Secure and efficient data transfer using spreading and assimilation in MANET

Mobile ad hoc Network (MANET) is a cluster of moveable devices connected through a wireless medium to design network with rapidly changing topologies due to mobility. MANETs are applicable in variety of innovative application scenarios where smart devices exchange data among each other. In this case, security of data is the major concern to provide dependable solution to users. This article presents a secure mechanism for data transfer where sender splits the data into fragments and receiver gets the actual data by assimilating the data fragments. We have presented an Enhanced Secured Lempel-Ziv-Welch (ES-LZW) algorithm that provides cryptographic operations for secure data transfer. In proposed model, we have utilized the disjoint paths to transfer the data fragments from sender side and assimilate these fragments at receiver to get the original data. The messages containing data fragments are compressed and encrypted as well. Our scheme ensures confidentiality, integrity, efficient memory utilization, and resilience against node compromising attacks. We have validated our work through extensive simulations in NS-2.35 using TCL and C language. Results prove that our scheme reduces memory consumption along with less encryption and decryption cost as compared to blowfish especially when plaintext has more repetitive data. We have also analyzed the impact of creating data fragments, fraction of communication compromised, and probability to compromise the data fragments by subverting intermediaries.     

Artificial neural network and dataset optimization for implementation of linear system models in resource-constrained embedded systems

On-board training of artificial neural network (ANN) is important in instances where real time data are required for model training. Provision of on-board intelligence enables the developed systems to self-recalibrate and enhances their efficiencies. In this work, investigations have been performed to determine optimized parameters of ANN model for linear systems. The performance parameters that is, model parameters, memory requirements, accuracy and processing time are chosen by considering the model to be installed on commercially available microcontrollers that have very limited on-board memory. Minimum data requirements for training ANN models of linear systems are also explored for better performance. All dataset ranges are normalized in order to exclude the effects of range differences. It is shown that for linear systems, 1–3–1 architecture produces best results against ≤100 data points when Bayesian Regularization (BR) training function is used along with Log Sigmoid Activation function. Simulations for 1–3–1 architecture are then performed for datasets having 10, 25, 50 and 100 data points. The results show that training with 25 data points produces over-all better performance than other datasets. A large dataset utilizes more training time and memory whereas a smaller dataset produces relatively lesser accuracy. The effects of clustered data and uniformly distributed data are also explored. It is found that total epochs in case of clustered data are significantly higher than uniformly distributed data. The combination of these optimized parameters that is, 1–3–1 architecture, with BR and Log function, for ≤100 data points can be used for the development and implementation of linear components or systems in resource-constrained embedded systems.     

New multi‐standard dual‐wideband and quad‐wideband asymmetric step impedance resonator filters with wide stop band restriction

New multi‐standard wide band filters with compact sizes are designed for wireless communication devices. The proposed structures realize dual‐wideband and quad‐wideband characteristics by using a new skew‐symmetrical coupled pair of asymmetric stepped impedance resonators, combined with other structures. The first and second dual‐wideband filters realize fractional bandwidths (FBW) of 43.2%/31.9% at the central frequencies (CF) of 1.875/1.63 GHz, and second bandwidths of 580 MHz/1.75 GHz at CF of 5.52/4.46 GHz, respectively. The proposed quad‐band filter realizes its first/second/third/fourth pass bands at CF 2.13/5.25/7.685/9.31 GHz with FBW of 46.0%/11.4%/4.6% and 5.4%, respectively. The wide pass bands are attributed to the mutual coupling of the modified ASIR resonators and their bandwidths are controllable by tuning relative parameters while the wide stop band performance is optimized by the novel interdigital cross coupled line structure and parallel uncoupled microstrip line structure. Moreover, the quad band is generated by introducing the novel defected rectangle structure. These multi‐standard filters are simulated, fabricated and measured, and measured results agree well with both simulated results and theory predictions. The good in‐band and out‐of‐band performances, the miniaturized sizes and simple structures of the proposed filters make them very promising for applications in future multi‐standard wireless communication.     

Application layer classification of Internet traffic using ensemble learning models

Accurate application layer classification of Internet traffic has been a necessary requirement for various regulatory, control, and operational purposes of Internet service provider (ISP). Due to the dynamic and ever evolving nature of Internet applications generating a diverse mixture of Internet traffic, it has been necessary to apply deep packet inspection (DPI) techniques for traffic classification. DPI methods offer accuracy but degrade overall network throughput and thus cause problems in ensuring quality of service (QoS) and maintaining service-level agreements. Moreover, Internet traffic is mostly end to end encrypted. This in turn limits the applicability of DPI techniques and renders them useless, unless the encryption tunnel is broken by the service provider which would risk violating user privacy. To address these trade-offs between classification accuracy, performance, and user privacy, we resort to machine learning (ML)-based algorithms. In this article, we apply three ensemble ML algorithms and report their performance metrics in the application layer classification of Internet traffic.     

Energy-efficient RRH-association and resource allocation in D2D enabled multi-tier 5G C-RAN

The device-to-device (D2D) enabled cloud radio access network (C-RAN) is considered as a promising network model which provides high data rate and energy efficiency. In this paper, we formulate a joint mode selection, subchannel assignment (SA), power allocation (PA) and remote radio head (RRH)-association problem in D2D enabled single carrier frequency division multiple access based C-RAN in the uplink. This problem is mixed-integer non-linear problem which is extremely difficult to solve in its original form. To solve this problem, we propose an iterative technique which solves this problem in two stages; mode selection stage and joint SA, PA and RRH-association (SAPARA) stage. For mode selection, a link quality based technique is presented while, for joint SAPARA, we developed an iterative technique that solves this this problem in three steps such that SA and PA are carried out in the first and second step, respectively, while RRH-association is performed in the third step. Our results show the efficiency of the presented techniques.

     

Performance evaluation of broadcasting strategies in cognitive radio networks

Broadcasting is an important phenomenon, because it serves as simplest mode of communication in a network, via which each node disseminates information to their neighboring nodes simultaneously. Broadcasting is widely used in various kind of networks, such as wireless sensor networks, wireless networks, and ad-hoc networks. Similarly, in cognitive radio networks (CRNs), broadcasting is also used to perform many tasks including neighbor discovery, spectrum mobility, spectrum sharing, and dissemination of message throughout the network. The traditional approach that has been used as broadcasting in CRNs is simple flooding in which a message is disseminated in the network without any strategy check. Simple flooding can cause major setbacks in the network, such as excessive redundant rebroadcasts, and collision drops which collectively are termed as broadcast storm problem. To reduce the effects of broadcast storm problem in wireless networks, we propose and compare four broadcasting strategies for cognitive radio networks in this paper. These four strategies are: (1) probability based, (2) counter based, (3) distance based, and (4) area based. Extensive NS-2 based simulations are carried out on different threshold values for each broadcasting strategy. After experimental evaluation, it is demonstrated that counter based broadcasting surpasses other broadcasting strategies by achieving maximum delivery ratio of 60% and by decreasing redundant rebroadcasts and collision drops up to 44 and 37% respectively.

     

A Simplified Model Predictive Control of Four-Leg Two-Level Inverter

Abstract

This article proposes current control strategy for four-leg two-level voltage source inverters (VSI). This strategy is based on model predictive control (MPC) approach and presents its comparison with finite control set (FCS) model technique. Besides providing a fast dynamic response, proposed technique replaces complex modulation stage and PI controller being used in earlier classical control techniques. Moreover, the proposed methodology selects the switching sequence which reduces tracking error between output and applied reference currents using cost function optimization. This shows the improvement of system response like FCS model technique. However, FCS model technique results in calculation stress and computation burden; resulting computational delay and extra power consumption by the processor. This problem is solved with the help of proposed single predictive technique which makes the inverter more suitable for large time horizon operations and small sampling time instants. The computational delay is reduced up to 6% compared to the case of FCS-MPC controlled inverter. Performance of proposed simplified technique is analyzed and compared with FCS-MPC controlled system with the help of different types of reference signals. This work will boost the industrial application of four-leg two-level VSI by increasing dynamic response and removing complex modulation stage.     

Preparation and Characterization of an Efficient Nano-Inorganic Composite of CuO/ZnO/Al<Subscript>2</Subscript>O<Subscript>3</Subscript> for the Catalytic Amination of Aryl Halides in Aqueous Conditions

In this research, an efficient recyclable nano-inorganic composite of CuO/ZnO/Al₂O₃ (CuO/ZnO/Al₂O₃ nanocatalyst) is prepared, characterized and used for the amination of aryl halides with aqueous ammonia in water. The catalyst was prepared by co-precipitation method and characterized by various techniques such as the X-ray diffraction, scanning electron microscope, energy dispersive spectroscopy, and brunauer–Emmett–Teller surface area analysis. Various aryl halides reacted with aqueous ammonia and corresponding products were obtained in high yields. CuO/ZnO/Al₂O₃ nanocatalyst as an efficient stable catalyst is recyclable up to five consecutive runs by simple filtration.

Graphical Abstract

An efficient recyclable nano-inorganic composite of CuO/ZnO/Al₂O₃ (CuO/ZnO-Al₂O₃ nanocatalyst) is prepared, characterized and used for the amination of aryl halides with aqueous ammonia in water.

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Ion exchange properties of carboxylated bagasse

Bagasse fibers were chemically modified with the aim to prepare lignocellulosic materials that have the ability to remove heavy metal ions from waste water. Three different reactions were used for the modification: etherification using monochloroacetic acid, esterification using succinic anhydride, and oxidation using sodium periodate and sodium chlorite. Bagasse was crosslinked using epichlorohydrin before chemical modification to avoid loss of its constituents during the chemical modification or application. The structure of the prepared derivatives was proofed using Fourier transform infrared and chemical methods. The ability of the prepared bagasse cation exchangers to adsorb heavy metal ions (Cu²⁺, Ni²⁺, Cr³⁺, Fe³⁺), on a separate basis or in a mixture of them, at different metal ion concentration was tested. Thermal stability of the different bagasse derivatives was studied using thermogravimetric analysis. © 2006 Wiley Periodicals, Inc. J Appl Polym Sci 102: 1399–1404, 2006     

Crystal structure transformation of TiO2 in presence of Fe2O3 and NiO in air atmosphere

TiO₂ is produced and marketed in two main grades viz. anatase and rutile. Both anatase and rutile have their own pigmentry properties and hence cannot be substituted by each other. Pure anatase on heating at higher temperatures undergoes crystallographic rearrangement to form rutile. This transformation in presence of NiO and Fe₂O₃ under air atmosphere was studied using XRD and SEM. The transformation temperature was found to be reduced much in presence of NiO and Fe₂O₃ and the extent of lowering was higher for NiO than Fe₂O₃. The activation energy for this transformation was also calculated. The method of preparation had major influence on the transformation.