Reliable Nodes Research Articles

EARLY: Efficient and Reliable Graph Neural Network for Dynamic Graphs

Graph neural networks have been widely used to learn node representations for many real-world static graphs. In general, they learn node representations by recursively aggregating information from neighbors. However, graphs in many applications are dynamic, evolving with continuous graph events, such as node feature and graph structure updates. These events require the node representations to be updated accordingly. Currently, due to the real-time requirement, how to efficiently and reliably update node representations under continuous graph events is still an open problem. Recent studies propose two solutions to partially address this problem, but their performance is still limited. First, local-based GNNs only update the nodes directly involved in events, suffering from the quality-deficit issue, since they neglect the other nodes affected by these events. Second, neighbor-sampling GNNs propose to sample neighbors to accelerate neighbor aggregation computations, encountering the neighbor-redundant issue. These sampled neighbors may be similar and cannot reflect the distribution of all neighbors, leading that node representations aggregated on these redundant neighbors may differ from those aggregated on all neighbors. In this paper, we propose an efficient and reliable graph neural network, namely EARLY, to update node representations for dynamic graphs. We first identify the top-k influential nodes that are most affected by graph events. Then, to sample neighbors diversely, we propose a diversity-aware layer-wise sampling technique. We theoretically demonstrate that this technique can decrease the sampling expectation error and learn more reliable node representations. Therefore, the top-k nodes selection and diversity-aware sampling enable EARLY to efficiently update node representations in a reliable way. Extensive experiments on the five real-world graphs demonstrate the effectiveness and efficiency of our proposed EARLY.

Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

Improved range-free localization algorithm based on reliable node optimization and enhanced sand cat optimization algorithm

Data Rate Aware Reliable Transmission Mechanism in Wireless Sensor Networks using Bayesian Regularized Neural Network approach

Wireless Sensor Networks consists of interconnected nodes that exchange information wirelessly enabling its deployment in innovative application areas. Network reliability streamline this exchange of information and communication technology to improve the overall performance of the network. The network reliability is categorized as: node reliability and link reliability. In this article, the node reliability is quantified with the help of packet reliability. The packet reliability is enhanced by optimizing the data rate using Bayesian Regularized Neural Network approach, which thus makes the network more reliable and sustainable. The optimization is carried out in three phases: network designing, data rate prediction and reliability evaluation. The network design includes the deployment of sensor nodes for gathering the communication data using NS-2.35. In the next phase, data rate prediction is carried out to enhance the reliability of the network. The reliability of a network is directly influenced by the packet loss ratio. According to research and the network experts, the acceptable threshold limit for the packet loss ratio is 5 percent. The data rate prediction is carried out to minimize the packet loss using the Bayesian Regularized Neural Network algorithm. The packet reliability is measured in terms of packet loss across the wireless network. Finally, a novel framework is presented for evaluating the packet reliability of the wireless network.

Existence of optimally‐greatest digraphs for strongly connected node reliability

AbstractIn this paper, we introduce a new model to study network reliability with node failures. This model, strongly connected node reliability, is the directed variant of node reliability and measures the probability that the operational vertices induce a subdigraph that is strongly connected. If we are restricted to directed graphs with vertices and or arcs, an optimally‐greatest digraph does not exist. Furthermore, we study optimally‐greatest directed circulant graphs when the vertices operate with probability near zero and near one. In particular, we show that the graph is optimally‐greatest for values of near zero. Then, we determine that the graph is optimally‐greatest for values of near one when is even. Next, we show that the graph is optimally‐greatest for values of near one when is odd and not divisible by three and that is optimally‐greatest for values of near one when is odd and divisible by three. We conclude with a discussion of open problems.

Wireless Distributed Consensus for Connected Autonomous Systems

Connected critical autonomous systems (C-CAS) are envisioned to significantly change our life and work styles through emerging vertical applications such as autonomous vehicles and cooperative robots. However, as the scale of the connected nodes continues to grow, their heterogeneity and cyber-security threats are more eminent, and conventional centralized communications and decision-making methodology are reaching their limit. This paper is the first exploration of a trustworthy and fault-tolerant framework for C-CAS for achieving hyper-reliable global decision-making in a trustless environment, where the connected sensors/nodes are less reliable due to either communication failure or local decision error (e.g., by sensing algorithm/AI, etc.). The proposed framework is based on two iconic distributed consensus (DC) mechanisms, practical Byzantine fault tolerance (PBFT) and Raft, under the proposed PICA (Perception-Initiative-Consensus-Action) protocol with wireless connections among the nodes. We first analytically derived consensus reliability in six different system models. The other fundamental performance metrics such as the consensus throughput and latency, node scalability and reliability gain are also analytically derived. These analytical results provide basic design guidelines for wireless Distributed Consensus (WDC) usage in the C-CAS systems. The results show that WDC significantly improves overall system reliability with the increasing number of participating nodes.

An Improved Byzantine Fault-Tolerant Algorithm Based on Reputation Model

This study proposes an improved Byzantine fault-tolerant consensus RB-BFT based on the reputation model to address the problems of low reliability of primary nodes and high communication complexity in the practical Byzantine algorithm (PBFT). First, this algorithm establishes a dynamic reputation model of nodes to distinguish honest and malicious nodes in the system, lowering the likelihood of malicious nodes being chosen as primary nodes and increasing the dependability of primary nodes. Second, the algorithm introduces supervisory nodes for information supervision while reducing the centrality of the system. Finally, this algorithm improves the consistency protocol of the PBFT algorithm by optimizing the process of mutual communication between nodes in the preparation and commitment phases, which reduces the algorithm communication complexity from O(n2) to O(n). Theoretical and practical studies reveal that the RB-BFT algorithm enhances performance and reliability greatly.

Improvement of PBFT Algorithm Based on CART

In response to the problems of the practical Byzantine fault-tolerant algorithm (PBFT), such as random selection of master nodes and poor scalability, a CART-based PBFT optimization algorithm is proposed, namely the C-PBFT algorithm. First, the introduction of weighted impurity variables improves the CART algorithm, overcomes the mutual influence of attributes between nodes, and improves the classification accuracy. Secondly, through the point grouping mechanism, the nodes are divided into three types: consensus nodes, candidate nodes, and alternate nodes, which are dynamically adjusted based on node behavior to ensure the reliability of consensus nodes. Finally, the voting weight is introduced into the consensus node, and the consensus is reached by using the voting weight exceeding the threshold, which reduces the message transmission in the blockchain network and improves the efficiency of the algorithm operation. The experimental results show that the improved consensus algorithm has better performance in terms of delay, throughput, and fault tolerance and reduces the frequent switching of views caused by the failure of the master node.

Cluster based energy efficient routing protocol for heterogeneous wireless sensor networks

SummaryWith the advancement in recent technologies, wireless sensor networks (WSNs) have emerged to become one of the key research areas. Sensor nodes in WSN are deployed randomly to sense the events that occur in an area. Clustering of sensor nodes in a wireless sensor network will result in better energy efficiency and topology management. In many scenarios, wireless sensor networks are deployed in remote areas and in hostile environments where batteries cannot be recharged or replaceable. In real world applications, heterogeneous sensor nodes with different initial energies in a WSN are more preferable to prolong stability period, network lifetime, and throughput. The main objective of this article is to design and develop a cluster‐based energy efficient routing protocol for heterogeneous wireless sensor networks (CEER). The proposed CEER routing protocol is based on the residual energy of all the sensor nodes in each round, distance of each sensor node from the base station, weighted election probabilities and reliability of a sensor node and the network. It is found from the results that, the proposed CEER routing protocol provides better stability, network lifetime, and throughput as compared with the existing routing protocols of heterogeneous wireless sensor networks.

PIRAP: Intelligent Hybrid Approach for Secure Data Transmission in Wireless Sensor Networks

The wireless sensor network (WSN) is a mobile adhoc network which has no support of infrastructure. The complexities in network management are critical to resolve even when choosing the cluster head is more efficient. From clustering procedure, appropriate cluster heads are selected with attention to energy saving among member nodes. When it comes to WSN security, trust-based cluster head selection is critical, assuming the cooperation of all sensor nodes. In light of this assumption, existing approaches were unable to assist in identifying the network’s ideal cluster head. Due to the dynamic topology and mobility of nodes in WSNs, security is a challenge. However, secrecy is often accomplished end-to-end via symmetric keys between two corresponding applications. The symmetric key is incompatible with the WSN environment. Additionally, the WSN nodes vary in their qualification for which portion of the data is accessible in which context. This work proposes an elliptic curve cryptography-enabled ciphertext policy attribute-based encryption (ECC-CP-ABE) algorithm for secure data transmission during intra-cluster communication and inter-cluster communication. In this work, we select a reliable node-based trust value mechanism and this reliable node acts as an attribute authority that inter-cluster provides a decryption private key to cluster members who are involved in intra- and inter-cluster communications. In ECC-CP-ABE, the cluster head node (CHN) and cluster member node (CMN) utilize CP-ABE for the encryption of network messages using an access policy matrix A that is computed from an AND–OR operation-based monotonic tree access structure which is defined over a various set of attributes. To ensure authenticity and integrity, the cluster member and CHN sign each ciphertext using an ECC algorithm. For performance evaluation, we use packet delivery ratio, energy consumption, encryption time, network lifetime, and decryption time as metric measures and compared the results of proposed ECC-CP-ABE with two benchmark methods, Secured WSN and Taylor-based Cat Salp Swarm Algorithm. From the results, we analyze that the proposed ECC-CP-ABE reduces energy consumption by 53.2%; increases packet delivery ratio by 98.6%; increases network lifespan by 97.5%, and reduces encryption time by 20[Formula: see text]s and decryption time by 15[Formula: see text]s.

Artificial Intelligence Machine Learning in Healthcare System for improving Quality of Service

A vital element of widespread patient monitoring is consistency in transmission between the patients and the healthcare professionals not including time and position dependencies. Artificial intelligence (AI) and machine learning (ML) techniques have a vast possibility to proficiently handle the automated function of the mobile nodes distributed in the Mobile ad-hoc network (MANET). ML is a part of AI in that the computer algorithms learn themselves by improving from historical experiences. The main issues in MANET are autonomous operation, maximization of a lifetime, coverage of the network, energy utilization, connectivity issues, quality of service, high bandwidth necessity, communication protocol design, etc. ML is valuable for data aggregation, and it saves the energy of mobile nodes and enhances the network lifetime. In this paper, we propose an Artificial Intelligence Machine Learning Algorithm for improving the Quality of Service in MANET (AIMQ). ML techniques based on artificial neural networks (ANN) algorithm is helpful for data aggregation tasks. This approach formed the clusters by node mobility and connectivity. Glowworm Swarm Optimization (GSO) algorithm is applied in every cluster to choose a proficient Cluster Head (CH). Here, we choose the CH by GSO fitness function based on mobile node degree, node distance, node reliability, and energy. ANN algorithm recognizes and chooses the data aggregator with great energy and more extended stability. It updates the weight of input parameters such as node energy, node degree, packet loss ratio, and node delay to reduce the errors. It minimizes the repetitive CHs selection and member nodes’ re-affiliation in a cluster.

Security for Multi-hop Communication of Two-tier Wireless Networks with Different Trust Degrees

Many effective strategies for enhancing network performance have been put forth for wireless communications’ physical-layer security. Up until now, wireless communications security and privacy have been optimized based on a set assumption on the reliability or network tiers of certain wireless nodes. Eavesdroppers, unreliable relays, and trustworthy cooperative nodes are just a few examples of the various sorts of nodes that are frequently categorized. When working or sharing information for one another, wireless nodes in various networks may not always have perfect trust in one another. Modern wireless networks’ security and privacy may be enhanced in large part by optimizing the network based on trust levels. To determine the path with the shortest total transmission time between the source and the destination while still ensuring that the private messages are not routed through the untrusted network tier, we put forth a novel approach. Toexamine the effects of the transmit SNR, node density, and the percentage of the illegitimate nodes on various network performance components, simulation results are provided.

Secure and Energy-Efficient Network Topology Obfuscation for Software-Defined WSNs

Network topology obfuscation (NTO) is generally considered as a promising proactive mechanism to mitigate traffic analysis attacks. The main challenge is to strike a balance among energy consumption, reliable routing, and security levels due to resource constraints in sensor nodes. Furthermore, software-defined wireless sensor networks (WSNs) are more vulnerable to traffic analysis attacks due to the uncovered pattern of control traffic between the controller and the nodes. In this article, a new energy-aware NTO mechanism is proposed, which maximizes the attack costs and is efficient and practical to be deployed. Specifically, first, a route obfuscation method is proposed by utilizing ranking-based route mutation, based on four different critical criteria: 1) route overlapping; 2) energy consumption; 3) link costs; and 4) node reliability. Then, a sink node obfuscation method is introduced by selecting several fake sink nodes that are indistinguishable from actual sink nodes, according to the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula> -anonymity model. As a result, the most suitable routes and sink nodes can be selected, and a highest obfuscation level can be reached without sacrificing energy efficiency. Finally, extensive simulation results demonstrate that the proposed methods can strongly mitigate traffic analysis attacks and achieve effective NTO for software-defined WSNs. In addition, the proposed methods can reduce the success rate of the attacks while achieving lower energy consumption and higher network lifetime.

A novel meshless method based on the virtual construction of node control domains for porous flow problems

In this paper, a novel meshless method that can handle porous flow problems with singular source terms is developed by virtually constructing node control domains. By defining a connectable node cloud, this method uses the integral of the diffusion term and generalized finite-difference operators to derive overdetermined equations of the node control volumes. An empirical method of calculating reliable node control volumes and a triangulation-based method to determine the connectable point cloud are developed. The method focuses only on the volume of the node control domain rather than the specific shape, so the construction of node control domains is virtual and does not increase the computational cost. To our knowledge, this is the first study to construct node control volumes in the meshless framework, termed the node control domain-based meshless method (NCDMM), which can also be regarded as an extended finite-volume method (EFVM). Taking two-phase porous flow problems as an example, discrete NCDMM schemes satisfying local mass conservation are derived by integrating the generalized finite-difference schemes of governing equations on each node control domain. Finally, commonly used low-order finite-volume method (FVM)-based nonlinear solvers for various porous flow models can be directly employed in the proposed NCDMM, significantly facilitating general-purpose application. Theoretically, the proposed NCDMM has the advantages of previous meshless methods for discretizing computational domains with complex geometries, as well as the advantages of traditional low-order FVMs for stably handling a variety of porous flow problems with local mass conservation. Four numerical cases are implemented to test the computational accuracy, efficiency, convergence, and good adaptability to the calculation domain with complex geometry and various boundary conditions.

Design and uncertainty analysis of ice cream bar turning mechanism based on axiom design and TRIZ theory

This article uses a combination of Axiomatic design (AD) and Theory of Innovation Problem Solving (TRIZ) as the basis of the mechanism design framework. First, the product structure design matrix is generated through the modular design process in AD, and then the contradiction analysis in TRIZ is used for the coupling design parameters in the design matrix to obtain a feasible solution of the design plan. To verify the reliability of the design scheme when considering the actual errors such as the size and shape of the ice cream bar, the known uncertain factors were quantified and Adams simulation was used. Finally, a secondorder moment method is used to analyze the reliability of the key nodes of the turning mechanism, and the reliability of the node is 90.66%, which verifies the reliability of the mechanism design.

Optimization and Analysis of Wireless Networks Lifetime using Soft Computing for Industrial Applications

Recently, wireless networks are applied in various engineering and industrial applications. One of the critical problems in wireless network system optimization in intelligent applications is obtaining an adequate energy fairness level. This issue can be resolved by applying effective cluster-based routing optimization with multi-hop routing. Hence a new network structure is developed that is derived from energy consumption architecture by applying soft computing strategies such as evolutionary operators in determining the exact clusters for optimizing energy consumption. The new effective evolutionary operators are tested in the optimization of a lifetime. The proposed method is simulated for different values of the routing factor, α, for different types of networks. The energy levels range from 0.4 to 0.8, achieving good results for nearly 2500 rounds. The proposed strategy optimizes the clusters, and its head is selected reliably. The optimization of cluster head choice has been done based on the base station distance, the energy of the node, and the node's energy efficiency. The reliability of the long-distance nodes is increased during the data transmission by modifying the size of the area of the candidate set of nodes in contrast the near-distance node's energy consumption is reduced. For the energy levels that range from 0.4 to 0.8, the higher network throughput is obtained at the same time network lifetime is optimized compared to other well-known approaches. The proposed model is expected for different industrial wireless network applications to optimize the systems during the long-run simulation and to achieve high reliability and sustainability.

From text to ties: Extraction of corruption network data from deferred prosecution agreements

Abstract Deferred prosecution agreements (DPAs) are a legal tool for the nontrial resolution of cases of corruption. Each DPA is accompanied by a Statement of Facts that provides detailed and publicly available textual records of the given cases, including summarized evidence of who was involved, what they committed, and with whom. These statements can be translated into networks amenable to social network analysis allowing an analysis of the structure and dynamics of each case. In this study, we show how to extract information about which actors were involved in a given case, the relations and interactions among these actors (e.g., communication or payments), and their relevant individual attributes (gender, affiliation, and sector) from five Statements of Fact. We code the extracted information manually with two independent coders and subsequently, we assess the inter-coder reliability. For assessing the coding reliability of nodes and attributes, we use a matching coefficient, whereas for assessing the coding reliability of ties, we construct a network from the coding of each coder and subsequently calculate the graph correlations of the two resulting networks. The coding of nodes and ties in the five extracted networks turns out to be highly reliable with only slightly lower coding reliability in the case of the largest network. The coding of attributes is highly reliable as well, although it is prone to missing data on actors’ gender. We conclude by discussing the flexibility of our data collection framework and its extension by including network dynamics and nonhuman actors (such as companies) in the network representation.

Оптимальний комплекс операцій технічного обслуговування і ремонту для підвищення надійності вузлів, систем та агрегатів мобільних машин

The work examines the improvement of the forms and methods of organizing maintenance and repair systems of mobile machines in order to increase their reliability by optimizing diagnostic operations and monitoring the technical condition of their units, systems and units. The proposed centralized system of maintenance and repair of machines includes the principles of production and is characterized by the wide use of technological complexes of operations of the system of maintenance and repair of nodes, systems, aggregates and mobile machines as a whole. A comparison was made of the methodology for implementing the proposed maintenance and repair system and the world-famous RCM (Reliability-centered Maintenance) methodology, focused on a certain level of reliability. Schematics of the technological process of implementing a complex of maintenance and repair operations based on one-stage and multi-stage control of the technical condition of nodes, systems, aggregates and the mobile machine as a whole are presented. A decision-making algorithm has been developed for the processes of a complex of maintenance and repair operations. Clarified technological and information support of complexes of maintenance and repair operations of nodes, systems, units of mobile machines. The role of the operator-diagnostic in relation to the probability of receiving errors of the 1st and 2nd type of defects of parts, as well as the use of computerized workplaces, is revealed. It is shown how these factors affect the level of reliability of nodes, systems, aggregates and mobile machines as a whole. It was determined that in order to reduce the influence of the human factor on the results of the diagnosis of the presence or absence of defects and the distribution of nodes, systems, aggregates according to the technological routes of optimal complexes of maintenance and repair operations, consideration of the repair and maintenance system as an artificial cognitive system can help.

Innovating Healthcare IoT with a Secure Blockchain Consensus Algorithm based on Verifiable Random Ranking

The Internet of Things (IoT) is helping the healthcare industry innovate. Medical professionals are no longer confined to hospitals or physical locations. IoT devices are used in healthcare for remote monitoring of vital signs, transmission and reception of private data, and other similar applications. Protection from cyber threats is the most vital aspect of IoT device operation. Malicious activities result in the leakage of sensitive data and the malfunctioning of devices, which becomes a critical issue. As a result, it is now a serious endeavor to create effective solutions that can safeguard the data and record exchange networks of every IoT device. To address some critical issues with current consensus algorithms, this paper proposes the verifiable random ranking (VRR) based Secure Blockchain Consensus (SBC) mechanism. In place of conventional algorithms, we developed new techniques for managing active nodes and evaluating node reliability and robustness. Our experiments show that the VRR-SBC algorithm speeds up consensus, improves security, and reduces the price of verification. The results of the simulation demonstrate the usefulness of the blockchain era for limited devices and provide an opportunity to assess the scope of the consensus algorithm's applicability.

System Reliability of a Semi-Submersible Drilling Rig Considering the Effects of the Main Nodes

In this paper, structural systems reliability analysis was used to estimate the system reliability of a typical semi-submersible based on the characteristic structure failure mechanisms under extreme wave loads. First, a three-dimensional simulation model was created for the chosen semi-submersible platform using an elastic-perfectly plastic material model, neglecting the effect of the strain hardening assumption. Then, under design wave conditions, and characteristic structural failure mechanisms of a semi-submersible platform were examined, and a system reliability model was developed based on the failure mechanism of the chosen platform. Finally, system reliability methods were used to determine the platform’s system reliability. It was discovered that the main node’s system reliability makes a significant contribution to the overall structure reliability, so it is strongly advised that the reliability of the main nodes be taken into account when determining the overall structure’s reliability. This research aims to apply reliability theory in offshore engineering and, ideally, to encourage the reliability-based design of semi-submersible platforms and other similar structures.

FRICTION PROPERTIES OF MАO COATINGS ON ALUMINUM ALLOYS

Aluminum alloys are widely used in mechanical engineering due to their physical and mechanical properties. However, their low wear resistance and burr resistance limit their use in friction nodes. In this regard, parts made of aluminum alloys operating under conditions of sliding friction require surface strengthening, which determines the friction wear parameters. Research works in this direction allow increasing the reliability and service life of friction nodes. Ensuring the compatibility of friction pairs allows reducing the coefficient of friction and, as a result, to increase wear resistance. One of the effective methods of forming wear-resistant coatings on aluminum alloys is the method of micro-arc oxidation (MAO). The use of micro-arc discharges as highly concentrated energy sources to create conditions for high-temperature electrotechnical processes and phase transformations in the surface layer makes it possible to form structures based on high-temperature oxides on the surface of metals, which provides qualitatively new surface properties: high hardness, wear resistance, corrosion resistance in various environments. However, the use of oxide coatings in bearings is mainly due to their high wear resistance and corrosion resistance. Information on the antifriction properties of aluminum oxide-based coatings is limited. The purpose of this work is to determine the coefficient of friction of MAO-coatings on D16 aluminum alloy in a pair with cast iron, steel and MAO-coating under conditions of oil and water lubrication, and to find out the feasibility of using MAO technology to create sliding bearings. The formation of MAO coatings was carried out in an alkaline-silicate electrolyte in the cathodic-anodic mode. The structure and morphology of the working layer of the MAO-coating surface, which works in friction pairs with cast iron, steel and MAO-coating under conditions of oil and water lubrication, was investigated. The benefit of the available porosity of the working layer of the MAO coating, which is 5-10% and contributes to the retention of lubricant in the friction zone, is shown. The phase composition, hardness of the coatings were studied and the coefficient of sliding friction was determined on the SMC-2 friction machine according to the "disk-pad" scheme when using water and oil as lubricants; the load varied from 0.2 to 2 kN, the nominal pressure from 1 to 11 MPa. It was established that the MAO coating has a crystalline structure, the phase composition is high-temperature modifications of aluminum oxide (α-Al2O3 and γ- Al2O3), and the hardness is 18000-20000 MPa. It was established that the coefficient of sliding friction of gray cast iron - MAO-coating, steel - MAO-coating and MAO on MAO decreases to the value of 0.01 - 0.013 under lubrication conditions. It was found that in a pair of MAO-coating - MAO-coating, anti-friction properties are manifested both under conditions of lubrication with oil and water. The low coefficient of friction for the coating-coating pair in the case of water lubrication is explained by the high hydrophilic properties of the oxide contacting surfaces, which makes it possible to recommend them for use in sliding bearings (liners) of hydroturbines. A conclusion was drawn regarding the reasons for the low coefficient of friction of the studied samples. Keywords: D16 aluminum alloy, cast iron, steel, micro-arc treatment, coating, phase composition, crystal structure, aluminum oxides, coating thickness, coating hardness, friction coefficient, antifriction properties