Node Data Research Articles

An Optimized Cluster Head Selection and Secured Wireless Sensor Network Using MRSA

Currently, Wireless Sensor Networks (WSNs) play a vital role in remote monitoring and sharing of information in an infrastructure. Even though WSNs have numerous advantages, their usage is reduced by node's lifetime and data loss. The aforementioned problems in WSNs were overcome by using energy efficient protocols by various researchers. But most of the authors focused on either trust model or energy efficient model, or analyzed both models using separate algorithms. However, this leads to repeated process and to high energy consumption. Here, the cluster head is selected by using the swarm intelligence algorithm and LEACH protocol. The hybrid ant colony and modified Particle swarm algorithm are used to select the cluster heads. The CH selection depends on the two parameters, namely residual energy and number of successful transmissions of node. The node's trust is assessed using these two parameters (transmission nodes and residual energy), which also assist in ensuring the safety of the data and energy. The proposed system uses a digital signature algorithm known as the modified RSA algorithm to achieve network security when performing cryptography on data. The performance obtained by the proposed method was compared to other existing techniques in terms of throughput, energy consumption, packet delivery ratio, and network lifetime.

SPEED-UP, and energy-efficient GPSR protocol for WSNs using IOT

The development of new applications for intelligent cities, medicine, agriculture and energy harvesting would be a promising concept using wireless sensor networks based on the Internet of Things (IoT-WSNs). At the moment, the research was aimed at reducing the limited energy supply of IoT devices, which has a serious impact on their architecture. To ensure rapid and stable connection, new and efficient methods of energy systems must be designed. To handle data transfer in WSNs and to handle the problem of detector energy supply, the authors presented an improved Speed Up-Greedy Perimeter Stateless Routing Protocol for Wireless Sensor Networks (SU-GPSR) and Energy Harvest Greedy Perimeters Stateless Routing Protocol (EH-GPSR). SU-GPSR, unlike other variations of GPSR algorithms, provides complete energy consumption while assessing the next jump for local nodes and mobile nodes in an integrated framework. Additionally, established measurements are recorded using the SU-GPSR skip selection method. To calculate the amount of energy collected, EH-GPSR uses an EH rate that is generated by Mobilized Minimum Path Recovery Time (MMRT) method, as it improves the method of greedy routing. To incrementally choose the next hop, it particularly uses a weighted method that merges the node's data cost with the EH rate. In both packed and systems, the simulation study infers that SU-GPSR performs better than EH-GPSR and FA-GPSR, whereas EH-GPSR increases the node life with a speed by 20% when contrasted to the other GPSR method.

Research on software trustworthiness measurement evaluation model based on data driven

in this paper, we construct a data-driven trust evaluation model based on perceptual source. The model takes the monitoring module as the evaluation unit, and the relay node completes the trust evaluation of the sensing node in its monitoring module. The direct trust calculation is realized by the relationship between the sensing node's own data, and the recommended trust is calculated by using the relationship between the neighbor nodes in the monitoring module. Combined with the historical trust, the comprehensive trust of the sensing node is output. In this paper, the credibility of existing database driven cognitive software is summarized, and then the key technologies of reliability evaluation and classification of database driven cognitive software are analyzed.

Minimizing Data Access Latencies for Virtual Machine Assignment in Cloud Systems

Cloud systems empower the big data management by providing virtual machines (VMs) to process data nodes (DNs) in a faster, cheaper and more effective way. The efficiency of a VM allocation is an important concern that is influenced by the communication latencies. In the literature, it has been proved that the VM assignment minimizing communication latency in the presence of the triangle inequality is 2-approximation. However, a 2-approximation solution is not efficient enough as data center networks are not limited to the triangle inequality. In this paper, we define the quadrilateral inequality property for latencies such that the time complexity of the VM assignment problem minimizing communication latency in the presence of the quadrilateral inequality is in P (polynomial) class. Indeed, we propose an algorithm for the problem of assigning VMs to DNs to minimize the maximum latency among allocated VMs in addition to DNs with their assigned VMs. This algorithm is latency optimal and 2-approximation for networks with the quadrilateral inequality and the triangle inequality, respectively. Besides, the extension of the proposed method can be applied to the cloud elasticity. The simulation results illustrate the good performance and scalability of our method in various known data center networks.

Fast Recovery Architecture by Selecting Surrogate Master Node (FSMN) in Industrial IoT Environment

This paper proposes an architecture that selects a sink node when a CPS is paralyzed. The CPS is a system (master node) with various sensor networks and machine networks that exchanges device information in real-time in the industrial IoT environment. When the CPS cannot perform normally due to internal/external factors, the last log of the master node is analyzed to determine the cause of the paralysis. The CPS select six attributes from the log data and convert them into datasets usable in this architecture. The normal category of the sink node is determined through the K-means clustering algorithm according to the sink node's profiling data. The centroid of the normal category is then determined. The normal category of the sink node is updated in real time until the CPS is paralyzed. When CPS paralysis occurs, the log data of the most recent sink node is applied to the K-means clustering that was formed in advance. We then determine the most available sink node among the sink nodes through the distance using vector value and priority algorithm between the central point and the sink node. Therefore, in the case of a failure of the master node, the high-priority surrogate master node can perform the role of the master node so that the data and the system can be maintained even in unexpected situations.

D2o: a distributed data object for parallel high-performance computing in Python

We introduce D2O, a Python module for cluster-distributed multi-dimensional numerical arrays. It acts as a layer of abstraction between the algorithm code and the data-distribution logic. The main goal is to achieve usability without losing numerical performance and scalability. D2O's global interface is similar to the one of a numpy.ndarray, whereas the cluster node's local data is directly accessible for use in customized high-performance modules. D2O is written in pure Python which makes it portable and easy to use and modify. Expensive operations are carried out by dedicated external libraries like numpy and mpi4py. The performance of D2O is on a par with numpy for serial applications and scales well when moving to an MPI cluster. D2O is open-source software available under the GNU General Public License v3 (GPL-3) at https://gitlab.mpcdf.mpg.de/ift/D2O

Data Density Correlation Degree Clustering Method for Data Aggregation in WSN

One data aggregation method in a wireless sensor network (WSN) is sending local representative data to the sink node based on the spatial-correlation of sampled data. In this paper, we highlight the problem that the recent spatial correlation models of sensor nodes' data are not appropriate for measuring the correlation in a complex environment. In addition, the representative data are inaccurate when compared with real data. Thus, we propose the data density correlation degree, which is necessary to resolve this problem. The proposed correlation degree is a spatial correlation measurement that measures the correlation between a sensor node's data and its neighboring sensor nodes' data. Based on this correlation degree, a data density correlation degree (DDCD) clustering method is presented in detail so that the representative data have a low distortion on their correlated data in a WSN. In addition, simulation experiments with two real data sets are presented to evaluate the performance of the DDCD clustering method. The experimental results show that the resulting representative data achieved using the proposed method have a lower data distortion than those achieved using the Pearson correlation coefficient based clustering method or the α-local spatial clustering method. Moreover, the shape of clusters obtained by DDCD clustering method can be adapted to the environment.

Dimension Projection Matrix/Tree: Interactive Subspace Visual Exploration and Analysis of High Dimensional Data

For high-dimensional data, this work proposes two novel visual exploration methods to gain insights into the data aspect and the dimension aspect of the data. The first is a Dimension Projection Matrix, as an extension of a scatterplot matrix. In the matrix, each row or column represents a group of dimensions, and each cell shows a dimension projection (such as MDS) of the data with the corresponding dimensions. The second is a Dimension Projection Tree, where every node is either a dimension projection plot or a Dimension Projection Matrix. Nodes are connected with links and each child node in the tree covers a subset of the parent node's dimensions or a subset of the parent node's data items. While the tree nodes visualize the subspaces of dimensions or subsets of the data items under exploration, the matrix nodes enable cross-comparison between different combinations of subspaces. Both Dimension Projection Matrix and Dimension Project Tree can be constructed algorithmically through automation, or manually through user interaction. Our implementation enables interactions such as drilling down to explore different levels of the data, merging or splitting the subspaces to adjust the matrix, and applying brushing to select data clusters. Our method enables simultaneously exploring data correlation and dimension correlation for data with high dimensions.

Tradeoff Between Throughput and Energy Consumption in Multirate Wireless Sensor Networks

In wireless networks, multirate capability will facilitate using channel resource and reducing the bit error rate effectively. Current contention-based MAC protocols, however, behave inefficiently in multirate situations, leading to the performance anomaly problem. This paper researches the throughput deterioration in multirate wireless sensor networks. Initially, this paper refers to the notion of fairness to solve the performance deterioration problem and demonstrates that the utility maximization of throughput will be achieved when each contending node receives an equal proportion of channel occupancy time. Besides, considering the intrinsic tradeoff between throughput optimization and energy consumption optimization, the paper converts the tradeoff to the regulation of single node's channel access probability, which is determined by node's data rate and energy level. Finally, based on the contention-based MAC algorithm, the paper proposes an improved MAC protocol. Simulation validates that different application demands between throughput and lifetime can be adjusted flexibly and efficiently by regulating the tradeoff factor in the algorithm.

A new self-diagnosing approach based on petri nets and correlation graphs for fault management in wireless sensor networks

In most fault-detection algorithms in wireless sensor networks (WSNs), each sensor compares its data with the data of neighboring nodes. The majority of comparative methods will not work properly if more than half of the neighboring nodes of a sensor are faulty. Moreover, such comparative methods are unable to detect common mode failures (CMFs). Hence, having noticed the deficiencies of the existing comparative methods and as a reaction against such problems, we introduced a novel self-diagnosing approach to reduce the effect of neighboring node's data in determining the status of nodes so that a sensor's status will be determined independently of any comparisons. A sensor will be deemed to be fault-free if its components and the inner links between the components are flawless. In this paper, the behaviors of the components of a sensor are independently analyzed by means of the proposed model based on Petri nets and the links of the sensor's components are investigated by means of the correlation graph. In addition, the authors extended and generalized the proposed method to all the nodes of a network and evaluated their operation. Simulation results showed that the modeling implemented by the HPSim tool can cover both permanent and transient faults accurately. Moreover, using the correlation graph and Pearson correlation coefficient helped us to gain confidence in the correctness of the inner links between the components of a sensor. Evaluation of the results indicated that the statistical results from the QI Macros tool were substantially similar to those from HPSim and Matlab tools. Furthermore, simulations results demonstrated that the detection accuracy and false alarm rate of the proposed method is acceptable even when the fault probability of each sensor is generally to be high. As a result, using these mechanisms leads to the development of the self-diagnosing capability in the sensors of a WSN.

An LMI-BasedH∞Control Approach for Networked Control Systems with Deadband Scheduling Scheme

Due to the bandwidth constraints in the networked control systems (NCSs), a deadband scheduling strategy is proposed to reduce the data transmission rate of network nodes. A discrete-time model of NCSs is established with both deadband scheduling and network-induced time-delay. By employing the Lyapunov functional and LMI approach, a state feedbackH∞controller is designed to ensure the closed-loop system asymptotically to be stable withH∞performance index. Simulation results show that the introduced deadband scheduling strategy can ensure the control performance of the system and effectively reduce the node's data transmission rate.

Power Allocation in Two-Dimensional Superposition Modulation Based Cooperative Wireless Communication System

Superposition modulation makes it possible for transmitting a node's own data along with its neighbors' relay data in a single time-slot and frequency, which can improve spectral efficiency of cooperative wireless communication system. First, we consider a two-user scenario and propose a design criterion for two-dimensional superposition modulation based cooperative transmission where the users cooperate to transmit their data to a base station. We investigate M-PSK superposition modulation based cooperative scenario for Rayleigh fading channels and thoroughly derive the average symbol error rate (SER) of such system with decode-and-forward relaying. Furthermore, we introduce a design parameter called power allocation ratio (PAR) which can be adaptively changed within the derived limits. We derive SER expressions in terms of the PAR and demonstrate its effect on average SER. Based on analytical and simulation results, we show that the proposed scheme provides reduced SER and increased spectral efficiency per link compared to existing schemes in practical scenarios. Finally, we investigate multi-user scenario, propose an adaptive power allocation scheme for superposition modulation, and confirm that SER is decreased with our proposed scheme.

Quantized Point‐Based Global Illumination

AbstractPoint‐based global illumination (PBGI) uses a dense point sampling of the scene's surfaces to approximate indirect light transport and is intensively used in 3D motion pictures and special effects. Each point caches the reflected light using a spherical function and is typically used in a subsequent rasterization process to compute color bleeding and ambient occlusion in an economic, noise‐free fashion. The entire point set is organized in a spatial tree structure which models the light transport hierarchically, enabling fast adaptive shading on receivers (e.g., unprojected pixels). One of the major limitations of PBGI is related to the size of this tree, which can quickly become too large to fit in memory for complex scenes. However, we observe that, just as with natural images, this point data set is extremely redundant. In this paper, we present a new method exploiting this redundancy by factorizing PBGI data over the tree nodes. In particular, we show that a k‐means clustering in the parameter space of the spherical functions allows to define a small number of representative nodes against which any new one can be classified. These representative functions, gathered in a pre‐process over a subset of the actual points, form a look‐up table which allows to substitute node's data by quantized integers in a streaming process, avoiding building the full tree before compressing it. Depending on the nodes' spherical function variance in the scene and the desired accuracy, our indexed PBGI representation achieves between one and two orders of magnitude compression of the nodes spherical functions, with negligible numerical and perceptual error in the final image. In the case of a binary tree with one surfel per leaf and no spherical functions in the leaves, this leads to compression rates ranging from 3× to 5× for the whole tree.

Exploratory Factor Analysis of Pathway Copy Number Data with an Application Towards the Integration with Gene Expression Data

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression.

A Fault-Tolerant Cooperative Spectrum Sensing Algorithm over Cognitive Radio Network Based on Wireless Sensor Network

A serious threat to cognitive radio networks that sense the spectrum in a cooperative manner is the transmission of false spectrum sensing data by malicious sensor nodes. SNR fluctuations due to wireless channel effects complicate handling such attackers even further. This enforces the system to acquire authentication. Actually, the decision maker needs to determine the reliability or trustworthiness of the shared data. In this paper, the evaluation process is considered as an estimation dilemma on a set of evidences obtained through sensor nodes that are coordinated in an underlying wireless sensor network. Then, a likelihood-based computational trust evaluation algorithm is proposed to determine the trustworthiness of each sensor node's data. The proposed procedure just uses the information which is obtained from the sensor nodes without any presumptions about node’s reliability. Numerical results confirm the effectiveness of the algorithm in eliminating malicious nodes or faulty nodes which are not necessarily conscious attackers.

Performance evaluation of an optical transparent access tier based on PON and spectral codes

The increasing amount of bandwidth requirements and quality of service needs for the next-generation access networks has boosted extensive research in the fiber-optics communication field. In this light, passive optical networks (PONs) combined with optical code division multiple access (OCDMA), provide a potentially cost-effective solution to meet such bandwidth demands. This work proposes an optical transparent architecture which enables all-optical communication between the network nodes. The encoded data streams are multiplexed at a merging point which results in multiple user interference (MUI), thus significantly reducing the network throughput. The networking nodes are able to monitor and record user activity in the PON, and further register the (past) state of activity at the merging point. In this work, we study the coherence of state between the networking nodes and the merging point, for different packet size distributions, in order to predict an optimal transmission instant of each node's data packets. We note that the states are coherent depending on the packet size distribution.

센서 네트워크상의 새로운 자동 위치결정 방법

Many sensor network applications require that each node's sensor data stream be annotated with its physical location in some coordinate system. Equipping GPS on every sensor node is often expensive and does not work in indoor deployments. Recently, cricket-based localization system is often used for indoor localization system. It is very important to know the exact position of beacons in cricket-based localization system for identifying moving sensor node's position. In this paper, a new method, Mobile Listener Detect Algorithm (MLD) which can automatically calculate the unknown newly installed beacons is proposed. For the verification of the feasibility of the proposed scheme, we have conducted several experiments.

Nonintrusive Fiber Monitoring of TDM Optical Networks

This paper introduces a new embedded nonintrusive fiber-monitoring technique for time-division-multiplexing optical networks. It allows an optical transmitter to characterize the fiber plant from reflections caused by data bursts transmitted across the network instead of dedicated test signals. The probing is performed with minimal burden on data traffic so that many measurements can be averaged to improve accuracy. The method is very suitable for embedded optical time-domain reflectometers (OTDR), which reuse a network node's optical data transmitter for OTDR excitations and embed a reflectometer inside the fiber endpoint. This paper models the OTDR with Laplace transforms, an approach previously unpursued, after which it is explained how reflections from multiple data bursts with arbitrary width can be converted into one normalized format. This new class of OTDR excites the fiber with a negative step of light instead of the conventional short pulse. The signal-to-noise ratio (SNR) for backscatter and Fresnel reflections caused by the negative step and pulse are compared theoretically. It is shown that negative-step OTDR breaks the tradeoff between excitation pulsewidth and distance resolution, has a natural separation between fiber backscatter and Fresnel reflectors, and improves the SNR of nonreflective events

The Imaging Node for the Planetary Data System

The Planetary Data System Imaging Node maintains and distributes the archives of planetary image data acquired from NASA's flight projects with the primary goal of enabling the science community to perform image processing and analysis on the data. The Node provides direct and easy access to the digital image archives through wide distribution of the data on CD-ROM media and on-line remote-access tools by way of Internet services. The Node provides digital image processing tools and the expertise and guidance necessary to understand the image collections. The data collections, now approaching one terabyte in volume, provide a foundation for remote sensing studies for virtually all the planetary systems in our solar system (except for Pluto). The Node is responsible for restoring data sets from past missions in danger of being lost. The Node works with active flight projects to assist in the creation of their archive products and to ensure that their products and data catalogs become an integral part of the Node's data collections.