Improve Content Delivery Research Articles

Resource allocation for content distribution in IoT edge cloud computing environments using deep reinforcement learning

This study presents a novel approach to optimize resource allocation, aiming to boost the efficiency of content distribution in Internet of Things (IoT) edge cloud computing environments. The proposed method termed the Caching-based Deep Q-Network (CbDQN) framework, dynamically allocates computational and storage resources across edge devices and cloud servers. Despite its need for increased storage capacity, the high cost of edge computing, and the inherent limitations of wireless networks connecting edge devices, the CbDQN strategy addresses these challenges. By considering constraints such as limited bandwidth and potential latency issues, it ensures efficient data transfer without compromising performance. The method focuses on mitigating inefficient resource usage, particularly crucial in cloud-based edge computing environments where resource costs are usage-based. To overcome these issues, the CbDQN method efficiently distributes limited resources, optimizing efficiency, minimizing costs, and enhancing overall performance. The approach improves content delivery, reduces latency, and minimizes network congestion. The simulation results substantiate the efficacy of the suggested method in optimizing resource utilization and enhancing system performance, showcasing its potential to address challenges associated with content spreading in IoT edge cloud calculating situations. Our proposed approach evaluated metrics achieves high values of Accuracy is 99.85%, Precision at 99.85%, specificity is 99.82%, sensitivity is 99.82%, F-score is 99.82% and AUC is 99.82%.

Analysis of learning styles in first semester students at the UACyA UAN: an exploration based on the VARK Model

This article analyzes the predominant learning styles in first semester students at the UACyA UAN, which classifies the styles as visual, auditory, reader-writer and kinesthetic. This approach provides a solid foundation for understanding individual learning preferences. The objective is to analyze and understand the predominant styles through a statistical analysis of the results, examining preference patterns and their correlation with academic performance and adaptation to the university environment. Pedagogical implications are explored to design personalized educational interventions that fit the most common learning styles. The results highlight the diversity of learning styles, providing valuable information to improve content delivery and the educational experience. This approach based on the VARK model offers a comprehensive perspective on learning preferences, promoting a more inclusive and effective pedagogy in university education.

SLAP: Segmented Reuse-Time-Label Based Admission Policy for Content Delivery Network Caching

‘‘Learned” admission policies have shown promise in improving Content Delivery Network (CDN) cache performance and lowering operational costs. Unfortunately, existing learned policies are optimized with a few fixed cache sizes while in reality, cache sizes often vary over time in an unpredictable manner. As a result, existing solutions cannot provide consistent benefits in production settings. We present SLAP , a learned CDN cache admission approach based on segmented object reuse time prediction. SLAP predicts an object’s reuse time range using the Long-Short-Term-Memory model and admits objects that will be reused (before eviction) given the current cache size. SLAP decouples model training from cache size, allowing it to adapt to arbitrary sizes. The key to our solution is a novel segmented labeling scheme that makes SLAP without requiring precise prediction on object reuse time. To further make SLAP a practical and efficient solution, we propose aggressive reusing of computation and training on sampled traces to optimize model training, and a specialized predictor architecture that overlaps prediction computation with miss object fetching to optimize model inference. Our experiments using production CDN traces show that SLAP achieves significantly lower write traffic (38%-59%), longer SSDs lifetime (104%-178%), a consistently higher hit rate (3.2%-11.7%), and requires no effort to adapt to changing cache sizes, outperforming existing policies.

Hop Count Based Interest Selection and Content Forwarding Scheme for Vehicular Named Data Networks

Vehicular Named Data Networks (VNDN) face challenges in efficiently disseminating content due to high mobility and intermittent connectivity. To address these challenges, a Hop Count based Interest Selection and Content Forwarding (HISCF) scheme for VNDNs is proposed. The scheme focuses on mitigating interest flooding, reducing data packet duplication, and alleviating network congestion. HISCF consists of two components: interest selection and content forwarding. The selection process chooses a vehicle based on hop count and Interest Satisfaction Ratio (ISR) to forward the interest packet. Content forwarding is performed considering a hop count limit and pending interests, ensuring efficient content delivery. The HISCF scheme is evaluated using extensive simulations in ns-3 with ndnSIM. Performance metrics such as Data Packet Replication Count (DPRC), total number of interest packets forwarded, Interest Response Time (IRT) and routing overhead are analysed. Results show that HISCF outperforms naïve VNDN, reducing DPRC, minimizing interest packets forwarded, and decreasing average IRT. The findings demonstrate that HISCF effectively mitigates interest broadcast storms, reduces data packet duplication, and improves content delivery efficiency in VNDNs. This study contributes to VNDN research advancement and provides insights for designing effective content forwarding mechanisms in vehicular networks.

ENCache: Improving content delivery with cooperative caching in Named Data Networking

Content centric networks offer better response time and resilience to failures by caching the content in the network routers. Owing to the limited capacity of caches and the quest for maximizing the utilization of available caches has lead to many interesting caching techniques. While on-path caching is a commonly adopted caching technique to reduce content access latency, it does not guarantee better cache hit ratio when considered across multiple receivers in the network. In this paper, we present eNCache which is a cooperative caching method that cooperates among the routers in the neighborhood to improve overall performance. It retains the simplicity of on-path caching and makes caching, routing (queries and replies) decisions by interacting with off-path routers in the neighborhood. We present the working of eNCache with algorithms for request handling and caching decisions at every router. We also formalize the interaction between a router and its neighbors by providing an extension to the Named Data Networking Interest packet structure. We evaluate and compare the performance of eNCache with other popular on-path caching strategies by performing simulations on standard RocketFuel Internet Service Provider (ISP) topologies using a discrete event simulator. We also compare the performance of a variant of enhanced NCache, i.e.,eNCache with hash based off-path caching methods. Our simulation results show that eNCache has reduced latency, better cache hit ratio and diversity.

Blockchain-Enabled On-Path Caching for Efficient and Reliable Content Delivery in Information-Centric Networks

As the demand for online content continues to grow, traditional Content Distribution Networks (CDNs) are facing significant challenges in terms of scalability and performance. Information-Centric Networking (ICN) is a promising new approach to content delivery that aims to address these issues by placing content at the center of the network architecture. One of the key features of ICNs is on-path caching, which allows content to be cached at intermediate routers along the path from the source to the destination. On-path caching in ICNs still faces some challenges, such as the scalability of the cache and the management of cache consistency. To address these challenges, this paper proposes several alternative caching schemes that can be integrated into ICNs using blockchain technology. These schemes include Bloom filters, content-based routing, and hybrid caching, which combine the advantages of off-path and on-path cachings. The proposed blockchain-enabled on-path caching mechanism ensures the integrity and authenticity of cached content, and smart contracts automate the caching process and incentivize caching nodes. To evaluate the performance of these caching alternatives, the authors conduct experiments using real-world datasets. The results show that on-path caching can significantly reduce network congestion and improve content delivery efficiency. The Bloom filter caching scheme achieved a cache hit rate of over 90% while reducing the cache size by up to 80% compared to traditional caching. The content-based routing scheme also achieved high cache hit rates while maintaining low latency.

의복구성 분야의 패턴제도를 위한 유튜브 교육 동영상 분석

This study aimed to prepare a database for constituting pattern making education content and producing video educational material by analyzing the video content organization and screen production method of YouTubers that provided pattern making information that is the most basic of clothing construction education. Four YouTubers in the clothing construction field with more than 10,000 subscribers were selected among domestic YouTubers, and 155 pattern making videos with more than 10,000 views were selected for the analysis within their channels. In the category of pattern education, topics of each YouTuber's videos were classified into dimensional measurement, basic foundation patterns, grading, and clothing classification, and the composition of lecture contents was analyzed. Each YouTuber's videos were analyzed in terms of lecture format and stage setting, cut composition, lecturer screen size and camera angle and movement, pattern making screen size and camera angle and movement, lighting and audio, subtitles and screen conversion effects, sound effects and background sounds. Comprehensive characteristics resulted from the analysis were that the videos were composed of contents based on a practical and trendy design that reflected the needs of the viewers, and special contents such as easy pattern making methods and detailed size application. It also consisted of professional content that could be applied to design changes or dimensional changes based on long practical experience. Content and technical efforts were made to build rapport with viewers. Screen composition and editing techniques to improve content delivery showed the composition of two shots, design explanation screen and pattern drawing screen, the use of appropriate subtitles, realistic lecture composition and editing, and reduced patterns. Based on this study, I would like to conduct a step-by-step analysis of various clothing construction training videos in the future.

CDCA: Transparent Cache Architecture to Improve Content Delivery by Internet Service Providers

CDCA: Transparent Cache Architecture to Improve Content Delivery by Internet Service Providers

UAV-Mounted Cloudlet Systems for Emergency Response in Industrial Areas

The advancements in Internet of Things (IoT) and embedded systems, 5G networks and next-generation wireless systems, and embedded and distributed artificial intelligence are empowering Industry 4.0. In this new industrial era, smart autonomous and connected systems will improve efficiency, reduce cost and pollution, and increase productivity and safety in industrial 4.0 based applications. Nevertheless, the new generation technologies mentioned above have not been explored to improve safety in multiplant and industrial zones. In this article, we shed light on the design of unmanned aerial vehicles (UAVs)-assisted systems for emergence response in multiplant and industrial zones. We discuss the important contributions of UAV-mounted cloudlet systems to support SAR missions by providing computation, communication, and storage resources to first responders, surveillance of the affected area, real-time monitoring of SAR members and victims, and caching nodes to improve content delivery among SAR teams. Besides, we design an envisioned UAV-cloudlet reference system for emergency response in industrial areas. Moreover, we highlight the current challenges that are being addressed in the literature aimed at making possible the efficient use of the designed UAV-mounted cloudlet systems for emergency response. Finally, we point out some future research directions that require further investigation.

A DRL-Driven Intelligent Optimization Strategy for Resource Allocation in Cloud-Edge-End Cooperation Environments

Complex dynamic services and heterogeneous network environments make the asymmetrical control a curial issue to handle on the Internet. With the advent of the Internet of Things (IoT) and the fifth generation (5G), the emerging network applications lead to the explosive growth of mobile traffic while bringing forward more challenging service requirements to future radio access networks. Therefore, how to effectively allocate limited heterogeneous network resources to improve content delivery for massive application services to ensure network quality of service (QoS) becomes particularly urgent in heterogeneous network environments. To cope with the explosive mobile traffic caused by emerging Internet services, this paper designs an intelligent optimization strategy based on deep reinforcement learning (DRL) for resource allocation in heterogeneous cloud-edge-end collaboration environments. Meanwhile, the asymmetrical control problem caused by complex dynamic services and heterogeneous network environments is discussed and overcome by distributed cooperation among cloud-edge-end nodes in the system. Specifically, the multi-layer heterogeneous resource allocation problem is formulated as a maximal traffic offloading model, where content caching and request aggregation mechanisms are utilized. A novel DRL policy is proposed to improve content distribution by making cache replacement and task scheduling for arriving content requests in accordance with the information about users’ history requests, in-network cache capacity, available link bandwidth and topology structure. The performance of our proposed solution and its similar counterparts are analyzed in different network conditions.

Context-based caching in mobile information-centric networks

Wireless networking is expected to sustain the direct interaction between personal users’ devices, and to provide connectivity on large-scale resource-constrained devices. However, conventional networking protocols fail in large scale mobile wireless environments, due to node mobility, dynamic topologies, and intermittent connectivity. Information-Centric Networking (ICN) has been considered the most promising candidate to overcome the drawbacks of host-centric architectures where Named Data Networking (NDN) is one of the well-known and studied architectures within the ICN paradigm.The main objective of this work is to improve both content availability and network performance in mobile environments regarding the ICN paradigm. This is provided through a context-based approach for the caching admission policy providing in-network caching and content replication, facilitating the efficient and timely delivery of information. Content popularity, freshness, proximity, source mobility type and network density are some of the metrics considered in the caching decision. We conducted a comparative study between our proposal and the NDN caching strategy by using two different datasets with real mobility and connectivity traces, addressing intermittent communication. According to our results, we observed that using a multi-criteria context-based cache admission policy improves cache hits, cache evictions, and request satisfaction ratios in mobile environments, thus improving content delivery and network efficiency.

Delay-Aware Content Delivery With Deep Reinforcement Learning in Internet of Vehicles

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.

On building human-aware opportunistic communication strategies for cost-effective content delivery

Mobile networks face challenges such as data traffic growth, increasing connected devices, spectrum limitations, and costly infrastructure updates. In this context, opportunistic communication strategies appear to assist in scenarios such as data offloading, expanding the capillarity given by users’ mobility, delivering content in challenging situations (e.g., emergency, rural areas, or crowded places), and fostering innovative applications. State-of-art opportunistic strategies extract human mobility characteristics (e.g., social and spatial) to improve content delivery’s cost-effectiveness. Nevertheless, due to constraints, such as the limited availability of real-world datasets or the lack of a human-centered vision in networking, most previous works relied on traditional or simplified human-aware metrics to reach their purposes. Next-generation proposals need a more in-depth vision of peculiar aspects hidden into mobility datasets while working with more realistic scenarios. While building TOOTS, a novel human-aware opportunistic communication strategy for improved content delivery cost-effectiveness, we learned that several phases play a significant role in reaching strategy’s superior performance. This work guides the reader through the whole process necessary for building the Tactful Opportunistic Communication Strategy (TOOTS). The proposal consists of the following steps: learning human-aspect best practices from state-of-art; characterizing the traces while obtaining strategy’s insights targeting superior performance; using, proposing, and analyzing human-aware metrics; combining such metrics and insights into a complete strategy for cost-effective content delivery in a more realistic scenario, and finally, evaluating the strategy through discussions of its best practices and shortcomings for future work. This work shows that TOOTS can improve the performance of an opportunistic scenario in terms of overhead, delivery rate, and delivery latency by following this proposed process.

Assessing the Effectiveness of flowcharts as a teaching and learning tool

The Integrated Cardiovascular Acute Care disease module in the second professional year at Western New England University, College of Pharmacy and Health Sciences, introduces disease states' pharmacotherapeutics such as coronary artery disease, shock, and arrhythmias. To improve learner understanding and application of this material, we introduced the use of flowcharts in the module. Flowcharts are skeletal outlines of key concepts that help summarize acute cardiovascular diseases by combining pharmacological concepts with evidence‐based pharmacotherapeutic decision‐making. This study aims to evaluate the effectiveness of flowcharts as an instructional tool in the therapeutics module.A skeletal outline of the flowcharts was provided to the students on the College’s learning management site. The instructors completed the charts using a doc‐cam system during class at the end of each disease state. A pre‐ and post‐survey was administered to evaluate the effectiveness of these flowcharts. The study was initiated in Spring 2019 and data was collected until Spring 2021.A pre‐survey showed that 83% (n=45) agreed that flowchart use would improve their knowledge; the post‐survey showed that 91% (n=60) agreed that flowcharts improved their overall knowledge in the course. While 90% (n=60) agreed that flowcharts improved their understanding of pharmacology, 95% of the surveyed participants agreed that flowcharts helped them understand the role of drugs in pharmacotherapy. Furthermore, 96% (n=60) of the participants agreed that they would like to see more use of flowcharts in the other pharmacotherapeutics modules. A topic‐wise comparison of the average scores demonstrated significant differences in the mean±SD score on summative assessments for the classes that received flowcharts versus those that did not use flowcharts.In order to gain insight into how the students use flowcharts for learning, we also conducted focus‐group interviews of the students using predesigned questions. A total of 16 students were interviewed in groups of 4‐5. Thematic coding of the interview transcripts is currently being performed. However, preliminary 1st and 2nd round coding identified emerging themes such as “visually useful,” “summarizes material,” “helps retain and memorize”, “helps in exam” etc.The results from our study indicate that the use of flowcharts improved content delivery and application in an acute care cardiovascular disease pharmacotherapeutics module. The flowcharts provide the students with a decision‐making framework to make informed therapeutic decisions. We believe that the use of these flowcharts benefit student learning and understanding and can be used in other disease state modules.

LA-MDPF: A forwarding strategy based on learning automata and Markov decision process in named data networking

Recently, Named Data Networking (NDN) has emerged as a leading paradigm among several Informa- tion-Centric Networking (ICN) schemes promising to address the shortcomings of TCP/IP architecture. Despite the great importance of the forwarding strategy in NDN, researches in this area are still somewhat limited. This paper presents a new strategy to improve the forwarding of request packets to the best interfaces, which improves the overall performance in NDN. Markov Decision Process (MDP) is employed to model this problem, where a router is presented as a decision-maker. To improve the performance of forwarding strategy, a Learning Automata (LA)-based algorithm is proposed, in which the experiences are considered in forwarding decisions. The proposed strategy (LA-MDPF) evaluates the previous decisions and responds by a penalty or a reward, affecting the probabilities of making the same decisions in the future. LA-MDPF can trace the best interface up to the original content or its cached replicas and overcome link failures and resource constraints. The LA-MDPF performance is evaluated by computer simulations using ndnSIM. Simulation results show that, the proposed algorithm improves content delivery performance in terms of network load balancing, average throughput, average packet drop, average Interest satisfaction ratio (ISR), and average content retrieval time at a reasonable computational cost compared to existing works.

Dynamic three‐dimensional virtual environment to improve learning of anatomical structures

Increasing number of medical students and limited availability of cadavers have led to a reduction in anatomy teaching through human cadaveric dissection. These changes triggered the emergence of innovative teaching and learning strategies in order to maximize students learning of anatomy. An alternative approach to traditional dissection was presented in an effort to improve content delivery and student satisfaction. The objective of this study is to acquire three-dimensional (3D) anatomical data using structured-light surface scanning to create a dynamic four-dimensional (4D) dissection tool of four regions: neck, male inguinal and femoral areas, female perineum, and brachial plexus. At each dissection step, identified anatomical structures were scanned using a 3D surface scanner (Artec Spider™). Resulting 3D color meshes were overlaid to create a 4D (3D+time) environment. An educational interface was created for neck dissection. Its implementation in the visualization platform allowed 4D virtual dissection by navigating from surface to deep layers and vice versa. A group of 28 second-year medical students and 17 first-year surgery residents completed a satisfaction survey. A majority of medical students (96.4%) and 100% of surgery residents said that they would recommend this tool to their colleagues. According to surgery residents, the main elements of this virtual tool were the realistic high-quality of 3D acquisitions and possibility to focus on each anatomical structure. As for medical students, major elements were the interactivity and entertainment aspect, precision, and accuracy of anatomical structures. This approach proves that innovative solutions to anatomy education can be found to help to maintain critical content and student satisfaction in anatomy curriculum.

SAFT-VNDN: A Socially-Aware Forwarding Technique in Vehicular Named Data Detworking

Vehicular Social Networks (VSNs) is the bridge of social networks and Vehicular Ad-Hoc Networks (VANETs). VSNs are promising as they allow the exchange of various types of contents in large-scale through Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Vehicular Named Data Networking (VNDN) is an auspicious communication paradigm for the challenging VSN environment since it can optimize content dissemination by decoupling contents from their physical locations. However, content dissemination and caching represent crucial challenges in VSNs due to short link lifetime and intermittent connectivity caused by vehicles’ high mobility. Our aim with this paper is to improve content delivery and cache hit ratio, as well as decrease the transmission delay between end-users. In this regard, we propose a novel hybrid VNDN-VSN forwarding technique based on social communities, which allows requester vehicles to easily find the most suitable forwarder or producer among the community members in their neighborhood area. Furthermore, we introduce an effective caching mechanism by dividing the content store into two parts, one for community private contents and the second one for public contents. Simulation results show that our proposed forwarding technique can achieve a favorable performance compared with traditional VNDN, in terms of data delivery ratio, average data delivery delay, and cache hit ratio.

Using Real-Time Data to Develop and Improve Teaching of Clinical Skills in Virtual Nursing Simulation Laboratories During COVID-19 Pandemic.

During COVID-19 pandemic public health measures, face-to-face simulation laboratories were cancelled. A rapid transition to online teaching environments required staff and students to rapid upskilling in digital literacy. The purpose of this article is to describe a model of virtual nursing simulation laboratory implemented in graduate entry to practice Master's nursing program to teach clinical skills. The model used cloud-based communication app Zoom and real time feedback data to improve content delivery, student engagement and confidence in skill development. This model was co-designed with the student cohort to ensure students, as stakeholders, had a voice in having their education needs met during these challenging times.

Project-based Community of Inquiry for Effective Online Graduate Course on Life Cycle Assessment

This article aimed to present the improvements on the instructional design of a postgraduate course, namely Life Cycle Assessment (LCA), for effective content delivery in online teaching and learning due to COVID-19 pandemic. The community of inquiry framework was used as the basis for this improvement. It was aided by project-based learning to enhance the educational experiences and improve content delivery efficiency. Conventional active learning teaching pedagogy was maintained in the early part of the course. Next, an open-ended project became the pillar of the second part of the course for performing a comparative LCA in a team. To ensure all students have high individual accountability, each team member performed a LCA study independently for a sub-scope of the project. Meanwhile, their teammates supported each other as each sub-scope is interdependent on the project. WhatsApp was used to promote interactions between students-students and students-instructors to provide just-in-time feedback, and weekly consultations were offered to students to monitor student progress. Based on the course evaluation, the students felt optimistic about the changes. They agreed that these new delivery methods helped them in mastering the subject matter.

Active Learning Strategies and Blended Learning Approach for Teaching Under Graduate Software Engineering Course

Software Engineering is a core theory course offered in Undergraduate Engineering programmes. It is one of the challenging courses for the teaching faculty. The course includes various systematic approaches, and methods that can be employed for designing, developing, testing and maintaining quality software applications. The course also focuses on the latest tools and technologies that are being practiced in the industry. Effective content delivery methods leveraging active learning strategies for the classical topics have been well researched and established. However, improvement in content delivery plan and execution is required for relatively new and evolving topics like DevOps and version controlling. This paper presents an experimental study of application of the various active learning techniques such as discussion forums, tech talks followed by quiz for such topics. Impact of the application of the improved content delivery plan on the course outcomes attainment by the learners has also been observed and presented. The enhanced approach has proved to improve student learning outcomes, which are measured and presented using a standard set of tools and metrics. This paper uses the logistic regression model for the study of impact of blended learning approach on student learning outcomwhich produes ces 60% accuracy.