where fog nodes act as caches for content storage. Appropriate placement of cache nodes is important for fast content distribution to users and maintaining the QoS. Another challenge is to minimize energy expenditure while deploying caches at scale. To deal with these issues, a Connected Dominating Set (CDS) and popularity-based caching strategy in content distribution fog networks are suggested in this work. The content is sorted based on its past popularity and then CDS based rules have been developed to recommend the most ideal fog nodes (FNs) to place popular content or files. The selection of fog nodes as caches is performed based on factors of storage capacity, energy consumption, and user density (i.e., the number of neighbors of a fog node). Experiment outcomes authenticate the efficiency of the anticipated scheme without caching and random method of cache placement. In comparison to the without caching and random method, the proposed method produces better fog nodes as cache nodes, incurs a lower energy consumption and consumes less bandwidth. [ABSTRACT FROM AUTHOR]

A two-stage adaptive non-orthogonal/orthogonal multiple access transmission scheme was designed for content delivery scenarios in wireless caching networks.For the scheme, a high-reliability user scheduling strategy was proposed, and the analytical expression of the outage probability and its asymptotic expression in high signal-to-noise ratio regime were derived by theoretical analysis.The analysis results indicate that the proposed strategy can achieve full spatial diversity gain.Simulation results demonstrate that compared with the instantaneous channel state information-based scheduling strategy, the random scheduling strategy, and the conventional orthogonal multiple access transmission strategy, the proposed strategy can achieve better content delivery reliability than existing strategies by obtaining the spatial diversity gain.

Today, the rapid development, large volume, and global access of multimedia applications have demanded the shifting of network functionality from the central server to the near user. In recent times, Edge Computing (EC) and Information Centric Networking (ICN) have been presented as arising advances for content dissemination near the end user. The EC aims to provide content locally by reducing the burden of the core networks, while ICN allows content routing and forwarding based on content names directly. The inherent in-network caching feature of ICN facilitates caching by intermediate network nodes. The combined use of EC and ICN can efficiently handle content dissemination and ultimately improves user experience. In this regards, an ICN based edge caching scheme has been proposed for handling multimedia big data traffic in IoT based smart cities, while incorporating four caching attributes in its proposed design. Firstly, a layered network architecture has been presented that offers Device-to-Device (D2D) communication and ICN support at the application layer of Base station (BS) for utilizing caching of the requested content at edge of the network. Secondly, a decision on caching of contents at network nodes in layered network architecture has been presented based on various centrality measures, which supports efficient caching. Thirdly, this work offers caching of content near the delivery path in ICN network tiers, while leveraging near path caching for fast content dissemination. Lastly, reproactive caching where proactive caching is followed by reactive caching for controlling the network traffic during peak hours has been integrated in to the design model. The performance of the proposed scheme has been evaluated by conducting simulations in Icarus- an ICN caching simulator against various caching benchmark schemes. The results received from the experiments have shown significant performance improvement of our proposed scheme for different performance metrics including cache hit ratio, content retrieval delay, content path stretch, and internal link load. [ABSTRACT FROM AUTHOR]

The vehicular named data networking (VNDN) is gaining more attention as a future vehicular networking (VN) model. The VNDN's fundamental principles of data naming, data-centric forwarding, and in-network caching make it inherently capable of mitigating the challenges in traditional VN. However, broadcast communication for content discovery and delivery causes VNDN to face challenges such as overhead due to packets redundancy, collision, and retransmission. In this paper, we introduce an Interest-Data-flow tracking-based forwarding scheme (IDTracS) for VNDN. IDTracS aims to improve VNDN performance by prioritizing the potential best forwarders and addressing Interest and Data packet broadcast issues. IDTracS is a receiver- and time-contention-based forwarding scheme that controls both Interest and Data packets forwarding. IDTracS uses the node's degree of centrality to the Interest/Data flow and the node's geo-distance closeness from the last Interest/Data forwarder for the requested content name-prefix. Accordingly, it computes an Interest/Data forwarding delay timer for prioritizing Interest/Data among nodes. The simulation findings reveal that the proposed IDTracS achieves an enhanced Interest satisfaction ratio, delivery delay and network transmission overhead compared to related geo-position- and distance-based protocols. [ABSTRACT FROM AUTHOR]

Traditional network architectures cannot meet user needs for content caching, and there is a conflict between low latency requirements and high communication costs in content delivery. To address these problems, an online content caching and delivery algorithm based on Lyapunov optimization and branch and bound method is proposed under the scenario of horizontal collaboration between edge nodes to balance delivery delay and cost and to make efficient decisions on content caching and content delivery. The proposed algorithm decomposes the continuous problem into a single slot online optimization problem based on Lyapunov optimization theory, and solves them using branch and bound algorithms. Simulation experiments showed that the proposed algorithm can achieve lower average content delivery delay and higher content hit rate under a limited content delivery cost budget. It can also adaptively balance content delivery delay and delivery cost. [ABSTRACT FROM AUTHOR]

Copyright of Journal on Communication / Tongxin Xuebao is the property of Journal on Communications Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

As delay-tolerant and large-size content, for example, software updates, TV series, and virtual reality related content, become more prevalent in mobile networks, the need for efficient content delivery mechanisms becomes increasingly important,the traffic that carries these contents is not suitable to be evaluated using traditional network performance metrics, e.g., delay, throughput, and jitter. Based on this insight, we propose the solution of content dissemination from opportunistic mobile social communications (CODOMOC)which utilizes energy cost as an alternative performance metric and exploits daily human activity mobility pattern to determine how, when, and where the contents should be disseminated. Then, we introduce two options in CODOMOC to achieve different network operators’ objectives. The two options are the Only Dense (OD) option which aims at minimizing energy consumption for network operators and the Broadcast Efficiency (BE) option to further reduce the total carbon footprint of network operators. CODOMOC is evaluated by comparing with a mobility-based broadcast method. The results show that CODOMOC reduces the average energy consumption by 51% and 60% in the OD and BE options respectively. The proposed solution equipped with the two modes is expected to provide a higher degree of flexibility and reduce energy consumption for mobile networks, while, admittedly, the application scope of the solution and the associated methodologies proposed in this paper is restricted to important but non-urgent traffic delivery.

While potential teaching activities in the online classroom are unlimited, an instructor’s teaching time is not. As such, it is essential that online instructors prioritize limited time to instructional strategies that have the greatest impact on student learning. A survey of 413 faculty and 2386 students examined faculty and student perceptions about instructional components or strategies that have greatest impact on student learning in the online classroom. Findings revealed significant differences in faculty and student perceptions with faculty giving the highest value ratings to non-instructor generated content and students prioritizing text-based instructional content (regardless of source). Overall, faculty tended to place more value on instructional components compared to students. Students rated faculty interaction and feedback as the most valuable component of their online learning experience. Findings explore how institutions can utilize teaching supplements to support faculty’s desire to provide content so that instructional time can focus on interaction and feedback.

Multi-access Edge Computing (MEC) is a new paradigm that brings storage and computing close to the clients. MEC enables the deployment of complex network-assisted mechanisms for video streaming that improve clients’ Quality of Experience (QoE). One of these mechanisms is segment prefetching, which transmits the future video segments in advance closer to the client to serve content with lower latency. In this work, for HAS-based (HTTP Adaptive Streaming) video streaming and specifically considering a cellular (e.g., 5G) network edge, we present our approach Segment Prefetching and Caching at the Edge for Adaptive Video Streaming (SPACE). We propose and analyze different segment prefetching policies that differ in resource utilization, player and radio metrics needed, and deployment complexity. This variety of policies can dynamically adapt to the network’s current conditions and the service provider’s needs. We present segment prefetching policies based on diverse approaches and techniques: past segment requests, segment transrating (i.e., reducing segment bitrate/quality), Markov prediction model, machine learning to predict future segment requests, and super-resolution. We study their performance and feasibility using metrics such as QoE characteristics, computing times, prefetching hits, and link bitrate consumption. We analyze and discuss which segment prefetching policy is better under which circumstances, as well as the influence of the client-side Adaptive Bit Rate (ABR) algorithm and the set of available representations (“bitrate ladder”) in segment prefetching. Moreover, we examine the impact on segment prefetching of different caching policies for (pre-)fetched segments, including Least Recently Used (LRU), Least Frequently Used (LFU), and our proposed popularity-based caching policy Least Popular Used (LPU).

Context • During the Covid pandemic, teachers and academics at all levels were abruptly required to learn and deploy generic online educational tools that do not adequately substitute for many classroom or lab practices. In the rush to make education viable during the pandemic, there was little time for critical analysis of the qualities of "online delivery," especially with regard to embodied dimensions of cognition. Conventional academic styles of pedagogy and testing were commonly emulated. In the attempt to simply "keep the train on the rails," there has been little time to assess what - cognitively or pedagogically - has been gained or lost. > Problem • Computer and internet-based interactive applications have specific cognitive affordances and constraints that differ from the tangible embodied scenarios they sometimes purport to emulate. Traditional pedagogy in many disciplines entails a substantial component of "hands-on" learning, a complementary knowing how that enables practice, builds skills and provides metaphors and concepts. Existing software and interfaces do not cater well to training embodied skills, and perhaps cannot. In the absence of hands-on lab, studio and clinical experience that usually complements textbook-lecture-test styles of pedagogy, a knowing that (as opposed to knowing how) orientation was reinforced. The traditional recognition that embodied experience is an integral component of effective learning has been elided (similar elision occurs with respect to some online research practices). In emphasizing "problem-solving," this instrumentalizing of a knowledge-that style of pedagogy usually elides the syncretic and creative cognitive corollary of problem framing. > Method • I discuss online pedagogy and research from an enactive/embodied critical perspective and juxtapose case examples of embodied practices. > Results • The conclusions drawn from this discussion are that important cognitive differences between face-to-face, hands-on pedagogy and research, and pedagogy and research conducted online are present and critical analysis of the situation is crucial for pedagogical and research effectiveness. > Implications • Whether in pharmacology, mechanical engineering or social media, new technologies and technological systems often have unexpected effects when embedded in society. This is the case with online pedagogy. Critical assessment is overdue. Such will motivate new education research and human-computer design initiatives. Researchers in human-computer interaction, cognitive psychology, education design and other fields should take up the challenge. It remains a possibility that some embodied practices may simply not be compatible with online environments. It is incumbent upon institutions to take such issues seriously, or risk substantial impoverishment of the educational experience. > Constructivist content • I analyze online pedagogy and research from an embodied and enactivist perspective, assessing the different qualities of sensorimotor engagement in screen-based activities as opposed to hands-on experiences. I argue that "knowing" is grounded in sensorimotorically multimodal embodied experiences, and recognize the limitations in the sensorimotor ecologies of online phenomena. [ABSTRACT FROM AUTHOR]

Pre-caching at edge nodes may improve resource efficiency for future content-centric cellular networks by bringing contents closer to users. However, which mode (muticast or unicast) should be selected for the efficient delivery of those cached contents is still not well addressed, especially at the situation that some key parameters such as content popularity and transmission environments are unknown. To solve this problem, the criterion of delivery mode selection is studied based on the learning policy of multi-armed bandit. According to the criterion, a mode selection algorithm is proposed. In this algorithm, edge nodes can choose the better delivery modes in the current slot only dependent on existing observations in the previous slot. Performance evaluation results validate our analyses and proposals on the mode selection. [ABSTRACT FROM AUTHOR]

The first outbreak of COVID-19 was reported in December 2019 and the disease took the shape of a pandemic in the next few months. Universities around the world imported lessons to their student mostly in online mode in 2020 and 2021. Thirty-five undergraduate computer science students were interviewed about their experience of attending online lectures during the COVID-19 pandemic. A quantitative analysis of their responses revealed that 43% of them felt that they can learn equally well from online and offline lectures, 49% felt that online lectures provide them flexibility which in turn helps them to perform better in academics and 54% felt that professors have improved their online teaching skills since the beginning of the pandemic. Further, a qualitative analysis revealed that students appreciate online lectures for allowing them to access ebooks and digital resources while attending lectures, and making it easier to study topics that require a lot of visualization and ask queries to professors. Consequently, 77% students said that a combination of online and offline lectures may be used in the future with students being allowed to choose how they learn. Alternatively, only online lectures may be scheduled on some days of the week so that students need not travel to the campus on those days. [ABSTRACT FROM AUTHOR]

The traditional Internet architecture provides applications with a stable end-to-end connection between a requester and a source node holding the information. However, in a vehicular environment, this connection is not stable due to the mobility of vehicles that results in location change, from the requester, the source, or both. To overcome these situations, Named Data Networking (NDN) has been proposed as new architecture for data retrieval and mobility support. This new paradigm enhances content access and dissemination by decoupling the content from its original location. In this paper, we introduce the forwarding in NDN-based VANETs and highlight its benefits and limitations. We propose a classification of NDN-based VANETs forwarding strategies, then detail the representative schemes. Furthermore, we provide a review and comparison of existing forwarding strategies in terms of various attributes such as transmission mode, forwarding strategies, changes of NDN architecture, application scenarios, problem addressed, evaluation metrics, simulation platform. Finally, we conclude our contribution by identifying main open research challenges that can be exploited for future works. We believe that this survey will help NDN-based VANETs researchers community to easily understand the forwarding in vehicular environment, overcome some repeated solutions and gives further inspiration to design a new protocols to improve NDN-based VANETs networks in this context for further relevant research works.

The majority of Web content is delivered by only a few companies that provide Content Delivery Infrastructures (CDIs) such as Content Delivery Networks (CDNs) and cloud hosts. Due to increasing concerns about trends of centralization, empirical studies on the extent and implications of resulting Internet consolidation are necessary. Thus, we present an empirical view on consolidation of the Web by leveraging datasets from two different measurement platforms. We first analyze Web consolidation around CDIs at the level of landing webpages, before narrowing down the analysis to a level of embedded page resources. The datasets cover 1(a) longitudinal measurements of DNS records for 166.5 M Web domains over five years, 1(b) measurements of DNS records for Alexa Top 1 M over a month and (2) measurements of page loads and renders for 4.3 M webpages, which include data on 392.3 M requested resources. We then define CDI penetration as the ratio of CDI-hosted objects to all measured objects, which we use to quantify consolidation around CDIs. We observe that CDI penetration has close to doubled since 2015, reaching a lower bound of 15% for all .com, .net, and .org Web domains as of January 2020. Overall, we find a set of six CDIs to deliver the majority of content across all datasets, with these six CDIs being responsible for more than 80% of all 221.9 M CDI-delivered resources (56.6% of all resources in total). We find high dependencies of Web content on a small group of CDIs, in particular, for fonts, ads, and trackers, as well as JavaScript resources such as jQuery. We further observe CDIs to play important roles in rolling out IPv6 and TLS 1.3 support. Overall, these observations indicate a potential oligopoly, which brings both benefits but also risks to the future of the Web. [ABSTRACT FROM AUTHOR]

Cache-enabled networks suffer hugely from the challenge of content caching and content delivery. In this regard, cache-enabled device-to-device (D2D) assisted multitier cellular networks are expected to relieve the network data pressure and effectively solve the problem of content placement and content delivery. Consequently, the user can have a better opportunity to get their favored contents from nearby cache-enabled transmitters (CETs) through reliable and good-quality links; however, as expected, designing an effective caching policy is a challenging task due to the limited cache memory of CETs and uncertainty in user preferences. In this article, we introduce a joint content placement and content delivery technique for D2D assisted multitier cellular networks (D2DMCN). A support vector machine (SVM) is employed to predict the content popularity to determine which content is to be cached and where it is to be cached, thereby increasing the overall cache hit ratio (CHR). The content request is satisfied either by the neighboring node through the D2D link or by the cache-enabled base stations (BSs) of the multitier cellular networks (MCNs). Similarly, to solve the problem of optimal content delivery, the Hungarian algorithm is employed aiming to improve the quality of satisfaction. The simulation results indicate that the proposed content placement strategy effectively optimizes the overall cache hit ratio of the system. Similarly, an effective content delivery approach reduces the request content delivery delay and power consumption. [ABSTRACT FROM AUTHOR]

The rapid development of the Internet of Vehicles (IoV) enables various vehicular applications, such as image-aided navigation and traffic information management. It is important to provide efficient content delivery services for these vehicular applications. Caching popular content at roadside units (RSUs) is a promising way to improve content delivery efficiency. However, due to RSUs with limited cache space, it is very challenging to develop an effective content delivery policy that satisfies the high quality of service (QoS) requirements for vehicular applications. In this paper, we investigate the user-centric content delivery problem with service delay constraints in the IoV, where the objective is to minimize the vehicle’s cost under usage-based pricing. The problem of finding an optimal content delivery policy is modeled as a finite-horizon Markov decision process (MDP). Since the cache state of each RSU, and the wireless channel qualities between the vehicle and RSUs, are usually unknown to the vehicle a priori, the vehicle must learn the optimal delivery policy by interacting with the environment. To solve this problem and optimize the vehicle’s cost, we propose a double deep Q network (DDQN)-based algorithm, which implements dynamic content delivery decisions. Furthermore, the double deep Q network can overcome the large-scale state space and reduce Q value over-estimation. Numerical results show that our policy achieves a near-optimal performance when compared to the optimal policy that knows precisely cache state and wireless channel state. We also compare the effects of different caching strategies and vehicle mobility on the performance of the algorithm. [ABSTRACT FROM AUTHOR]

For wireless caching networks, the scheme design for content delivery is non-trivial in the face of the following tradeoff. On one hand, to optimize overall throughput, users can associate their nearby APs with great channel capacities; however, this may lead to unstable queue backlogs on APs and prolong request delays. On the other hand, to ensure queue stability, some users may have to associate APs with inferior channel states, which would incur throughput loss. Moreover, for such systems, how to conduct predictive scheduling to reduce delays and the fundamental limits of its benefits remain unexplored. In this paper, we formulate the problem of online user-AP association and resource allocation for content delivery with predictive scheduling under a fixed content placement as a stochastic network optimization problem. By exploiting its unique structure, we transform the problem into a series of modular maximization sub-problems with matroid constraints. Then we devise PUARA, a Predictive User-AP Association and Resource Allocation scheme which achieves a provably near-optimal throughput with queue stability. Our theoretical analysis and simulation results show that PUARA can not only perform a tunable control between throughput maximization and queue stability, but also incur a notable delay reduction with predicted information. [ABSTRACT FROM AUTHOR]

Online education is of various types and can be defined in numerous ways. Over the last few decades, online education has gained popularity, but has also been regarded with reluctance and scepticism as to the benefits it may have. The COVID- 19 pandemic was declared on the 11th of March 2020 and forced many countries to impose lockdowns and restrictions. As such, several activities were shut down, schools and universities were closed, thus, the need for rapid solutions arose. Since the online environment was there for the taking, physical classrooms were virtualised overnight, homes became learning and teaching spaces for students and educators, whereas the latter were faced with adapting content and transferring it in order to suit its delivery via online platforms. With little to no experience in online education, educators sought to find ways to continue their work bringing emergency modifications. Although digital transformation is not a new phenomenon, the transit from physical classrooms to online ones has been and still is a rough learning curve for many educators. Apart from optimising digital technology to apply to education, another difficult task of any online teacher is to keep students motivated and involved, which can be achieved in different ways. This article discusses methods of content and course delivery, the technology-induced problems that online teaching implies, assessment, ethical behaviour in a virtual setting, and the risk of cheating in online examinations, as well as ways of preventing cheating. Nevertheless, the abrupt onlinification of education may lie at the basis of future exploration and research.

The traditional Internet architecture provides applications with a stable end-to-end connection between a requester and a source node holding the information. However, in a vehicular environment, this connection is not stable due to the mobility of vehicles that results in location change, from the requester, the source, or both. To overcome these situations, Named Data Networking (NDN) has been proposed as new architecture for data retrieval and mobility support. This new paradigm enhances content access and dissemination by decoupling the content from its original location. In this paper, we introduce the forwarding in NDN-based VANETs and highlight its benefits and limitations. We propose a classification of NDN-based VANETs forwarding strategies, then detail the representative schemes. Furthermore, we provide a review and comparison of existing forwarding strategies in terms of various attributes such as transmission mode, forwarding strategies, changes of NDN architecture, application scenarios, problem addressed, evaluation metrics, simulation platform. Finally, we conclude our contribution by identifying main open research challenges that can be exploited for future works. We believe that this survey will help NDN-based VANETs researchers community to easily understand the forwarding in vehicular environment, overcome some repeated solutions and gives further inspiration to design a new protocols to improve NDN-based VANETs networks in this context for further relevant research works. [ABSTRACT FROM AUTHOR]

Recent publications abound that criticize instruction that takes learning styles into consideration in teacher training, lesson planning, and content delivery. What contribution does the recent movement to debunk the validity of learning styles make to teaching adults? This article challenges the purpose for debunking learning styles and emphasizes the need to have students develop a deeper knowledge of their own learning preferences while encouraging instructors to provide variety in delivering content to appeal to the large number of learning preferences perceived by their students. [ABSTRACT FROM AUTHOR]

This paper studies joint cache placement and cooperative multicast beamforming to provide content-centric data services for mobile users in the integrated satellite-terrestrial network (ISTN). Specifically, in the ISTN, users requesting the same content are arranged into a multicast group and served by the cache-enabled base stations (BSs) and low earth orbit (LEO) satellite via cooperative beamforming. To maximize the network utility that takes network throughput and backhaul traffic into consideration, the cache placement, LEO satellite and BS clustering, and multicast beamforming are jointly designed and formulated as a two-timescale optimization problem. However, the original problem is anti-causal since the cache placement strategy and content delivery policy are coupled in different timescales. By utilizing historical information, we propose a two-step scheme to decompose the problem into a short-term content delivery subproblem and a long-term cache placement subproblem. As the former subproblem is nonconvex with mixed-integer variables and coupling constraints, we transform it into an equivalent problem and propose a penalty concave-convex procedure based algorithm to solve it. To address the latter subproblem, a centralized iterative algorithm and a distributed alternating algorithm with low complexity are developed, respectively. Simulation results validate that the proposed schemes can effectively enhance the network throughput and reduce the backhaul traffic compared with benchmark scheme. [ABSTRACT FROM AUTHOR]

Cloud Content Delivery Networks (CCDNs) scale their content delivery services by taking advantage of the flexible cloud resources and management mechanisms so that users can get the content nearby and reduce traffic of the central network. The content delivery efficiency of CCDNs and quality of service can be improved by rationally planning routing. However, the existing routing planning strategies are not suitable for CCDNs because the number of nodes and the network traffic in CCDNs are dynamic. In this paper, we propose a routing planning method that considers the influence of these factors. In our method, we first build a new framework called the collaborative reinforcement learning based CCDN to capture the dynamic characteristics of the above factors through the V -value feedback model, and we quantify the node transmission efficiency with the Q value. Based on this framework, we propose a collaborative reinforcement learning based delivery tree construction algorithm to obtain a delivery tree. Through numerical simulation, it is found that the delivery path constructed by this method can effectively improve the efficiency of content delivery.

In view of the problem of satellite mobility and terrestrial network resource constraints, a pre-placement strategy for terrestrial network content based on named data networking was proposed; Then, local content requests under the coverage of multiple base stations was fully considered.Based on the diff erence in distribution, a content distribution strategy based on fairness in satellite networks was proposed.The numerical results showed that the proposed content pre-placement strategy was eff ective in terms of satellite network content injection.In addition, in terms of satellite network content distribution, the proposed fair-based content distribution strategy could maximized content satisfaction.

Information-Centric Networking (ICN) has been recently recognized as a promising future Internet paradigm by providing content-oriented functionality and in-network caching characteristic rather than relying on overlay or end-to-end communication mode. However, the deployment of ICN on the real network is very difficult or even almost impossible at the current stage. In other words, the end-to-end IP network always has the overwhelming development strengths. In spite of this, the end-to-end IP network has no the adequate in-network caching resources to offload the traffic. At the right time, as the essential and significant characteristic of ICN, in-network caching has great potential to be applied into IP network in order to dramatically reduce load pressure of Origin Server (OS). Therefore, we in this paper enable IP network to have the capacity of in-network caching, and on this base, we investigate the comprehensive routing and content delivery. Firstly, the network topology is divided into a number of Autonomous Systems (ASs), and each AS has an Information Management Center (IMC) which has rich functionalities by maintaining Content Index Table (CIT), Physical Topology Index Table (PTIT) and Response Request Table (RRT). Then, CIT is used to conduct both intra-AS routing and explicitly collaborative caching, and PTIT is used to conduct both inter-AS routing and adaptive content delivery including on-path and off-path. In particular, RRT is only sent to OS so that the end-to-end communication connection is switched off. Finally, the experimental results reveal that the proposed scheme can reduce load pressure of OS around 45.7% over Deltacom and 54.8% over GTS. In addition, compared to the previous work, the proposed scheme has better performance. [ABSTRACT FROM AUTHOR]

Poorly designed PowerPoint presentations used by most lecturers for content delivery at conferences and in classrooms are seen as an inhibitor to effective teaching and learning. These kinds of presentations often have too many tiny words with no infographics or multimedia elements nor conformed with multimedia design principles. This indicates a lack of requisite skills by lecturers to design compelling and memorable presentations. The present study therefore developed an Interactive PowerPoint Presentation Design Training Package (IPPDTP) for use by lecturers in mastering PowerPoint presentation design that leads to effective content delivery at the tertiary level of education. The study adopted Research and Development (R&D) design with three research questions and one null hypothesis, and it was carried out in four phases: Phase I--needs assessment; Phase II--IPPDTP development; Phase III--IPPDTP validation by ICT experts; and Phase IV--trial-testing of IPPDTP by users. The population for the study was sixty-nine participants. The instruments for data collection were the IPPDTP questionnaire, expert validation, and user evaluation questionnaires. The instruments yielded reliability coefficients of 0.78, 0.81, and 0.84, respectively, and an overall reliability coefficient of 0.87. Mean and standard deviation were used to analyze the research questions while t-test statistical tool was used to test the null hypothesis at 0.05 level of significance. Findings revealed that IPPDTP was functional and very effective in mastering PowerPoint presentation designs while users expressed satisfaction. It was therefore recommended that lecturers should obtain and use IPPDTP to learn how to create effective PowerPoint presentations as well as improve their presentation skills to keep abreast with global best practices. [ABSTRACT FROM AUTHOR]

The outbreak of COVID-19 brought about global lockdown and educational managers were confronted with the unenviable choice of closing institutions until the pandemic plummeted or learning new ways of content delivery. The purpose of this article was to investigate use of social media on content delivery in higher education in Kenya: A case of School of Education, University of Nairobi. The objectives were to determine the social media platforms used in content delivery and the individual characteristics which determined their use. The case study research design included a target population of 150 Master of Education (M.Ed.) school-based students and 20 lecturers. The findings indicated that WhatsApp was the most popular platform for both lecturers and students, followed by YouTube and Facebook. Reasons for use of the said social media platforms were convenience, cost-friendliness, timesaving, and access of information to many. It was observed that there were no clear patterns on the types of social media platforms used by gender, age, year of study and teaching experience. The study concludes that while social media has been entrenched in content delivery; exploitation of the same to its full potential remains untapped. The study recommends that the University management reconsider the mobile provider for supply of SIM cards and bundles on basis of internet coverage and reliability of connectivity.

We present a new spatiotemporal offloading (STO) framework that can deal with the time-varying traffic demand in an overloaded location using proactive caching during off-peak times. Focusing on a conventional cellular network, the system-wide mobility can be captured by the inter-cell transitions along individual users' mobility pattern. It is opposed to the existing geographical caching for content placement during off-peak times, which treats a system built around multiple base stations as a single service area, thus limiting its performance to low hit probability. We formulate a system-level STO problem to determine a content placement strategy that minimizes the access link load by delivering the requested data through device-to-device (D2D) links, rather than through a cellular link, particularly targeting potentially overloaded cells. Because its complexity is seriously attributed to the individual users' cell-by-cell mobility and content preferences, an aggregated flow model has been proposed to reduce the search space by invoking the law of segment conservation. Simulation results clearly demonstrate the effect of the spatiotemporal offloading that can be realized through inter-cell mobility-aware proactive caching. An extremely important implication of the current framework is that a varying traffic demand can be spatiotemporally distributed in a cellular system, overcoming the traditional limitation that arises from conservative or inefficient resource allocation due to a reliance on over-dimensioning.

Device-to-device (D2D) communications have shown a huge potential in cellular offloading and become a potential technology in 5G and beyond. In D2D networks, the requested contents by user devices (UDs) can be delivered via D2D links, thus offloading the content providers (CPs). In this work, we address the problem of minimizing the delay of delivering content in a decentralized and partially D2D connected network using network coding (NC) and cooperation among the UDs. The proposed optimization framework considers UDs' acquired and missing contents, their limited coverage zones, NC, and content's erasure probability. As such, the completion time for delivering all missing contents to all UDs is minimized. The problem is modeled as a coalition game with cooperative-players wherein the payoff function is derived so that increasing individual payoff results in the desired cooperative behavior. Given the intractability of the formulation, the coalition game is relaxed to a coalition formation game (CFG). A distributed coalition formation algorithm relying on merge-and-split rules is developed for solving the relaxed problem at each transmission. The effectiveness of the proposed solution is validated through computer simulation against existing schemes.

In this paper, we investigate the content delivery in edge caching networks where requesters can get the interested contents from surrounding helpers by device-to-device (D2D) communications. Taking the finite service capacity and cache space of helpers into account, the problem is to maximize the number of requesters obtaining all desired contents by optimizing the selection of content sources. The hypergraph theory is first applied to analyze the cumulative content effect caused by multiple content sources, and the problem is formulated as a hypergraph based cooperation cache game with at least one pure Nash equilibrium (NE). The log-linear based cooperation caching algorithm (LBCC) is proposed to find the corresponding NE points. Simulation results verify the superiority of the proposed LBCC. [ABSTRACT FROM AUTHOR]

Multi-source device-to-device (D2D) communication allows the base station (BS) to serve the content requests of the users locally through D2D links. As a result, the load on the base stations (BSs) and the consumption of radio resources reduces significantly. Clearly, the success of multi-source content delivery relies on the willingness of the content owners (COs), i.e., sources, to deliver their content to requesting user. Consequently, in this article, we investigate the economic interaction between single BS and multiple COs for content delivery. In view of the fact that participating COs are heterogeneous with respect to the amount of content in their cache and the sensitivity towards energy consumption, there is a need to design a fair incentive mechanism which motivates COs for content delivery. To this end, we model the interaction among the BS and multiple COs as one-to-many bargaining game and design an incentive mechanism based on the Nash bargaining framework. Specifically, we obtain the optimal amount of content delivered by participating COs and their corresponding incentives under two variants of one-to-many bargaining, namely sequential bargaining and concurrent bargaining. For both variants of bargaining, the obtained optimal solutions are capable of minimizing the amount of content delivered by the BS while ensuring fair incentive transfer among the participating COs. [ABSTRACT FROM AUTHOR]

Although the future Internet paradigm especially Information‐Centric Networking (ICN) has been recognized and accepted in recent years, the end‐to‐end IP network structure always shows the overwhelming development tendency due to the deployment difficulty of ICN on the real and large‐scale network environment. However, each coin has two sides, that is to say, ICN also has its own unique value, that is, in‐network caching feature which is not included in the current IP network. In this letter, we propose an In‐network caching enabled IP network paradigm, which is a novel concept different from both ICN and the traditional IP network. Based on such paradigm, we present a light content delivery scheme depending on identity, with the chain‐based fine‐grained caching support. Given the NP‐hardness of delivery scheme in mesh topology, we make a case study in Metropolitan Area Network (MAN) to address the large‐scale content delivery scenario. The experimental results show that the proposal in this letter can effectively relieve the load pressure of origin server, and can also greatly improve the delivery efficiency. [ABSTRACT FROM AUTHOR]