Optimal Data Transmission Research Articles

RETRACTION: Data Location Integration With Stable Routing: Stable and Optimal Data Transmission in Wireless Sensor Networks

RETRACTION: A. Gayathri, D. Ruby, N. Manikandan, T. Gopalakrishnan, K. Anusha, and P. Narayanasamy, “,” Trans Emerg Telecommun Technol (Early View): doi:10.1002/ett.4627.The above article, published online on 22 August 2022 in Wiley Online Library (wileyonlinelibrary.com), has been retracted by agreement between the journal Editor‐in‐Chief, Changqiao Xu; and John Wiley & Sons Ltd. The article was submitted as part of a guest‐edited special issue. Following publication, it has come to the attention of the journal that the article was accepted solely on the basis of compromised editorial handling and peer review processes. Furthermore, parts of the experimental methods in this manuscript lack sufficient detail to interpret and reproduce the findings. Therefore, the decision to retract this article was taken. The authors have been informed of the retraction and Dr. Ruby, on behalf of the authors, disagrees with this decision.

Fractional Strong Metric Dimension of Convex Polytopes and its applications

The fractional versions of various metric related parameters have recently gained importance due to their applications in the fields of sensor networking, robot navigation and linear optimization problems. Convex polytopes are collection of those polytopes of Euclidean space which are their convex subsets. They have key importance in the field of network designing due to their stable and resilient structure which aids optimal data transfer. The identification and removal of components (nodes) of a communication network causing abruption in its flow is of key importance for optimal data transmission. These components are referred as strong resolving neighbourhood (SRNs) in graph theory and assigning least weight to these components aids the computation of fractional strong metric dimension (FSMD). In this paper, we compute FSMD for certain convex polytopes which include $\mathbb{P}_{n}$, $\mathbb{P}_{n}^{1}$ and $\mathbb{P}_{n}^{2}$. In this regard, it is shown that for $n \geq 3$, FSMD of $\mathbb{P}_{n}$ and $\mathbb{P}_{n}^{2}$ is $n$ and $\frac{3n}{2}$, respectively. Also, FSMD of $\mathbb{P}_{n}^{1}$ is $n$ when $n$ is odd and $\frac{3n}{2}$ when $n$ is even. Finally, an application of FSMD in the context of internet connection networks is furnished.

A deep reinforcement learning-based intelligent QoS optimization algorithm for efficient routing in vehicular networks

With the rapid development of Telematics, Vehicle Self-Organizing Networks (VANETs) play an increasingly critical role in Intelligent Transportation Systems (ITS). Especially in the environment without roadside assistance units (RSUs), how to effectively manage inter-vehicle communication and improve the stability and communication efficiency of the network has become a hot topic of current research. In this paper, a Deep Reinforcement Learning-based Intelligent QoS-optimized efficient routing algorithm for vehicular networks (DRLIQ) is proposed for VANETs with/without RSU environments, and routing methods are proposed respectively. Among them, in RSU-free environment, the DRLIQ algorithm utilizes the powerful processing capability of deep reinforcement learning to intelligently select the optimal data transmission path by dynamically learning and adapting to the changes in the vehicular network, thus effectively reducing communication interruptions and delays, and improving the accuracy of data transmission. The performance of the DRLIQ algorithm under different vehicle densities is evaluated in simulation experiments and compared with current popular algorithms. The experimental results show that the DRLIQ algorithm outperforms the comparison algorithms in reducing the number of communication interruptions, BER and network delay, especially in vehicle-dense environments. In addition, the DRLIQ algorithm shows higher adaptability and stability in coping with network topology changes and vehicle dynamics.

IMPROVING THE PROCESSES OF ENSURING THE AIRWORTHINESS OF AIRCRAFT BY CREATING A HIGH-SPEED HETEROGENEOUS INFORMATION SYSTEM

The problem of forming the appearance of a high-speed heterogeneous information system for monitoring airworthiness during the operation of aviation equipment is considered. A mixed-integer model has been formed for forming the optimal data transmission path for verifying the authenticity of airway components, where integer variables determine the set of external systems from where and where data is transmitted, a set of data warehouses and connections between them, and continuous variables – characteristics of data transfer rates by web services and bandwidth of data transmission channels. The criterion for choosing the optimal solution is proposed – the time of data transfer between external data source systems and external data consumer systems, taking into account data warehouses. An algorithm for optimizing the structure and parameters of a heterogeneous information system has been developed, which ensures the organization of information interaction between heterogeneous data sources/consumers in a minimum time. It is shown that the problem under consideration relates to difficult combinatorial optimization problems, that is, there is no polynomial algorithm for solving it in a reasonable time. However, taking into account the specifics of the task, in some cases, allows you to build calculation schemes that provide sufficient accuracy of the solution and an acceptable time. After conducting a brief analysis of possible solutions, including Dijkstra, Johnson, Floyd – Warshall, the authors implemented computational procedures based on the Bellman – Ford method, the main advantages of which are the possibility of obtaining an accurate solution, no restrictions when working with negative edges of the network graph, low value of the algorithm execution time. The results of calculations are presented and the possibility of reducing the data transfer time by 20,2 % is shown by choosing the optimal connections between data sources and consumers, taking into account the data formats of the sources used and the compatibility of web services providing their processing.

Modification of the collective dynamic routing method in heterogeneous networks

In the heterogeneous networks, a complex signal-interference environment is formed, characterized by a intra-system interference high level, which significantly reduces the heterogeneous networks throughput. One of the ways to increase such networks throughput by effectively reducing the intra-system interference influence is to use the integrated optimization method of these networks. However, its application is hampered by the complexity of implementing the collective dynamic routing method that underlies it. Therefore, modification of the collective dynamic routing method in relation to large heterogeneous networks is relevant. As a result of the application analysis of the collective dynamic routing method for optimizing the data flows distribution in heterogeneous networks, it is shown that the main limitation of this method applicability is the computational complexity of finding the optimal data transmission route. For this value the corresponding expressions are obtained. A modification of the collective dynamic routing method has been developed, in which the influence of intra-system interference is taken into account at the subnet segment level, and data distribution between subnets is carried out at the zone level. Using the obtained expressions, it is shown that the computational complexity of implementing the collective dynamic routing method in this case depends only on the number of users in the zone, which ensures the scalability of such a solution. Therefore, the use of the developed modification of the collective dynamic routing method will make it possible to use it to increase the throughput of large heterogeneous networks.

Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused clustering Protocol for Wireless Sensor Networks

Nowadays, Wireless Sensor Networks (WSNs) plays several important application fields, specially monitoring events without any human interference. The sensor nodes (SNs) have lesser life time in wireless sensor network owing to its continual sensing with battery drains very rapidly. As a result of the heavy traffic, sensors nearby quickly expire and energy hole issues start to appear. To overcome these issues, an Energy Efficient Spiking Deep Residual Network and Binary Horse Herd Optimization Espoused Clustering Protocol for Wireless Sensor Networks (EE-SDRN-BHHO-CP-WSN) is proposed in this manuscript for sustaining the energy efficiency in the wireless sensor network. One of the main issues in wireless sensor network assisted applications is making use of the available energy. An optimum path selection is a key process of saving energy from sensor nodes to the sink. Spiking Deep Residual Network (SDRN) and Binary Horse Herd Optimization (BHHO) optimize energy efficiency in WSN. The cluster head (CH) is designated by effectual fitness function generated by multi-objectives. It benefits in low power consuming and decreases a count of idle sensor nodes. After CH selection, Binary horse herd optimization algorithm is considered for optimal route selection and data transmission by sink node. The EE-SDRN-BHHO-CP-WSN method is implemented in MATLAB and the efficiency of the proposed method is analyzed with different metrics, like network lifetime, number of alive nodes, number of dead nodes, throughput, energy consumption, packet delivery ratio (PDR). The proposed EE-SDRN-BHHO-CP-WSN method attains 32.35 %, 42.34 %, 49.27 % higher network lifetime, 26.34 %, 31.94 % and 39.35 % higher number of alive nodes and 46.26 %, 38.64 % and 27.83 % lower number of dead nodes compared with other existing models, such as Multi-objective CH based Energy-aware Optimized Routing Algorithm in WSN (MCH-EOR-WSN), Energy efficient CH selection utilizing improved Sparrow Search Algorithm in WSN (EECH-ISSA-WSN), and New Energy Aware CH Selection Algorithm for WSN (NEA-CHSA-WSN respectively).

MR-SFAMA-Q: A MAC Protocol based on Q-Learning for Underwater Acoustic Sensor Networks

<p>In recent years, with the rapid development of science and technology, many new technologies have made people’s exploration of the ocean deeper and deeper, and due to the requirements of national defense and marine development, the underwater acoustic sensor network (UASN) has been paid more and more attention. Nevertheless, the underwater acoustic channel has the properties of considerable propagation delay, limited bandwidth, and unstable network topology. In order to improve the performance of the medium access control (MAC) protocol in UASN, we propose a new MAC protocol based on the Slotted-FAMA of Multiple Reception (MR-SFAMA) protocol. The protocol uses the Q-Learning algorithm to optimize the multi-receiver handshake mechanism. The current state is judged according to the received node request, and the Q-table is established. Through the multi-round interaction between the node and the environment, the Q-table is continuously updated to obtain the optimal strategy and determine the optimal data transmission scheduling scheme. The reward function is set according to the total back-off time and frame error rate, which can reduce the packet loss rate during network data transmission while reducing the delay. In addition, the matching asynchronous operation and uniform random back-off algorithm are used to solve the problem of long channel idle time and low channel utilization. This new protocol can be well applied to unstable network topology. The simulation results show that the protocol performs better than Slotted-FAMA and MR-SFAMA regarding delay and normalized throughput.</p> <p> </p>

Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

Enhancement in Optimal Resource-Based Data Transmission Over LPWAN Using a Deep Adaptive Reinforcement Learning Model Aided by Novel Remora With Lotus Effect Optimization Algorithm

Novel methods in utilizing wireless sensor networks with blockchain technology: A review

Wireless Sensor Networks (WSNs) play a vital role in achieving quick and efficient results. These networks comprise small, affordable sensors that collect valuable data from their surroundings. The core objective of this project is to optimize the pathways for data transmission, improve communication among sensor nodes, and filter out irrelevant or erroneous data prior to sending it to the central station. This project introduces a new method by implementing blockchain intelligence, which stores and provides optimal data transmission routes. This empowers sensor nodes to identify the most efficient paths for data sharing, eliminating unnecessary routes. Additionally, the blockchain acts as a filter, ensuring that only relevant data is transmitted to the central station. This approach not only saves time by streamlining data routes but also secures data security through the validity of blockchain technology. Furthermore, increases data quality by discarding unnecessary information. This project showcases a smart solution that tackles common WSN challenges, promising to elevate technological capabilities for a variety of applications.

A Spatial Clustering and Matching Game–Based Multihop Routing Algorithm for Heterogeneous UAVs

Unmanned aerial vehicle (UAV) clusters are increasingly deployed in military and civilian applications, necessitating efficient and reliable communication networks for cooperative operations. However, heterogeneous UAVs pose significant challenges for data transmission within and outside the cluster due to their high mobility, rapid topology changes, and limited node energy. For the routing planning problem of transmitting data from autonomous, heterogeneous UAVs to the cloud, existing methods struggle to accommodate the heterogeneity of UAVs and the communication bandwidth requirements, given the NP‐hard nature of the problem. To address this issue, this paper first constructs an exact model based on mixed‐integer linear programming to describe the heterogeneous UAVs, comprehensively searches the solution space, and generates a reasonable routing scheme for the heterogeneous UAVs, guiding the cluster communication network system design. Secondly, a heuristic algorithm is proposed that leverages density–based spatial clustering and matching game theory. The algorithm calculates relay nodes based on local density and relative distance. It generates an approximate optimal data transmission path for each UAV through a multilevel matching process, effectively reducing communication delay within the cluster. Finally, the paper conducts simulation experiments in randomly generated regional scenarios to validate the efficiency and effectiveness of the proposed method.

Optimal data transmission for decentralized IoT and WSN based on Type-2 Fuzzy Harris Hawks Optimization

Due to the widespread availability of services and smart devices, the decentralized internet of things and wireless sensor network-based systems (DIoT and WSN) have drawn the attention of academics and researchers and are still under constant development. Many challenges face the DIoT and WSN networks and need to be solved. The main issue among these challenges is reducing energy consumption in order to increase network lifetime. Energy-efficient routing protocols and the clustering method are the best solutions to this problem. In this paper, Type-2 Fuzzy Harris Hawks Optimization (T2FHHO) is used to choose the best path (route) from the source (cluster head (CH)) to the destination nodes (base station (BS)). Along with this, we propose a new fitness function to find the next hop based on residual energy, distance, traffic, and buffer size. Then, we apply the T2FHHO method to get the best CH. This protocol seeks to increase the long-term reward earned by each node. The best path in terms of the minimum cost obtained using the proposed method is found. The two main contributions of this study are as follows: firstly, we present a new efficient routing approach that minimizes costs and maximizes efficiency. Secondly, we apply the T2FHHO to the network parameters of energy consumption, network lifetime, and convergence curves. The results indicate that T2FHHO outperforms competing methods. The proposed method holds the promise of significantly advancing the state of the art in energy-efficient routing for DIoT and WSN networks, with significant implications in various sectors and applications, ensuring extended network lifetimes and sustainable IoT implementations.

Internet of Things (IoT)-Based Smart Healthcare System for Efficient Diagnostics of Health Parameters of Patients in Emergency Care

The Internet of Things has been instrumental in bringing about several advancements and innovations in the domain of healthcare. Healthcare professionals are essentially life savers when it comes to handling emergency cases such as accidents, heart attacks, etc. Only the patient’s vital parameters generally characterize emergency cases, and the doctors must wait for additional details for a wholesome diagnosis. As a result, the treatment processes and procedures sometimes get hastened and, in turn, put the patients’ lives at risk. It would always be helpful for doctors to be equipped with the medical requirements in advance for deciding the right course of action, thereby increasing the scope and chances of recovery. In this work, Multi-Model IoT (MMIoT) devices are deployed to monitor and collect health data from different body parts simultaneously. The healthcare data comprises signals and imagery captured from the MMIoT devices. Both the U-Net model and LSTM model are used to analyze the data automatically. The data processing is carried out by the server connected to the MMIoT network. All the medical IoT devices experimented with in this work are interconnected using a potential 5G network for optimal data transmission. The output obtained from the U-Net and the LSTM are channelized through a dense layer to classify the health anomalies accurately. It would not only facilitate but also educate medical professionals to handle unseen and typical cases in the future confidently. It can improve the overall quality of treatment and save lives with the best available resources.

Multipath Routing Scheme for Optimum Data Transmission in Dense Internet of Things

The Internet of Things (IoT) is an emerging technology that has recently gained significant interest, especially with the dramatic increase in connected devices. However, IoT networks are not yet standardized, and the design of such networks faces many challenges, including scalability, flexibility, reliability, and availability of such networks. Routing is among the significant problems facing IoT network design because of the dramatic increase in connected devices and the network requirements regarding availability, reliability, latency, and flexibility. To this end, this work investigates deploying a multipath routing scheme for dense IoT networks. The proposed method selects a group of routes from all available routes to forward data at a maximum rate. The choice of data transmission routes is a complex problem for which numerical optimization methods can be used. A novel method for selecting the optimum group of routes and coefficients of traffic distribution along them is proposed. The proposed method is implemented using dynamic programming. The proposed method outperforms the traditional route selection methods, e.g., random route selection, especially for dense IoT networks. The model significantly reduced the number of intermediate nodes involved in routing paths over dense IoT networks by 34%. Moreover, it effectively demonstrated a significant decrease of 52% in communication overhead and 40% in data delivery time in dense IoT networks compared to traditional models.

Optimization of Delay Using Killer Whale Algorithm (KWA) on NB-IoT

Abstract: NB-IoT is designed to connect IoT devices with low-power, wide-area coverage and efficient costs. Ensuring optimal data transmission delay is a challenge in NB-IoT implementation. Inadequate coverage can hinder IoT adoption. Optimization balances energy saving and delay trade-off. The Killer Whale Algorithm (KWA) optimizes delay by adjusting repetition variables. KWA addresses dimensions, variable limits. Applying KWA in NB-IoT optimizes transmission, enhancing QoS. Optimizing delay involves reducing latency in uplink data transmission using repetition variables. This study applies KWA to optimize NB-IoT delay. Analysis in Table 4 shows non-linear repetition-distance correlation. Interestingly, delay outcomes exhibit a contrasting relationship. Still, delay remains advantageous, remaining under 1 second even at 10 km, specifically 9.2674 ms (0.0092674 seconds). This thesis aims to optimize delay in NB-IoT network transmission using the Killer Whale Algorithm (KWA), crucial for modern communication networks and IoT applications. Leveraging KWA, the research identifies solutions to reduce transmission delay, enhancing efficiency and meeting IoT communication demands for speed and timeliness

Performance Evaluation of Single-Carrier and Orthogonal Frequency Divison Multiplexing-Based Autoencoders in Comparison with Low-Density Parity-Check Encoder

Recently, the growing demands for ultra-high speed applications require more advanced and optimal data transmission techniques. Wireless autoencoders have gained significant attention since they provide global optimization of the transceiver structure. This article explores the application of autoencoders to enhance the performance of wireless communication systems. It provides the performance evaluation of the systems using single-carrier and OFDM-based autoencoders under the conditions of AWGN and fading channels. Then, in terms of the BLER metric, the wireless systems with autoencoders are compared with conventional systems using LDPC coding and quadrature amplitude modulation for various configurations. Simulation results indicate that for high-modulation orders (QAM-64 or QAM-256), communication systems employing autoencoders provide superior performance compared to systems using LDPC channel encoding in regions with a low signal-to-noise (SNR) ratio. Specifically, a gain of 1–2 dB in signal power is obtained for single-carrier autoencoders and 0.3–2 dB is obtained for OFDM-based autoencoders. Therefore, wireless communication systems utilizing autoencoders can be considered as a promising candidate for future wireless communication systems.

Deep optimization algorithm for emergency-aware data transmission in Mobile-Ad Hoc Networks

Mobile Adhoc Network (MANET) is a dynamic network with mobility nodes. Emerging applications for MANETs in real-time present numerous research challenges. Specifically, the mobile nodes’ dynamic character hinders the routing efficacy in MANET. Previous algorithms for routing like DSDV DSR, AODV, and are inefficient due to an ineffective route discovery method. Route selection becomes more complex and energy-intensive for large-scale applications, such as air pollution monitoring. For air pollution monitoring applications, this research seeks to improve data delivery while reducing energy consumption. In this work, we proposed DeepOptimizer for achieving optimal data transmission. First, the network is segregated into multiple clusters using the Rough set theory. In the all clusters, Cluster Head is accountable for split a data into normal and emergency. This process is performed by grouping data by K++ means algorithm. For emergency data, Graph-based Route Selection (GRS) algorithm. This is the fast algorithm that selects the optimal route. On the other hand, the normal data transmission route is selected by the Deep-SpikeQNetwok-based Whale Optimization (WO) algorithm. Finally, the network is tested through simulations made in ns-3 based on network lifetime, throughput, energy level, delay and packet delivery ratio.

Utilizing the intelligence edge framework for robotic upper limb rehabilitation in home

Robotic devices are gaining popularity for the physical rehabilitation of stroke survivors. Transition of these robotic systems from research labs to the clinical setting has been successful, however, providing robot-assisted rehabilitation in home settings remains to be achieved. In addition to ensure safety to the users, other important issues that need to be addressed are the real time monitoring of the installed instruments, remote supervision by a therapist, optimal data transmission and processing. The goal of this paper is to advance the current state of robot-assisted in-home rehabilitation. A state-of-the-art approach to implement a novel paradigm for home-based training of stroke survivors in the context of an upper limb rehabilitation robot system is presented in this paper. First, a cost effective and easy-to-wear upper limb robotic orthosis for home settings is introduced. Then, a framework of the internet of robotics things (IoRT) is discussed together with its implementation. Experimental results are included from a proof-of-concept study demonstrating that the means of absolute errors in predicting wrist, elbow and shoulder angles are 0.89180,2.67530 and 8.02580, respectively. These experimental results demonstrate the feasibility of a safe home-based training paradigm for stroke survivors. The proposed framework will help overcome the technological barriers, being relevant for IT experts in health-related domains and pave the way to setting up a telerehabilitation system increasing implementation of home-based robotic rehabilitation. The proposed novel framework includes:•A low-cost and easy to wear upper limb robotic orthosis which is suitable for use at home.•A paradigm of IoRT which is used in conjunction with the robotic orthosis for home-based rehabilitation.•A machine learning-based protocol which combines and analyse the data from robot sensors for efficient and quick decision making.

Fast Determination of Optimal Transmission Rate for Wireless Blockchain Networks: A Graph Convolutional Neural Network Approach.

One of the primary challenges in wireless blockchain networks is to ensure security and high throughput with constrained communication and energy resources. In this paper, with curve fitting on the collected blockchain performance dataset, we explore the impact of the data transmission rate configuration on the wireless blockchain system under different network topologies, and give the blockchain a utility function which balances the throughput, energy efficiency, and stale rate. For efficient blockchain network deployment, we propose a novel Graph Convolutional Neural Network (GCN)-based approach to quickly and accurately determine the optimal data transmission rate. The experimental results demonstrate that the average relative deviation between the blockchain utility obtained by our GCN-based method and the optimal utility is less than 0.21%.

ROS-Based Collaborative Driving Framework in Autonomous Vehicular Networks

This paper investigates the optimal data transmission for collaborative driving in autonomous vehicular networks (AVNs). We consider that vehicles communicate with each other based on the definition of Robot Operating System (ROS), which is a pervasively adopted middleware operating system for autonomous vehicles such as Baidu Apollo. ROS defines a publish/subscribe scheme for inter-vehicular communications, in which vehicles subscribe to “topics” published by adjacent vehicles; a “topic” is related to the real-time sensing/computing data that a vehicle intends to share with nearby neighbors, <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">e.g.</i> , radar or camera images captured. By sharing various sensing data using different topics at the real-time, vehicles in the same area therefore can collaboratively drive with cooperative perception and computing. However, multiple data flows for different topics would coexist along overlapping transmission paths during this process. As a result, a fundamental issue raised is how to schedule the contending data flows in the mobile and bandwidth-constrained vehicular networks. In this paper, we model the ROS-based publish/subscribe scheme as an optimization problem which jointly considers the power allocation and conflict avoidance of the communication process in AVNs. By applying the Lyapunov Optimization, we decompose the model to calculate the power and sub-carrier proportion on each node while avoiding link conflicts, and finally obtain the optimal resource allocation strategy. By combining the corresponding link conflict constraints, we are able to encapsulate the optimization model in a stable set to efficiently avoid link conflicts, and thereby reduce the resource waste caused by the link interference and data flow conflicts. Using simulations, we show that our method has good advantages in resource optimization.

An Enhanced QoS-Aware Multipath Routing Protocol for Real-Time IoT Applications in MANETs

Recent transmission of large volumes of data through mobile ad hoc networks (MANETs) has resulted in degraded Quality of Service (QoS) due to factors such as packet loss, delay, and packet drop in multipath routing. To address this issue, a traffic-aware Enhanced QoS-Aware Multipath Routing Protocol (EQMRP) has been proposed for real-time IoT applications in MANETs. EQMRP efficiently switches between multiple paths and monitors traffic conditions to maintain an optimal data transmission rate. The proposed method considers different delay sensitivity levels and link expiration time (LTE) to maintain QoS in each path. Through IoT application data analysis, EQMRP maintains QoS in each path more efficiently than conventional methods. The proposed method has been simulated and validated using MATLAB, and the performance analysis shows that EQMRP achieves a higher packet delivery ratio, lower delay, and reduced packet drop compared to conventional methods. In conclusion, the traffic-aware EQMRP protocol offers a significant improvement in QoS for real-time IoT applications in MANETs.