Data Routing Research Articles

A Hybrid Biologically-Inspired Optimization Algorithm for Data Gathering in IoT Sensor Networks

The wireless sensor networks are autonomous Ad hoc Networks that form the backbone of IoT technology. Their vital role is to collect data from their physical environments and transfer these data to the Base Station (BS). This task implies high energy consumption when a suitable data routing policy is not applied. Then, the data collection method determines the efficiency of WSNs. Moreover, the IoT-based applications that require the mobility of the IoT sensor nodes introduce a new challenge regarding network connectivity. The IoT sensor node’s mobility leads to regular changes in the network topology, resulting in high packet loss during communication. The management of network energy use in order to prolong the battery life of IoT sensor nodes is a crucial issue, as IoT sensor nodes possess limited battery capacity. This paper addresses these issues by proposing an efficient Hybrid Biologically-Inspired Optimization Algorithm, named HBIP, for Data Gathering in IoT Sensor Networks. The flagship idea of the HBIP algorithm is to incorporate the swarming stage of the Bacterial Foraging Optimization (BFO) into the Artificial Bee Colony (ABC) Optimization’s exploitation phase. Simulation results show that the HBIP protocol outperforms the LEACH protocol and ABC and BFO metaheuristics regarding network energy consumption, lifetime, and data packets received at the BS. In practice, we demonstrated that the HBIP protocol increases the data collection by 84.40% over LEACH, 19.43% over BFO, and 7.26% over ABC in the network scenario considered. Further on, we extend the study of the HBIP algorithm on Mobile IoT Sensor Networks by proposing a Multi-Objective Optimization-based Data Gathering Protocol (MOO-DGP). The MOO-DGP protocol employs the HBIP algorithm to determine the best solution that balances Velocity, Cluster Stability, Link Quality, and Energy Consumption. An extensive evaluation of the MOO-DGP protocol shows it outperforms the existing Mobility-Based Clustering (MBC) protocol regarding effective data packet delivery rate, average control overhead, network lifespan, and energy consumption.

A Load‐Balancing Enhancement to Schedule‐Aware Bundle Routing

ABSTRACTDelay‐ and disruption‐tolerant networking (DTN) enables communication in networks afflicted by long propagation delays and sporadic connectivity. DTN routing techniques such as schedule‐aware bundle routing (SABR) exist to route data bundles in deterministic networks, such as those found in deep‐space environments, where node contacts are predictable. This article begins with an overview of DTN architecture and SABR. SABR's method of final route selection (forwarding rules) is closely examined. The article then addresses a limitation of SABR whereby the algorithm may overlook parallel channels, leading to network congestion. To mitigate this, an enhancement is proposed. This enhancement aims to optimize data bundle distribution across candidate routes in networks with parallel channels, thus alleviating congestion and enhancing overall network performance. This is achieved with simple modifications to SABR's forwarding rules to avoid the concentration of data bundles on a minority of node contacts. The enhancement is demonstrated through simulations in a reference scenario implemented in DtnSim.

Enhancing Data Routing in Wireless Sensor Networks through Predictive Mobile Sink Trajectory Modeling with ALDS-Net

Enhancing Data Routing in Wireless Sensor Networks through Predictive Mobile Sink Trajectory Modeling with ALDS-Net

Vertex Coloring and Eulerian and Hamiltonian Paths of Delaunay Graphs Associated with Sensor Networks

In this paper, we explore the connection between sensor networks and graph theory. Sensor networks represent distributed systems of interconnected devices that collect and transmit data, while graph theory provides a robust framework for modeling and analyzing complex networks. Specifically, we focus on vertex coloring, Eulerian paths, and Hamiltonian paths within the Delaunay graph associated with a sensor network. These concepts have critical applications in sensor networks, including connectivity analysis, efficient data collection, route optimization, task scheduling, and resource management. We derive theoretical results related to the chromatic number and the existence of Eulerian and Hamiltonian trails in the graph linked to the sensor network. Additionally, we complement this theoretical study with the implementation of several algorithmic procedures. A case study involving the monitoring of a sugarcane field, coupled with a computational analysis, demonstrates the performance and practical applicability of these algorithms in real-world scenarios.

Assessing Urban Park Accessibility and Equity Using Open-Source Data in Jiujiang, China

Urban parks have become more important in residents’ daily lives owing to both rapid urbanization and increasing environmental pressures. Globally, there is growing concern regarding equitable access to urban parks, particularly in densely populated countries such as China. This study focuses on the accessibility and equity of urban parks in Jiujiang using walking route data obtained from an open-source platform through an application programming interface to assess park accessibility. We explored the equity of park accessibility from three perspectives: spatial, opportunity, and group equity. The results indicated that urban parks in central Jiujiang have significantly better accessibility than those in suburban areas. Less than half of the study area was covered within a 1500 m walking distance. There is a large service blind catchment in park accessibility. Similarly, disparities in the equity of park accessibility were observed. The Penpu sub-district has the best accessibility; in contrast, the Qili Lake sub-district benefits from less than 10% of park accessibility. Wealthier communities benefit more from park accessibility than disadvantaged communities. Our study aims to provide strategies for urban planning for policymakers. Strategies such as increasing park entrances, opening gated communities, and creating shared green spaces may help ensure environmental equity.

An Enhanced Lightweight Secure Authentication and Privacy-Preserving Approach for VANETs

Aims and Background: Vehicular-Adhoc-Networks (VANETs) have gained a lot of attention in the past ten years. Used extensively in intelligent transport systems (ITS), it facilitates the sharing of traffic data between vehicles and their immediate surroundings, resulting in a more pleasant driving experience. When it comes to VANET security, privacy and security are the two biggest obstacles. To improve security in VANET, authentication and privacy-preserving methods are required because any specific disclosure of vehicle specifics, including route data, might have devastating consequences. Objectives & Methodology: In light of this need, this research introduces a novel framework for VANETs called enhanced timed efficient stream loss-tolerant authentication (ETESLTA), which enables a new kind of lightweight authentication while simultaneously protecting users' privacy. Initialisation, registration, mutual authentication, broadcasting and verification, and vehicle revocation are all parts of the suggested model. Furthermore, the ETESLTA method requires very little memory while yet providing excellent broadcast authentication, just like TESLTA. In addition, the ETESLTA method incorporates a cuckoo filter to record the real details of cars inside the RSU's detection range. Results: The suggested approach provides strong anonymity to achieve privacy and resists common assaults, and it features lightweight mutual authentication between the parties. A wide scale of experiments are conducted and the outcomes are evaluated in terms of numerous metrics to ensure the ETESLTA technique performs well. Conclusion: The experimental results demonstrated that the ETESLTA method was superior to the most current state-of-the-art approaches.

Monitoring ride‐hailing passenger security risk: An approach using human geography data

AbstractRide‐hailing services pose significant security challenges for passengers, underscoring the need for effective security risk monitoring. While extensive research has addressed various aspects of ride‐hailing, few studies specifically focus on passenger security risk monitoring. This paper introduces onSecP, an online approach designed to monitor the security risks faced by ride‐hailing passengers using human geography data. onSecP comprises two phases that set it apart from conventional anomalous trajectory detection methods. First, it employs an anomalous trajectory detection model using the LCSS‐Kmeans‐Geoinformation technique, which identifies and scores anomalous ride‐hailing trajectories. Second, it utilizes a multi‐parameter risk evaluation model enhanced by the AHP‐Entropy‐Cluster weighting method to perform real‐time calculations of passenger security risks by integrating factors such as driver characteristics, trip details, geographical environment, trajectory anomaly scores, abnormal stop duration, and passenger information. Our approach leverages diverse data sources, including ride‐hailing driver information, Point of Interest (POI) data as well as optimal route data from AMap, Global Positioning System (GPS) data, expert assessments, and passenger demographic surveys. Experimental evaluations demonstrate that onSecP effectively differentiates between unsafe trips and normal or abnormal trajectories, thereby significantly improving security risk monitoring for ride‐hailing passengers. Consequently, onSecP offers a robust tool for enhancing ride‐hailing security warning systems.

Optimized Task Deployment in Dynamic Voltage and Frequency Scaling-Enabled Network-on-Chip Systems: Enhancing Energy Efficiency and Real-Time Responsiveness

In modern multi-design computing systems, which employ dynamic voltage and frequency scaling (DVFS) and network-on-chip (NoC) communications, the optimization of task deployment is precarious for enhancing overall system performance. It introduces a comprehensive methodology that integrates task allocation, scheduling, frequency management, redundancy handling, and diverse data routing approaches. The aim is to optimize energy intake, real-time responsiveness, and system heftiness. The system design features a primary processing element associated with three slave computing units (CUs) within a 2D mesh network, with the primary CU connected to a co-processor for dependent task scheduling. This research also proposes innovative algorithms for co-processor and real-time operating system (RTOS) scheduling to reduce latency and boost power efficiency. These methodologies aim to maximize job scheduling efficiency in RTOS environments, thus refining overall system performance.

Congestion control in internet of things (IoT) using auction theory

The Internet of Things (IoT) facilitates data transmission through communication networks, preventing congestion when input data rate exceeds output, and congestion control in computer networks modulates traffic entry. This paper proposes a fusion of auction theory with reinforcement learning as a means of managing congestion in the IoT. The proposed technique seeks to enhance network performance by utilizing object trustworthiness evaluation and auction-based route selection to manage congestion during data routing. The suggested method calculates the believability of objects by analyzing their historical performance in data forwarding and congestion avoidance, utilizing a learning automaton. The auction approach is employed to determine the most efficient ways for transmitting data. The IoT topology is initially organized into a collection of dependable links known as the Connected Dominating Set (CDS). Active objects employ the learning automata model to assess the reliability of their neighbors. The auction model ultimately chooses the optimal route based on characteristics such as credibility, energy, and delay. The experimental results demonstrate that the proposed methodology surpasses existing comparison methods in the initial scenario, exhibiting a 24.13% reduction in energy usage.

Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs

This paper proposes a novel Neuro-fuzzy-based Data Routing (NFDR) mechanism for efficient data routing and dynamic cluster formation in Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). The NFDR mechanism incorporates optimal scalability factors computed from past and present network parameter values, acting as an additional buffer factor to sustain nodes within clusters, even with partial satisfaction of network parameter values. The neural network determines cluster formation requirements, while the objective function adjusts according to the updated fuzzy logic of identified cluster members. Super heads are initially selected, and cluster member size is adjusted to sustain maximum data transmission without affecting network parameter thresholds. Simulation results demonstrate that the NFDR mechanism enhances clustering range and cluster members while sustaining evaluation metrics such as 75% energy retention, 20% end-to-end delay reduction, and a 15% reduction in dead nodes, ultimately contributing to the development of efficient and robust IoT-enabled WSNs.

Cyclic-coverings of Line graph L(Sn) and its Disjoint Union

A graph is a particular representation of a static network and labeling of a graph can be think of as automatic routing of data in a network topology. A visual representation of data in the form of a graph help us to look deep insight. The data science (e.g., Python Package: a computer programming language) uses graph theory concepts to study and analyze the networks. Analyzing these network is equivalent of finding a set of edges E′ for a graph G such that every vertex of G is incident with at least one edge in E′. Then E′ is called an edge-covering of G. A spanning tree of a connected graph is an example of edge-covering. A finite simple graph G is an (ad, d)-H-antimagic if the following three conditions are satisfied: G has an H-covering (H a subgraph of G), there exists a bijection α : V (G) ∪ E(G) → {1, 2, 3, . . . , |V (G) ∪ E(G)|} and the H-weights constitute an arithmetic progression with common difference d. The above said labeling is called super if α(V ) = {1, 2, . . . , |V |}. In this research article, we focused on studying super C3-antimagic labeling of line graph of a sun-let graph for several differences and C3-supermagic labeling of its disjoint union.

Enhanced lion swarm optimization and elliptic curve cryptography scheme for secure cluster head selection and malware detection in IoT-WSN

Wireless Sensor Networks present a significant issue for data routing because of the potential use of obtaining data from far locations with greater energy efficiency. Networks have become essential to modern concepts of the Internet of Things. The primary foundation for supporting diverse service-centric applications has continued to be the sensor node activity of both sensing phenomena in their local environs and relaying their results to centralized Base Stations. Malware detection and inadequate Cluster Heads node selection are issues with the current technology, resulting in a drastic decrease in the total Internet of Things-based performance of sensor networks. The paper proposes an Enhanced Lion Swarm Optimization (ELSO) and Elliptic Curve Cryptography (ECC) scheme for secure cluster head selection and malware detection in IoT-based Wireless Sensor Networks (WSNs). The paper includes network models, choice of Cluster Head (CH) and attack detection procedures. The proposed method chooses the Cluster Head with the best fitness function values, increasing data transmission speeds and energy efficiencies. Minimum Hop Detection has been implemented to provide the best routing paths against attack nodes. Security level for quick data transmissions via the Internet of Things using Wireless Sensor Networks strengthen sinkhole attacks and black hole nodes, which are successfully removed using this method. The proposed method integrates the use of Lion Swarm Optimization and Elliptic Curve Cryptography (ECC) enhances network security by ensuring secure data transmission and preventing unauthorized access, which is particularly important in IoT-WSN environments. The proposed method achieves less End delay, increased throughput of 93%, lower energy utilization of 4%, increased network lifetime of up to 96%, Packet Delivery Ratio of up to 98% and 97% of malicious node detection efficiently compared to existing methods.

A Scalable Framework for Secure and Reliable Wireless-based Fog Cloud Communication

—Wireless telecommunication systems are essential in transferring data through fog cloud servers. However, the fog cloud servers suffer from unreliable and non-secure routing and limited power resources when mobile users are mobile. This paper introduces a scalable framework (SFRRDC) for reliable wireless routing and secure data transfer. We aim to address security and reliability issues by combining pheromone termite (PT) characteristics and ant colony optimization (ACO) algorithms for high-performance, more secure, highly reliable data transfer among the fog cloud servers. For that, the proposed SFRRDC supports a user authentication mining (UAM) algorithm to secure the confidentiality of the users. Based on testing results, it is confirmed that the proposed SFRRDC provides a better solution for confidentiality and fast routing for a wireless telecommunication system. The results show that the proposed SFRRDC framework’s energy consumption outperforms competing frameworks’ energy consumption with a better-achieved latency. For example, the suggested SFRRDC method uses 748.5 Joules during 72 hours of operation, competing frameworks use more energy, and DDFQFR uses 976.22 Joules. It also shows that the proposed SFRRDC is immune to malicious attacks. The results show that with 3,600 Fog cloud users, the proposed HEE protocol reduces the number of attacks to only 48 compared to 58, 72, and 77415 expected malicious attacks when using the ICDRP-F-SDVN, ACO, and DDFQFR frameworks, respectively.

A Digital-based Model Proposal for Optimum Building Orientation in Architecture in Ecological Context Nur Sumeyye

In order to make optimum use of the heat energy coming from the sun and the cooling effect of the wind, it is very important that the buildings are positioned on the land in positions that can provide the highest level of user comfort with minimum energy consumption. Determining the layout of the building is a critical design parameter that affects energy consumption. However, when there are vistas such as sea, forest, etc. that are desired to be seen from the first degree interior volumes around the building, this factor becomes decisive in the building orientations. As a result of this, climatic comfort factors such as natural ventilation provided by wind and heat energy from the sun are ignored. While the orientation of the interior spaces towards the landscape is preferred by the users, the climate element and the comfort it provides should be the priority in the architectural design. In this study, an orientation optimization model has been proposed for building orientation based on utilizing vista and climatic data in the context of passive design. With the parametric and numerical-based approach developed in the study, the way to use the vista factor, which has a multivariate and complex structure, as an “input data” has been investigated. In order to test and verify the developed optimization model, “The Canal Istanbul” artificial waterway project area, which is a very up-to-date subject, was chosen as the plot research area. The Canal feasibility studies have been prepared by the authorities. The data obtained from these reports were used quantitatively in the model. In this context, the technical and ecological data of the canal route were investigated from the reports prepared for the Canal Istanbul route. Thus, as a result of the data accessed from various sources, the climate type and ecological characteristics of the region have been moved to a quantitative dimension as numerical data. An optimization model has been developed for optimum building orientation in terms of regional climate, taking into account the zoning decisions and basic planning criteria determined in the focus of these data. The developed model primarily places the building in the desired point position over the 1000 and 5000 zoning plans of the region, with the vista as priority. Afterwards, the model perceives the vista spline that enters the field of view of the building. Then, staying within the boundaries of this spline, it turns the structure in alternative directions and analyzes it according to each direction. At this stage, the model presents a mathematical dataset by comparing the alternative orientations of the building with the “optimal direction”, “prevailing wind direction” and “north direction” angles. Finally, these data are optimized within the model and suggestions are made to the designer to make a choice, based on the results obtained.

Optimizing wireless sensor network routing with Q-learning: enhancing energy efficiency and network longevity

Abstract WSNs have gained importance in the recent years in the fields of industries, automobiles, military, agriculture and health care sectors, and others for data acquisition and monitoring. WSNs are made up of a large number of low-power nodes implemented with requisite sensing, computational, and wireless communication functionalities in relation to other nodes or a BS. LEACH is hierarchical routing protocol that adapts nodes into clusters, with an objective of achieving an equal distribution of energy load. However, the drawback of LEACH is that the selection of cluster head (CH) and the usage of single hop data transmission to the base station (BS) depends on the random probability, which is not efficient in large network. New additions involve the integration of a k-means clustering algorithm and reinforcement learning Q-learning for the selection of CHs and routing of data. For instance, Q-Learning improves the reliability and flexibility of a given network as it allows for acquisition of the best routes that involve multi-hope communication. An assessment of Q-Learning based routing is conducted on WSNs, based on energy depletion rate, node duration, and packet delivery ratio. Theoretical analysis shows that the Q-Learning based algorithm outperforms the traditional algorithms such as LEACH and k-means by adopting better energy utilization, reduced node mortality and high throughput. Overall, this study exposes the ability of Q-Learning algorithm in enhancing WSN life and efficiency in the modern world, and thus could be considered an optimal solution for changeability and limited resources in WSN networks.

(Digital Presentation) Fuel Cells, Electrolyzers, and Energy Conversion

1. Introduction Content: Hydrogen stands at the forefront of alternative energy solutions, offering a versatile and powerful approach to achieving a sustainable energy future. As the lightest and most abundant element in the universe, hydrogen can be produced from various resources and utilized in diverse applications—from fueling vehicles to storing excess renewable energy. Embracing hydrogen technology is critical for reducing global dependence on fossil fuels, minimizing environmental impact, and enhancing energy resilience. This clean energy vector plays a pivotal role in integrating renewable sources into the energy grid, facilitating a smooth transition to low-carbon energy systems worldwide. Visuals: Use an image of Earth, emphasizing areas where hydrogen production and usage are prominent, illustrated with symbols like hydrogen pumps, refineries, and transport vehicles. Highlight key regions with advanced hydrogen infrastructure and potential growth areas, effectively showcasing hydrogen's role in powering a cleaner, more sustainable planet.2. Section on Fuel Cells Sub-Title: "Harnessing Chemical Energy Directly from Hydrogen" Content:Explanation of how fuel cells work.Types of fuel cells (PEM, SOFC, etc.).Applications (transport, stationary power, etc.). Visuals: Diagrams of fuel cell operation, photos of fuel cell vehicles or power systems.3. Section on Electrolyzers Sub-Title: "Splitting Water to Power the Future" Types of Electrolyzers: PEM (Proton Exchange Membrane): Delivers high-purity hydrogen, ideal for dynamic energy inputs. Alkaline: Traditional technology, cost-effective for steady, large-scale hydrogen output. Solid Oxide Electrolyzer (SOE): Operates at high temperatures, increasing efficiency by using heat to assist electrolysis. Anion Exchange Membrane (AEM): Combines advantages of PEM and alkaline, with potential for lower costs and improved durability. Membrane-Free Electrolyzer: Employs bipolar membranes, potentially reducing costs and complexity in certain applications.4. Section on Energy Conversion Sub-Title: "Efficient Energy Use and Storage" Content:Role of energy conversion in renewable energy.Methods of storing and converting energy (batteries, mechanical systems, etc.). Visuals: Charts comparing energy storage methods, images of large-scale energy storage facilities.5. Case Studies Hydrogen Buses in California, USA: Overview: California has pioneered the adoption of hydrogen fuel cell buses in public transportation systems across several cities. Impact: These buses significantly reduce urban air pollution and carbon emissions, contributing to cleaner city air and sustainable urban transport. Visuals: Map of California showing cities with hydrogen bus fleets, infographics of emission reductions, and passenger statistics. Green Hydrogen in Neom, Saudi Arabia: Overview: The Neom project aims to build a hydrogen-based economy, planning one of the world's largest green hydrogen plants. Impact: This initiative is set to position Saudi Arabia as a leader in green hydrogen production, diversifying its economy and reducing its dependence on oil exports. Visuals: Illustrative map of the Neom area, diagrams of the hydrogen production process, and projected economic impacts. Hydrogen Energy Storage in Germany: Overview: Germany's Energiewende (energy transition) includes investments in hydrogen technology for storing excess renewable energy. Impact: Enhances grid stability and maximizes the use of renewable energy sources, reducing reliance on fossil fuel-based power plants. Visuals: German national grid map with key hydrogen storage sites, charts showing energy storage capacity, and utilization rates. Hydrogen Trains in Germany: Overview: Introduction of the world’s first hydrogen-powered passenger trains in Lower Saxony. Impact: Provides a more sustainable alternative to diesel engines with zero emissions, showcasing the practical application of hydrogen in rail transport. Visuals: Routes of hydrogen trains, operational data, and comparisons with traditional diesel train emissions. Conclusion These case studies demonstrate hydrogen technology's versatility and capacity to support sustainable energy goals across transportation and energy storage sectors. By implementing similar technologies, nations can achieve significant environmental and economic benefits.6. Future Outlook and Challenges Content:Potential advancements in technology.Current challenges facing scalability and cost. Visuals: Future-oriented imagery, possibly futuristic cityscapes.7. Conclusion Content: Hydrogen technologies have the potential to revolutionize the energy landscape by providing a sustainable, clean, and versatile energy solution. As technological advancements continue and infrastructure develops, hydrogen can play a pivotal role in achieving a sustainable energy future. With strong policy support and global collaboration, the transition to a hydrogen economy can significantly contribute to reducing carbon emissions, enhancing energy security, and promoting economic growth. Visuals: Inspirational quote or image that highlights innovation. Figure 1

Development of Geographic Information System (GIS) on Web-Based Medan City Transportation Route Application at PT. Rahayu Medan Ceria

The aim of this project is to provide a modern and efficient solution for managing and navigating transport routes in Medan city. This system is designed to facilitate users in obtaining accurate and real-time information about available transport routes. The web-based application leverages GIS technology to display interactive maps showing transport routes, bus stop locations, and estimated vehicle arrival times. The development of this system involves several stages: requirements analysis, system design, and implementation. The route data utilized includes 8 routes, consisting of both outbound and return paths for Medan city transport operated by PT. Rahayu Medan Ceria. Additionally, to support speedy route search processes, a binary search method is employed. Testing results indicate that the system can provide route information quickly and accurately. This application is expected to enhance the quality of transportation services in Medan city and offer significant benefits to the wider community.

Data aggregation and routing in Mobile Ad hoc network: Introduction to Self-Adaptive Tasmanian Devil Optimization

Mobile Ad-Hoc Network (MANETs) is referred to as the mobile wireless nodes that make up ad hoc networks. The network topology may fluctuate on a regular basis due to node mobility. Each node serves as a router, passing traffic throughout the network, and they construct the network’s infrastructure on their own. MANET routing protocols need to be able to store routing information and adjust to changes in the network topology in order to forward packets to their destinations. While mobile networks are the main application for MANET routing techniques, networks with stationary nodes and no network infrastructure can also benefit from using them. In this paper, we proposed a Self Adaptive Tasmanian Devil Optimization (SATDO) based Routing and Data Aggregation in MANET. The first step in the process is clustering, where the best cluster heads are chosen according to a number of limitations, such as energy, distance, delay, and enhanced risk factor assessment on security conditions. In this study, the SATDO algorithm is proposed for this optimal selection. Subsequent to the clustering process, routing will optimally take place via the same SATDO algorithm introduced in this work. Finally, an improved kernel least mean square-based data aggregation method is carried out to avoid data redundancy. The efficiency of the suggested routing model is contrasted with the conventional algorithms via different performance measures.

Incentivized BiLSTM with Triplet Attention for Predecting Congestion in Network Traffic

In the world of online live video streaming, network traffic congestion poses a serious risk to user experience and service quality. Several factors contributing to the congestion include insufficient bandwidth, an increase in user demand, as well as ineffective data routing. The incapacity of the prediction algorithms to Adjust to modifications in network circumstances and challenges in detecting long-range relationships may restrict their ability to effectively estimate congestion in dynamic contexts when it comes to online streaming video. In order to overcome these constraints, this study created an Incentivized-RF-Triplet ASTM (“Incentivized learning-based triplet attention enabled rat fierce hunting optimized Bidirectional Long Short-Term Memory) for network traffic congestion prediction in online streaming video. A reward” scheme built into the Incentivized learning mechanism pushes the algorithm to prioritize online live video streaming congestion prediction. The sequential structure of network traffic data is handled by the BiLSTM architecture, that is well-known for capturing temporal dependencies. By utilizing the triplet Attention method, the model is better able to identify relevant regions within the input data as well as identify congestion patterns more successfully. The RFSO method incorporates social behavior with selection and searching qualities to further improve the classifier's parameters. Therefore, the characteristics of the model can be tuned more effectively and robustly, improving its effectiveness in congestion detection. The outcomes of the experiment show how well the Incentivized-RF-Triplet ASTM technique works to precisely estimate traffic congestion for the Darpa99week1 dataset. 95.97% accuracy, 96.08% specificity, and 0.22 mean square error are reported.

AI-federated novel delay-aware link-scheduling for Industry 4.0 applications in IoT networks

PurposeWith the advent of AI-federated technologies, it is feasible to perform complex tasks in industrial Internet of Things (IIoT) environment by enhancing throughput of the network and by reducing the latency of transmitted data. The communications in IIoT and Industry 4.0 requires handshaking of multiple technologies for supporting heterogeneous networks and diverse protocols. IIoT applications may gather and analyse sensor data, allowing operators to monitor and manage production systems, resulting in considerable performance gains in automated processes. All IIoT applications are responsible for generating a vast set of data based on diverse characteristics. To obtain an optimum throughput in an IIoT environment requires efficiently processing of IIoT applications over communication channels. Because computing resources in the IIoT are limited, equitable resource allocation with the least amount of delay is the need of the IIoT applications. Although some existing scheduling strategies address delay concerns, faster transmission of data and optimal throughput should also be addressed along with the handling of transmission delay. Hence, this study aims to focus on a fair mechanism to handle throughput, transmission delay and faster transmission of data. The proposed work provides a link-scheduling algorithm termed as delay-aware resource allocation that allocates computing resources to computational-sensitive tasks by reducing overall latency and by increasing the overall throughput of the network. First of all, a multi-hop delay model is developed with multistep delay prediction using AI-federated neural network long–short-term memory (LSTM), which serves as a foundation for future design. Then, link-scheduling algorithm is designed for data routing in an efficient manner. The extensive experimental results reveal that the average end-to-end delay by considering processing, propagation, queueing and transmission delays is minimized with the proposed strategy. Experiments show that advances in machine learning have led to developing a smart, collaborative link scheduling algorithm for fairness-driven resource allocation with minimal delay and optimal throughput. The prediction performance of AI-federated LSTM is compared with the existing approaches and it outperforms over other techniques by achieving 98.2% accuracy.Design/methodology/approachWith an increase of IoT devices, the demand for more IoT gateways has increased, which increases the cost of network infrastructure. As a result, the proposed system uses low-cost intermediate gateways in this study. Each gateway may use a different communication technology for data transmission within an IoT network. As a result, gateways are heterogeneous, with hardware support limited to the technologies associated with the wireless sensor networks. Data communication fairness at each gateway is achieved in an IoT network by considering dynamic IoT traffic and link-scheduling problems to achieve effective resource allocation in an IoT network. The two-phased solution is provided to solve these problems for improved data communication in heterogeneous networks achieving fairness. In the first phase, traffic is predicted using the LSTM network model to predict the dynamic traffic. In the second phase, efficient link selection per technology and link scheduling are achieved based on predicted load, the distance between gateways, link capacity and time required as per different technologies supported such as Bluetooth, Wi-Fi and Zigbee. It enhances data transmission fairness for all gateways, resulting in more data transmission achieving maximum throughput. Our proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation.FindingsOur proposed approach outperforms by achieving maximum network throughput, and less packet delay is demonstrated using simulation. It also shows that AI- and IoT-federated devices can communicate seamlessly over IoT networks in Industry 4.0.Originality/valueThe concept is a part of the original research work and can be adopted by Industry 4.0 for easy and seamless connectivity of AI and IoT-federated devices.