Network Protocols Research Articles

Fuzzy-based Bi-objective Energy Efficient Routing Protocol for Large Scale Mobile Ad-hoc Network

MANET is an infrastructure-less network, which is comprised of a greater number of nodes that are moving in a random manner. Establishing efficient and reliable routes is the crucial design issue in MANET. In addition, providing energy-efficient routes for performing communication operations in MANET is also a challenging goal. Increasing network lifetime and decreasing overall traffic load are the significant considerations while designing the MANET. An energy optimization routing mechanism with a fuzzy basis that is state-based and influenced by COVID-19 is used to prevent and control epidemics. Based on their lifespan and varying mobility speeds, it is dependent on energy optimization yield under various scenarios. Fuzzy-based bi-objective criteria in energy-efficient routing protocol for the mobile ad-hoc network to select the different constraints like link stability, bandwidth, and battery life. The main goal of this paper is to obtain the optimal solution to choose the best route, limit battery life, and change the movement of mobile nodes for large-scale mobile ad-hoc networks. It is demonstrated that the possible solution relates to the domain of selecting the variables by outlining the connection between the energy function’s variables. Researchers optimize a path's link lifespan, resulting in the choice of the shortest path being particularly efficient in COVID pandemic prevention and treatment.

Reliable Vehicular Ad Hoc Networks for Intelligent Transportation Systems based on the Snake Optimization Algorithm

Vehicular Ad Hoc Networks (VANETs) represent an environment in which mobility exceeds the normal values, topology changes rapidly, and safety constraints are too high. The fundamental problem with VANETs is making transmission, acceptance, and sending out of messages between vehicles as timely, reliable, and secure as possible. The current study aims to address these challenges by applying the Snake Optimization Algorithm (SOA), enhancing network protocol efficiency, performance, and robustness. In this work, a comprehensive examination of the effects of optimal SOA on VANET protocols is provided over networks with different node sizes of 100, 250, and 500. End-to-end delay, path delivery overhead, and average number of hops improved after the utilization of SOA in all the considered networl configurations.

Robust and energy-efficient RPL optimization algorithm with scalable deep reinforcement learning for IIoT

The increasing complexity and quantity of the Industrial Internet of Things (IIoT) pose new challenges to the traditional routing protocol for low-power and lossy networks (RPL) in terms of dynamic management, data transmission reliability, and energy efficiency optimization. This paper proposes a scalable deep reinforcement learning (DRL) algorithm with a multi-attention actor double critic model for routing optimization (MADC) to meet the requirements of IIoT for efficient and intelligent routing decisions while improving data transmission reliability and energy efficiency. Specifically, MADC employs the centralized training and decentralized execution (CTDE) learning paradigm to decouple the model’s training and inference tasks, which reduces the difficulty and computational cost of model learning and improves the training efficiency. In addition, a lightweight actor network based on multi-scale convolutional attention mechanism is designed in MADC, which can provide intelligent and real-time decision-making capabilities for resource-constrained nodes with low computational and storage complexities. Moreover, a scalable critic network utilizing multiple attention mechanisms is proposed. It is not only suitable for dynamic and changing network environments but also can more comprehensively and accurately evaluate local observation states, providing more accurate and efficient guidance for model optimization. Furthermore, MADC incorporates a double critic network architecture to mitigate potential overestimation issues during training, thereby ensuring the model’s robustness and reliability. Simulation results demonstrate that MADC outperforms existing RPL optimization algorithms in terms of energy efficiency, data transmission reliability, and adaptability.

An Intelligent Medical Monitoring System using Narrow Band –IOT

With the emergence of technology, the healthcare sector has seen tremendous growth, providing creative ways to improve patient care, increase productivity, and save expenses. Of these developments, the Internet of Things' (IoT) incorporation into the healthcare industry has emerged as a key component for developing intelligent medical systems that are able to remotely and in real-time monitor patients' health problems. Narrowband IoT (NB-IoT), a low-power, wide-area network (LPWAN) protocol created expressly to meet the expanding demands of the IoT ecosystem, particularly in healthcare, is one exciting technology facilitating this shift. Vital health indicators including heart rate, blood pressure, glucose levels, and oxygen saturation can be tracked by healthcare practitioners with the help of an NB-IoT-enabled smart healthcare monitoring system, which can monitor patients continuously and remotely. By providing more timely and individualized care, decreasing hospital stays, and enabling faster reaction times, this technology has the potential to completely transform the healthcare industry.

Autonomic Resilience in Cybersecurity: Designing the Self-Healing Network Protocol for Next-Generation Software-Defined Networking

Autonomic Resilience in Cybersecurity: Designing the Self-Healing Network Protocol for Next-Generation Software-Defined Networking

Cross-layer Authentication Mechanism Model Combined with 5G Converged Channel Fingerprint

In the actual communication environment, once the attacker successfully steals the legitimate channel information, the key information can be cracked according to the authentication response, so there is a security risk of key leakage. This paper combines the physical layer channel characteristics with the shared key, and combines it into a joint key to design a challenge-response physical layer authentication mechanism based on interpolation polynomial. Then, this method is applied to the EAP-AKA’ authentication protocol of 5G network, and a cross-layer authentication mechanism for 5G converged channel fingerprint is proposed. Finally, using the captured high-level authentication challenge response data, a simulation environment is built in MATLAB and the feasibility and security of the scheme are verified. The experimental results show that the authentication mechanism has better authentication performance and security performance. Compared with the traditional high-level authentication mechanism, it has certain advantages in computing overhead and can meet the 5G application scenarios of large-scale IoT terminals. Moreover, it combines physical layer authentication with high-level authentication, realizes mutual complementarity and mutual enhancement, and enhances the security performance of authentication.

Performance evaluation for Q-learning based anycast routing protocol in unmanned aerial vehicle networks with multiple base stations

Unmanned Aerial Vehicle (UAV) networks can be used for data transmission in emergency scenarios, relaying data from ground users to base stations (BSs). While UAV networks collaborating with multi-BSs can significantly enhance performance, existing UAV routing protocols predominantly focus on unicast routing and often neglect critical aspects such as base station discovery. In addition, the high mobility of UAVs and rapid changes in network topology also pose great challenges for existing multi-BS routing protocols to maintain efficient data transmission. Aiming at the above problems, this paper abstracts the routing of multi-base station UAV networks as anycast routing for dynamic networks and proposes a distributed anycast routing protocol called QARP to improve the data transmission performance. In QARP, base stations can be discovered automatically and parameters of Q-learning are dynamically adjusted to improve the efficiency of data transmission. The Link Duration Estimation is used to influence routing decision and dynamically adjust the hello message interval. A multiple base stations transmission value function is designed to indicate the performance of data transmission and is used to calculate the reward and update Q-table. The experimental results show that the QARP proposed in this paper outperforms existing multi-BS routing and Q-learning based routing protocols in terms of delay, packet delivery ratio and throughput in single base station and multiple base stations scenarios.

A self-contained emulator for the forensic examination of IoE scenarios

With the number of cyberincidents on the Internet of Everything (IoE) increasing every year, so does the amount of forensic investigations that are carried out in this environment. As the research community is avidly working on the development of solutions that can assist in the examination process, it is crucial to, firstly, have access to a resource that can facilitate the process of learning the characteristics of these investigations, and, secondly, to have a testbed that allows evaluating the effectiveness and feasibility of new solutions. Likewise, from an educational standpoint, having access to assets that allow interacting with these devices in a simple and efficient way can lead to learners getting a better understanding of the forensic characteristics and requirements of this environment. In view of this, a self-contained emulator for the forensic examination of these scenarios is presented in this article that mirrors their static and dynamic by emulating both the firmware of the devices that comprise them and the multiple network protocols used in them. Additionally, the emulator offers the capability to deploy digital twins within IoE scenarios, enhancing its utility for cybersecurity forensic investigations and training sets. To demonstrate its feasibility and convenience, two case studies are presented that emulate different IoE forensic contexts, showing that the proposal is capable of emulating their static and dynamic behaviour, and that it can be used to perform different forensic tasks.

Spectrum Monitoring in Cognitive Radio Networks for Reduced Data Loss, Resource Wastage, and Interference

ABSTRACTCognitive radio is a technology that allows the unlicensed users to access the licensed spectrum based on sensing information perceived by the users. Cognitive radio user generally attempts to access the unutilized band of licensed user. However, the emergence/reappearance of primary users during ongoing cognitive user's transmission creates the interference at the primary receiver and data loss at cognitive receiver. In this context, the spectrum monitoring is a vital process of the cognitive radio network during data transmission to continuously monitor the primary user's presence or absence on a licensed channel. In this manuscript, we have utilized the spectrum monitoring technique on earlier existing MAC protocol for CR network to detect the emergence/reappearance of primary users throughout the data transmission period of cognitive users. The spectrum monitoring is employed to reduce the interference at primary receiver, which is measured in terms of interference power (IP) and data loss at CU receiver. The closed form expressions of data loss and IP for the monitoring and without monitoring scenarios are provided. In addition, the probability of monitoring error's (which causes misinterpretation of channel's status by the cognitive users) impact on these performance parameters is also provided and simulated. Further, earlier MAC protocol has significant amount of bandwidth wastage in case primary user disappears or reappears during cognitive user's data transmission, which is shown mathematically and simulated as well. However, incorporation of the monitoring phenomenon even with significant amount of monitoring error has reduced bandwidth wastage, which is measured in bps. In addition, the throughput calculation of the proposed system and the effect of monitoring error on the throughput of the proposed system are also seen. As per the results, the maximum data loss and IP occurred at licensed channel's busy probability value of 0.5, which is 23.22 bps and 3.487 W, respectively, at maximum monitoring error value. However, minimum data loss and IP are achieved at licensed busy channel's probability values of zero and one. In addition, the resource wastage at licensed channel's busy probability value of 0.5 is varying from 2.322 to 23.22 bps for varying the monitoring error value from 0.1 to 1, respectively, giving highest resource wastage at maximum monitoring error value.

Reducing tail latency for multi-bottleneck in datacenter networks: A compound approach

The effectiveness of network congestion control fundamentally depends on the accuracy and granularity of congestion feedback. In datacenter networks, precise feedback is essential for achieving high performance. Most existing approaches use either Explicit Congestion Notification (ECN) or network delay (e.g., RTT) independently as congestion indicators. However, in multi-bottleneck networks, the limitations of these signals become more pronounced: ECN struggles with large cumulative end-to-end latency, while RTT lacks the precision needed to control queuing delays at individual hops. To address these challenges, we propose Cocktail, a simple yet effective transport protocol for datacenter networks that combines both ECN and RTT congestion signals to more effectively handle multi-bottleneck scenarios. By leveraging the ECN signal, Cocktail bounds per-hop queue lengths, enhancing its ability to control single-hop latency and prevent packet loss. Additionally, by estimating RTT, Cocktail effectively manages end-to-end delay, resulting in lower Flow Completion Time (FCT). Extensive experimental evaluations in Mininet demonstrate that Cocktail significantly reduces the average and 99th-percentile completion times for small flows by up to 20% and 29%, respectively, compared to current practices under production workloads.

FRR: A Fast Routing Recovery mechanism minimizing network formation time in smart grids

In recent years, Internet of Things (IoT) technologies have been applied to the most diverse types of applications, highlighting their use in Smart Grids (SGs), such as smart metering, demand response, fault detection and renewable energy integration, as well as in Smart Cities, such as smart lighting, smart parking and environmental monitoring. A technology that has been widely used in these contexts is the Wireless Smart Ubiquitous Networks Field Area Network (Wi-SUN FAN) standard, which defines the implementation and behavior of interoperable networks based on open standards such as IEEE 802.15.4 and the IPv6 Routing Protocol for Low-Power and Lossy Networks (RPL). The adopted standards allow Wi-SUN FAN networks to take advantage of features such as security, frequency hopping, multi-hop packet routing, and IPv6 addressing, ensuring a wide coverage area and immunity against electromagnetic interference. Despite all the advantages, a shortcoming of Wi-SUN FAN networks is the long network formation time. In this article, we propose a mechanism called Fast Routing Recovery (FRR), which aims to accelerate the reconnection process in networks that use the RPL protocol by memorizing information about the nodes’ preferred parents. We evaluate the FRR mechanism by simulations and experiments. The results indicate that FRR can reduce the network formation time by up to 50% in certain network scenarios.

Lightweight, Post-Quantum Secure Cryptography Based on Ascon: Hardware Implementation in Automotive Applications

With the rapid growth of connected vehicles and the vulnerability of embedded systems against cyber attacks in an era where quantum computers are becoming a reality, post-quantum cryptography (PQC) is a crucial solution. Yet, by nature, automotive sensors are limited in power, processing capability, memory in implementing secure measures. This study presents a pioneering approach to securing automotive systems against post-quantum threats by integrating the Ascon cipher suite—a lightweight cryptographic protocol—into embedded automotive environments. By combining Ascon with the Controller Area Network (CAN) protocol on an Artix-7 Field Programmable Gate Array (FPGA), we achieve low power consumption while ensuring high performance in post-quantum-resistant cryptographic tasks. The Ascon module is designed to optimize computational efficiency through bitwise Boolean operations and logic gates, avoiding resource-intensive look-up tables and achieving superior processing speed. Our hardware design delivers significant speed improvements of 100 times over software implementations and operates effectively within a 100 MHz clock while demonstrating low resource usage. Furthermore, a custom digital signal processing block supports CAN protocol integration, handling message alignment and synchronization to maintain signal integrity under automotive environmental noise. Our work provides a power-efficient, robust cryptographic solution that prepares automotive systems for quantum-era security challenges, emphasizing lightweight cryptography’s readiness for real-world deployment in automotive industries.

Source-Based Disjoint Multipath Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

Source-Based Disjoint Multipath Routing Protocol for Underwater Acoustic Networks: Protocol Design and Sea Trials

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Retraction Note: Optical wireless communication based mobile edge computing integrated channel allocation using scheduling with machine learning protocols in advanced 5G networks

Buffer Occupancy-Based Congestion Control Protocol for Wireless Multimedia Sensor Networks

Wireless multimedia sensor networks (WMSNs) have stringent constraints and need to deliver data packets to the sink node within a predefined limited time. However, due to congestion, buffer overflow occurs and leads to the degradation of the quality-of-service (QoS) parameters of event information. Congestion in WMSNs results in exhausted node energy, degraded network performance, increased transmission delays, and high packet loss. Congestion occurs when the volume of data trying to pass through a network exceeds its capacity. First, the BOCC protocol uses two congestion indicators to detect congestion. One is the buffer occupancy and other is the buffer occupancy change rate. Second, a rate controller is proposed to protect high-priority I-frame packets during congestion. BOCC sends a congestion notification to the source node to reduce congestion in the network. The source node adjusts its data transmission rate after receiving the congestion notification message. In the proposed algorithm, the rate adjustment is made by discarding low-priority P-frame packets from the source nodes. Third, to further improve the performance of the BOCC protocol, the problem is formulated as a constrained optimization problem and solved using convex optimization and sequential quadratic programming (SQP) methods. Experimental results based on Raspberry Pi sensor nodes show that the BOCC protocol achieves up to 16% reduction in packet loss and up to 23% reduction in average end-to-end delay compared to state-of-the-art congestion control algorithms.

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

An hybrid machine learning and improved social spider optimization based clustering and routing protocol for wireless sensor network

Analytical Evaluation of the WebSocket and WebTransport Protocols for the Class of SCADA Systems Operating Within the Intranet

The purpose of research. In recent years, web technologies have been actively used in many process control systems, including SCADA. Such systems allow continuous monitoring of the test object in real time. In this regard, there is a need for frequent data transmission for their further display. To solve this problem, network protocols are used, which continue to develop rapidly to this day.Materials and methods. SCADA systems are used in many industries: aviation, ground transportation, rocket and space technology, systems for receiving and processing telemetry information. Among them, a class of SCADA systems operating within the intranet stands out. This can be an internal network of the enterprise, a hangar or a small test stand for tracking the indexes of measuring equipment. Therefore, today, more than ever, open and publicly available materials are important for understanding modern network solutions that reduce the time spent on data transmission and their further visualization. Such information will be of particular value to students studying network and web technologies.Results. As a result of the research, taking into account the limitations of the SCADA system and network being developed, the time characteristics for the WebSocket and WebTransport protocols have been determined, and the most optimal solution for a typical load is proposed. In the presented paper, the issues of data transfer between the client and server parts for a class of SCADA systems operating within an intranet are considered. In this regard, it is proposed to consider the two most suitable solutions for this task. These are application layer network protocols of the OSI model: WebSocket and WebTransport. In addition, transport layer protocols (TCP and QUIC) are taken into account, since they can also affect time characteristics. Data transmission issues are considered in the context of the SCADA data display and monitoring system. The server part is developed using C#, and the client is developed using JavaScript. A number of experiments are carried out for boundary cases and typical loads, on the basis of which the dependence of data transmission time on their volume is determined.Conclusion. The approach given in the paper on collecting network traffic and analyzing time characteristics will also be useful as part of the educational process for teaching students in the courses “Computer Networks” and “Internet Technologies”.

OCTOPUS: operation control system for task optimization and job parallelization via a user-optimal scheduler

The material acceleration platform, empowered by robotics and artificial intelligence, is a transformative approach for expediting material discovery processes across diverse domains. However, the development of an operating system for material acceleration platform faces challenges in simultaneously managing diverse experiments from multiple users. Specifically, when it is utilized by multiple users, the overlapping challenges of experimental modules or devices can lead to inefficiencies in both resource utilization and safety hazards. To overcome these challenges, we present an operation control system for material acceleration platform, namely, OCTOPUS, which is an acronym for operation control system for task optimization and job parallelization via a user-optimal scheduler. OCTOPUS streamlines experiment scheduling and optimizes resource utilization through integrating its interface node, master node and module nodes. Leveraging process modularization and a network protocol, OCTOPUS ensures the homogeneity, scalability, safety and versatility of the platform. In addition, OCTOPUS embodies a user-optimal scheduler. Job parallelization and task optimization techniques mitigate delays and safety hazards within realistic operational environments, while the closed-packing schedule algorithm efficiently executes multiple jobs with minimal resource waste. Copilot of OCTOPUS is developed to promote the reusability of OCTOPUS for potential users with their own sets of lab resources, which substantially simplifies the process of code generation and customization through GPT recommendations and client feedback. This work offers a solution to the challenges encountered within the platform accessed by multiple users, and thereby will facilitate its widespread adoption in material development processes.

ML-based Anomaly Detection for CAN Bus Network in Agriculture Machinery

The adoption of advanced automation and next-generation technologies like the Internet of Things (IoT) and modern communication networks has revolutionized the food and agriculture sector, boosting the efficiency and precision of farm machinery. However, this increased inter-connectivity has also exposed significant vulnerabilities, particularly in Controller Area Network (CAN) protocols, widely used in advanced agricultural machinery and equipment. Due to its lack of inherent security features, CAN is susceptible to various cyber-attacks, potentially leading to severe consequences if these attacks remain undetected and unmitigated. This paper introduces a supervised machine learning (ML)-based anomaly detection system (CAN-ADS) designed to detect various cyber-attacks on CAN-based agricultural machinery. The system leverages network traffic augmentation and data balancing techniques to train ML algorithms on CAN-specific datasets. Experimental results show that CAN-ADS achieves high accuracy (approx 98%) and true-positive rates with low false-negative rates (approx 1%).

Survey of AI-driven routing protocols in underwater acoustic networks for enhanced communication efficiency

The high-speed growth of undersea communication networks requires sophisticated routing protocols to deal with challenging underwater conditions including large latencies, limited bandwidths and varying topologies. In this paper, we examine the use of Artificial Intelligence (AI), Machine Learning (ML), Reinforcement Learning (RL) and fuzzy logic to optimize routing protocols for underwater networks. We provide a comprehensive survey of existing AI-based approaches, emphasizing their novelties and constraints underwater.To assess the efficiency of these AI-based routing protocols, we carry out extensive simulations across various underwater environments where metrics such as packet delivery ratio, energy consumption, end-to-end delay, and computational efficiency are focused on. The results reveal that AI-aided protocols excel over conventional methods particularly in situations involving complex environmental dynamics as well as resources limitation.However, there are practical implementation issues which must be solved before the real-world application of AI-based routing such as hardware constraints, concerns on energy usage , and scalability. This study provides valuable insights into the integration of AI technologies into underwater communication networks, paving the way for more reliable and efficient underwater operations. Our findings contribute to the growing body of knowledge in this field and offer a foundation for future advancements in underwater communication technologies.