Active Nodes Research Articles

Lightweight and Identifier-Oblivious Engine for Cryptocurrency Networking Anomaly Detection

The distributed cryptocurrency networking is critical because the information delivered through it drives the mining consensus protocol and the rest of the operations. However, the cryptocurrency peer-to-peer (P2P) network remains vulnerable, and the existing security approaches are either ineffective or inefficient because of the permissionless requirement and the broadcasting overhead. We design and build a Lightweight and Identifier-Oblivious eNgine (LION) for the anomaly detection of the cryptocurrency networking. LION is not only effective in permissionless networking but is also lightweight and practical for the computation-intensive miners. We build LION for anomaly detection and use traffic analyses so that it minimally affects the mining rate and is substantially superior in its computational efficiency than the previous approaches based on machine learning. We implement a LION prototype on an active Bitcoin node to show that LION yields less than 1% of mining rate reduction subject to our prototype, in contrast to the state-of-the-art machine-learning approaches costing 12% or more depending on the algorithms subject to our prototype as well, while having detection accuracy of greater than 97% F1-score against the attack prototypes and real-world anomalies. LION therefore can be deployed on the existing miners without the need to introduce new entities in the cryptocurrency ecosystem.

Aging transition in coupled quantum oscillators.

Aging transition is an emergent behavior observed in networks consisting of active (self-oscillatory) and inactive (non-self-oscillatory) nodes, where the network transits from a global oscillatory state to an oscillation collapsed state when the fraction of inactive oscillators surpasses a critical value. However, the aging transition in quantum domain has not been studied yet. In this paper we investigate the quantum manifestation of aging transition in a network of active-inactive quantum oscillators. We show that, unlike classical case, the quantum aging is not characterized by a complete collapse of oscillation but by sufficient reduction in the mean boson number. We identify a critical "knee" value in the fraction of inactive oscillators around which quantum aging occurs in two different ways. Further, in stark contrast to the classical case, quantum aging transition depends upon the nonlinear damping parameter. We also explain the underlying processes leading to quantum aging that have no counterpart in the classical domain.

A cellular learning automata-based approach for self-protection and coverage problem in the Internet of Things

In the Internet of Things (IoT) sensing layer, targets in the monitoring area are covered by sensor nodes. So the coverage problem is a key issue of IoT application. Self-protection is an effective way to resist external attacks, which can cover the remaining nodes with a set of nodes. This paper considers the coverage problem combined with node self-protection and proposes a cellular learning automata-based method. In this method, nodes are mapped to the cellular structure, and each cell is equipped with learning automata. At the initialization stage, the activation probability is assigned to each node by the residual energy, and updated according to the reward or penalty feedback from neighbors and the random environment. At the adjustment stage, partial self-protection is applied to reduce the number of activated nodes. Consequently critical nodes could acquire protection with high priority than other normal nodes. When the desired coverage is not met, sleeping nodes will be activated according to the coverage increment value. Meanwhile, a local strategy based on the cellular structure is designed to heal the coverage hole caused by failed nodes. In accordance to this strategy, sleeping nodes around the failed nodes could be quickly activated. The simulation results show that the proposed scheme outperforms the existing methods with respect to the number of active nodes, energy efficiency, and coverage reliability.

Induced diffusion percolation model: Examining the role of superactive nodes in the diffusion of innovations

Building on a recent study on scientific collaboration networks, we propose an induced diffusion percolation model that brings superactive nodes into focus. Defined as active nodes surrounded by at least k active or superactive neighbors, superactive nodes play a key role in innovation diffusion by inducing their neighbors to adopt an innovation. We investigate the induced diffusion percolation model using the modified Newman–Ziff algorithm on two-dimensional lattices (square and triangular lattices) and regular random networks with and without clustering. The induction by superactive nodes leads to a first-order percolation phase transition in two-dimensional lattices and a double transition – a continuous percolation transition followed by a discontinuous jump of the order parameter of the largest cluster’s strength – in regular random networks. Whereas clustering increases the percolation threshold in the classical percolation model on regular random networks, it decreases the critical initial activation probability that triggers a discontinuous jump of the induced activation.

Bootstrap percolation on the stochastic block model

We analyze the bootstrap percolation process on the stochastic block model (SBM), a natural extension of the Erdős–Rényi random graph that incorporates the community structure observed in many real systems. In the SBM, nodes are partitioned into two subsets, which represent different communities, and pairs of nodes are independently connected with a probability that depends on the communities they belong to. Under mild assumptions on the system parameters, we prove the existence of a sharp phase transition for the final number of active nodes and characterize the sub-critical and the super-critical regimes in terms of the number of initially active nodes, which are selected uniformly at random in each community.

Optimization of 3D Spectrum Management in Future Wireless Networks

In this paper, we propose an application customized network slicing mechanism to cope with dynamic traffic in future networks and various service requirements for network users, namely, context-aware network slicing (CANS). The resource allocation is carried out in frequency (f), space (s) and time (t) domains (referred to as three domains (3D) resource allocation) while the customization targets at low latency and high reliability, characteristic for IoT applications. We first formulate CANS as a two-step matching game with unknown preferences for access and back-haul networks. To dynamically resize mobile users' network slices (NSs), we propose application customized CANS++ method based on a coalition game to minimize the hand-off risk in the network. We then develop two-step stable matching (TSSM) and <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"><tex-math notation="LaTeX">$\epsilon$</tex-math></inline-formula> -Pareto stability techniques to find the solutions to CANS and CANS++, respectively. To improve energy and cost efficiency, an artificial intelligence-based Dynamic Network Architecture (DNA) with ON/OFF scheduling (AI-DOS) is proposed to dynamically maximize NS utilization. Finally, we carry out extensive numerical analysis and demonstrate that context-aware 3D network slicing and resizing schemes can improve at least three times the users' utilities. These utilities are enhanced up to 180% by minimizing their hand-off risk. Furthermore, NS shrinking technique can reduce the number of active nodes (controlled in the s-domain) by factor five while the average utility of macro base stations, controlled in the t-domain, is improved between 70% and 500%.

Energy efficient target tracking in wireless sensor network using PF-SVM (particle filter-support vector machine) technique

When using a Wireless Sensor Network (WSN) for target tracking applications, optimum selection of right functioning nodes can reduce the number of active nodes and also ensuring tracking reliability requirement. Due to the limitations of the WSN's sensing range, it is crucial to create a mechanism that allows nodes to coordinate in order to follow the target reliably and with a high probability. By doing this, the network's overall energy consumption can be decreased, resulting in a longer network lifetime. Target tracking (TT) is a well observed and significant application of WSNs. In simple words, it maintains a proper trade-off between tracking quality and energy consumption. In the proposed work, Particle Filter (PF) with a machine learning technique called Support Vector Machine (SVM) based energy efficient target tracking used in WSN's. PF is considered to be the most accepted filtering algorithm in various tracking and localization problems. Simulation results show greater performance in determining the target location and maintain lower energy consumption.

Strategies for reducing polarization in social networks

The widespread usage of social networks has intensified the phenomenon of opinion polarization. This polarization has a corrosive effect on the functioning of public opinions, societies, and democracies. Hence, it is vital to devise strategies for reducing polarization. In this paper, we propose a depolarization model based on the modified linear threshold model and the Hegselmann-Krause model. Using this model, we then devise three depolarization strategies (building heterogeneous edges, embedding local neutral opinion, and introducing global neutral opinion), and conduct depolarization experiments with varied parameters, including self-belief parameter, social influence moderating parameter, and the selection mode of new edges and active nodes. Our experiments with synthesized and real-world datasets have yielded some interesting results.

Synergies between resource sustainability and energy performance of cloud servers: The role of virtual machine repacking approach

Dynamic virtual machine (VM) consolidation to combine the workload of active devices and switch idle nodes into low-power states while preserving the quality of service (QoS) guarantee is the primary proposed solution’s goal to maintain dependable services between providers and customers. We propose a multi-step resource provision algorithm to address this issue efficiently. The forwarding step unites the iterative search for critical paths and the priority selection for task layers to reduce the makespan and reach an optimal critical task placement. The backward step trains processing servers against different loads using eclectic utilization levels to guarantee those servers function at their most efficient utilization values. Furthermore, this step optimizes energy consumption and resource diffusion by reallocating the existent VMs and mitigating an attempted overload of machines. Different evaluation metrics are assessed with respect to the number of tasks and VMs. The results show that the proposed paradigm optimizes energy consumption and resource utilization efficiently, lowering the makespan, overhead time, and network utilization and maximizing the throughput productivity and QoS satisfaction.

VKECE-3D: Energy-Efficient Coverage Enhancement in Three-Dimensional Heterogeneous Wireless Sensor Networks Based on 3D-Voronoi and K-Means Algorithm

During these years, the 3D node coverage of heterogeneous wireless sensor networks that are closer to the actual application environment has become a strong focus of research. However, the direct application of traditional two-dimensional planar coverage methods to three-dimensional space suffers from high application complexity, a low coverage rate, and a short life cycle. Most methods ignore the network life cycle when considering coverage. The network coverage and life cycle determine the quality of service (QoS) in heterogeneous wireless sensor networks. Thus, energy-efficient coverage enhancement is a significantly pivotal and challenging task. To solve the above task, an energy-efficient coverage enhancement method, VKECE-3D, based on 3D-Voronoi partitioning and the K-means algorithm is proposed. The quantity of active nodes is kept to a minimum while guaranteeing coverage. Firstly, based on node deployment at random, the nodes are deployed twice using a highly destructive polynomial mutation strategy to improve the uniformity of the nodes. Secondly, the optimal perceptual radius is calculated using the K-means algorithm and 3D-Voronoi partitioning to enhance the network coverage quality. Finally, a multi-hop communication and polling working mechanism are proposed to lower the nodes' energy consumption and lengthen the network's lifetime. Its simulation findings demonstrate that compared to other energy-efficient coverage enhancement solutions, VKECE-3D improves network coverage and greatly lengthens the network's lifetime.

Active Nodes for Passivity and Finite-Gain Stability of Non-autonomous Networks

This paper studies the role of a class of node systems with adjustable damping coefficients in the node dissipation inequalities, called the active nodes, for achieving passivity and finite-gain stability of network systems with external inputs and outputs. The paper first discusses the relation between the active nodes with passivity-type dissipation inequalities and those with L2-stability-type dissipation inequalities, especially focusing on how to transform one type into the other. Then, the paper proceeds to discuss the graph-theoretic condition on the locations of active nodes to achieve network passivity or finite-gain stability. Thereafter, a method for network reduction by exploiting active nodes is presented. Finally, a human-in-the-loop stabilization problem is solved for a network system by exploiting the results developed in the paper.

An Ageing-Aware and Temperature Mapping Algorithm for Multilevel Cache Nodes

Increase in chip inactivity in the future threatens the performance of many-core systems and therefore, efficient techniques are required for continuous scaling of transistors. As of a result of this challenge, future proposed many-core system designs must consider the possibility of a 50% functioning chip per time as well maintaining performance. Fortunately, this 50% inactivity can be increased by managing the temperature of active nodes and the placement of the dark nodes to leverage a balance working chip whilst considering the lifetime of nodes. However, allocating dark nodes inefficiently can increase the temperature of the chip and increase the waiting time of applications. Consequently, due to stochastic application characteristics, a dynamic rescheduling technique is more desirable compared to fixed design mapping. In this paper, we propose an Ageing Before Temperature Electromigration-Aware, Negative Bias Temperature Instability (NBTI) & Time-dependent Dielectric Breakdown (TDDB) Neighbour Allocation (ABENA 2.0), a dynamic rescheduling management system which considers the ageing and temperature before mapping applications. ABENA also considers the location of active and dark nodes and migrate task based on the characteristics of the nodes. Our proposed algorithm employ Dynamic Voltage Frequency Scaling (DVFS) to reduce the Voltage and Frequency (VF) of the nodes. Results show that, our proposed methods improve the ageing of nodes compared to a conventional round-robin management system by 10% in temperature, and 10% ageing.

Slice Aware Baseband Function Placement in 5G RAN Using Functional and Traffic Split

5G and Beyond 5G (B5G) are undergoing numerous architectural changes to enable higher flexibility and efficiency in mobile networks. Unlike traditional mobile networks, baseband functions in 5G are disaggregated into multiple components - Radio Unit (RU), Distributed Unit (DU), and Centralized Unit (CU). These components can be placed in different geographical locations based on the latency sensitivity and available capacity in the network. Processing baseband functions in a centralized location offer various advantages (known as centralization benefit in RAN) to mobile network operators, which have been a point of interest for several research works. However, achieving maximum centralization is challenging due to various factors such as limited capacity in the midhaul network, delay requirement of different functional splits and network slices, etc. In this work, we aim to address these challenges by proposing a slice-aware baseband function placement strategy. Our primary objective is to maximize the degree of centralization in the network by appropriate selection of functional split. To achieve this objective, we jointly consider functional split, traffic split, different placement options for baseband functions, and network slice-specific requirements. We also consider the minimization of active processing nodes in cloud infrastructure of different levels (edge and regional) to provide additional resource efficiency. To this end, we formulate an optimization model using Mixed Integer Linear Programming (MILP) and compare its performance with different baseline techniques. We show that the proposed model achieves 6.5% more degree of centralization than the state-of-the-art while placing baseband functions in the network. To tackle the high computational complexity of the MILP model, we also present a polynomial-time heuristic algorithm for solving the problem in large-scale scenarios. We show that although the optimization model achieves around 4% more degree of centralization than the heuristic, the heuristic solves the problem in a reasonable amount of time, making it suitable for real deployment scenarios.

Study on Active Sleeping Node Detection Method in Sensor Network Based on Multi-dimensional Sliding Window

Study on Active Sleeping Node Detection Method in Sensor Network Based on Multi-dimensional Sliding Window

AINL: Network Topology Identification of Multiple Communication Modes via Active Intercepting and Node Locating

AINL: Network Topology Identification of Multiple Communication Modes via Active Intercepting and Node Locating

RSG-GCN: Predicting Semantic Relationships in Urban Traffic Scene With Map Geometric Prior

Automated identification of the relationships between traffic actors and surrounding objects, in order to describe their behavior and predict their intentions, has become the focus of increasing attention in the field of autonomous driving. Therefore, in this work, we propose a Road Scene Graphs-Graph Convolutional Network (RSG-GCN) as a novel, graph-based model for predicting the topological graph structure of a given traffic scene. The status of the actors and HD map information are integrated as prior knowledge, allowing the edges linking the actor nodes to capture potential semantic relationships, such as “vehicle approaching pedestrian” and “pedestrian waiting at intersection”. To train this model, we created our own RSG dataset, as well as a relational dataset and benchmark derived from nuScenes. Our extensive range of experiments demonstrate that our model can more accurately predict semantic relationships and behavior in a given traffic scene than other popular traffic scene prediction models. In particular, regarding the use of HD map prior knowledge, we found that the resulting increase in accuracy significantly outweighs performance loss caused by the increase in graph size. The downstream applications of RSG include traffic scene retrieval and synthetic traffic scene generation, which are briefly described.

Experimental and analytical method for determining specific losses in the presence of a surface effect

In this article, based on the accumulated experience of determining the intensity of the internal loss function in the active nodes of electromechanical devices, an experimental and analytical method for determining this intensity in time by the coordinates of the area under study is proposed. The error in determining the intensity of internal losses is determined based on comparing the results of calculating the integration of two variants of the two-dimensional heat equation.

Optimal Node Scheduling based on Randomized Sleep Assignment and Active Node Failure for Energy-Efficient Coverage of WSNs

Optimal Node Scheduling based on Randomized Sleep Assignment and Active Node Failure for Energy-Efficient Coverage of WSNs

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.

AN EVALUATION OF COMPUTING IN COMPUTER CLUSTER

A computer cluster is a collection of connected computers that work together to create a single computer. Cluster components are typically connected through LAN. Most often, clusters are used to increase performance and/or availability over what is offered by a single computer. It is more economical than alternatives. The cluster computer’s primary goal is to utilize processing node groupings to finish the task more quickly. By moving loads from busy to idle nodes, it is accomplished. Transferring additional loads from active nodes to idle nodes is the method used to accomplish this goal.