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.

     

Using Markov Learning Utilization Model for Resource Allocation in Cloud of Thing Network

Wireless Personal Communications - The integration of the Internet of Things (IoT) and cloud environment has led to the creation of Cloud of Things, which has given rise to new challenges in IoT...     

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.     

Additive manufacturing of monolithic supercapacitors with biopolymer separator

Journal of Applied Electrochemistry - In this paper, additive layer-by-layer fabrication of a fully screen printed monolithic supercapacitor exhibiting performance comparable with supercapacitors...     

Removal of melanoidin from molasses spent wash using fly ash-clay adsorbents

Removal of melanoidin pigment from molasses spent wash was investigated using a new adsorbent. Solid adsorbents were fabricated from charcoal fly ash and clay. The effect of various molasses concentration (6 to 12 g/l) on removal efficiency was studied. The obtained results revealed that maximum removal efficiency of 82% was achieved at the molasses concentration of 6 g/l and contact time of 7 h. The saturated porous adsorbents were regenerated and reused to conduct similar experiments. The achieved data showed that more than 90% of the capacity of the fresh adsorbent was recovered after regeneration. Various adsorption isotherms of Langmuir, Freundlich, Temkin and Harkins-Jura were applied to interpret the obtained experimental data. The obtained results revealed that the sorption data were well described by the Harkins-Jura model. Also, various kinetic models of pseudo-first order, pseudo-second order, Elovich and intra-particle diffusion were used to predict the characteristic parameters which are useful in process design. It was concluded that the best fit was obtained with pseudo-second order kinetic model at low molasses concentrations.     

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.     

Fabrication and Characterization of Functionally Graded Al/SiCp Composites Produced by Remelting and Sedimentation Process

A new process termed here as remelting and sedimentation (RAS) was developed to produce functionally graded Al/SiC composites with a smooth concentration gradient of SiC particles along the height of samples, as opposed to a step change. For this purpose, first settling velocities of different-sized SiC particles in aluminum A356 melt were measured, and the results exhibited a reasonably good agreement with those predicted via the modified Stokes law. Then slices of particulate Al/SiC composites with different SiC contents of 5, 10, 15, and 20 vol.% were stacked in a cast iron mold and heated at 650 °C resulting in remelting and unification of the different composite parts. Considering the preliminary settling experiments, the composite slurry was held at this temperature for three different times to investigate the optimum holding time for obtaining a smooth gradient of SiC concentration along the height of the sample. After quenching, the samples were sectioned and subjected to metallographic studies and hardness measurements. The results confirmed that holding the melt for 60 s provides sufficient settling and redistribution of SiC particles and results in successful production of a functionally graded material.     

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.     

Enhancing performance of failure-prone clusters by adaptive provisioning of cloud resources

In this paper, we investigate Cloud computing resource provisioning to extend the computing capacity of local clusters in the presence of failures. We consider three steps in the resource provisioning including resource brokering, dispatch sequences, and scheduling. The proposed brokering strategy is based on the stochastic analysis of routing in distributed parallel queues and takes into account the response time of the Cloud provider and the local cluster while considering computing cost of both sides. Moreover, we propose dispatching with probabilistic and deterministic sequences to redirect requests to the resource providers. We also incorporate checkpointing in some well-known scheduling algorithms to provide a fault-tolerant environment. We propose two cost-aware and failure-aware provisioning policies that can be utilized by an organization that operates a cluster managed by virtual machine technology, and seeks to use resources from a public Cloud provider. Simulation results demonstrate that the proposed policies improve the response time of users’ requests by a factor of 4.10 under a moderate load with a limited cost on a public Cloud.     

Boundary query solution using convex hull algorithm

An important issue in database (DB) systems is responding to different users’ queries in an acceptable time. To do this, we should define different queries based on users’ real needs and we should consider suitable solutions. In this article, we express a new query called ‘boundary query’ which is used for achieving an overall view of a subject in the DB. This query does not return all query answers but it returns boundary values that cover all answers for the related query. In this article, we map a DB environment to a vector space based on necessary attributes. Then we implement the proposed method, and based on the results, we observe that the proposed method's run time is acceptable for huge DBs.