Reliable Data Transmission Research Articles

Energy efficient multipath routing in IoT-wireless sensor network via hybrid optimization and deep learning-based energy prediction

ABSTRACT Efficient data transmission in Wireless Sensor Networks (WSNs) is a critical challenge. Traditional routing protocols focus on energy efficiency but do not consider other factors that might degrade performance. This research proposes a novel Hybrid Beluga Whale-Coati Optimization (HBWCO) algorithm to address these issues, focusing on optimizing energy-efficient data transmission. In the proposed approach, initially, sensor nodes and field dimensions are initialized. Then, K-means clustering is applied to grouping nodes. The Deep Q-Net model is used to predict energy levels of nodes. CH is selected as per the node having higher energy. Multipath routing is performed through the HBWCO algorithm, which optimally selects the best routing paths by considering factors like reliability, residual energy, predicted energy, throughput, and traffic intensity. If link breakage occurs, a route maintenance phase is initiated using Source Link Breakage Warning (SLBW) message strategy to notify the source node about the issue of choosing another path. This work offers a comprehensive approach to enhancing energy efficiency in networks. The suggested HBWCO approach is in contrast to the traditional methods. The HBWCO approach has achieved the highest reliability of 0.948 and the highest throughput of 3496. Therefore, the HBWCO algorithm offers an effective solution for data transmission and routing reliability.

Digital Modulation in Modern Communication Systems

Digital modulation is a fundamental technology in modern communication systems, enabling efficient and reliable data transmission over wired and wireless networks. Compared to analog modulation, digital techniques such as Phase Shift Keying (PSK), Quadrature Amplitude Modulation (QAM), and Orthogonal Frequency Division Multiplexing (OFDM) offer higher data rates, superior noise immunity, and better spectral efficiency. These advantages make digital modulation essential for applications in mobile communication, satellite systems, and the Internet of Things (IoT). Additionally, digital modulation supports advanced error correction, encryption, and digital signal processing (DSP), ensuring robust and secure communication. This paper explores the principles, advantages, and applications of digital modulation, emphasizing its critical role in modern communication technologies such as 4G, 5G, and beyond. Keywords: Digital Modulation, PSK, QAM, OFDM, Noise Immunity, Spectral Efficiency, 5G, IoT, DSP, Wireless Communication

A Comprehensive Review on OFDM, 5G and Various PAPR Minimization Techniques based on Machine Learning

The use of modulation methods in Fifth Generation (5G) wireless communication systems is essential for fulfilling the requirements of increased data rates, reduced latency, and enhanced connection. This entails the optimization of power spectral density (PSD), the improvement of data transmission reliability, and the reduction of bit error rates (BER) and interference. Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technique used in 4G and 5G wireless communication networks. OFDM results in a significant Peak to Average Power Ratio (PAPR) in the time domain because of the constructive interference between many subcarriers. This leads to increased complexity and expense of amplifiers, as well as higher costs and complexity of networks. Hence, it is essential to devise novel approaches to mitigate the PAPR in OFDM systems. ML has become a potential approach for addressing PAPR concerns. This study provides a thorough examination of ways for optimizing PAPR, with a specific emphasis on ML approaches.

Modification of the Peterson algebraic decoder

The aim of the study is to increase the reliability of data transmission based on the modification of the Peterson algorithm. The modification of the Peterson algorithm is based on the control of the correctness of the error locator polynomial. An additional stage of checking the error locator polynomial is proposed. This check is carried out using the recursive calculation of syndrome components. This approach increases the reliability of data transmission by reducing the probability of erroneous decoding at noise levels exceeding the constructive minimum of the code distance. The recursive calculation of syndrome components ensures the efficient implementation of the algorithm and minimises computational complexity.

PRIVACY CHALLENGES AND FRAMEWORK DESIGN FOR IOT DEVICES: A COMPREHENSIVE APPROACH

This article discusses the use of Internet of Things (IoT) technologies for monitoring air quality in the city. The developed system collects data from temperature, humidity, pressure, gas, time, and GPS sensors and transmits it to the server via the LoRaWAN network. The main advantages of this technology are low power consumption and the ability to transmit data over long distances. The system architecture allows for easy scaling of the network and integration of additional sensors for more accurate monitoring. The data is stored in a MySQL database, where it is pre-processed to eliminate inconsistencies between different types of sensors. This article considers the security of IoT devices, including the use of AES-128 and ECDH for data protection. Experimental results show that the proposed approach provides reliable data transmission, attack resistance, and efficient resource management. This article highlights the importance of proper data processing and protection for the successful deployment of IoT systems in smart cities.

Advancing Artificial Intelligence of Things Security: Integrating Feature Selection and Deep Learning for Real-Time Intrusion Detection

The size of data transmitted through various communication systems has recently increased due to technological advancements in the Artificial Intelligence of Things (AIoT) and the industrial Internet of Things (IoT). IoT communications rely on intrusion detection systems (IDS) to ensure secure and reliable data transmission, as traditional security mechanisms, such as firewalls and encryption, remain susceptible to attacks. An effective IDS is crucial as evolving threats continue to expose new security vulnerabilities. This study proposes an integrated approach combining feature selection methods and principal component analysis (PCA) with advanced deep learning (DL) models for real-time intrusion detection, significantly improving both computational efficiency and accuracy compared to previous methods. Specifically, five feature selection methods (correlation-based feature subset selection (CFS), Pearson analysis, gain ratio (GR), information gain (IG) and symmetrical uncertainty (SU)) were integrated with PCA to optimise feature dimensionality and enhance predictive performance. Three classifiers—artificial neural networks (ANNs), deep neural networks (DNNs), and TabNet–were evaluated on the RT-IoT2022 dataset. The ANN classifier combined with Pearson analysis and PCA achieved the highest intrusion detection accuracy of 99.7%, demonstrating substantial performance improvements over ANN alone (92%) and TabNet (94%) without feature selection. Key features identified by Pearson analysis included id.resp_p, service, fwd_init_window_size and flow_SYN_flag_count, which significantly contributed to the performance gains. These results indicate that combining Pearson analysis with PCA consistently improves classification performance across multiple models. Furthermore, the deployment of classifiers directly on the original dataset decreased the accuracy, emphasising the importance of feature selection in enhancing AIoT and IoT security. This predictive model strengthens IDS capabilities, enabling early threat detection and proactive mitigation strategies against cyberattacks in real-time AIoT environments.

ELBPFO: Energy-Aware Load Balanced Packet Failure Optimization for Provisioning Time-Driven IoT Application using Wireless Sensor Networks

Ensuring efficient and reliable data transmission is crucial for time-driven Internet of Things (IoT) applications using Wireless Sensor Networks (WSNs). These applications require optimized communication strategies that minimize latency and energy consumption while maintaining network stability in heterogeneous environments. However, achieving real-time data delivery often leads to excessive energy depletion and increased packet failures, particularly in sensor nodes near the sink. Traditional clustering approaches help manage energy consumption but introduce additional load imbalances and energy burdens on cluster heads, impacting overall network efficiency. To address these challenges, this study proposes Energy-Aware Load Balanced Packet Failure Optimization (ELBPFO), a strategy designed to enhance network lifetime and reduce packet loss while ensuring energy-efficient data transmission. ELBPFO optimizes clustering and routing mechanisms to balance network load, minimize hop count, and lower communication delay, thereby improving the performance of time-sensitive IoT applications. Comparative analysis demonstrates that ELBPFO outperforms conventional models in reducing latency and energy consumption, making it a robust solution for real-time IoT-WSN deployments.

A novel network lifetime maximization technique in WSN using energy efficient algorithms

Recent advances in wireless communication have enabled the development of small, low-cost, wearable sensors, which play a crucial role in applications such as healthcare monitoring, environmental sensing, and industrial automation. However, maximizing network lifetime (NL) and optimizing energy consumption remain key challenges in Wireless Sensor Networks (WSNs). Existing routing algorithms often struggle to balance energy efficiency and service quality, leading to premature network failures. To address this gap, this paper proposes a novel approach that integrates a near-optimal Single Objective Genetic Algorithm (SOGA) and an Advanced Exhaustive Search Algorithm (AESA) to enhance NL in fully connected WSNs. The proposed method optimizes energy-efficient routing by incorporating four quality parameters: proximity ranging, network lifetime, interaction counting, and link effectiveness. By leveraging multi-hop communication and efficient node connectivity, our approach significantly outperforms existing routing protocols in terms of energy conservation and data transmission reliability. A network simulator is utilized to evaluate key performance indicators, including average edge delay, latency, and packet delivery ratio, comparing them against conventional routing protocols such as ad hoc on-demand routing discovery, dynamic source routing, and simplified connection state routing. The findings demonstrate that the proposed method effectively extends NL while maintaining an optimal balance between energy consumption and service quality. This research contributes to the state of the art by providing an advanced energy-efficient routing solution for WSNs, with potential implications for various real-world applications, including smart cities, healthcare monitoring, and industrial IoT deployments.

Secured Multi-Objective Optimisation-Based Protocol for Reliable Data Transmission in Underwater Wireless Sensor Networks

Underwater wireless sensor network (UWSN) requirements have increased beyond applications in environmental monitoring and underwater exploration to military surveillance. The complex underwater environment raises many challenges due to high propagation delays, limited bandwidth, high error rates, and dynamic underwater currents. Most traditional clustering algorithms do not consider the multifaceted requirements of UWSNs. In most cases, a single objective is optimised at the cost of other essential factors, such as energy consumption, network robustness, and data transmission reliability. This paper proposes a new UWSN protocol based on the tiger beetle optimisation (TBO) algorithm for multiobjective K-means clustering (TBO-MOK). The protocol comprises adaptive search procedures motivated by tiger beetle hunting behaviors and lightweight AES-based encryption for data security. TBO-MOK is excellent in multiobjective optimisation since it simultaneously considers performance metrics of more than one aspect. Many problems are resolved by TBO-MOK, which optimises all the involved performance metrics to provide balanced energy usage and robust communication links. Comprehensive simulations demonstrate that TBO-MOK outperforms the traditional LEACH, PSO, and GA approaches in grossly enhancing network lifetime, energy efficiency, load balancing, and data transmission reliability. These results show the potential of TBO-MOK to provide a more effective and resilient solution for UWSNs.

Optimization of network performance of distributed storage system for biomechanical big data based on cloud computing

This study proposes a network performance optimization strategy based on cloud computing to address the stringent demands of biomechanical big data on the efficiency of distributed storage systems. Biomechanical data, including motion capture, force plate measurements, and tissue strain analysis, involve large-scale, high-frequency, and heterogeneous datasets that necessitate efficient storage and real-time processing. By optimizing data transmission paths, designing an efficient caching mechanism, dynamically allocating bandwidth resources, and implementing network congestion control, the system significantly enhances throughput, reduces latency, and improves bandwidth utilization and data transmission reliability.

Real-Time Telemetry Transmission for CubeSat Mission using KL AP – APRS

This paper describes the design and implementation of a ground station to receive Automatic Packet Reporting System (APRS) signals from the KLAP transmitter module designed for the KLSAT 1U CubeSat. The KLAP module enables fast transmission of telemetry data using the APRS protocol, specifically operating in the AX.25 standard. The ground station includes the use of software to determine APRS signals, a Terminal Node Controller (TNC) integrated with the antenna for signal processing, and an increase in the operating frequency to 144.390 MHz (APRS frequency). The system works with the PINPOINT APRS application and APRS.fi, allowing continuous monitoring and analysis of telemetry data from CubeSats. The test demonstrated that the KLAP module provides reliable and efficient data transmission from space to ground, demonstrating the potential of APRS technology in the development of small satellite communications. The design ensures data integrity and low cost, making it suitable for small satellite missions. We conducted extensive practical tests to evaluate the performance of the KLAP module and ground station and demonstrated that the system can instantly and reliably identify and analyse telemetry data from CubeSats. The results highlight the effectiveness of APRS technology in improving small-scale radio communications, providing high-resolution and efficient field telemetry solutions. This study demonstrates the potential for APRS to be more widely used in satellite communications, especially in the field where limited use requires new methods of information transmission and resception.

Cross-Chain Technology of Consortium Blockchain Based on Identity Authentication

With consortium blockchain becoming the mainstream form of blockchain applied to various industries, the proportion of nonasset data in blockchain applications is gradually increasing. However, there is currently no cross-chain solution for nonasset data. The aim of this study is to explore the cross-chain problem of nonasset data and design a cross-chain solution that is suitable for the application scenarios of consortium blockchains. We achieved cross-chain identity authentication through an integrated distributed trust model. We then proposed cross-chain anchor nodes as alternatives to traditional routing, eliminating third-party Relay risks while ensuring secure information transmission through smart contracts. Finally, on the basis of ensuring the timeliness and reliability of data transmission, combined with the consortium blockchain organizational structure, cross-chain technology is more in line with the characteristics of data element circulation. This study provides an effective and secure solution for cross-chain interaction and application data flow in consortium blockchains through comprehensive smart contract protection mechanisms and rigorous access controls. The proposed approach is expected to promote the safe application and development of consortium blockchain technology in various industries.

Optimization of Wireless Mesh Networks for Disaster Response Communication

Wireless Mesh Networks (WMNs) have emerged as a resilient and adaptable solution for disaster response communication, offering self-healing and self-organizing capabilities that ensure uninterrupted connectivity in emergency scenarios. Traditional communication infrastructures often fail due to network congestion, power outages, and physical damage during disasters, necessitating an optimized approach for rapid and reliable data transmission. This study presents an AI-optimized WMN framework aimed at enhancing network performance by improving packet delivery ratio (PDR), reducing end-to-end delay, optimizing energy consumption, increasing network throughput, and strengthening security. Simulations conducted in MATLAB Simulink compare the performance of AI-optimized routing with conventional protocols such as AODV (Ad hoc On-Demand Distance Vector) and OLSR (Optimized Link State Routing). Results demonstrate that AI-optimized routing achieves a 15.5% higher PDR, 43% lower delay, 49% increased throughput, and 30% reduced energy consumption compared to traditional approaches. Furthermore, an AI-driven Intrusion Detection System (IDS) improves network security by increasing attack detection accuracy to 94.6% while reducing false positive rates to 5.2%. The findings highlight the significance of AI-based routing optimization in disaster scenarios, ensuring robust, energy-efficient, and secure communication for first responders and affected communities. Future research will explore hybrid AI-blockchain security mechanisms, 5G and satellite network integration, and real-world experimental validation to further enhance WMN resilience in extreme disaster conditions.

Energy‐Efficient Cluster‐Based Reliable Routing Using Hybrid Nutcracker and Improved Sand Cat Optimization Algorithm for Extending Network Lifetime in WSNs

ABSTRACTIn wireless sensor networks (WSNs), sensor nodes are deployed in a target region for sensing environmental physical parameters to attain the objective of reactive decision‐making. These sensor nodes necessitate energy for processing and forwarding the sensed data to the base station (BS) for better data delivery in WSNs. Balanced energy utilization in WSNs prevents the problem of hotspot, and dynamic cluster head (CH) selection with reliable route establishment is a vital decision‐making approach that helps in optimal path selection with maximized energy conservation. In this paper, a nutcracker and sand cat optimization algorithm (NCSCOA)–based multiobjective CH selection and sink node mobility scheme is propounded for enabling rapid and reliable data transmission with reduced energy consumption in heterogeneous WSNs. This NCSCOA handled the problem of hotspot as well as isolated nodes and facilitated loop‐free routing with the support of the improved nutcracker optimization algorithm (INCOA) that makes the decision of routing using local and global search optimization processes. It constructed an energy‐level matrix (ELM) by deriving the impactful factors of intercluster formation, distance between CH and BS, residual energy (RE), and node density for achieving optimal CH selection and route determination. In specific, improved sand cat optimization algorithm (ISCOA) is used during the intercluster formation phase by discovering the optimized path between source and destination during route establishment. Simulation‐based findings of the proposed NCSCOA confirmed its efficacy by improving the mean number of alive nodes by 23.18%, reducing energy consumption and delay by 21.86% and 20.98% compared to benchmarked protocols.

Transforming Patient Care with IoT Integration: A Technical Perspective

The healthcare industry is experiencing a profound digital transformation through the integration of Internet of Things (IoT) technologies. This technical article examines how IoT is revolutionizing patient care by enabling real-time monitoring, data-driven treatment plans, and improved operational efficiency. The article explores the three-layer IoT architecture in healthcare: the perception layer that interfaces with physical environments, the network layer that ensures reliable data transmission, and the application layer that transforms raw data into actionable insights. The article further discusses critical implementation considerations including interoperability standards, security frameworks, and privacy protections essential for healthcare deployments. Technical applications such as real-time monitoring systems and automated medication administration demonstrate significant improvements in patient outcomes. Advanced analytics and machine learning integration enable predictive capabilities that anticipate clinical events before conventional detection, while specialized implementation strategies address the unique challenges of healthcare data pipelines. The case study on remote patient monitoring architecture illustrates how multiple technical components work in concert to deliver comprehensive care solutions. Through detailed analysis of implementation approaches and technological frameworks, this article provides a technical perspective on how IoT is transforming healthcare delivery across clinical, operational, and financial dimensions.

Intelligent Congestion Control of Data Transmission in Low Earth Orbit Satellite Networks Based on Reinforcement Learning: Analysis and Optimization

Intelligent Congestion Control of Data Transmission in Low Earth Orbit Satellite Networks Based on Reinforcement Learning: Analysis and Optimization

HYBRID CHAOS-PERMUTATION EVOLUTIONARY ALGORITHM FOR ENERGY-EFFICIENT CLUSTERING AND ROUTING IN WIRELESS SENSOR NETWORKS

Efficient resource allocation in Wireless Sensor Networks (WSNs) is critical due to the constrained energy resources of sensor nodes and the dynamic nature of network topologies. Traditional clustering and routing algorithms often struggle to maintain energy efficiency and network stability, leading to reduced network lifespan and suboptimal performance. To address these challenges, a Hybrid Chaos-Permutation Evolutionary Algorithm (HCPEA) is proposed, integrating chaotic permutation theory with an adaptive evolutionary framework for energy-efficient clustering and routing. The HCPEA optimizes cluster head selection and transmission paths by leveraging chaotic maps for enhanced population diversity and permutation-based refinements to avoid premature convergence. Simulation results demonstrate that HCPEA significantly outperforms conventional methods, including Particle Swarm Optimization (PSO) and Genetic Algorithm (GA), in terms of energy efficiency, packet delivery ratio, and network lifetime. Compared to GA and PSO, HCPEA achieves an 18.5% improvement in energy efficiency, a 22.3% increase in packet delivery ratio, and a 25.7% extension in network lifetime under varying network scales and dynamic conditions. These findings establish HCPEA as a robust and scalable solution for sustainable WSN operations, ensuring reliable data transmission and prolonged network functionality.

Development of a High-Reliability Hybrid Data Transmission System for Unmanned Surface Vehicles Under Interference Conditions

This paper discusses modern approaches to the creation of a highly reliable data transmission system for unmanned surface vehicles (USVs) operating under interference conditions. In contrast to existing solutions, an improved communication algorithm is proposed to ensure uninterrupted transmission of video, telemetry, and control signals even in highly unstable environments. The study identifies the main technical requirements for data transmission and evaluates the key parameters of the communication channel. The proposed hybrid communication system utilizes adaptive channel switching, data compression, and equipment reconfiguration, improving data transmission stability and reducing latency. A comparative analysis of existing communication technologies reveals the limitations of acoustic, optical, and radio wave systems. A conceptual architecture combining these technologies provides optimal data transmission by adapting to the environment. Experimental results confirm the effectiveness of the system, demonstrating reliable operation even with 80% packet loss in public Internet networks. The system’s adaptability, low latency, and dynamic routing make it suitable for real-time USV operations, including environmental monitoring, scientific research, and search and rescue missions. Its potential extends to commercial and dual applications requiring sustained data transmission in challenging maritime environments.

Developing IoT Performance in Healthcare Through the Integration of Machine Learning and Software-Defined Networking (SDN)

In this era of rapid growth of the Internet of Things (IoT) in healthcare, the networking solution must be robust and efficient for smooth data transfer and the network. It is very well established that Deep Learning (DL) is a subset of Machine Learning (ML) and has shown great promise in enhancing network performance through intelligent data analysis and making smart decisions. The wide deployment of Wireless sensor networks and Mobile ad hoc networks (MANET) is why they are used in healthcare applications, as they offer low-cost deployment and real-time data exchange. Nevertheless, WSNs suffer limitations in energy availability due to their inherent constraints. However, the energy consumption in a WSN routing is a significant problem because of resource limitations. Integrating ML in a Software Defined Networking (SDN) framework shows the possibility of network resource optimization and a sustainable network architecture. Typically, conventional routing annoys most about shortest path algorithms, where nodes are used unevenly, and critical nodes die early from battery depletion. This work proposes a new DRL-based algorithm for maximizing traffic distribution and maintaining energy consumption equilibrium across the nodes in WSNs to improve their lifetime. The model proposes dynamically changing routing paths to preserve the nodes’ loading with a limited power supply system to persist the network lifespan and thus improve efficiency. However, the approach adds some hops to the average packet transmissions per hop. Still, it delays the first node’s energy depletion long enough to provide sustained network operability in IoT-driven healthcare environments. The experimental results validate the effectiveness of the proposed method in furthering the network functionality and reliability for data transmission, thereby making it a viable solution for the next generation of IoT healthcare networks.

Research on rechargeable agricultural wireless sensor network based on ZigBee immune routing repair algorithm

WSN (wireless sensor network) plays a very important role in the agricultural environment monitoring. Although solar energy and other power supply methods are used to solve the node energy problem, the monitoring equipment works outdoors for a long time, which is easily affected by the environment. The supply is unstable to cause abnormalities in some nodes. So this study proposes a ZIRRA algorithm (ZigBee immune routing repair algorithm) for the rechargeable agricultural WSN. It simulates the working mechanism of the immune system and designs modules such as identification, processing, cloning and storage, which can provide a better repair strategy for abnormal nodes. Then it compares the quality of the backup nodes and replaces the backup nodes with poor quality, so that the optimal paths are maintained between source nodes and middle relay nodes, which increases the optimization ability of the algorithm. The experimental results show that the ZIRRA algorithm shows significant advantages in routing node repair mechanism. Compared with the LFRA, AR-TORA and ICCO algorithms, the average routing energy consumption of the ZIRRA algorithm reduced 35.33%, 58.37% and 45.15% , the data transmission delay reduced by 23.72%, 36.74% and 16.28%, and the average node survival time extended 25.08%, 33.55% and 13.88%. In addition, the maximum communication time and network throughput of the ZIRRA algorithm increased 44.49% and 13.03% at the scale of 1000 to 2000 nodes. These quantitative results show that the ZIRRA algorithm can improve the energy efficiency, transmission reliability and stability. The ZIRRA algorithm draws on the working principle of the immune system and repairs abnormal nodes through identification, processing, cloning and storage modules. Unlike the traditional node repair algorithms, the ZIRRA algorithm has higher efficiency and accuracy in identifying and processing abnormal nodes through the improved clone tracking algorithm. It uses an improved clone tracking algorithm in the learning module, improves the cloning and mutation mechanisms, and generates the optimal antibodies for repairing abnormal nodes. It also integrates an adaptive energy management strategy to cope with fluctuations in energy levels by prioritizing the transmission of critical data and reducing the frequency of non-essential communications, which improves the network stability and data transmission reliability.