Candidate Nodes Research Articles

Detection of Malicious Circuitry using Transition Probability Based Node Reduction Technique

In recent years, serious concerns have been raised against the tampering of integrated circuits due to outsourcing of circuits for fabrication. It has led to the addition of malicious circuitry known as Hardware Trojan. In this paper, a transition probability based node reduction technique for faster and efficient Hardware Trojan (HT) detection has been attempted. In the proposed method, the fact that the least controllable and observable nodes or the nodes with least transition probability are more vulnerable as Trojan sites is taken into consideration. The nodes that have lesser activity than the threshold are the candidate nodes. At each candidate node, segmentation is done for further leakage power analysis to detect the presence of Trojans. Experimental results observed on ISCAS’85 and ISCAS’89 benchmark circuits illustrate that the proposed work can achieve remarkable node reduction upto 78.81% and time reduction upto 58.7%. It was also observed that the circuit activity can be increased by varying the input probability. Hence, for further reduction in the Trojan activation time, the weighted input probability was obtained.

Broadband Spectrum Sensing of Distributed Modulated Wideband Converter Based on Markov Random Field

The Distributed Modulated Wideband Converter (DMWC) is a networking system developed from the Modulated Wideband Converter, which converts all sampling channels into sensing nodes with number variables to implement signal undersampling. When the number of sparse subbands changes, the number of nodes can be adjusted flexibly to improve the reconstruction rate. Owing to the different attenuations of distributed nodes in different locations, it is worthwhile to find out how to select the optimal sensing node as the sampling channel. This paper proposes the spectrum sensing of DMWC based on a Markov random field (MRF) to select the ideal node, which is compared to the image edge segmentation. The attenuation of the candidate nodes is estimated based on the attenuation of the neighboring nodes that have participated in the DMWC system. Theoretical analysis and numerical simulations show that neighboring attenuation plays an important role in determining the node selection, and selecting the node using MRF can avoid serious transmission attenuation. Furthermore, DMWC can greatly improve recovery performance by using a Markov random field compared with random selection.

A heuristic survivable virtual network mapping algorithm

Network virtualization is a promising solution to attack Internet ossification. Virtual network mapping (or embedding) problem is the core of it and is proved to be NP-hard. In this paper, virtual network mapping problem with survivability is formulated and solved with a heuristic algorithm. Firstly, network link resources are divided into primary flow resources and secondary flow resources. The former are used under normal network operation, whereas the latter are used as backup resources once the networks fail. Secondly, we introduce a novel metric named global resource capacity (GRC) which is recently proposed for measuring node mapping capacity to improve network load balance. At last, a heuristic survivable virtual network embedding algorithm (GRC-SVNE) is proposed. In node mapping phase, we calculate the mapping capacity of all nodes and then some nodes are selected as candidate nodes for virtual network embedding and the goal is to improve mapping successful ratio. After that, link mapping is performed with Dijkstra algorithm. Simulation results show that GRC-SVNE outperforms the traditional greedy algorithm (GREEDY), randomized algorithm (R-ViNE) as well as deterministic algorithm (D-ViNE) and demonstrates desirable results in terms of acceptance ratio, network load balance and network revenue.

Multiscale Site Matching for Vision-Only Self-Localization of Intelligent Vehicles

Self-localization is a challenging issue in intelligent vehicle (IV) systems. Traditional self-localization methods, such as the Global Navigation Satellite System (GNSS), Inertial Navigation System (INS) and vision simultaneous localization and mapping (vSLAM), are subject to low accuracy, high cost or low robustness. To this end, this paper proposes a new multi-scale site matching localization (MS2ML) method for IV systems by using one single monocular camera. The MS2ML consists of a coarse localization, an image-level localization and a metric localization. In coarse localization, the proposed MS2ML calls the Bayesian vision-motion topological localization to obtain a set of nodes from a visual map. Furthermore, the holistic feature is generated for each query image, and hence, the holistic feature matching is implemented to realize image-level localization. A node is then selected from the candidate nodes. In metric localization, the closest node and vehicle pose are calculated through matching local features with three-dimension (3D) data. In order to evaluate the proposed MS2ML, real-world driving tests have been carried out in three different routes, two of which are from an urban roadway and an industrial park in Wuhan, China and the third one is from public KITTI (Karlsruhe Institute of Technology and Toyota Technology Institute) data set. The total lengths of these routes are more than 7 km. The experiment results demonstrate that the average localization errors of the proposed MS2ML method are less than 0.45 frame and the pose errors are less than 0.59 m. As a result, the proposed method remains high accuracy and great robustness in various environments.

A 2-Layers Virtual Network Mapping Algorithm Based on Node Attribute and Network Simplex

Network virtualization is one of the most important techniques to overcome Internet ossification, which can enable multiple networks to run on a common infrastructure. Virtual network mapping is a key process of network virtualization to efficiently map virtual network onto the substrate network within constraints of node and link resource. However, previous VNM heuristic algorithms completely separate the node mapping stage from link mapping stage and ignore the effect of link mapping results on physical node ranking, which may lead to higher mapping cost and distributed distribution of virtual nodes on the substrate network. The shortest path algorithm is used at most to resolve virtual link mapping which results in longer mapping time. To address these issues, a two-layer virtual network mapping algorithm named simplex-VNM is proposed based on node attribute and network simplex in this paper. In node mapping layer, a new node ranking approach which considers topology attributes, global network resources, and mapped physical node information provides candidate nodes and node mapping cost for link mapping layer. Then, in link mapping layer, network simplex algorithm which has smaller time complexity compared to traditional shortest path algorithms has been introduced to find the optimal link mapping solution under the result of node mapping layer. Finally, according to the results of two layers, the virtual node and relevant links will be mapped with minimal total mapping cost. Extensive simulation results have demonstrated that our proposed method behaves better than four other algorithms that consider node attribute and use shortest path algorithm in terms of total mapping cost and mapping time.

De-Anonymizing Social Networks With Random Forest Classifier

Personal privacy is facing severe threats as social networks are sharing user data with advertisers, application developers, and data mining researchers. Although these data are anonymized by removing personal information, such as user identity, nickname, or address information, personal information still could not be protected effectively. In order to arouse the attention of people from academia and industry for privacy protection, we propose a random forest method to de-anonymize social networks. First, we convert the social network de-anonymization problem into a binary classification problem between node pairs. In order to partition large sparse social networks, we use the spectral partition method to partition large graphs into a number of small subgraphs. Then, we use the features of the network structure to train the random forest classifier. As a result, candidate node pairs from anonymous network and auxiliary network can be classified as matched pair by the random forest classifier. Furthermore, we improve the efficiency of our solution through parallelizing proposed method. The experiments conducted on the real data sets show that our solution’s area under the curve is 19% higher than baseline methods on average. Besides that we test the robustness of the proposed algorithm by adding some noisy data, and the result demonstrates that our solution has good robustness.

A Seed Expansion Graph Clustering Method for Protein Complexes Detection in Protein Interaction Networks.

Most proteins perform their biological functions while interacting as complexes. The detection of protein complexes is an important task not only for understanding the relationship between functions and structures of biological network, but also for predicting the function of unknown proteins. We present a new nodal metric by integrating its local topological information. The metric reflects its representability in a larger local neighborhood to a cluster of a protein interaction (PPI) network. Based on the metric, we propose a seed-expansion graph clustering algorithm (SEGC) for protein complexes detection in PPI networks. A roulette wheel strategy is used in the selection of the seed to enhance the diversity of clustering. For a candidate node u, we define its closeness to a cluster C, denoted as NC(u, C), by combing the density of a cluster C and the connection between a node u and C. In SEGC, a cluster which initially consists of only a seed node, is extended by adding nodes recursively from its neighbors according to the closeness, until all neighbors fail the process of expansion. We compare the F-measure and accuracy of the proposed SEGC algorithm with other algorithms on Saccharomyces cerevisiae protein interaction networks. The experimental results show that SEGC outperforms other algorithms under full coverage.

Air parcel network design considering pure freighters

This paper proposes a selection model of candidate nodes and an air parcel network hub location model to estimate the efficiency of the parcel network when adding pure freighters. Using the data on real demands among airports in practice, this article analyzes the hub location problem of the parcel network for DHL Express in the Asia-Pacific region. We find that Beijing, Hong Kong, Singapore, and Mascot are potential choices to be hub airports. The four hubs have parcel production of 10.1, 5.36, 2.75 and 0.84 million tons in the planning horizon, respectively. A sensitivity analysis is conducted to analyze the influence of different numbers of pure freighters on the air parcel network and the results indicate that the number of pure freighters can alleviate parcel pressure.

Fundamental principles governing sporulation efficiency: A network theory approach

Using network theory on an integrated time-resolved genome-wide gene expression data, we investigated the intricate dynamic regulatory relationships of transcription factors and target genes to unravel signatures that contribute to extreme phenotypic differences in yeast, Saccharomyces cerevisiae. We performed a comparative analysis of the gene expression profiles of two yeast strains SK1 and S288c which are known for high and low sporulation efficiency, respectively. The results based on various structural attributes of the networks, such as clustering coefficient, degree-degree correlations, and betweenness centrality suggested that a delay in crosstalk between functional modules can be construed as one of the prime reasons behind low sporulation efficiency of S288c strain. A more hierarchical structure in the late phase of sporulation in S288c indicated an attempt of this low sporulating strain to obtain modularity, which is a feature of early sporulation phase. Further, the weak ties analysis revealed that mostly meiosis-associated genes were the end nodes of the weak ties for the high sporulating SK1 strain, while for the low sporulating S288c strain these nodes were mitotic genes. This again was a clear indication of the delay in regulatory activities in the S288c strain, which are essential to initiate sporulation. Our results demonstrate the potential of this framework in identifying candidate nodes contributing to phenotypic diversity in natural populations with application prospects in drug target discovery and personalized health.

Cooperative task allocation in heterogeneous wireless sensor networks

In a wireless sensor network, sensor nodes are strictly energy and capacity constrained, which makes it necessary for them to collaboratively execute a complex task. Thus, task allocation becomes a fundamental and crucial issue in wireless sensor networks. Most previous studies developed centralized methods to solve this problem. In addition, a common assumption is that all the sensor nodes are homogeneous, which is unfavorable in many real applications. In this article, a distributed task allocation strategy which can handle the problem in a heterogeneous wireless sensor network is proposed. The task is propagated from nodes to nodes and each node matches its own capacity with the required capacities until all the demanded capacities of the task are obtained. Building on this, an enhanced task allocation strategy based on self-organization is developed. By utilizing previous assigning information, the nodes with proper capacities will be selected as candidate nodes, then the paths to these nodes will be ...

ZBTB16 and Metabolic Syndrome: a Network Perspective

Metabolic syndrome is a prevalent, complex condition. The search for genetic determinants of the syndrome is currently undergoing a paradigm enhancement by adding systems genetics approaches to association studies. We summarize the current evidence on relations between an emergent new candidate, zinc finger and BTB domain containing 16 (ZBTB16) transcription factor and the major components constituting the metabolic syndrome. Information stemming from studies on experimental models with altered Zbtb16 expression clearly shows its effect on adipogenesis, cardiac hypertrophy and fibrosis, lipid levels and insulin sensitivity. Based on current evidence, we provide a network view of relations between ZBTB16 and hallmarks of metabolic syndrome in order to elucidate the potential functional links involving the ZBTB16 node. Many of the identified genes interconnecting ZBTB16 with all or most metabolic syndrome components are linked to immune function, inflammation or oxidative stress. In summary, ZBTB16 represents a promising pleiotropic candidate node for metabolic syndrome.

A 38 pJ/b Optimal Soft-MIMO Detector

An optimal soft multiple-input multiple-output (MIMO) detector is proposed with linear complexity for a general spatial multiplexing system with two transmitting symbols and $\boldsymbol {N_{R}\ge 2}$ receiving antennas. The computational complexity of the proposed scheme is independent of the operating signal-to-noise ratio and grows linearly with the constellation order. It provides the soft maximum-likelihood solution using an efficient log-likelihood ratio calculation method, avoiding the exhaustive search on all the candidate nodes. Moreover, an efficient pipelined hardware implementation of the detection algorithm is proposed, which is fabricated and fully tested in a 130-nm CMOS technology. Operating at 1.2 V supply with 412-MHz clock, the chip achieves up to 5 Gb/s throughput with 192-mW power dissipation and an energy efficiency of 38 pJ/b, showing great potential to be used in next generation Gbps wireless systems. The proposed MIMO detector is perfectly suitable to be applied to the long-term evolution modem as well as Wi-Fi and WiGig devices with more than 1 RF chain. Synthesis results in a 90-nm CMOS technology demonstrate that the design can operate at a sustained throughput of 6.2 Gb/s and an energy efficiency of 28 pJ/b at 1.2 V supply. For applications demanding a lower throughput regime, the core can operate at 0.9 V supply consuming 42 mW providing a throughput of 1 Gb/s. 1 1 U.S. patent issued in 2009.

Supervised ranking framework for relationship prediction in heterogeneous information networks

In recent years, relationship prediction in heterogeneous information networks (HINs) has become an active topic. The most essential part of this task is how to effectively represent and utilize the important three kinds of information hidden in connections of the network, namely local structure information (Local-info), global structure information (Global-info) and attribute information (Attr-info). Although all the information indicates different features of the network and influence relationship creation in a complementary way, existing approaches utilize them separately or in a partially combined way. In this article, a novel framework named Supervised Ranking framework (S-Rank) is proposed to tackle this issue. To avoid the class imbalance problem, in S-Rank framework we treat the relationship prediction problem as a ranking task and divide it into three phases. Firstly, a Supervised PageRank strategy (SPR) is proposed to rank the candidate nodes according to Global-info and Attr-info. Secondly, a Meta Path-based Ranking method (MPR) utilizing Local-info is proposed to rank the candidate nodes based on their meta path-based features. Finally, the two ranking scores are linearly integrated into the final ranking result which combines all the Attr-info, Global-info and Local-info together. Experiments on DBLP data demonstrate that the proposed S-Rank framework can effectively take advantage of all the three kinds of information for relationship prediction over HINs and outperforms other well-known baseline approaches.

Analysis of SCERP: A Cluster Based Routing Protocol for Energy Balancing in Wireless Sensor Network

Prolonging lifetime of wireless sensor network is a most significant problem due to energy constraint nature of sensor nodes. It is difficult to recharge nodes during network lifetime, to increase application area of WSN there is a need to design energy efficient clustering protocol for WSN. In this article there is a discussion about problems in Leach protocol and propose an improvement on the Leach routing protocol to reduce energy consumption and to extend network lifetime. Proposed self organized cluster based energy balanced routing protocol (SCERP) selects a cluster head node by considering probability based on ratio of residual energy of the node and the average energy level of nodes in network, and the geometric distance between the candidate node to the BS as key parameters. The outcome of simulation shows that proposed protocol is better than Leach in terms of balancing energy consumption of nodes and extending WSN lifetime.

Hardware Trojan Detection Based on Logical Testing

In recent years, hardware Trojans (HTs) have become one of the main challenging concerns within the chain of manufacturing digital integrated circuit chips. Because of their diversity in chips, HTs are difficult to detect and locate. This paper attempted to propose a new improved method for detection and localization of HTs based on the real-time logical values of nodes. The algorithm extracts the nodes with special attributes. At the next stage, the nodes with the greatest similarity in terms of logical value are selected as targets. Depending on the size of the circuit, the extraction continues until a sufficient number of similar nodes has been selected. The logical relationship between the candidate nodes yields a function, the logical values of which differ in the Trojan-free and Trojan-infected modes, thus detecting the potential Trojans. This method is scalable, overcoming the problems of noise and Process variation. The success rate of Trojan detection in this method is more than 80%. The most overhead is 13% for power consumption and 15% for area.

Prior node selection for scheduling workflows in a heterogeneous system

Many workflow scheduling algorithms for heterogeneous systems have been developed to satisfy multiple requirements such as minimizing schedule length while maximizing throughput. In particular, in list-based scheduling approaches, the schedule length depends on the given nodes as well as the task allocation and ordering policies. This is because the scheduling priority is derived by averaging the execution time and communication time of the given nodes. If the set of nodes can be adjusted before the scheduling tasks, a small schedule length can be achieved. In this paper, we propose a prior node selection algorithm, called lower bound based candidate node selection (LBCNS) to select a subset of given nodes to minimize the schedule length while fairly scheduling each job. Our proposal has two approaches: (i) LBCNS_DEFAULT, which considers the job characteristics and each node’s performance, and (ii) priority-based LBCNS, which additionally takes each scheduling priority into account for a dedicated task scheduling algorithm.The experimental results of extensive simulations show that LBCNS_DEFAULT has the best fairness for scheduling multiple workflow jobs, while priority-based LBCNS achieves the minimum schedule length with the highest efficiency for a single workflow job and multiple workflow jobs.

Delay Analytical Models for Opportunistic Routing in Wireless Ad Hoc Networks

Opportunistic routing (OR) is a promising technology for improving the throughput of wireless networks. In contrast to traditional routing techniques that forward messages along predetermined paths, OR schemes allow selection of a set of candidate nodes as potential next-hop forwarders to utilize the broadcast nature of the wireless medium. An analysis of transmission latency and signaling overhead depend on the number of hops required to transmit a packet from source to destination. In this paper, we propose three models to analyze the number of transmissions for successfully delivering a packet with three OR schemes: single-forwarder OR with known topologies, multiple-forwarder OR with known topologies, and multiple forwarder OR with unknown topologies. These models are useful in the network planning. The simulation results verified the accuracy and usability of the three models.

SRTS : A Self-Recoverable Time Synchronization for sensor networks of healthcare IoT

Sensor networks for healthcare IoT (Internet of Things) have advanced rapidly in recent years, which has made it possible to integrate real-time health data by connecting bodies and sensors. Body sensors require accurate time synchronization in order to collaboratively monitor health conditions and medication usage. Self-recovery and high accuracy are crucial for time synchronization protocols in sensor networks for healthcare IoT. Because body sensors are generally deployed with unstable energy sources, nodes can fail because of inadequate power supply. This influences the efficiency and robustness of time synchronization protocols. Tree-based protocols require stable root nodes as time references. The time synchronization process cannot be completed if a root node fails. To address this problem, we present a Self-Recoverable Time Synchronization (SRTS) scheme for healthcare IoT sensor networks. A recovery timer is set up for candidate nodes, which are dynamically elected. The candidate node whose timer expires first takes charge of selecting a new root node. Meanwhile, SRTS combines the two-points least-squares method and the MAC layer timestamp to significantly improve the accuracy of PBS. Furthermore, SRP and RRP models are used in SRTS. Thus, our approach provides higher accuracy than PBS, while consuming a similar amount of energy. We use NS2 network tools to evaluate our approach. The simulation results show that SRTS exhibits better self-recovery than time synchronization protocols STETS and GPA under different network scales. Moreover, accuracy and clock drift compensation are better than those of PBS and TPSN.

Algorithm of inclusion and interchange of variables for capacitors placement

This work develops the algorithm of inclusion and interchange of variables to obtaining the sizes, the placements and the control schemes of the capacitors banks that minimize the annual total cost while complying with the maximum and minimum voltage constraints. This is a new and very efficient search method that improves the results of the previous contributions. The method considers all the possible locations for capacitors placement as well as the correct interrelation between sizes of all the capacitors of fixed and switched type. Instead of to place the capacitors on candidate nodes that are previously selected by sensitivity factors and then to select their time-controls, the presented method examines the placement of the capacitors in all available positions (node, control) by evaluating directly the effects of the inclusion and the interchange of variables (capacitors) in the value of the objective function. The fact that the optimization of the placements and the time-controls of the capacitors is coordinated with the selection of their sizes, allows the obtaining of a high quality solutions for the optimization problem. The effectiveness of the proposed method is tested by solving various examples and by comparing the obtained results with several previously published solutions for these examples.

Research on Interference Optimization Mechanism of Wireless Routing Signal Transmission Based on Fuzzy Comprehensive Evaluation Method

Inherently dynamic in nature, wireless sensor network transmits data less efficient and reliable, and thus the conventional routing protocol is inapplicable to the new type of wireless sensor network. Such being the case, this paper first analyzes this problem and proposes a routing interference optimization mechanism FCE based on distributed fuzzy comprehensive evaluation. The fuzzy principle is introduced to the priority calculation of the node. In doing so, the fuzzy linear transformation principle and the maximum membership principle are used to classify the dynamic nature of candidate nodes and to select well-performed candidates from the optimal candidate nodes. In spite of the few number of those candidates, they are qualified to compete for the right of next-hop forwarding, such that the routing interference will become less probable and the data forwarding is rendered more efficient. Finally, through a simulation test, our method is verified effective.