944 research outputs found
Bayesian fusion algorithm for inferring trust in wireless sensor networks
This paper introduces a new Bayesian fusion algorithm to combine more than one trust component (data trust and communication trust) to infer the overall trust between nodes. This research work proposes that one trust component is not enough when deciding on whether or not to trust a specific node in a wireless sensor network. This paper discusses and analyses the results from the communication trust component (binary) and the data trust component (continuous) and proves that either component by itself, can mislead the network and eventually cause a total breakdown of the network. As a result of this, new algorithms are needed to combine more than one trust component to infer the overall trust. The proposed algorithm is simple and generic as it allows trust components to be added and deleted easily. Simulation results demonstrate that a node is highly trustworthy provided that both trust components simultaneously confirm its trustworthiness and conversely, a node is highly untrustworthy if its untrustworthiness is asserted by both components. © 2010 ACADEMY PUBLISHER
Can we trust trusted nodes in wireless sensor networks?
In this paper we extend our previously designed trust model in wireless sensor networks to include both; communication trust and data trust. Trust management in wireless sensor networks is predominantly based on routing messages; whether the communication has happened or not (successful and unsuccessful transactions). The uniqueness of sensing data in wireless sensor networks introduces new challenges in calculating trust between nodes (data trust). If the overall trust is based on just the communication trust, it might mislead the network, that is; untrustworthy nodes in terms of sensed data can be classified as trusted nodes due to their communication capabilities. Hence we need to develop new trust models to address the issue of the actual sensed data. Here we are comparing the two trust models and proving that one model by itself is not enough to decide on the trustworthiness of a node, so new techniques are required to combine both data trust and communication trust. ©2008 IEEE
Recursive bayesian approaches for auto calibration in drift aware wireless sensor networks
The purpose for wireless sensor networks is to deploy low cost sensors with sufficient computing and communication capabilities to support networked sensing applications. Even when the sensors are properly calibrated at the time of their deployment, they develop drift in their readings leading to biased sensor measurements. Noting that a physical phenomenon in a certain area follows some spatio-temporal correlation, we assume that the sensors readings in that area are correlated. We also assume that the instantiations of drifts are uncorrelated. Based on these assumptions, and inspired by the resemblance of registration problem in radar target tracking with the bias error problem in wireless sensor networks, we follow a Bayesian framework to solve the Drift/Bias problem in wireless sensor networks. We present two methods for solving the drift problem in a densely deployed sensor network, one for smooth drifts and the other for unsmooth drifts. We also show that both methods successfully detect and correct sensor errors and extend the effective life time of the sensor network
Topology of event distribution as a generalized definition of phase transitions in finite systems
We propose a definition of phase transitions in finite systems based on
topology anomalies of the event distribution in the space of observations. This
generalizes all the definitions based on the curvature anomalies of
thermodynamical potentials and provides a natural definition of order
parameters. The proposed definition is directly operational from the
experimental point of view. It allows to study phase transitions in Gibbs
equilibria as well as in other ensembles such as the Tsallis ensemble.Comment: 4 pages, 3 figure
CentFlow: Centrality-Based Flow Balancing and Traffic Distribution for Higher Network Utilization
Next-generation networks (NGNs) are embracing two key principles of software defined networking (SDN) paradigm functional segregation of control and forwarding plane, and logical centralization of the control plane. A centralized control enhances the network management significantly by regulating the traffic distribution dynamically and effectively. An eagle-eye view of the entire topology opens up the opportunity for an SDN controller to refine the routing. Optimizing the network utilization in terms of throughput is majorly dependent on the routing decisions. Open Shortest Path First (OSPF) and Intermediate System to Intermediate System (IS-IS) are well-known traditional link state routing protocols proven with operation over operator networks for a long time. However, these classical protocols deployed distributively fall short of expectation in addressing the current routing issues due to the lack of a holistic view of the network topology and situation, whereas handling enormous traffic and user quality of experience (QoE) requirements are getting critical. IP routing in NGN is widely expected to be supported by SDN to enhance the network utilization in terms of throughput. We propose a novel routing algorithm-CentFlow, for an SDN domain to boost up the network utilization. The proposed weight functions in CentFlow achieve smart traffic distribution by detecting highly utilized nodes depending on the centrality measures and the temporal node degree that changes based on node utilization. Furthermore, the frequently selected edges are penalized thereby augmenting the flow balancing and dispersion. CentFlow reaps greater benefits on an SDN controller than the classical OSPF due to its comprehensive view of the network. Experimental results show that CentFlow enhances the utilization of up to 62% of nodes and 49% of links, respectively, compared to an existing Dijkstra algorithm-based routing scheme in SDN. Furthermore, nearly 6.5% more flows are processed networ- wide
Composition of Binary Compressed Sensing Matrices
In the recent past, various methods have been proposed to construct deterministic compressed sensing (CS) matrices. Of interest has been the construction of binary sensing matrices as they are useful for multiplierless and faster dimensionality reduction. In most of these binary constructions, the matrix size depends on primes or their powers. In this study, we propose a composition rule which exploits sparsity and block structure of existing binary CS matrices to construct matrices of general size. We also show that these matrices satisfy optimal theoretical guarantees and have similar density compared to matrices obtained using Kronecker product. Simulation work shows that the synthesized matrices provide comparable results against Gaussian random matrices
Bioanalytical Method Development and Validation of Memantine in Human Plasma by High Performance Liquid Chromatography with Tandem Mass Spectrometry: Application to Bioequivalence Study
A simple, sensitive, and rapid HPLC-MS/MS method was developed and validated for quantitative estimation of memantine in human plasma. Chromatography was performed on Zorbax SB-C18 (4.6 × 75 mm, 3.5 μm) column. Memantine (ME) and internal standard Memantine-d6(MED6) were extracted by using liquid-liquid extraction and analyzed by LC-ESI-MS/MS using multiple-reaction monitoring (MRM) mode. The assay exhibited a linear dynamic range of 50.00–50000.00 pg/ml for ME in human plasma. This method demonstrated an intra- and interday precision within the range of 2.1–3.7 and 1.4–7.8%, respectively. Further intra- and interday accuracy was within the range of 95.6–99.8 and 95.7–99.1% correspondingly. The mean recovery of ME and MED6 was 86.07 ± 6.87 and 80.31 ± 5.70%, respectively. The described method was successfully employed in bioequivalence study of ME in Indian male healthy human volunteers under fasting conditions
A Prospective study on the assessment of risk factors for type 2 diabetes mellitus in outpatients department of a south Indian tertiary care hospital: A case-control study
Background: Type 2 diabetes mellitus (T2DM) is the most general type of diabetes. In India, the risk factors (modifiable and nonmodifiable) for diabetes are seen more frequently and there is lack of perception about this problem.Objective: The objective of the study was to assess the incidence and risk factors for T2DM in a south Indian tertiary care hospital.Materials and Methods: A prospective study was conducted on 1161 subjects (with or without T2DM) from November 2014 to April 2015 in general medicine department of Dr. Pinnamaneni Siddhartha Institute of Medical Sciences and Research Foundation, Andhra Pradesh, south India. Chi-square test was used to evaluate the incidence of T2DM and odds ratios were calculated in univariate logistic regression analysis for risk factors.Results: T2DM was significantly higher in the subjects of age above 41 years (86.3%, P<0.0001), married (95.4%, P=0.002), educators (degree and above, 13.2%, P<0.0001), known family history (50.8%, P<0.0001), BMI (>25 kg/m2,58.7%; P<0.0001), Govt. job holders (5.5%, P<0.0001), business people (12%, P<0.0001), house wives (38.3%, P<0.0001), high economic status (34.9%, P<0.0004), preexisting hypertension (40.2%, P<0.0001), urban residence (50.4%, P<0.0001), physical inactivity (45.3%, P<0.001), stress (61.0%, P=0.01), consumption of tea and coffee (daily thrice or more, 6.3%, P=0.0003), soft drinks (weekly thrice or more, 4%, P=0.0008) and junk foods (weekly thrice or more 2.6%, P=0.025) than non-diabetic subjects. Univariate logistic regression analysis showed that the age (above 41 years), marital status, education, family history, BMI (>25 kg/m2), high economic status, co-morbidities (hypertension and thyroid disorders) urban residence, physical inactivity, stress, consumption of tea and coffee (daily thrice or more), soft drinks (weekly thrice or more) and junk foods are the significantly risk factors for T2DM.Conclusion: The present study results suggested that beware of hypertension, thyroids disorders, physical inactivity, stress, soft drinks and junk foods, which are major risk factors of T2DM.Â
Moisture retention and release characteristics of some soils developed on different parent materials and landforms
Abstract Ten soils developed under different landforms and parent materials in a micro-watershed of Wunna catchment were studied for soil properties and also for moisture retention and release characteristics. These soils show considerable variations in physical and chemical characteristics and also in retention and release of soil water. The variation in the amount of water retention of soils on basalt as well as sandstone are attributed to relative proportion of soil separates and type and amount of clay minerals. The water holding capacity of heavy textured soils are more as compared to light textured soils. The plant available water capacity of soils on basalt is relatively higher as compared to soils on sandstone indicating high amount of water storage in the profile during crop growth. The per cent moisture release was maximum between soil matrix suction of 33 kPa and 500 kPa of basaltic soils (73 to 82 %) as well as sandstone soils (65 to 87 %). The percent moisture release decreased nearer to wilting point. The release of soil moisture is gradual between 33 to 1000 kPa matric suction in case of soils on basalt. But in case of sandstone soils, sudden release is observed between 33 to 500 kPa and therefore sandy soils show moisture stress during crop growth. The study indicates that the rate of release of moisture in fine textured soils is more gradual than the relatively coarser textured ones
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