Network Server Research Articles

HELP: Everlasting Privacy through Server-Aided Randomness

Everlasting (EL) privacy offers an attractive solution to the Store-Now-Decrypt-Later (SNDL) problem, where future increases in the attacker's capability could break systems which are believed to be secure today. Instead of requiring full information-theoretic security, everlasting privacy allows computationally-secure transmissions of ephemeral secrets, which are only "effective" for a limited periods of time, after which their compromise is provably useless for the SNDL attacker. In this work we revisit such everlasting privacy model of Dodis and Yeo (ITC'21), which we call Hypervisor EverLasting Privacy (HELP). HELP is a novel architecture for generating shared randomness using a network of semi-trusted servers (or "hypervisors"), trading the need to store/distribute large shared secrets with the assumptions that it is hard to: (a) simultaneously compromise too many publicly accessible ad-hoc servers; and (b) break a computationally-secure encryption scheme very quickly. While Dodis and Yeo presented good HELP solutions in the asymptotic sense, their solutions were concretely expensive and used heavy tools (like large finite fields or gigantic Toeplitz matrices). We abstract and generalize the HELP architecture to allow for more efficient instantiations, and construct several concretely efficient HELP solutions. Our solutions use elementary cryptographic operations, such as hashing and message authentication. We also prove a very strong composition theorem showing that our EL architecture can use any message transmission method which is computationally-secure in the Universal Composability (UC) framework. This is the first positive composition result for everlasting privacy, which was otherwise known to suffer from many "non-composition" results (Müller-Quade and Unruh; J of Cryptology'10).

IFM/IFEk/1 Queuing Model with Erlang Service under Heptagonal and Octagonal Intuitionistic Fuzzy Numbers

Objectives: This research identifies the steps involved in determining the membership and non-membership functions of significant State execution proportions in Erlang services and models α-cut operations. Additionally, we applied the DSW Algorithmic Rule to intuitionistic fuzzy heptagonal and octagonal numbers. In this case, the inter-entry rate is Poisson and exponential, both of which have an intuitionistic Erlang nature. The main objective of this research is to evaluate the performance of a single server queuing model in terms of fuzzy and intuitionistic queuing theory. Fuzzy and intuitionistic fuzzies are the characteristics of the entering and exiting rates, respectively. Using a predetermined technique, an approach is carried out to assess the process metrics, taking the fuzzy numbers at face value without converting them into crisp values. Methods: We use the -cut method in IFM/IFEk/1 Single Server Queuing Model in FCFS with Erlang service. By applying increasing and decreasing entrance costs, the Poisson process is used to manage the arrival rates while delivering the input. Intuitionistic fuzzy queuing models are used to extend the queuing model. Findings: We noticed that the model indicated that performance can be enhanced by adding more factors. Comparing this procedure to fuzzification, accuracy is better. Shopkeepers, dealers, and companies will all benefit from the suggested model's assistance in precisely identifying the best performance metrics for the awaiting system. Novelty: Using an intuitionistic fuzzy queue, this research presents a novel method of assessing the M/Ek/1 queuing model in Erlang services. The computation of -cut intervals is an arithmetic procedure for intuitionistic fuzzy numbers. This novel approach assesses the on-server performance of an Erlang queuing model by extending fuzzy into intuitionistic fuzzy. The fuzzy waiting theory modeling evaluation metrics are given as a range of values, in contrast to the intuitionistic model, which offers a wide range of values. Applications: Several emails arrive at a network device in a sequential manner according to a Poisson arrival process, and each email's execution time is random. When mail arrives, it is queued and processed in the order that it arrives if the network server is busy. Using fuzzy queuing theory, the emails are "customers" and the network is the "server". The single server mathematical framework is a simple tool that can be used to restore the fuzzy queuing model. The parameters are all uncertain. Keywords: Queuing Theory, Heptagonal and Octagonal Fuzzy Numbers, Heptagonal and Octagonal Intuitionistic Fuzzy Numbers, Erlang Service, DSW Algorithm, Performance Measures

Computer Vision System for Multi-Robot Construction Waste Management: Integrating Cloud and Edge Computing

Sorting is an important construction waste management tool to increase recycling rates and reduce pollution. Previous studies have used robots to improve the efficiency of construction waste recycling. However, in large construction sites, it is difficult for a single robot to accomplish the task quickly, and multiple robots working together are a better option. Most construction waste recycling robotic systems are developed based on a client-server framework, which means that all robots need to be continuously connected to their respective cloud servers. Such systems are low in robustness in complex environments and waste a lot of computational resources. Therefore, in this paper, we propose a pixel-level automatic construction waste recognition platform with high robustness and low computational resource requirements by combining multiple computer vision technologies with edge computing and cloud computing platforms. Experiments show that the computing platform proposed in this study can achieve a recognition speed of 23.3 fps and a recognition accuracy of 90.81% at the edge computing platform without the help of network and cloud servers. This is 23 times faster than the algorithm used in previous research. Meanwhile, the computing platform proposed in this study achieves 93.2% instance segmentation accuracy on the cloud server side. Notably, this system allows multiple robots to operate simultaneously at the same construction site using only a single server without compromising efficiency, which significantly reduces costs and promotes the adoption of automated construction waste recycling robots.

OGC API: Ldproxy, an Updated and Contemporary Approach to Geodata Provision in Disaster Mapping

Abstract. Natural disaster occurrences have been rising for the past decades. It causes severe damage both in human properties and lives. To minimize the risk caused by disaster and accelerate the recovery, the accessibility of geospatial data is critical. WebGIS utilizes the internet to make the geospatial data accessible to a wider audience. In addition, the emergence of novel standards from OGC APIs also accelerates the accessibility of geospatial data. However, the abundance of Geospatial data requires a better performance of the webGIS. Ldproxy as a tool that follows OGC API standards could be an alternative to traditional tools such as Geoserver. This study focuses on developing a WebGIS application for accessing disaster-related geospatial data and evaluating the performance of Ldproxy in comparison to Geoserver. The method is to build a Single Page Application (SPA) using open-source data from Google Earth Engine (GEE) and OpenStreetMap (OSM). The functionalities of the webGIS enable users to retrieve OSM and GEE data and examine the impact of the disasters. The performance of Ldproxy and Geoserver is tested using Google Light House for comparison. The parameters that are tested are Speed Index, Total Blocking Time, Time to Interactive, Max Potential First Input Delay, Network Server Latency, and Total Byte Weight. As a result, Ldproxy shows slower performance in NSL compared to Geoserver. However, it offers an mvt feature format that makes the feature data can be loaded more efficiently and quickly.

Comprehensive cost analysis of voice call recording storage with and without NAS and GSM gateway integration via unified communication server

This study is an extension of existing research, which previously demonstrated the effective integration of Network Attached Storage (NAS) with a GSM Gateway into Unified Communication (UC) Platform, thereby realizing remarkable enhancements in system scalability, data security, privacy, network and overall server performance. Although the technical and operational benefits of this integration were well understood and documented, however there was an important gap that is cost analysis. This paper addresses the gap by providing a comprehensive cost analysis of voice call archiving with and without NAS and GSM Gateway integration using UC. The analysis considers essential financial factors, including initial investment, ongoing operational costs, and long-term savings due to lower maintenance and scalability. While the findings reveal that the upfront costs of NAS and GSM Gateway integration are higher, these are offset by long-term operational efficiencies and system resilience, leading to substantial financial gains. The evaluation results offer organizations a strategic model to assess the cost-effectiveness of using this NAS-GSM Gateway integrated approach for voice data archiving in UC environments. This custom-costing solution provides a much-needed holistic perspective on the combined technical and financial benefits.

PKM IMPROVEMENT COMPETENCY CURRICULUM UJI KOMPETENSI KEAHLIAN (UKK) AND NETWORK AUTOMATION TO IMPROVE SOFT SKILL ASPECTS AT SMKS NUSANTARA

The implementation of Program Pengabdian kepada Masyarakat (PKM), to improve the competence of SMK TKJ Nusantara 1 Ciputat students in terms of skills and soft skills in facing the Uji Kompetensi Keahlian (UKK) through a series of structured network training and practicum activities. The methods used include theoretical training and intensive practicum implementation that focuses on 1) Network Security Design, Installation and Configuration of Dynamic Routing 2) Wired and Wireless Network Design with VLAN and Routing 3) Troubleshooting Network Services on Client Server Networks 4) Troubleshooting Network Security on WAN Networks 5) Network Automation on MikroTik routers using Paramiko and Netmiko Python. The results achieved showed a significant increase in the technical skills and soft skills of the students. The final scores of the student groups ranged from 8.05 to 9.15. These results indicate that each student is increasingly mastering the technical skills needed for the configuration and management of questions and assignments tested on the UKK. The results of this PKM activity also include the preparation of UKK modules and writing books for Network automation on MikroTik RouterOS using Python, which is intended as a reference and ongoing learning material for students and teachers in practical learning and teaching activities.

Using DL Models in the Service Layer to Enhance the Fault Tolerance of IoT Networks

In an IoT network, the networked servers form a service layer, providing services to the users and the devices. The request to the service servers is routed through the gateway on one side of the services layer and the networked controllers on the other side. Data are transported from the sensors/devices through cluster heads en route to base stations and the controllers to the service servers, where the data are processed and sent for storage in the cloud through gateways. When any device is broken down or becomes non-operational, the inputs are not sensed, creating a gap in the data. The data transmitted from the devices would then become an incomplete flow; such data are not suitable for undertaking data analytics or predictions. The missing data must be first identified as the data flow and estimated or predicated to complete the data before they are transmitted through the cloud for storage and subsequent retrievals. This paper proposes a recurrent (RNN) neural network to predict the missing data. Two models are tested to predict the missing data: the multi-layer perceptron (MLP) model and a long short-term memory (LSTM)-based RNN model. The RNN-based model provides 99.66% accurate data prediction compared to other models.

Improving the efficiency of resource allocation in educational content delivery networks using the 1C:ESB integration bus

Learning Management Systems (LMS) play a key role in the educational process by providing students with various educational content and instructors with tools to monitor student knowledge and assess progress. However, as the number of students increases, the load on data transmission channels increases, which can lead to a decrease in the quality of learning. To solve this problem, Content Delivery Networks (CDN) are used to speed up the loading of websites and reduce the load on servers. CDN ensure the availability and security of websites and improve their scalability (resistance to smooth load growth) by distributing content across a network of servers around the world, allowing users to access content faster and more efficiently.Integration data exchange bus (Enterprise Service Bus, ESB) can be an effective tool for optimizing CDN content distribution algorithm. It allows integration of different systems and applications, enabling data exchange between them. This is particularly useful in the context of distance learning, which requires fast and reliable delivery of educational content, feedback, learning analytics, etc. Implementing an integration bus of data exchange with LMS-based distance learning system can be a key factor in ensuring an efficient and quality educational process. The article considers the properties and characteristics of the integration bus and justifies the use of the integration bus to optimize the algorithm of CDN content distribution, in particular in LMS.

Dynamic resource allocation and offloading optimization for network slicing in B5G multi-tier multi-tenant systems

For differentiating and customizing different types of flows guaranteeing individual 5QI QoS requirements, 5 G and Beyond 5 G (B5G) first specify several key technologies, e.g., 1) virtualizing radio resources, network functions and network servers, 2) network slicing, 3) Service Function Chaining (SFC) and flow steering, etc. Furthermore, for reducing E2E delay and path/link traffic congestion for diverse flowing while accessing cloud computing, Multi-access Edge Computing (MEC) is specified in 5 G/B5G ETSI. Although several extensively related studies discussed network slicing, SFC and MEC, they seldom consider both resource allocation and traffic offloading in a tenant efficiently, simultaneously. Thus, for efficiently addressing above critical issues, three motivations are proposed, including 1) to dynamically allocate resource for B5G with multi-tier multi-tenant networking, 2) to propose adaptively vertical and horizontal offloading to the computing node for diverse types of flows, and 3) to minimize the blocking rate while guaranteeing the delay constraint. Two novel efficient algorithms are proposed: Fast Latency Decrease Resource Allocation (FLDRA) and Minimum Cost Resource Allocation (MCRA). These two proposed algorithms achieve dynamic E2E resource allocation and optimal vertical and horizontal offloading to the computing node while guaranteeing the E2E latency and 5QI QoS requirements for different types of flows. Numerical results demonstrate that FLDRA minimizes resource allocation while MCRA balances the loading of resource availability. The proposed algorithms of FLDRA and MCRA significantly outperform the compared approaches in blocking rates. Moreover, the proposed MCRA algorithm yields the highest resource utilization, the least network delay, etc.

The Use of PFSense and Suricata as a Network Security Attack Detection and Prevention Tool on Web servers

This research explores the effectiveness of PFSense and Suricata integration in detecting and preventing network security attacks on web servers. The experimental method was conducted by testing the performance of these two open-source tools in the face of four types of attacks: Ping of Death, SYN Flood, SQL Injection, and Brute Force Attack on SSH. The tests were conducted in an environment with hardware specifications such as [specify specifications, e.g. CPU, RAM, and device type], and software including [specify operating system and version of PFSense and Suricata]. The results showed that Suricata was able to detect threats with an accuracy rate of 92% and successfully blocked 85% of the attacks. The average response time of the system to detect attacks was 250 ms. The integration between PFSense and Suricata proved effective in identifying attack patterns and preventing further potential damage. With proper configuration, this combination not only keeps the web server network secure, but also provides a quick response to complex cyber threats. This research contributes to the development of more reliable network security solutions by demonstrating how the integration of PFSense and Suricata can be effectively used to protect web servers. The findings provide practical guidance for practitioners and academics in implementing more innovative approaches to enhance network security in the digital era.

Collision Avoidance Adaptive Data Rate Algorithm for LoRaWAN

Long-Range Wide-Area Network (LoRaWAN) technology offers efficient connectivity for numerous end devices over a wide coverage area in the Internet of Things (IoT) network, enabling the exchange of data over the Internet between even the most minor Internet-connected devices and systems. One of LoRaWAN’s hallmark features is the Adaptive Data Rate (ADR) algorithm. ADR is a resource allocation function which dynamically adjusts the network’s data rate, airtime, and energy dissipation to optimise its performance. The allocation of spreading factors plays a critical function in defining the throughput of the end device and its robustness to interference. However, in practical deployments, LoRaWAN networks experience considerable interference, severely affecting the packet delivery ratio, energy utilisation, and general network performance. To address this, we present a novel ADR framework, SSFIR-ADR, which utilises randomised spreading factor allocation to minimise energy consumption and packet collisions while maintaining optimal network performance. We implement a LoRa network composed of a single gateway that connects loads of end nodes to a network server. In terms of energy use, packet delivery rate, and interference rate (IR), our simulation implementation does better than LoRaWAN’s legacy ADR scheme for a range of application data intervals.

A multiple-input fluid queue model for performance evaluation of fog server in an intelligent vehicular network

Nowadays, there is a growing trend towards fog computing which is a relatively new concept that has been introduced as an extension of cloud computing. It is considered as a promising paradigm for intelligent vehicular networks due to its ability to reduce delays and enhance network efficiency. The utilization of fog computing plays a crucial role in tackling the distinct obstacles encountered in vehicular computing, including issues like real-time data processing, and limitations on bandwidth. By relocating computational resources closer to the network’s edge, it enhances the effectiveness, dependability, and safety of vehicular applications while simultaneously boosting privacy and security measures. In addition, fog offloading has played a pivotal role in fog computing and edge computing structures. Its objective is to efficaciously allocate the data and assign the processing of tasks among edge devices, fog nodes, and cloud resources. Efficient fog offloading strategies are adaptable, scalable, and are able to handle faults, making them indispensable for optimizing these architectures. Although efficiency and quality of service are crucial objectives in a fog computing based intelligent vehicular network environment, performance remains a significant concern that cannot be overlooked. In light of this, to evaluate the efficacy of the probabilistic offloading approach on a fog server, a thorough performance evaluation is necessary. This research proposes a fluid queue approach that accounts for the constant flow of data packets while evaluating a fog server’s performance. For a fog computing-based intelligent vehicular network (FCIVN) with numerous heterogeneous smart vehicles (SVs), we construct a multiple-input fluid queue to model the tasks handed over to the fog server in order to evaluate its performance. In an FCIVN, the arrivals are drawn from various sources at the fog server. Accordingly, in the proposed model the fluid queue is modulated by more than one independent and distinct finite-state birth–death processes (BDPs) which control the variable inflow, and another BDP which controls the variable outflow. We present the details concerning an intermediate fog server’s buffer occupancy distribution within an FCIVN. Further, we assess the performance measures in terms of the offered load, expected buffer-level, average throughput and average latency of the tasks in an FCIVN. Finally, quantitative illustrations are presented to demonstrate the appropriateness of the fluid queue model developed in this study. The results are found to be in consistence with the expected behaviour of these performance indicators.

Secure device authentication and key agreement mechanism for LoRaWAN based IoT networks

SummaryThe proposed work introduces two schemes for secure device authentication and key agreement (SDA & KA) mechanisms. Initially, an efficient implicit certificate approach based on the Elliptic curve Qu–Vanstone (EIC‐EcQuV) scheme is developed in the first stage to instantly concur on the session key. The proposed scheme implicitly performs quick authentication of the public key. Also, this scheme prevents the attacker from creating fake key combinations. Through EIC‐EcQuV, the implicit certificate (IC) is distributed which helps to implicitly authenticate the user. This work also proposes ithe developed Public Key Certificateless Cryptosystem (PKCIC) scheme in the second stage, whch was also for the SDA & KA mechanism. In the EIC‐EcQuV scheme, efficient authentication is enabled, but public key theft is possible. However, in the PKCIC scheme, authentication is performed through partial keys, and the public key is secured via the Schnorr signature. The efficiency of the proposed schemes is proved by comparing the attained results with previous schemes. The proposed method obtains the computational cost of 0.0583 s for end‐to‐end devices, 0.06111 for network servers, and 0.00071 s for the gateway, with an execution time of 78.624 for 1000 devices. The attained key agreement of the proposed EIC‐EcQuV is 0.953 s, and PKCIC is 0.9988 s.

Optimization challenges of resources in it outsourcing companies in uncertain market conditions

The introduction of computer networks can significantly speed up the execution of information processes, as well as increase the efficiency of distribution and use of hardware, software and information resources. In a network, different computers perform different functions. Computers that give other computers access to their resources and manage the distribution of network resources are called servers. Those computers that use the servers' resources are known as clients. The article is devoted to the study of the important issue of the selection and use of tools for the order of server administration, which manage and maintain the server infrastructure in the environment of the network of computer clubs in order to determine the classification of system server administration tools, which are necessary for effective management of servers in computer networks. Carrying out various tasks requires making decisions about choosing server administration tools from the many available or developing new ones for managing and maintaining the server infrastructure, which leads the company to achieve business goals. Difficulties with the choice of effective tools for solving tasks lie in the competence of network administrators. This issue is especially important when it comes to training system administrators. That is, it is necessary to achieve certain educational goals, after achieving which the network administrator will be able to: describe the functions of the administration center, describe the rules for the effective use of package tools for system administration, determine the procedure for dispatching servers; use a variety of remote server administration tools to manage servers; apply cross-platform administration tools, apply tools for automating the performance of individual administration tasks, etc. To solve the problem, a list of typical groups has been defined: 1) administration of user groups; 2) administration of security measures; 3) administration of local and global networks; 4) monitoring of events and resources; 5) data archiving and recovery. Classes of tools for automating system administration tasks are offered: tool packages for system administration; cross-platform administration tools; tools for individual administration tasks. Considerable attention in the course is given to the analysis of the classes of system administration tools, since knowledge of system administration tools is necessary to troubleshoot, test, communicate, and fix the systems needed to continue operation. With the right tools, the job becomes less tedious as many tasks can be accomplished with these system administration tools. Overall, the article provides valuable information for system administrators looking for ways to improve their system administration skills and knowledge and may be useful for companies looking for ways to optimize their server infrastructure.

Blockchain-enhanced efficient and anonymous certificateless signature scheme and its application

Although the Internet of Things (IoT) brings efficiency and convenience to various aspects of people’s lives, security and privacy concerns persist as significant challenges. Certificateless Signatures eliminate digital certificate management and key escrow issues and can be well embedded in resource-constrained IoT devices for secure access control. Recently, Ma et al. designed an efficient and pair-free certificateless signature (CLS) scheme for IoT deployment. Unfortunately, We demonstrate that the scheme proposed by Ma et al. is susceptible to signature forgery attacks by Type-II adversaries. That is, a malicious-and-passive key generation center (KGC) can forge a legitimate signature for any message by modifying the system parameters without the user’s secret value. Therefore, their identity authentication scheme designed based on vehicular ad-hoc networks also cannot guarantee the claimed security. To address the security vulnerabilities, we designed a blockchain-enhanced and anonymous CLS scheme and proved its security under the Elliptic curve discrete logarithm (ECDL) hardness assumption. Compared to similar schemes, our enhanced scheme offers notable advantages in computational efficiency and communication overhead, as well as stronger security. In addition, a mutual authentication scheme that satisfies the cross-domain scenario is proposed to facilitate efficient mutual authentication and negotiated session key generation between smart devices and edge servers in different edge networks. Performance evaluation shows that our protocol achieves an effective trade-off between security and compute performance, with better applicability in IoT scenarios.

Design and Implementation of Long Range Wide Area Networks for Future Industrial IoT Applications

Background: The evolution of Long Range Wide Area Networks (LoRaWAN) is a potential candidate for next generation networks for managing the massive number of devices in the Industrial Internet of Things (IIoT). LoRaWAN is more suitable for transmitting smaller intermittent data but using smaller bandwidth over a long distance. Objective: This research paper proposes to design a Low Power Wide Area Network (LPWAN) to provide long-range data transfer for Industrial IoT applications, especially for industrial sensor data. Methods: This research work deploys the experimental setup of LoRaWAN devices using LoRa gateways and Internet of Things network server to evaluate its performance for IIOT applications. Results: The deployment of this LoRaWAN has demonstrated its long range, low power, stability and low deployment cost through extensive performance evaluation carried out. LoRaWAN has been analyzed for its coverage and throughput performance. Conclusion: Through performance analysis, potential enhancements have been identified to overcome its shortcomings. This paper concludes the feasibility of deploying LoRaWAN technology for the future generation IIOT applications.

Q-ID: Lightweight Quantum Network Server Identification Through Fingerprinting

Q-ID: Lightweight Quantum Network Server Identification Through Fingerprinting

Designing A Web-Based Mikrotik Hotspot Server Monitoring Application

This research was conducted to build a hotspot network server monitoring system and implement a hotspot network server management system and hotspot network users. In this study using the Experiment research method. In this study, a web-based proxy hotspot server monitoring application was designed. The results of the experiment are then documented to analyze so that the right recommendations are produced for designing a web-based proxy hotspot server monitoring application. From the results of this analysis, conclusions will be drawn about the benefits, functions and advantages of the system that has been built. The results of this study indicate that the web-based proxy hotspot server monitoring application can be applied to hotspot servers on proxy routers, where the test results show that the application can monitor users with the results there is 1 user who is active using user1 login and getting IP address 10.5.50.253 with an uptime of 1 minute 49 seconds. This application will display all users who are using the hotspot network, where the number of users displayed will adjust how many users are currently connected. The hotspot server monitoring application can also monitor the amount of download (RX) traffic of 14.04 MB and upload (TX) of 2.43 MB on the ether1 interface and the amount of download (RX) traffic of 2.44 MB and upload (TX) of 14.47 MB on the ether2 interface. The amount of this traffic depends on the use of the hotspot network by users in the hotspot network. In addition, this application can also monitor hotspot server logs by displaying activities that are happening on the proxy router such as login request activities, IP address requests, login attempts by users and others. This application can also be used for hotspot user management where test results show the application can add and also delete users who can connect to the hotspot network.

Inner External DQN LoRa SF Allocation Scheme for Complex Environments

In recent years, with the development of Internet of Things technology, the demand for low-power wireless communication technology has been growing, giving rise to LoRa technology. A LoRa network mainly consists of terminal nodes, gateways, and LoRa network servers. As LoRa networks often deploy many terminal node devices for environmental sensing, the limited resources of LoRa technology, the explosive growth in the number of nodes, and the ever-changing complex environment pose unprecedented challenges for the performance of the LoRa network. Although some research has already addressed the challenges by allocating channels to the LoRa network, the impact of complex and changing environmental factors on the LoRa network has yet to be considered. Reasonable channel allocation should be tailored to the situation and should face different environments and network distribution conditions through continuous adaptive learning to obtain the corresponding allocation strategy. Secondly, most of the current research only focuses on the channel adjustment of the LoRa node itself. Still, it does not consider the indirect impact of the node’s allocation on the entire network. The Inner External DQN SF allocation method (IEDQN) proposed in this paper improves the packet reception rate of the whole system by using reinforcement learning methods for adaptive learning of the environment. It considers the impact on the entire network of the current node parameter configuration through nested reinforcement learning for further optimization to optimize the whole network’s performance. Finally, this paper evaluates the performance of IEDQN through simulation. The experimental results show that the IEDQN method optimizes network performance.

Broadcasting Adaptation in the Streaming Era: Industrial Transformation in the Digital Revolution

In an era of ever-evolving streaming revolution, the broadcasting industry is facing major transformation to adapt and survive. Broadcasters need to adopt new strategies in business models and monetization, such as combining subscriptions, individual purchases, data-driven advertising, as well as partnerships with large streaming platforms through a hybrid approach. Investing in producing unique and engaging original content is also critical to differentiate yourself from competitors and cater to diverse audience preferences by exploring new genres and formats. In terms of technological infrastructure, increasing network capacity, servers and hardware are needed to support high-quality streaming, as well as paying attention to security and redundancy to ensure continuity of operations. Moreover, data measurement and analytics are key by developing new, relevant metrics to measure content performance, understand consumer behavior and preferences, and optimize monetization strategies. Leveraging data also allows for better personalization of content according to viewers' individual interests. By adopting a comprehensive approach in all these aspects, broadcasters can adapt effectively and achieve long-term success in an increasingly competitive streaming era.