Node Discovery Research Articles

In-Depth Analysis of Molecular Network Based on Liquid Chromatography Coupled with Tandem Mass Spectrometry in Natural Products: Importance of Redundant Nodes Discovery.

The identification of molecules within complex mixtures is a major bottleneck in natural products (NPs) research. Liquid chromatography-tandem mass spectrometry (LC-MS/MS) has emerged as the main tool for the high-throughput characterization of NPs. The large amount of data sets by LC-MS/MS presents a challenge for data processing and interpretation, and the LC-MS/MS molecular network (MN) is one of the most prominent tools for analyzing large MS/MS data sets, widely used for rapid classification, identification, and structural speculation of unknown compounds. However, the existence of a large number of redundant nodes leads to false-positive results. To solve this problem, we proposed the in-depth analysis of MN. In this study, in-depth analysis of MN of five NPs representing the common structures of saponin, steroid, flavonoid, alkaloid, and phenolic acid revealed the presence of redundant nodes (including other adducts, isotope, and in-source fragmentation) in addition to the normal nodes, which can lead to false-positive identification results. Additionally, the reasons for different redundant nodes are discussed and experimentally verified, and it was found that the impact of redundant nodes can be mitigated by optimizing the experimental conditions and employing Feature-Based Molecular Networking. Furthermore, Ion Identity Molecular Networking can rapidly discover and screen redundant nodes, simplifying the in-depth analysis of MN and improving the network connectivity of structurally related molecules. Finally, a combination formulation of 7 NPs is used as an example to provide a guide for in-depth analysis of MN for comprehensive characterization of complex systems. This study highlights the importance of an in-depth analysis of MN for better understanding and utilization of MS/MS data in complex systems to reduce the false-positive rate of identification by screening and filtering redundant nodes.

Electrodiagnostic Findings of Median Nerve Motor and Sensory Conduction in Neurological Asymptomatic Newly Diagnosed Patients with Hypothyroidism

BACKGROUND: The influence/impact of thyroid hormones on peripheral nervous system has still not been thoroughly/substantially studied/examined. Neurographic studies, which measure motor and sensory nerve conduction along with F-wave, have an important role in diagnosing diseases of the peripheral nerves since electrophysiological sings of neuropathy can be detected even in subclinical state/cases. AIM: The aims of this study were to assess the neurophysiological parameters of the median nerve by electrodiagnostic measurements using electromyoneurogaphy (EMNG) in the region of radiocarpal joint in patients with newly diagnosed and medically untreated primary hypothyroidism who had no symptoms and signs of peripheral nerve damage/injury/lesion. Furthermore, it was our aim to make an insight of the possible early detection of the latent damage of peripheral nervous system in untreated hypothyroidism. MATERIAL AND METHODS: This was a prospective clinical study that comprised 78 subjects/patients according to inclusion criteria, divided into two groups: one with primary hypothyroidism and the other group involving euthyroid subjects. Thyroid status was examined in each subject; anthropometric parameters were analyzed, and electromyoneurographic imaging (EMNG) of hand median nerve was realized. RESULTS: Of the total number of 78 subjects included in the study, 58 (74.36%) had hypothyroidism and 20 were euthyroid subjects (25.64%). The calculated mean levels of thyroid status parameters in hypothyroid patients, TSH, aTPO and FT4, were 6.19±1.85 mIU/L, 872.22±296.66 U/mL and 0.78±0.11 ng/dL, respectively. In euthyroid patients, the mean levels of TSH, aTPO and FT4 were 0.27±0.08 mIU/L, 31.2071±5.65 U/mL and 1.31±0.25 ng/dL, respectively. Statistically significant differences were obtained between hypothyroid and euthyroid subjects regarding: age (p=0.0147, r=0.3239), body weight (p=0.0441, r= -0.531), body mass index (p=0.0050, r= -0.301), WHO classification for BMI (p=0.0032, r= -0.250), TSH (p<0.0001, r=-0.309), aTPO (p<0.0001, r=0.5554) and FT4 (p<0.0001, r= -0.317). In the group of 58 hypothyroid subjects, 38 (65.52%) were women and 20 (34.48%) were men. A strong correlation and significant statistical difference were registered in EMNG pathological findings in patients with pathologic BMI and with mild predilection for the sensory part of the median nerve. Of the 58 hypothyroid patients, EMNG pathologic findings were present in 18 that, in terms of their features, indicated a distal lesion of the median nerve. These 18 subjects had an increased body mass index (30.83±5.65 kg/m2). CONCLUSIONS: Discovery, definition and explanation of etiopathogenic nodes in peripheral nervous system diseases will require extensive and multidisciplinary strategies in the next decades. These complex conditions, in our opinion, will claim a steady/a step-by-step/ approach to the problems, as well as consistency and symmetry in the design and methodology of the research procedures.

Priority-based fault tolerance mechanism with neighbour candidate node discovery algorithm and task processing by replication and forwarding technique under Fog-IoT wireless computing environments

The real-world exponential increase in data traffic has brought attention to a new computing paradigm termed Fog Computing (FC), which is intended for task offloading in fault-free fog networks. It is a potential aid which that provides greater processing aids at lower costs and with greater availability, flexibility, and cost. The issue typically arises due to the high task count and impacts task offloading in fog scenarios. In order to address a problem that arises in the Fog-IoT network for providing dependable and error-free transmission, an appropriate technique is required. Based on fault minimization and cost optimisation, the novel FT mechanism is proposed in this research. First, proposed Priority based Task offloading with Fault Tolerance (PToFT) scheme is used to identify the faulty-FNs using FN's remaining residual energy. To find the neighbour candidate Fog access node for replacing the faulty-FNs, the Min-cost Neighbour Candidate Node Discovery based on replication and forwarding (MNCND-RaF) technique is proposed for effective task processing and also tracks the task information towards the new nodes. These proposed methods are simulated, evaluated, and compared with the current Fault Tolerance (FT) techniques. The results shows that the compared results of the proposed methods will outperforms with current approaches like Without FT, NFT-WOA, and DFTLA methods, as 42.3 %, 36.2 %, and 27.7 %, respectively. Additionally, it utilized 1.53 J of residual energy as compared with HBI-LB and 0.84 J without replicas.

Key Vulnerable Nodes Discovery Based on Bayesian Attack Subgraphs and Improved Fuzzy C-Means Clustering

Aiming at the problem that the search efficiency of key vulnerable nodes in large-scale networks is not high and the consideration factors are not comprehensive enough, in order to improve the time and space efficiency of search and the accuracy of results, a key vulnerable node discovery method based on Bayesian attack subgraphs and improved fuzzy C-means clustering is proposed. Firstly, the attack graph is divided into Bayesian attack subgraphs, and the analysis results of the complete attack graph are quickly obtained by aggregating the information of the attack path analysis in the subgraph to improve the time and space efficiency. Then, the actual threat features of the vulnerability nodes are extracted from the analysis results, and the threat features of the vulnerability itself in the common vulnerability scoring standard are considered to form the clustering features together. Next, the optimal number of clusters is adaptively adjusted according to the variance idea, and fuzzy clustering is performed based on the extracted clustering features. Finally, the key vulnerable nodes are determined by setting the feature priority. Experiments show that the proposed method can optimize the time and space efficiency of analysis, and the fuzzy clustering considering multiple features can improve the accuracy of analysis results.

Minimum-Cost-Based Neighbour Node Discovery Scheme for Fault Tolerance under IoT-Fog Networks

The exponential growth in data traffic in the real world has drawn attention to the emerging computing technique called Fog Computing (FC) for offloading tasks in fault-free environments. This is a promising computing standard that offers higher computing benefits with a reduced cost, higher flexibility, and increased availability. With the increased number of tasks, the occurrence of faults increases and affects the offloading of tasks. A suitable mechanism is essential to rectify the faults that occur in the Fog network. In this research, the fault-tolerance (FT) mechanism is proposed based on cost optimization and fault minimization. Initially, the faulty nodes are identified based on the remaining residual energy with the proposed Priority Task-based Fault-Tolerance (PTFT) mechanism. The Minimum-Cost Neighbour Candidate Node Discovery (MCNCND) algorithm is proposed to discover the neighbouring candidate Fog access node that can replace the faulty Fog node. The Replication and Pre-emptive Forwarding (RPF) algorithm is proposed to forward the task information to the new candidate Fog access node for reliable transmission. These proposed mechanisms are simulated, analysed, and compared with existing FT methods. It is observed that the proposed FT mechanism improves the utilization of an active number of Fog access nodes. It also saved a residual energy of 1.55 J without replicas, compared to the 0.85 J of energy that is used without the FT method.

Developing a method for the detection and identification of rootstock blockchain network nodes

The object of this study is the protocol for detecting nodes in the Rootstock blockchain network and crawling tools. Node discovery protocols are the foundation of any decentralized peer-to-peer network. In blockchain systems, full nodes store and maintain a complete copy of all transactions performed in the network. However, they do not store information about all other nodes, such as their IDs or IP addresses. Each node usually maintains an incomplete list of nodes to which it connects to exchange blockchain data. In decentralized networks, nodes join and leave the network and their IP addresses can change, making it impractical to maintain a complete, up-to-date list of all nodes. Therefore, the only way to get a list of all nodes in the network is to poll each node sequentially. The developed method involves sending specially formed messages to nodes to obtain their neighbors. The graph search algorithm is used to traverse all received neighboring nodes. This allows one to consistently detect all network nodes. Identifying the desired sequence of messages requires a preliminary analysis of the node software RSKj in the part of node discovery protocol. Effectiveness of the proposed method was verified using the developed software and an experiment in the main network. 6 verification nodes were deployed in different physical locations and at different times. All test nodes were detected in less than 10 minutes. The developed method found 222 nodes that have 209 unique IP addresses. Results of this study show how to perform analysis of node discovery protocol. They provide the means to obtain information about the available nodes of the Rootstock blockchain system, enabling the analysis of both the blockchain network in general and individual node

An algorithm for discovering vital nodes in regional networks based on stable path analysis

Vital node discovery is a hotspot in network topology research. The key is using the Internet’s routing characteristics to remove noisy paths and accurately describe the network topology. In this manuscript, a vital regional routing nodes discovery algorithm based on routing characteristics is proposed. We analyze the stability of multiple rounds of measurement results to overcome the single vantage point’s path deviation. The unstable paths are eliminated from the regional network which is constructed through probing for target area, and the pruned topology is more in line with real routing rules. Finally, we weight the edge based on the actual network’s routing characteristics and discover vital nodes in combination with the weighting degree. Unlike existing algorithms, the proposed algorithm reconstructs the network topology based on communication and transforms unweighted network connections into weighted connections. We can evaluate the node importance in a more realistic network structure. Experiments on the Internet measurement data (275 million probing results collected in 107 days) demonstrate that: the proposed algorithm outperforms four existing typical algorithms. Among 15 groups of comparison in 3 cities, our algorithm found more (or the same number) backbone nodes in 10 groups and found more (or the same number) national backbone nodes in 13 groups.

Hybrid Interval Type-2 Fuzzy AHP and COPRAS-G-based trusted neighbour node Discovery in Wireless Sensor Networks

In Wireless Sensor Networks (WSNs), reliable and rapid neighbour node discovery is considered as the crucial operation which frequently needs to be executed over the entire lifecycle. Several neighbour node discovery mechanisms are proposed for reducing the latency or extending the sensor nodes’ lifetime. But majority of the existing neighbour node discovery mechanisms failed in addressing the critical issues of real WSNs related to energy consumptions, constraints of latency, uncertainty of node behaviors, and communication collisions. In this paper, Hybrid Interval Type-2 Fuzzy Analytical Hierarchical Process (AHP) and Complex Proportional Assessment using Grey Theory (COPRAS-G)-based trusted neighbour node discovery scheme (FAHPCG) is proposed for better data dissemination process. In specific, Interval Type 2 Fuzzy AHP is applied for determining the weight of the evaluation criteria considered for neighbour node discovery, and then Grey COPRAS method is adopted for prioritizing the sensor nodes of the routing path established between the source and destination. It adopted the merits of fuzzy theory for handling the uncertainty and vagueness involved in the change in the behavior of sensor nodes during the process of neighbour discovery. It is proposed with the capability of exploring maximized number of factors that aids in exploring the possible dimensions of sensor nodes packet forwarding potential during the process of neighbour node discovery. The simulation results of the proposed FAHPCG scheme confirmed an improved neighbour node discovery rate of 23.18% and prolonged the sensor nodes lifetime to the maximum of 7.12 times better than the baseline approaches used for investigation.

EASND: Energy Adaptive Secure Neighbour Discovery Scheme for Wireless Sensor Networks

Wireless Sensor Network (WSN) is defined as a distributed system of networking, which is enabled with set of resource constrained sensors, thus attempt to providing a large set of capabilities and connectivity interferences. After deployment nodes in the network must automatically affected heterogeneity of framework and design framework steps, including obtaining knowledge of neighbor nodes for relaying information. The primary goal of the neighbor discovery process is reducing power consumption and enhancing the lifespan of sensor devices. The sensor devices incorporate with advanced multi-purpose protocols, and specifically communication models with the pre-eminent objective of WSN applications. This paper introduces the power and security aware neighbor discovery for WSNs in symmetric and asymmetric scenarios. We have used different of neighbor discovery protocols and security models to make the network as a realistic application dependent model. Finally, we conduct simulation to analyze the performance of the proposed EASND in terms of energy efficiency, collisions, and security. The node channel utilization is exceptionally elevated, and the energy consumption to the discovery of neighbor nodes will also be significantly minimized. Experimental results show that the proposed model has valid accomplishment.

MAD‐DDS: Memory‐efficient automatic discovery data distribution service for large‐scale distributed control network

AbstractThe rampant deployment of data distribution service (DDS) as middle‐ware service providers for industrial network platforms has been widely investigated. All DDS‐based node discovery protocols establish communication with its intended target systems by accessing matched endpoints/nodes information. These matched endpoints/nodes information is usually embedded with the system’s programmable control plane network which acts as the conveyor vehicle. The introduction of software defined networking (SDN) is to characterize the control plane from embedded data plane. The DDS implements the simple discovery protocol (SDP) as its inherent node discovery protocol. Deploying DDS for data packet exchange in server‐based collaborative distributed networked control (DNC) systems has gained traction. The current automatic discovery protocol (ADP) based on SDP is fraught with real‐time limitations such as high memory consumption and poor packet transmission. This work presents novel memory‐efficient automatic discovery data distribution service (MAD‐DDS) with enhanced threshold bloom filters (ETBF) where ETBF stores transmission packets at simulation end‐nodes. The packet is further adjusted using optimized binarization and decision thresholds inside ADP, hence guaranteeing memory reduction. The testbed computation recorded significantly improved quality of service (QoS) whereas numerical results depict significant decline in memory consumption with consistent packet transmission rates that produces increased computational capacity.

LoyalDE: Improving the performance of Graph Neural Networks with loyal node discovery and emphasis

Recent years have witnessed an increasing focus on graph-based semi-supervised learning with Graph Neural Networks (GNNs). Despite existing GNNs having achieved remarkable accuracy, research on the quality of graph supervision information has inadvertently been ignored. In fact, there are significant differences in the quality of supervision information provided by different labeled nodes, and treating supervision information with different qualities equally may lead to sub-optimal performance of GNNs. We refer to this as the graph supervision loyalty problem, which is a new perspective for improving the performance of GNNs. In this paper, we devise FT-Score to quantify node loyalty by considering both the local feature similarity and the local topology similarity, and nodes with higher loyalty are more likely to provide higher-quality supervision. Based on this, we propose LoyalDE (Loyal Node Discovery and Emphasis), a model-agnostic hot-plugging training strategy, which can discover potential nodes with high loyalty to expand the training set, and then emphasize nodes with high loyalty during model training to improve performance. Experiments demonstrate that the graph supervision loyalty problem will fail most existing GNNs. In contrast, LoyalDE brings about at most 9.1% performance improvement to vanilla GNNs and consistently outperforms several state-of-the-art training strategies for semi-supervised node classification.

Enlightening Network Lifetime based on Dynamic Time Orient Energy Optimization in Wireless Sensor Network

Mobile Ad-hoc Networks (MANET) are a set of Large-scale infrastructure and mobile device networks that build themselves without centralized control to provide various services through mobile. However, the quality of service of MANET is highly dependent on multiple parameters. Many routing schemes in literature use hop count, mobility speed, direction, etc. Similarly, the flow-based approach chooses long routes, which increases latency and reduces throughput efficiency. However, not all methods work well with all Quality of Service (QoS) parameters. To introduce a Dynamic Time Orient Energy Optimization (DTOEO) algorithm to construct the energy-based tree formation to achieve the minimum energy consumption network. Energy-based Dynamic Tree Routing to provide higher energy node and shortest route estimation that help to better transmission quality. In this proposed DTOEO method, perform three stages, there are i). Source node discovery process, ii). Time-orient density estimation, and iii). Energy-based Dynamic Tree Routing. In this stage, orient density estimation evaluates the data transmission size for each window period. To assess the consuming energy in the overall network. The proposed method of performance evaluation using various QoS matrices and its comparison to the existing process provides better performance.

TriBeC: identifying influential users on social networks with upstream and downstream network centrality

The complex heterogeneous nature of social networks generates colossal user data, hence requiring exhaustive efforts to accelerate the propagation of information. This necessitates the identification of central nodes that are considered substantial for information spread and control. Our research proposes a novel centrality metric, TriBeC to identify the significant nodes in online social networks by utilizing the impact of weighted betweenness extended with network quartiles. The proposed approach introduces a user data-driven centrality measure for the discovery of influential nodes in online social networks. This is based on locating the median with the information flowing upstream and downstream, thereby considering the impact of border nodes lying farthest in the network circumference. Experimental outcomes on Twitter, Facebook, BlogCatalog, Scale-free and Random networks show the outperforming results of topmost 1% TriBeC central nodes over existing counterparts in terms of the percentage of the network being infested with information over time.

A core IoT ontology for automation support in edge computing

Service providers provision more and more Internet-of-Things (IoT) services in the cloud for dynamicity and cost-effectiveness purposes. This is made possible thanks to the introduction of edge computing that brings additional computing and resources for analytics close to the data sources and thus enables meeting the low latency requirement. Edge nodes should support (i) the heterogeneity of IoT devices (e.g., sensor, actuator) and (ii) characteristics (e.g., mobility, location awareness). IoT is already integrated to the hybrid cloud/edge environment. However, the ecosystem lacks of automation due to the previously mentioned characteristics. Indeed, edge nodes are often manually selected during deployment time, and most of the regular quality-of-service (QoS) management procedures remain difficult to implement. This paper introduces a comprehensive semantic model called EdgeOnto. It encompasses all concepts related to IoT applied in the context of edge computing. The ultimate goal of EdgeOnto is to automate the several steps that make up the IoT services lifecycle in hybrid cloud/edge environment. On the one hand, semantics enable an automatic discovery of the relevant edge nodes that are suitable to host and execute IoT services considering their requirements. On the other hand, it allows supporting the specific QoS procedures that are related to such setting (e.g., low latency, mobility, jitter). The core ontology was designed with the Protégé open-source tool. A smart strawberry farming use case was implemented and evaluated for illustration purposes. The results validate the accuracy and the precision of the designed semantic matchmaker.

Rogue Node Detection Based on a Fog Network Utilizing Parked Vehicles

Rogue nodes in the Internet of vehicles (IoV) bring traffic congestion, vehicle collision accidents and other problems, which will cause great social losses. Therefore, rogue node discovery plays an important role in building secure IoV environments. Existing machine learning-based rogue node detection methods rely too much on historical data, and these methods may lead to long network delay and slow detection speed. Moreover, methods based on Roadside Units (RSUs) have poor performance if the number of RSUs is insufficient. Based on the widespread presence of ground vehicles, we propose a rogue node detection scheme based on the fog network formed by roadside parked vehicles. To achieve efficient rogue node discovery, a fog network composed of stable roadside parked vehicles is dynamically established, in which each fog node firstly collects the information of moving vehicles on the road in its coverage range, and then fog nodes use the U-test method to determine the rogue nodes in parallel, so as to find the bad nodes efficiently. Simulation results show that the proposed algorithm has higher detection accuracy and stability than the other rogue node detection schemes.

Influenced node discovery in a temporal contact network based on common nodes.

Verification is the only way to make sure if a node is influenced or not because of the uncertainty of information diffusion in the temporal contact network. In the previous methods, only $ N $ influenced nodes could be found for a given number of verifications $ N $. The target of discovering influenced nodes is to find more influenced nodes with the limited number of verifications. To tackle this difficult task, the common nodes on the temporal diffusion paths is proposed in this paper. We prove that if a node $ v $ is confirmed as the influenced node and there exist common nodes on the temporal diffusion paths from the initial node to the node $ v $, these common nodes can be regarded as the influenced nodes without verification. It means that it is possible to find more than $ N $ influenced nodes given $ N $ verifications. The common nodes idea is applied to search influenced nodes in the temporal contact network, and three algorithms are designed based on the idea in this paper. The experiments show that our algorithms can find more influenced nodes in the existence of common nodes.

Retraction Note: Node discovery with development of routing tree in wireless networks

Retraction Note: Node discovery with development of routing tree in wireless networks

Safe Routing Approach by Identifying and Subsequently Eliminating the Attacks in MANET

Wireless networks that are decentralized and communicate without using existing infrastructure are known as mobile ad-hoc networks. The most common sorts of threats and attacks can affect MANETs. Therefore, it is advised to utilize intrusion detection, which controls the system to detect additional security issues. Monitoring is essential to avoid attacks and provide extra protection against unauthorized access. Although the current solutions have been designed to defeat the attack nodes, they still require additional hardware, have considerable delivery delays, do not offer high throughput or packet delivery ratios, or do not do so without using more energy. The capability of a mobile node to forward packets, which is dependent on the platform's life quality, may be impacted by the absence of the network node power source. We developed the Safe Routing Approach (SRA), which uses behaviour analysis to track and monitor attackers who discard packets during the route discovery process. The attacking node recognition system is made for irregular routing node detection to protect the controller network's usual properties from becoming recognized as an attack node. The suggested method examines the nearby attack nodes and conceals the trusted node in the routing pathway. The path is instantly assigned after the initial discovery of trust nodes based on each node's strength value. It extends the network's life span and reduces packet loss. In terms of Packet Delivery Ratio (PDR), energy consumption, network performance, and detection of attack nodes, the suggested approach is contrasted with AIS, ZIDS, and Improved AODV. The findings demonstrate that the recommended strategy performs superior in terms of PDR, residual energy, and network throughput.

Intelligent context-aware fog node discovery

Fog computing has been proposed as a mechanism to address certain issues in cloud computing such as latency, storage, network bandwidth, etc. Fog computing brings the processing, storage, and networking to the edge of the network near the edge devices, which we called fog consumers. This decreases latency, network bandwidth, and response time. Discovering the most relevant fog node, the nearest one to the fog consumers, is a critical challenge that is yet to be addressed by the research. In this study, we present the Intelligent and Distributed Fog node Discovery mechanism (IDFD) which is an intelligent approach to enable fog consumers to discover appropriate fog nodes in a context-aware manner. The proposed approach is based on the distributed fog registries between fog consumers and fog nodes that can facilitate the discovery process of fog nodes. In this study, the KNN, K-d tree, and brute force algorithms are used to discover fog nodes based on the context-aware criteria of fog nodes and fog consumers. The proposed framework is simulated using OMNET++, and the performance of the proposed algorithms is compared based on performance metrics and execution time. The accuracy and execution time are the major points of consideration in the selection of an optimal fog search algorithm. The experiment results show that the KNN and K-d tree algorithms achieve the same accuracy results of 95%. However, the K-d tree method takes less time to find the nearest fog nodes than KNN and brute force. Thus, the K-d tree is selected as the fog search algorithm in the IDFD to discover the nearest fog nodes very efficiently and quickly.

An Improved Model for Node Discovery using Election Algorithm in Wireless Sensor Network

Mobile wireless devices are constrained based on resources. Computation offloading provides a unique method for taking advantage of multiple mobile wireless devices to save energy and increase performance of these devices. In order to build a robust serious solution for a mobile wireless sensor network. There must be a scheme to ensure that the resources of the mobile wireless sensor network is managed adequately. However, computational offloading scheme was proposed by researchers. But this solution was dependent on a super node which manages the offloading process and is required to be online on the network at all time which consume time. The objectives of this study is to create a “broadcast and receive” table that can access a given node at a particular time interval without the need of having a super node online all the time. This method ensures that as nodes enter the network, they announce their resources which is then saved in the table. This information is updated at a given time interval by a monitoring service. When a node is to be removed from the network, the details of the resources of the node is removed from the table. The focus of the study is to evaluate the resource aware of node discovery in wireless sensor network.