Delivery Network Research Articles

Optimization of trucks and drones in tandem delivery network with drone trajectory planning

Drones are widely recognized as an efficient method for delivering small and lightweight parcels, such as medicines, to customers who are difficult to reach by logistic vehicles. Various studies and projects have been conducted to integrate drones into the traditional logistic service. In this approach, drones stay on trucks, take off from trucks at specific locations to make deliveries to customers, and return to the trucks to replenish and recharge. However, drone trajectories could overlap when multiple drones are used for delivery, which can lead to the risk of collisions. Consequently, the need to address the trajectory planning of drones becomes essential. This work introduces a novel truck-drone routing problem. The novelty presented by this problem lies in the integration of drone trajectory planning problems into the truck-drone routing to generate collision-free routes for drones. This concept has not been previously explored in the existing body of literature. The problem is formulated as a mixed-integer programming problem, and a matheuristic is proposed to solve it. Computational experiments show that the proposed matheuristic outperforms the commercial solver and genetic algorithm in terms of solution quality and search efficiency. Results demonstrate that the integrated truck-drone delivery mode has a lower operating cost than the mere truck mode, and including trajectory planning can prevent potential collisions.

Urban Farms for Neighborhood Improvement and Social Well-being of Urban Areas in Kandy


 
 
 In the latter half of the 20th century, as urban populations increased in the majority of emerging nations, the reliability of urban food production and delivery networks decreased. Urban hunger increased concurrently with the urban population, speeding up by in response, urban farms spread over a growing worldwide. However, later other purposes were added to it and urban farms were nurtured. Globally, urban farms are changing as a result of political, economic, environmental, and technical advancements. With time, urban farming activities are becoming a reason to improve the value and quality of life in terms of economic, social, and other elements. In this research, it was investigated how urban farming affects the social well-being and neighborhood improvements of urban areas. Furthermore, it focuses on urban farms' relative attachment to the neighborhood and confidence, their impact on agriculture literacy and youth development, and the economic and food crises in the urban areas. It was sought to understand community perceptions of how urban farms can benefit cities through a qualitative study conducted in two urban farms in Kandy. Interviews with visitors, laborers, and professionals related to urban farming revealed the pathways by which community members view farms as improving neighborhoods and social well-being. According to this study, six characteristics are highlighted and evaluated: neighborhood attachment, neighborhood confidence, youth development, food access, agricultural literacy, and economic development. Benefits stemmed from a primary changes urban farms made to neighborhood improvement: the creation of public project involvement, the physical improvement of degraded space, the production of local food, and the creation of new businesses. These changes led to multiple perceived benefits, including increased social connectedness, a transformed physical landscape, an improved neighborhood reputation, and employment opportunities. But there were some points that should be developed. especially in cases of food access and youth development. Urban farms with strong social aims may appear to contribute little to economic development if measured using traditional indicators of success such as job creation or fiscal impacts, but they provide numerous other benefits for community development.
 Keywords: Urban farms, Social connectedness, Physical improvement, Food access, Youth engagement, Economic development
 
 

CTJIF-ICN: A Coadjuvant Trust Joint Interest Forwarding Mechanism in Information Centric Networks

The Information centric networks (ICN) transforms the focal point of current Internet paradigm to data centric approach from host centric approach by allowing content driven forwarding and in-network caching mechanisms. Though NDN (Named data networking) paradigm of ICN assures a secure content communication, it is vulnerable to different attacks by the malicious nodes. To minimize the hazards from compromised nodes and to improve the network security, the remaining nodes should transparently receive information about such nodes. This will restrict the forwarding strategy to exploit these malicious nodes for forwarding interest and content as well. Our protocol introduces a dynamic model for prediction of trust in order to evaluate the node trust. Proposed approach observes the historical behaviors of node and uses extended fuzzy logic rules for the prediction of future behaviors to evaluate the node’s trust value. This prediction model is incorporated within the trust based forwarding mechanism that aims to forward interest through secure and shortest path. The extensive simulation study has been carried out to analyze the protocol performance in ns-3 driven ndnSIM-2.0 simulator for performance metrics such as data discovery latency, packet delivery ratio, network overhead, detection ratio and cache hit ratio. When we integrate our trust joint forwarding strategy to state-of-the-art protocols, their performance is significantly improved up to approximately 10-35% against stated performance measures for realistic network topology.

Analysing Smart Power Grid against different Cyber Attacks on SCADA System

The integration of information and communications technology (ICT) with traditional power grid is transforming the power grid into a more reliable and smarter cyber physical system. A supervisory control and data acquisition (SCADA) system is used to implement the cyber layer for power grid, which is responsible for real-time monitoring and control of power grid and power delivery network. A SCADA system combined with other devices creates smart power grid environment, but it also makes power grid vulnerable against different cyber-attacks as it requires connection with various kinds of open networks. Smart power grids are a extremely critical infrastructure and attack of any kind can affect socio economical condition of the region. In this paper we present a non-complex co-simulation environment for analysing smart power grid and also includes two different cyber-attack scenarios. Index Terms- co-simulation; smart power grid; SCADA system; cyber-attack scenarios.

TrafAda: Cost-Aware Traffic Adaptation for Maximizing Bitrates in Live Streaming

The business growth of live streaming causes expensive bandwidth costs from the Content Delivery Network service. It necessitates traffic adaptation, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">i.e.</i> , adapting video bitrates for cost-efficient bandwidth utilization, especially under the 95 <sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <i>th</i> </sup> percentile pricing. However, our data-driven investigations indicate the existing methods are hard to achieve bitrate-cost balance in a long month-level billing cycle due to dynamic traffic patterns. We propose <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> , a learning-based cost-aware traffic adaptation method consisting of i) an ultra-long-term bandwidth demand forecasting model to learn complex bandwidth usage patterns, and ii) an imitation learning-based bitrate decision mechanism to optimize the ultra-long-term objective. We have implemented and deployed <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> on a large-scale live streaming system in China serving over one billion viewers from 388 cities. The results show that <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">TrafAda</i> improves peak-hour bitrate, quality of experience (QoE), and watching time by 34.75%, 44.56%, and 10.68%, respectively, without extra bandwidth cost, which can be converted to a considerable value for a commercial system.

PowerID: Using Supply-Side Impedances of Power Delivery Networks as Signatures for Consumer Electronics

PowerID: Using Supply-Side Impedances of Power Delivery Networks as Signatures for Consumer Electronics

Toward Inference Delivery Networks: Distributing Machine Learning With Optimality Guarantees

Toward Inference Delivery Networks: Distributing Machine Learning With Optimality Guarantees

Safe Drip – IOT Empowered Saline Monitoring and Reversal Prevention

The proposed system is an IoT-based alert system for saline reversal in medical settings.Saline is administered to patients in medical facilities to aid in their recovery from dehydration and enhance their overall well-being. The occurrence of saline reversal arises when medical staff inject saline into a patient without being fully conscious of the situation in the hospital.The system utilizes sensors placed at various points in the saline delivery network to detect the flow of saline in the wrong direction i.e the back flow of blood. The system sends alerts to medical staff via message to their mobile when a saline reversal is detected. If the saline decreases over a certain threshold level the sensor will stops the saline flow and the medical staff will get a alert message that the “bottle is empty”, Also we can monitor the saline flow rate via a web application. This system aims to improve patient safety by reducing the occurrence of saline reversals, which can cause serious harm to patients. The system proposes the combination of sensors to detect the saline level, The system uses ESP8266 as master, a load cell to measure the weight of the saline bottle and the system uses servo motor to stop the saline drip. If the weight decreases under a certain threshold level, the load cell will send a signal to the master controller and the controller will stop the drip flow using servo motor. To alert the medical staff in the hospital, the system uses IFTT server. Through IFTT the medical staff will get a alert via SMS/call. Also the system had developed a web application, Through the web application the medical staff can monitor the current status of the saline level.

RAMPART: Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation

In the field of multi-agent autonomous transportation, such as automated payload delivery or highway on-ramp merging, agents routinely exchange knowledge to optimize their shared objective and adapt to environmental novelties through Cooperative Multi-Agent Reinforcement Learning (CMARL) algorithms. This knowledge exchange between agents allows these systems to operate efficiently and adapt to dynamic environments. However, this cooperative learning process is susceptible to adversarial poisoning attacks, as highlighted by contemporary research. Particularly, the poisoning attacks where malicious agents inject deceptive information camouflaged within the differential noise, a pivotal element for differential privacy (DP)-based CMARL algorithms, pose formidable challenges to identify and overcome. The consequences of not addressing this issue are far-reaching, potentially jeopardizing safety-critical operations and the integrity of data privacy in these applications. Existing research has strived to develop anomaly detection-based defense models to counteract conventional poisoning methods. Nonetheless, the recurring necessity for model offloading and retraining with labeled anomalous data undermines their practicality, considering the inherently dynamic nature of the safety-critical autonomous transportation applications. Further, it is imperative to maintain data privacy, ensure high performance, and adapt to environmental changes. Motivated by these challenges, this paper introduces a novel defense mechanism against stealthy adversarial poisoning attacks in the autonomous transportation domain, termed Reinforcing Autonomous Multi-agent Protection through Adversarial Resistance in Transportation (RAMPART). Leveraging a GAN model at each local node, RAMPART effectively filters out malicious advice in an unsupervised manner, whilst generating synthetic samples for each state-action pair to accommodate environmental uncertainties and eliminate the need for labeled training data. Our extensive experimental analysis, conducted in a Private Payload Delivery Network (PPDN) —a common application in the autonomous multi-agent transportation domain—demonstrates that RAMPART successfully defends against a DP-exploited poisoning attack with a \(30\% \) attack ratio, achieving an F1 score of 0.852 and accuracy of \(96.3\% \) in heavy-traffic environments .

A Novel Approach to Improve Quality of Service in Cloud-Based Content Delivery Network

A content delivery network (CDN) improves normal website functions and increases customer satisfaction. In addition, CDNs foster a seamless online experience, ensuring reliable performance and bolstering cybersecurity measures for enhanced user trust and data protection. It improves High-speed content delivery, Real-time streaming, Multi-user scaling etc. One of the most notable achievements is the remarkable reduction in latency, thanks to advanced CDN technologies that position content closer to end-users, enhancing responsiveness and making real- time applications, online gaming, and interactive content more enjoyable. Furthermore, cost optimization has been a focal point, with CDNs efficiently managing resources and data transfer to deliver content while keeping operational expenses in check. In essence, the ongoing enhancements in CDNs underscore their pivotal role in shaping the digital content landscape. With a focus on speed, scalability, latency reduction, cost efficiency, security, global reach, and data- driven decision-making, CDNs have become indispensable for organizations seeking to excel in serving an increasingly diverse and demanding global audience. In a world where digital content reigns supreme, these improvements empower organizations to meet and exceed user expectations, contributing to their overall success in the digital age. Keywords: Content Delivery Network, Cloud-based Content Delivery Network, Quality metrics and monitoring, Resource management, Performance optimisation.

Design and Simulation of a System-in-Package Chip for Combined Navigation.

This paper proposes a system-in-package combination navigation chip. We used wire bonding, chip stacking, surface mount, and other processes to integrate satellite navigation chips, inertial navigation chips, microprocessor chips, and separation devices. Finally, we realized the hardware requirements for combined navigation in a 20 mm × 20 mm chip. Further, we performed a multi-physics simulation analysis of the package design. For antenna signals, the insertion loss was greater than -1 dB@1 GHz and the return loss was less than -10 dB@1 GHz. The amplitude of these noises of the signal between the MCU and the IMU was approximately 20%, and the maximum value of the coupling coefficient between signal lines on the top surface was 13.4174%. The ninth mode of the power plane yielded a maximum voltage of 55 mV, and all power delivery networks had a DC voltage drop of less than 2%. The highest temperature in the microsystem was approximately 42 °C. These results show that our design performed well in terms of signal, power, and thermal performance.

Comparing first-line (1L) atezolizumab plus bevacizumab (A+B) to lenvatinib (L) or sorafenib (S) in patients with unresectable hepatocellular carcinoma (uHCC): Findings from the National Veteran Health Administration (VHA) database.

446 Background: The approval of A+B has reshaped the treatment paradigm for patients with uHCC, establishing a new standard of care for 1L uHCC treatment. The VHA is the largest independent healthcare delivery network and the largest provider of liver-related care in the US. We aimed to compare patient characteristics and clinical outcomes between veterans who received 1L A+B and those who received L or S for unresectable HCC. Methods: Using data from the VHA National Corporate Data Warehouse (1/1/2017 - 12/31/2022), we conducted a retrospective cohort study in patients diagnosed with uHCC who initiated A+B after 2020 or initiated L or S after 2018. Child-Pugh (CP) scores were calculated using a previously validated algorithm. Kaplan-Meier and multivariable Cox regression methods were applied to compare overall survival (OS) between the groups. Results: We identified 405 eligible patients who received A+B, 453 received L, and 1,016 received S. Compared with patients receiving L or S, those treated with A+B had a higher proportion of CP A5 (47% and 35% vs. 54%, respectively; both p&lt;0.05), a similar proportion of A6 (30% and 32% vs. 31%, p=0.683 and 0.681, respectively), a lower proportion of B7 (15% and 20% vs. 9%, respectively; both p&lt;0.05), and a greater comorbidity burden as measured by the mean Charlson Comorbidity Index (CCI) (5.8 [L or S] vs 6.6 [A+B], both p&lt;0.05). Median OS (mOS) was 12.8 months (95% CI: 10.6, 17.1) in the A+B cohort, compared to 9.5 months [95% CI: 7.8, 11.4] in the L cohort, and 8.0 months [95% CI: 7.1, 8.6] in the S cohort. In subgroup analyses, longer mOS was observed with A+B than with L or S in CP A patients and those with ALBI grades 1-2A; similar mOS was observed among those with more severe liver dysfunction (Table). In multivariable analysis, A + B was associated with a 26% lower risk of death relative to L (HR=0.74, 95% CI: 0.62-0.88) and 30% lower risk of death compared to S (HR = 0.70, 95%CI 0.60-0.82), both p&lt;0.001. Conclusions: In a real-world VHA experience with A+B among diverse patient groups, a clinically notable survival benefit was observed with A+B compared to L or S. Future evaluations of the safety of using A+B in patients with greater liver dysfunction, including CP B and cirrhosis, are needed to evaluate the expanded use of A+B. [Table: see text]

Health equity and gendered border blindness: an exploration of healthcare services at the international border in Rajasthan

ObjectivesCitizens' access to health care and the delivery of the healthcare services is significantly affected by the spatiality of the regions and the connectedness of the elements of the healthcare system. This network of healthcare system, region and delivery of services faces myriad challenges in the borderland geography, which is characterised by accentuated military presence, poor physical infrastructure, disinterest of habitation near the border, lack of adequate, necessary and allied services such as schools and industry, social seclusion, migration etc. All these factors amalgamate to create an effect of gender-blind phenomenon as well as border-blind phenomenon. This is particularly acute for women and children. Study designA phenomenology research design has been used for the study. It encapsulates qualitative aspects of the views of those who experience marginalisation inclusive of gender-blind experiences. Marginalised women and frontline healthcare workers at the borderland were considered for the inquiry in this study. The study is a composite description of the phenomenon. MethodsThe border districts of Rajasthan from the Radcliffe line of Rajasthan have been identified for the purpose of the study. Using narrative ethnography along with interviews, an examination was executed from health professionals and marginalised women to comprehend health care access and equity from the service provider's perspective as well as the beneficiary's perspective. ResultsThe study provides a range of attributions based on which it could be established that health inequities exist in bordering rural areas. ConclusionsThe study realises the geopolitical influence of the Radcliffe line, where borderlands are commonly vital to the continuum struggle between the centre and state. Yet, there remains a gap towards implementing various schemes and services due to varied reasons for access from main to far-off border areas, making the situation vulnerable from a resource deficiency point of view. The Health Equity Framework cannot meet Social Determinants of Health in borderland areas of Rajasthan.

Abstract B057: An organoids-on-a-chip model to recapitulate and dissect the tumor microenvironment of PDAC

Abstract Abstract Body: Characteristics of the PDAC tumor microenvironment (TME) such as desmoplasia and an immunosuppressive landscape contribute to its resistance to chemotherapy and immunotherapy approaches. To recapitulate and dissect the in vivo PDAC TME, we bioengineered a PDAC organoid-on-a-chip model by integrating patient-derived PDAC organoids on a microfluidic-based organ-on-a-chip. While the patient-derived organoids (PDOs) maintain the characteristics of the in vivo counterparts with high fidelity, the organ-on-a-chip provides a controllable and reproducible environment with stromal and immune niche cells, creating an in vitro model that may providing insights into PDAC drug resistance and allow testing for new therapies. The multicellular organoid-on-a-chip model is composed of 1) a central PDAC niche including patient-derived PDAC organoids, patient-derived cancer-associated macrophages (CAFs), tumor-associated macrophages (TAMs) and blood vessels, and 2) surrounding vascular networks for nutrient transport and drug delivery. We reproduced the hypovasculariy of PDAC stromal niche on the chip and found a lower level of CD31 expression in the PDAC organoid niche than in the normal pancreatic organoid niche (p&amp;lt;0.0001). Comparing with the niche without PDOs, the PDAC niche on chip demonstrated a higher expression level of α-smooth muscle actin (α-SMA) on CAFs (fold-change=2.32, p&amp;lt;0.0001) and a more extensive deposition of ECM components (e.g., collagen I, collagen III, collagen IV and hyaluronic acid, fold-change&amp;gt;=2.25, p⇐0.0002), indicating the existence of desmoplasia on chip. Profiling of cytokine levels in the media revealed that, compared to models used with normal pancreatic epithelial cells with the other cellular components, the PDAC organoid TME induced the upregulation of a series of pro-tumor cytokines including MIP-3α, IL-8, CXCL5, IL-13 (fold-change&amp;gt;=5.02), the inflammation-related cytokines MIF, IL-5, MCP-1 (fold-change&amp;gt;=1.82), and CAF-derived cytokines like CXCL10, HGF, CXCL1, CXCL12 (fold-change&amp;gt;=1.82). The immunosuppressive cytokine TGF-β was increased after PDOs were loaded on the chip (762.3 pg/mL vs. 222.6 pg/mL in conditioned medium), as was the proinflammatory cytokine IL-6 (1089.5 pg/mL vs. 81.3 pg/mL), was maintained at a higher level than that in the niche with normal organoids (380.5 pg/mL vs. 60.4 pg/mL at day 13). We also found that PDOs induced TAMs inclined to be M2-like (CD68+CD163+) phenotype on chip. In turn niche cells (TAMs and CAFs) contributed to the immunosuppression in PDAC TME: the secretion of cytokines like IL-6, IL-13, MIP-1β and HGF were downregulated without niche cells (fold-change⇐0.646). In conclusion, we have developed a PDAC organoid-on-a-chip recapitulates the PDAC TME and is promising to serving as a platform to study mechanisms of drug resistance in PDAC as well as test novel therapies. Citation Format: Lunan Liu, Diane M. Simeone, Weiqiang Chen. An organoids-on-a-chip model to recapitulate and dissect the tumor microenvironment of PDAC [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B057.

SWARAM: Osprey Optimization Algorithm-Based Energy-Efficient Cluster Head Selection for Wireless Sensor Network-Based Internet of Things.

The Internet of Things (IoT) has transformed various aspects of human life nowadays. In the IoT transformative paradigm, sensor nodes are enabled to connect multiple physical devices and systems over the network to collect data from remote places, namely, precision agriculture, wildlife conservation, intelligent forestry, and so on. The battery life of sensor nodes is limited, affecting the network's lifetime, and requires continuous maintenance. Energy conservation has become a severe problem of IoT. Clustering is essential in IoT to optimize energy efficiency and network longevity. In recent years, many clustering protocols have been proposed to improve network lifetime by conserving energy. However, the network experiences an energy-hole issue due to picking an inappropriate Cluster Head (CH). CH node is designated to manage and coordinate communication among nodes in a particular cluster. The redundant data transmission is avoided to conserve energy by collecting and aggregating from other nodes in clusters. CH plays a pivotal role in achieving efficient energy optimization and network performance. To address this problem, we have proposed an osprey optimization algorithm based on energy-efficient cluster head selection (SWARAM) in a wireless sensor network-based Internet of Things to pick the best CH in the cluster. The proposed SWARAM approach consists of two phases, namely, cluster formation and CH selection. The nodes are clustered using Euclidean distance before the CH node is selected using the SWARAM technique. Simulation of the proposed SWARAM algorithm is carried out in the MATLAB2019a tool. The performance of the SWARAM algorithm compared with existing EECHS-ARO, HSWO, and EECHIGWO CH selection algorithms. The suggested SWARAM improves packet delivery ratio and network lifetime by 10% and 10%, respectively. Consequently, the overall performance of the network is improved.

Heuristic approach for optimising reliable supply chain network using drones in last-mile delivery under uncertainty

ABSTRACT In addressing the challenges of last-mile logistics, the reliability of the supply chain network becomes paramount. These challenges are intensified due to drone performance limitations and various uncertainties in supply chain operations. While recent literature recognises the potential of drones for last-mile delivery, it falls short in effectively considering these uncertainties in drone-enabled supply chain models. Our research addresses this gap with two major contributions: first, a novel stochastic mixed-integer programming model is developed to construct a feasible delivery network, including warehouses and recharging stations, enhancing both coverage and reliability. Second, a modification in the genetic algorithm by considering each scenario independently improves computational efficiency, outperforming commercial software by an average of 40% and up to 55%. Empirical findings reveal that strategic investments in system hardening can yield substantial improvements in reliability. Despite the absence of real-world stochastic parameters as a limitation, this research pioneers the design of reliable networks under uncertainties and extends drone coverage through strategic charging stations. This work sets a significant milestone for future optimization in drone logistics, offering practical implications for supply chain managers considering drone adoption.

MOODLE MANUAL UPGRADE METHODOLOGY

This abstract outlines a systematic four-step methodology for upgrading Moodle from version 3.6.5 to 3.9.9. The process begins with comprehensive backup and preparation, followed by the replacement of the Moodle codebase and execution of the upgrade script. Post-upgrade checks are performed to ensure the smooth functioning of core and third-party components. Additionally, the abstract explores the actions and functions of the cron script, emphasizing its role in executing scheduled tasks critical for Moodle's operation.&#x0D; Furthermore, the document sheds light on common reasons behind slow Moodle operation and provides practical tips for enhancing system speed. Recommendations include optimizing server resources, database maintenance, strategic caching implementation, and the judicious use of content delivery networks (CDNs). The importance of upgrading PHP and database versions is highlighted, alongside the benefits of disabling unused features and employing server monitoring tools. The abstract emphasizes the significance of staying abreast of Moodle updates for continued performance improvements and bug fixes, offering a holistic guide for upgrading and optimizing Moodle systems.

Holistic Service Provisioning in a UAV-UGV Integrated Network for Last-Mile Delivery

Holistic Service Provisioning in a UAV-UGV Integrated Network for Last-Mile Delivery

Beyond 1 Million Nodes—Crowdsourced Video CDN: Architecture, Technology, and Economy

The video service has already become one of the Internet killer applications. To deliver the ever growing video traffic with the Quality of Experience (QoE) guarantee, deploying the infrastructure of Content Delivery Network (CDN) closer and closer to users has been a noticeable trend. And this trend becomes more promising when recently CDNs providers start utilizing the storage and network resources of the edge network devices (e.g., the Wi-Fi Access Points) for content delivery [13], i.e., crowdsourced CDN. In this article, we envision the crowdsourced CDN and propose a set of practical strategies to guide the implementation of such new paradigm. Firstly, we perform a large-scale measurement to explore the geo-content popularity and the geo-heterogeneity of the resource distribution. Benefiting from the measurement observations, we design the key solutions for the system operation, including regional content popularity prediction based on Markov Decision Process (MDP), region partition for the edge device organization and the collaborative content replication. Moreover, we consider the economic issues and propose a sequential auction mechanism to cope with the resource allocation when multiple Content Providers (CPs) compete for the edge resources for their content caching. We also demonstrate the performance gain of our design with data-driven simulation.

Bayesian K-means clustering based quality of experience aware multimedia video streaming

&lt;span&gt;Media streaming is an essential approach for delivering multimedia information from the source distributor to the end-user through the Internet. Along with the development of more number of users and the spread of mobile devices, the availability and diversity of multimedia applications has also increased. Multimedia users primarily prioritize quality of experience (QoE), as they seek to access multimedia content with high availability and enjoy smooth video streaming in the shortest possible time. The impact of video delivery plays a significant role in QoE, which is efficiently made by delivering the content through a specialized content delivery network architecture. In this research, a Bayesian K-means clustering algorithm is proposed for the identification of QoE in multimedia video streaming. In this multimedia video streaming, the Amazon Prime video dataset is utilized for determining the performance of the proposed model. The proposed method is developed from the ‘Patching Up’ the video quality problem (PatchVQ) model, the from patches to pictures (PaQ-2-PiQ) model is utilized for the spatial feature extraction, and 3D ResNet-18 is utilized for temporal feature extraction. The proposed Bayesian K-means achieved a QoE reward function of 5,237.42 and 5841.36 as well as a fairness reward function of 5,841.36 and 8,732.08 at the speed of 1,500 kB/s and 2,000 kB/s respectively.&lt;/span&gt;