Network Protocols Research Articles

FCFS: A Dual-Channel and Reservation-Based MAC Protocol for Underwater Acoustic Networks

FCFS: A Dual-Channel and Reservation-Based MAC Protocol for Underwater Acoustic Networks

Machine learning for QoS and security enhancement of RPL in IoT-Enabled wireless sensors

Internet of Things (IoT) networks rely on wireless sensors for data collection and transmission, making them vulnerable to security threats that undermine their Quality of Service (QoS). The Routing Protocol for Low-Power and Lossy Networks (RPL) is crucial for efficient data transmission in IoT networks, but its performance can be significantly degraded by attacks such as Rank, Sinkhole and Wormhole attacks. These threats disrupt network integrity by manipulating routing information, attracting traffic through malicious nodes and tunneling data to malicious endpoints. This paper presents a novel machine learning-based framework to enhance RPL's security and QoS. Our approach integrates a random forest model for precise traffic classification, a reinforcement learning module for dynamic and adaptive routing, and a modified RPL objective function that incorporates classification outcomes into routing decisions. Simulations demonstrate that our framework significantly improves network throughput, reduces latency, and enhances packet delivery ratios while maintaining low jitter. Furthermore, it achieves a high detection rate, minimal false positives, and swift response to security incidents, thereby robustly securing the RPL protocol and enhancing QoS in IoT-enabled wireless sensor networks. The findings of this research offer substantial contributions to the field, providing a comprehensive solution to strengthen RPL against prevalent security threats.

Energy­efficient routing protocol in wireless sensor networks

Recently, self-organizing networks have gained popularity, which are distinguished by the possibility of connecting terminals anywhere and at any time without the need for a centralized network infrastructure. Mobile self-organizing networks are a promising direction for the development of information and communication systems. However, one should take into account the problems arising from the very nature of such networks: mobile nodes lead to an increase in the dynamism of the network topology due to the possibility of disconnection and the probability of movement; the limited supply of power sources of nodes requires careful management of power consumption in the design of hardware and protocols. Energy-saving design in self-organizing networks is more complex and important than in other wireless networks. The lack of infrastructure requires mobile nodes to act as routers, which increases traffic and therefore power consumption. Also, the energy-saving mode must take into account different network performance criteria, which can be caused by different protocols and power limitations. Thanks to the latest advances in technology and the rapid development of the production of small and affordable sensors, it has become possible to use them technically and economically effectively. These electronic sensors, which have high sensitivity, measure environmental parameters and convert the received data into an electronic signal that is transmitted over a wireless channel. After processing this signal, you can get information about the properties of objects or events that occur near the sensor. The application of a large number of such sensors, which can monitor a large area and exchange data wirelessly, forms a single wireless sensor network. Route allocation is a key challenge for resource-constrained networks. With the growth of computing power in wireless sensor technology, these sensor nodes are required to be equipped with more and more capabilities to perform more complex tasks. Each sensor is limited by its energy level, processing power, and ability to perceive information. Thus, the network of these sensors forms a more reliable and accurate structure. The article discusses the issue of using the HABRP protocol for its application in wireless sensor networks. The use of the HABRP protocol allows for improvement in the efficiency of wireless sensor networks, namely their energy efficiency. Therefore, the energy efficiency and simplicity of the HABRP protocol make it an excellent candidate for use in wireless sensor networks.

Enhanced Multicast Protocol for Low-power and Lossy IoT Networks

Enhanced Multicast Protocol for Low-power and Lossy IoT Networks

Provably Secure and Lightweight Authentication and Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks

Provably Secure and Lightweight Authentication and Key Agreement Protocol for Fog-Based Vehicular Ad-Hoc Networks

A secure three-factor authentication protocol for mobile networks

A secure three-factor authentication protocol for mobile networks

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

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

On the Impact of Deployment Errors in Location-Based Key Predistribution Protocols for Wireless Sensor Networks

On the Impact of Deployment Errors in Location-Based Key Predistribution Protocols for Wireless Sensor Networks

Cluster-based energy-efficient routing protocol in next generation sensor networks

Cluster-based energy-efficient routing protocol in next generation sensor networks

Advanced Recursive Best-First Search (RBFS) based Routing Protocol for Multi-hop and Multi-Channel Cognitive Wireless Mesh Networks

Cognitive Wireless Mesh Network (CWMN) is an opportunistic network in which radio channels can be assigned according to their availability to establish connections among nodes. After establishing a radio connection among nodes, it is necessary to find an optimal route from the source node to the destination node in the network. If there remain more channels among nodes, the minimum weighted channel should be taken into account to establish expected routes. The graph theoretic approach fails to model the multi-channel cognitive radio networks due to abrupt failure in finding new successful routes as it can’t figure multi-channel networks. In this paper, a multi-edged graph model is being proposed to overcome the problems of cognitive radio networks, such as flooding problems, channel accessing problems etc. A new channel accessing algorithm has been introduced, and optimal routes have been selected using a heuristic algorithm named RBFS. Simulation results are compared with DJKSTRA based routing protocols.

Optimizing Software-Defined Networks with Fuzzy Logic-Based Enhancement of Openflow Protocol

Today, humans have a strong need to control their devices from a distance so that they can control the world more than before and explore it for various purposes such as how the universe came into being, discovering the way of creation, observing the events in Global situation and so on. Communication with remote devices can be possible in various ways. SDN networks provide a possibility to exchange information between heterogeneous nodes. Considering that in SDN networks, the nodes are very expensive and these nodes themselves are performing many tasks and various vital tasks; Therefore, the cost of each byte of memory occupied on these nodes is very expensive and must be managed in such a way that they have the highest efficiency. Therefore, to solve this problem, it is very necessary and costly to carry out large projects. In the proposed method of this research, by improving the OpenFlow protocol in software-based networks, it is tried to avoid the cooperation of nodes in the directional distribution (not dissemination) of a small data, from the accumulation of extra information in the nodes' memories. Finally, after the simulation, it was observed that the improvement rate of the proposed method has improved by 0.38%, 0.05%, and 0.04%, respectively, compared to RD, FLCFP, and LEACH2013 methods. The improvement rate of the proposed method compared to RD, FLCFP, and LEACH2013 methods was 0.65%, 0.059%, and 0.331%, respectively.

Using electronic health records to enhance surveillance of diabetes in children, adolescents and young adults: a study protocol for the DiCAYA Network

IntroductionTraditional survey-based surveillance is costly, limited in its ability to distinguish diabetes types and time-consuming, resulting in reporting delays. The Diabetes in Children, Adolescents and Young Adults (DiCAYA) Network seeks...

Cache Improvement Based on Routing Protocol for AD-HOC Networks: Systematic Literature Review

Cache Improvement Based on Routing Protocol for AD-HOC Networks: Systematic Literature Review

A Unified Random Access Procedure for an NB-IoT Protocol in Nonterrestrial and Terrestrial Networks

A Unified Random Access Procedure for an NB-IoT Protocol in Nonterrestrial and Terrestrial Networks

Robust and energy-efficient authentication protocols for medical sensor networks

The growing incorporation of medical sensor networks into healthcare systems has emphasized the crucial requirement for secure and energy-efficient authentication protocols. The purpose of this study is to present a new method called Sidechain-based Trusted Platform Module (s-TPM) authentication, which aims to overcome the inherent difficulties in ensuring the security of medical sensor networks. This study performs a thorough comparison of s-TPM with two well-established authentication protocols, LEAP and TinySec, across multiple evaluation parameters, such as security, energy efficiency, and performance. The s-TPM authentication method utilizes sidechain technology to strengthen the resilience of authentication procedures in medical sensor networks. s-TPM utilizes a decentralized and tamper-proof system to reduce security weaknesses, offering a robust protection against unauthorized entry and data breaches. The results indicate that s-TPM surpasses LEAP and TinySec in terms of security resilience, guaranteeing a dependable protection against diverse cyber threats. Moreover, s-TPM demonstrates exceptional energy efficiency, which enhances the longevity of medical sensor devices’ operation while upholding a high standard of security. The results emphasize the importance of utilizing innovative methods, like s-TPM, to create authentication protocols that are specifically designed for the distinct difficulties encountered in medical settings.

Performance Analysis of 6TiSCH Networks Using Discrete Events Simulator

The Internet of Things (IoT) empowers small devices to sense, react, and communicate, with applications ranging from smart ordinary household objects to complex industrial processes. To provide access to an increasing number of IoT devices, particularly in long-distance communication scenarios, a robust low-power wide area network (LPWAN) protocol becomes essential. A widely adopted protocol for this purpose is 6TiSCH, which builds upon the IEEE 802.15.4 standard. It introduces time-slotted channel hopping (TSCH) mode as a new medium access control (MAC) layer operating mode, in conjunction with IEEE 802.15.4g, which also defines both MAC and physical layer (PHY) layers and provides IPv6 connectivity for LPWAN. Notably, 6TiSCH has gained adoption in significant standards such as Wireless Intelligent Ubiquitous Networks (Wi-SUN). This study evaluates the scalability of 6TiSCH, with a focus on key parameters such as queue size, the maximum number of single-hop retries, and the slotframe length. Computational simulations were performed using an open-source simulator and obtained the following results: increasing the transmission queue size, along with adjusting the number of retries and slotframe length, leads to a reduction in the packet error rate (PER). Notably, the impact of the number of retries is particularly pronounced. Furthermore, the effect on latency varies based on the specific combination of these parameters as the network scales.

DemePro: DEcouple packet Marking from Enqueuing for multiple services with PROactive congestion control

Most of current Data Center Network (DCN) protocols leverage Explicit Congestion Notification (ECN) for congestion control to maintain low latency and high throughput. However, the majority of them only consider single-queue scenario in each switch port, making their performance inferior in multiple-service multiple-queue scenario. To this end, we propose DemePro, a DCN scheme for multiple-service multiple-queue scenario. In face of congestion signal, DemePro also leverages ECN for congestion notification, while decouples packet marking from enqueuing, in order to ensure fairness among multiple services. We also question the effectiveness of the congestion signal derived from the single threshold for port buffer queue length, via a set of experiments. Then, DemePro utilizes multiple thresholds for proactive congestion control, and packets are encapsulated to carry congestion extent information, in order to notify TCP senders for precise and fine-grained congestion control. Experiments show that DemePro has better performance than MQ-ECN [1] which is currently the best DCN scheme for multiple-service multiple-queue scenario, and DemePro can also well guarantee the fairness.

Analysis of the photographers' quality control in the diabetic retinopathy screening at the IOBA's Reading Center

Aims/Purpose: Diabetic retinopathy (DR) is the most frequent ocular complication of diabetes and one of the leading causes of avoidable blindness. Teleophthalmology screening by taken retinal images is an effective tool in its early detection and delivering prompt treatment. IOBA's Reading Center (RC) of the University of Valladolid is in charge for the Regional Health System (SACYL) DR screening program. Retinographies are taken at Primary Care Centers, under mydriasis, following the three‐field Joslin Vision Network protocol by certified photographers. Quality control (QC) is an indispensable process in a RC to ensure an accurate retinography reading. An upper limit of 5% of photographs taken with low quality has been set as acceptable. This study aimed to analyse the quality of the retinographies taken in the DR screening at the IOBA's RC during 2021 and 2022.Methods: A retrospective study was designed analysing the quality outcomes of DR screening. Certified in image reading Optometrists determined if images received at the RC had enough quality according to seven factors (halo >1/3, inadequate vessels/parenchyma sharpness, inadequate central, nasal, temporal or fundus reflex image and inadequate disk sharpness). Statistical analysis was performed using SPSS 26 (Chicago, USA). Chi‐square test was used to compare frequencies between 2021 and 2022 outcomes.Results: 4474 retinographies were taken during 2021 and 6345 during 2022. 14 photographers took part in the program (8 in both years, 2 left in 2021, and 4 joined in 2022). All analysed factors showed less than 3% of incorrect images. 6.1% of images showed at least one error in 2021 and 4.4% in 2022 (p < 0.01). Every analysed factor showed a significant improvement (p < 0.02) between 2021 and 2022 except for disk sharpness in both eyes (p > 0.05).Conclusions: The quality of the images of the IOBA RC is adequate for the SACYL DR screening program showing values below the fixed upper limit in all the analysed factors with a significant decrease in almost all values that can be attributed to increased experience of capture personnel.

Enhancing DDoS detection in SDIoT through effective feature selection with SMOTE-ENN.

Internet of things (IoT) facilitates a variety of heterogeneous devices to be enabled with network connectivity via various network architectures to gather and exchange real-time information. On the other hand, the rise of IoT creates Distributed Denial of Services (DDoS) like security threats. The recent advancement of Software Defined-Internet of Things (SDIoT) architecture can provide better security solutions compared to the conventional networking approaches. Moreover, limited computing resources and heterogeneous network protocols are major challenges in the SDIoT ecosystem. Given these circumstances, it is essential to design a low-cost DDoS attack classifier. The current study aims to employ an improved feature selection (FS) technique which determines the most relevant features that can improve the detection rate and reduce the training time. At first, to overcome the data imbalance problem, Edited Nearest Neighbor-based Synthetic Minority Oversampling (SMOTE-ENN) was exploited. The study proposes SFMI, an FS method that combines Sequential Feature Selection (SFE) and Mutual Information (MI) techniques. The top k common features were extracted from the nominated features based on SFE and MI. Further, Principal component analysis (PCA) is employed to address multicollinearity issues in the dataset. Comprehensive experiments have been conducted on two benchmark datasets such as the KDDCup99, CIC IoT-2023 datasets. For classification purposes, Decision Tree, K-Nearest Neighbor, Gaussian Naive Bayes, Random Forest (RF), and Multilayer Perceptron classifiers were employed. The experimental results quantitatively demonstrate that the proposed SMOTE-ENN+SFMI+PCA with RF classifier achieves 99.97% accuracy and 99.39% precision with 10 features.

Tying up loose ends together: Cetaceans, maritime traffic and spatial management tools in the Strait of Gibraltar

Abstract The transboundary area of the Strait of Gibraltar is home to seven protected cetacean species that are threatened by high intensity of maritime traffic. More comprehensive knowledge of cetaceans and maritime traffic is required, together with analyses of legislations, strategies and policies. This study quantitatively investigates cetacean distribution and maritime traffic intensity and, for the species of community interest bottlenose dolphin, habitat suitability. Results are qualitatively discussed considering the overlap of cetacean hot spots with different maritime activities and the consistency of spatial conservation management measures in force. The Fixed Line Transect Mediterranean Monitoring Network protocols were followed for 59 visual surveys using ferries as observation platforms for monitoring cetaceans and maritime traffic. Surveys were carried out along the transects between Algeciras and Ceuta and between Algeciras and Tanger‐Med, in 2018 and 2019. 264 cetacean sightings, including seven different species and four near‐miss collision events (involving pilot, sperm and fin whales), were reported. Monitoring cetaceans from ferries in the Strait provided insights into cetacean distribution and maritime traffic, enabling the identification of cetacean hot spots, suitable habitats and maritime traffic high‐risk zones. A transboundary management effort is required, together with an adaptive approach for protecting highly mobile species such as cetaceans. Proposals include a long‐term cetacean monitoring program carried out by dedicated observers on board ferries as a cost‐effective methodology and mandatory training for crew members, to increase cetacean knowledge and reduce collision risk. The designation of an international temporal or, in some zones, permanent speed reduction area (i.e., Cetacean Critical Navigation Zone, with a maximum speed of 13 knots) and of a micro‐sanctuary with a seasonal no‐take zone in the Bay between Algeciras and Gibraltar, together with international surveillance, are recommended measures for the enhancement of conservation efforts in the Strait.