A Full Connectable and High Scalable Key Pre-distribution Scheme Based on Combinatorial Designs for Resource-Constrained Devices in IoT Network

Considering the internet of things (IoT), end nodes such as wireless sensor network, RFID and embedded systems are used in many applications. These end nodes are known as resource-constrained devices in the IoT network. These devices have limitations such as computing and communication power, memory capacity and power. Key pre-distribution schemes (KPSs) have been introduced as a lightweight solution to key distribution in these devices. Key pre-distribution is a special type of key agreement that aims to select keys called session keys in order to establish secure communication between devices. One of these design types is the using of combinatorial designs in key pre-distribution, which is a deterministic scheme in key pre-distribution and has been considered in recent years. In this paper, by introducing a key pre-distribution scheme of this type, we stated that the model introduced in the two benchmarks of KPSs comparability had full connectivity and scalability among the designs introduced in recent years. Also, in recent years, among the combinatorial design-based key pre-distribution schemes, in order to increase resiliency as another criterion for comparing KPSs, attempts were made to include changes in combinatorial designs or they combine them with random key pre-distribution schemes and hybrid schemes were introduced that would significantly reduce the design connectivity. In this paper, using theoretical analysis and maintaining full connectivity, we showed that the strength of the proposed design was better than the similar designs while maintaining higher scalability.

     

A note on the Consecutive Ones Submatrix problem

A binary matrix has the Consecutive Ones Property (C1P) for columns if there exists a permutation of its rows that leaves the 1's consecutive in every column. The problem of Consecutive Ones Property for a matrix is a special variant of Consecutive Ones Submatrix problem in which a positive integer K is given and we want to know if there exists a submatrix B of A consisting of K columns of A with C1P property. This paper presents an error in the proof of NP-completeness for this problem in the reference cited in text by Garey and Johnson [Computers and Intractability, A Guide to the Theory of NP-Completeness, 1979].     

A Microfabricated Flow Controller for Refrigerant Expansion

A flow controller for refrigerant expansion is reported. Devices are fabricated using a micromolding technique that is developed for thick nickel electrodeposition. The device consists of a short-tube restrictor with an integrated normally open valve, which, when actuated, presents a controllable blockage into the flow passage to obstruct the flow. Fabricated devices are evaluated with compressed air, with up to 22% reduction in mass flow rate at maximum actuation of the restrictor. The devices are also evaluated in an R134a vapor compression system of 1.5-2 kW, with the ability to control mass flow that is found to be greatly influenced by the vapor compression cycle's operational parameters. After the inlet pressure, the level of subcooling proved to be the most important parameter. For a cycle operating between 29 deg C and 4 deg C , saturation temperatures in the condenser and evaporator, respectively, actuation of the device reduced the refrigerant mass flow rate by 3.5% with 0.6 ^oC subcooling and up to 10.8 % with 5 deg C subcooling.     

Dynamics and organization of MAP kinase signal pathways

In the budding yeast, Saccharomyces cerevisiae, four separate but structurally related mitogen-activated protein kinase (MAPK) activation pathways are known. The best understood of these regulates mating. Pheromone binding to receptor informs cells of the proximity of a mating partner and induces differentiation to a mating competent state. The MAPK activation cascade mediating this signal is made up of Ste11 (a MEK kinase [MEKK]), Ste7 (a MAPK/ERK kinase [MEK]), and the redundant MAPK-related Fus3 and Kss1 enzymes. Another MAPK activation pathway is important for cell integrity and regulates cell wall construction. This cascade consists of Bck1 (a MEKK), the redundant Mkk1 and Mkk2 enzymes (MEKs), and Mpk1 (a MAPK). We exploited these two pathways to learn about the coordination and signal transmission fidelity of MAPK activation cascades. Two lines of evidence suggest that the activities of the mating and cell integrity pathways are coordinated during mating differentiation. First, cells deficient in Mpk1 are susceptible to lysis when they make a mating projection in response to pheromone. Second, Mpk1 activation during pheromone induction coincides with projection formation. The mechanism underlying this coordination is still unknown to us. Our working model is that projection formation generates a mobile second messenger for activation of the cell integrity pathway. Analysis of a STE7 mutation gave us some unanticipated but important insights into parameters important for fidelity of signal transmission. The Ste7 variant has a serine to proline substitution at position 368. Ste7-P368 has higher basal activity than the wild-type enzyme but still requires Ste11 for its function. Additionally, the proline substitution enables the variant to transmit the signal from mammalian Raf expressed in yeast. This novel activity suggests that Ste7-P368 is inherently more permissive than Ste7 in its interactions with MEKKs. Yet, Ste7-P368 cross function in the cell integrity pathway occurs only when it is highly overproduced or when Ste5 is missing. This behavior suggests that Ste5, which has been proposed to be a tether for the kinases in the mating pathway, contributes to Ste7 specificity and fidelity of signal transmission.     

Principle Component Analysis in Conjuction with Data Driven Methods for Sediment Load Prediction

This study investigates sediment load prediction and generalization from laboratory scale to field scale using principle component analysis (PCA) in conjunction with data driven methods of artificial neural networks (ANNs) and genetic algorithms (GAs). Five main dimensionless parameters for total load are identified by using PCA. These parameters are used in the input vector of ANN for predicting total sediment loads. In addition, nonlinear equations are constructed, based upon the same identified dimensionless parameters. The optimal values of exponents and constants of the equations are obtained by the GA method. The performance of the so-developed ANN and GA based methods is evaluated using laboratory and field data. Results show that the expert methods (ANN and GA), calibrated with laboratory data, are capable of predicting total sediment load in field, thus showing their transferability. In addition, this study shows that the expert methods are not transferable for suspended load, perhaps due to insufficient laboratory data. Yet, these methods are able to predict suspended load in field, when trained with respective field data.     

Solving a bi-objective cell formation problem with stochastic production quantities by a two-phase fuzzy linear programming approach

We propose a bi-objective cell formation problem with demand of products expressed in a number of probabilistic scenarios. To deal with the uncertain demand of products, a framework of two-stage stochastic programming model is presented. The proposed model considers minimizing the sum of the miscellaneous costs (machine constant cost, expected machine variable cost, cell fixed-charge cost, and expected intercell movement cost) and expected total cell loading variation. Because of conflicting objectives, we develop a two-phase fuzzy linear programming approach for solving bi-objective cell formation problem. To show the effectiveness of the proposed approach, numerical examples are solved and the results are compared with the two existing approaches in the literature. The computational results show that the proposed fuzzy method achieves lower objective functions as well as higher satisfaction degrees.     

Household Activity Pattern Problem with Autonomous Vehicles

Networks and Spatial Economics - The pace of changes in automating cars has sped up in the last few decades. Autonomous Vehicles (AVs) will dramatically change the future of transportation, and...     

Topic detection and tracking on heterogeneous information

Given the proliferation of social media and the abundance of news feeds, a substantial amount of real-time content is distributed through disparate sources, which makes it increasingly difficult to glean and distill useful information. Although combining heterogeneous sources for topic detection has gained attention from several research communities, most of them fail to consider the interaction among different sources and their intertwined temporal dynamics. To address this concern, we studied the dynamics of topics from heterogeneous sources by exploiting both their individual properties (including temporal features) and their inter-relationships. We first implemented a heterogeneous topic model that enables topic–topic correspondence between the sources by iteratively updating its topic–word distribution. To capture temporal dynamics, the topics are then correlated with a time-dependent function that can characterise its social response and popularity over time. We extensively evaluate the proposed approach and compare to the state-of-the-art techniques on heterogeneous collection. Experimental results demonstrate that our approach can significantly outperform the existing ones.     

A hybrid root transformation and decision on belief approach to monitor multiattribute Poisson processes

Most of industrial applications of statistical process control involve more than one quality characteristics to be monitored. These characteristics are usually correlated, causing challenges for the monitoring methods. These challenges are resolved using multivariate quality control charts that have been widely developed in recent years. Nonetheless, multivariate process monitoring methods encounter a problem when the quality characteristics are of the attribute type and follow nonnormal distributions such as multivariate binomial or multivariate Poisson. Since the data analysis in the latter case is not as easy as the normal case, more complexities are involved to monitor multiattribute processes. In this paper, a hybrid procedure is developed to monitor multiattribute correlated processes, in which number of defects in each characteristic is important. Two phases are proposed to design the monitoring scheme. In the first phase, the inherent skewness of multiattribute Poisson data is almost removed using a root transformation technique. In the second phase, a method based on the decision on belief concept is employed. The transformed data obtained from the first phase are implemented on the decision on belief (DOB) method. Finally, some simulation experiments are performed to compare the performances of the proposed methodology with the ones obtained using the Hotelling T ² and the MEWMA charts in terms of in-control and out-of-control average run length criteria. The simulation results show that the proposed methodology outperforms the other two methods.     

Decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for a Lorenz chaotic problem

This paper introduces a decoupled adaptive neuro-fuzzy (DANF) sliding mode control system for the chaos control problem in a system without precise system model information. It has on-line learning ability to deal with the parametric uncertainty and disturbance by adjusting the control parameters and no constrained conditions and prior knowledge of the controlled plant is required in the design process. Also, a decoupled adaptive sliding mode controller is developed to control the chaotic Lorenz system for comparison. Finally, the effectiveness of the proposed decoupled adaptive sliding mode and DANF sliding mode controllers are demonstrated by some simulated results.