BSLPSDN: Base station location privacy via software-defined networking (SDN) in wireless sensor networks

Base station's location privacy in a wireless sensor network (WSN) is critical for information security and operational availability of the network. A key part of securing the base station from potential compromise is to secure the information about its physical location. This paper proposes a technique called base station location privacy via software-defined networking (SDN) in wireless sensor networks (BSLPSDN). The inspiration comes from the architecture of SDN, where the control plane is separated from the data plane, and where control plane decides the policy for the data plane. BSLPSDN uses three categories of nodes, namely, a main controller to instruct the overall operations, a dedicated node to buffer and forward data, and lastly, a common node to sense and forward the packet. We employ three kinds of nodes to collaborate and achieve stealth for the base station and thus protecting it against the traffic-analysis attacks. Different traits of the WSN including energy status and traffic density can actively be monitored by BSLPSDN, which positively affects the energy goals, expected life of the network, load on common nodes, and the possibility of creating diversion in the wake of an attack on the base station. We incorporated multiple experiments to analyze and evaluate the performance of our proposed algorithm. We use single controller with multiple sensor nodes and multiple controllers with multiple sensor nodes to show the level of anonymity of BS. Experiments show that providing BS anonymity via multiple controllers is the best method both in terms of energy and privacy.     

The Double Edge Sword Based Distributed Executor Service

Scalability is one of the most important quality attribute of software-intensive systems, because it maintains an effective performance parallel to the large fluctuating and sometimes unpredictable workload. In order to achieve scalability, thread pool system (TPS) (which is also known as executor service) has been used extensively as a middleware service in software-intensive systems. TPS optimization is a challenging problem that determines the optimal size of thread pool dynamically on runtime. In case of distributed-TPS (DTPS), another issue is the load balancing b/w available set of TPSs running at backend servers. Existing DTPSs are overloaded either due to an inappropriate TPS optimization strategy at backend servers or improper load balancing scheme that cannot quickly recover an overload. Consequently, the performance of software-intensive system is suffered. Thus, in this paper, we propose a new DTPS that follows the collaborative round robin load balancing that has the effect of a double-edge sword. On the one hand, it effectively performs the load balancing (in case of overload situation) among available TPSs by a fast overload recovery procedure that decelerates the load on the overloaded TPSs up to their capacities and shifts the remaining load towards other gracefully running TPSs. And on the other hand, its robust load deceleration technique which is applied to an overloaded TPS sets an appropriate upper bound of thread pool size, because the pool size in each TPS is kept equal to the request rate on it, hence dynamically optimizes TPS. We evaluated the results of the proposed system against state of the art DTPSs by a client-server based simulator and found that our system outperformed by sustaining smaller response times.     

Query-oriented topical influential users detection for top-k trending topics in twitter

Applied Intelligence - Online Social Networks (OSNs) have become inevitable for any new methodology both for viral promoting applications and instructing the creation of inciting information and...     

Green synthesised zinc oxide nanostructures through Periploca aphylla extract shows tremendous antibacterial potential against multidrug resistant pathogens

To grapple with multidrug resistant bacterial infections, implementations of antibacterial nanomedicines have gained prime attention of the researchers across the globe. Nowadays, zinc oxide (ZnO) at nano‐scale has emerged as a promising antibacterial therapeutic agent. Keeping this in view, ZnO nanostructures (ZnO‐NS) have been synthesised through reduction by P. aphylla aqueous extract without the utilisation of any acid or base. Structural examinations via scanning electron microscopy (SEM) and X‐ray diffraction have revealed pure phase morphology with highly homogenised average particle size of 18 nm. SEM findings were further supplemented by transmission electron microscopy examinations. The characteristic Zn–O peak has been observed around 363 nm using ultra‐violet–visible spectroscopy. Fourier‐transform infrared spectroscopy examination has also confirmed the formation of ZnO‐NS through detection of Zn–O bond vibration frequencies. To check the superior antibacterial activity of ZnO‐NS, the authors'' team has performed disc diffusion assay and colony forming unit testing against multidrug resistant E. coli, S. marcescens and E. cloacae. Furthermore, protein kinase inhibition assay and cytotoxicity examinations have revealed that green fabricated ZnO‐NS are non‐hazardous, economical, environmental friendly and possess tremendous potential to treat lethal infections caused by multidrug resistant pathogens.Inspec keywords: nanomedicine, zinc compounds, II‐VI semiconductors, wide band gap semiconductors, nanoparticles, scanning electron microscopy, X‐ray diffraction, antibacterial activity, transmission electron microscopy, particle size, Fourier transform infrared spectra, ultraviolet spectra, visible spectra, enzymes, biochemistry, molecular biophysics, microorganisms, drugs, toxicology, bonds (chemical), semiconductor growth, nanofabrication, vibrational modesOther keywords: green synthesised zinc oxide nanostructures, Periploca aphylla extract, antibacterial potential, multidrug resistant pathogens, multidrug resistant bacterial infections, antibacterial nanomedicines, P. aphylla aqueous extract, structural examinations, scanning electron microscopy, X‐ray diffraction, pure phase morphology, homogenised average particle size, SEM, transmission electron microscopy, Fourier‐transform infrared spectroscopy, bond vibration frequency, antibacterial activity, disc diffusion assay, colony forming unit testing, S. marcescens, E. cloacae, E. coli, ultraviolet‐visible spectroscopy, protein kinase inhibition assay, cytotoxicity, lethal infections, ZnO     

Autonomous Magnetic Microrobots by Navigating Gates for Multiple Biomolecules Delivery

The precise delivery of biofunctionalized matters is of great interest from the fundamental and applied viewpoints. In spite of significant progress achieved during the last decade, a parallel and automated isolation and manipulation of rare analyte, and their simultaneous on‐chip separation and trapping, still remain challenging. Here, a universal micromagnet junction for self‐navigating gates of microrobotic particles to deliver the biomolecules to specific sites using a remote magnetic field is described. In the proposed concept, the nonmagnetic gap between the lithographically defined donor and acceptor micromagnets creates a crucial energy barrier to restrict particle gating. It is shown that by carefully designing the geometry of the junctions, it becomes possible to deliver multiple protein‐functionalized carriers in high resolution, as well as MCF‐7 and THP‐1 cells from the mixture, with high fidelity and trap them in individual apartments. Integration of such junctions with magnetophoretic circuitry elements could lead to novel platforms without retrieving for the synchronous digital manipulation of particles/biomolecules in microfluidic multiplex arrays for next‐generation biochips.     

Biofilm reduction,cell proliferation,anthelmintic and cytotoxicity effect of green synthesised silver nanoparticle using Artemisia vulgaris extract

Infectious diseases are caused by etiological agents. Nanotechnology has been used to minimise the effect of clinical pathogens which have resistance to antibiotics. In current research synthesis, characterisation and biological activities of green synthesised nanoparticles using Artemisia vulgaris extract have been done. The characterisation of AgNPs was carried out using Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, and scanning electron microscopy. Anti‐biofilm, cell viability, antibacterial, brine shrimp lethality, and deoxyribonucleic acid protection effects have been screened. UV‐Vis spectra showed the absorption peak of synthesised nanoparticles at 400 nm. FT‐IR indicated the involvement of the functional group in the preparation of AgNPs. SEM showed the spherical shape of AgNPs with 30 nm diameter. Biological screening results revealed the antibacterial effect against clinical bacterial pathogens. Biofilm reduction and cell viability assay also supported the antibacterial effect. Cytotoxicity effect was recorded as 100% at 200 μg/ml through brine shrimp lethality assay. Protein kinase inhibition zones recorded for AgNPs (16 mm bald) compared with A. vulgaris extract (11 mm bald). It has been concluded that green synthesised AgNPs are more effective against infectious pathogens and could be used as a potential source for therapeutic drugs.Inspec keywords: cellular biophysics, toxicology, silver, nanoparticles, nanomedicine, diseases, microorganisms, ultraviolet spectra, visible spectra, Fourier transform infrared spectra, enzymes, molecular biophysicsOther keywords: biofilm reduction, cell proliferation, anthelmintic effect, cytotoxicity effect, green synthesised silver nanoparticle, Artemisia vulgaris extract, infectious diseases, aetiological agents, Fourier transform infrared spectroscopy, UV‐Vis spectrophotometry, scanning electron microscopy, SEM, antibiofilm, cell viability, brine shrimp lethality, deoxyribonucleic acid protection effects, AgNP, cytotoxicity, protein kinase inhibition zones, therapeutic drugs     

The Role of Deep Learning in Parking Space Identification andPredictionSystems

In today’s smart city transportation, traffic congestion is a vexing issue, and vehicles seeking parking spaces have been identified as one of the causes leading to approximately 40% of traffic congestion. Identifying parking spaces alone is insufficient because an identified available parking space may have been taken by another vehicle when it arrives, resulting in the driver’s frustration and aggravating traffic jams while searching for another parking space. This explains the need to predict the availability of parking spaces. Recently, deep learning (DL) has been shown to facilitate drivers to find parking spaces efficiently, leading to a promising performance enhancement in parking identification and prediction systems. However, no work reviews DL approaches applied to solve parking identification and prediction problems. Inspired by this gap, the purpose of this work is to investigate, highlight, and report on recent advances in DL approaches applied to predict and identify the availability of parking spaces. A taxonomy of DL-based parking identification and prediction systems is established as a methodology by classifying and categorizing existing literature, and by doing so, the salient and supportive features of different DL techniques for providing parking solutions are presented. Moreover, several open research challenges are outlined. This work identifies that there are various DL architectures, datasets, and performance measures used to address parking identification and prediction problems. Moreover, there are some open-source implementations available that can be used directly either to extend existing works or explore a new domain. This is the first short survey article that focuses on the use of DL-based techniques in parking identification and prediction systems for smart cities. This study concludes that although the deployment of DL in parking identification and prediction systems provides various benefits, the convergence of these two types of systems and DL brings about new issues that must be resolved in the near future.     

Recent Advances in Rolling 2D TMDs Nanosheets into 1D TMDs Nanotubes/Nanoscrolls

Transition metal dichalcogenides (TMDs) van der Waals (vdW) 1D heterostructures are recently synthesized from 2D nanosheets, which open up new opportunities for potential applications in electronic and optoelectronic devices. The most recent and promising strategies in regards to forming 1D TMDs nanotubes (NTs) or nanoscrolls (NSs) in this review article as well as their heterostructures that are produced from 2D TMDs are summarized. In order to improve the functionality of ultrathin 1D TMDs that are coaxially combined with boron nitride nanotubes and single-walled carbon nanotubes. 1D heterostructured devices perform better than 2D TMD nanosheets when the two devices are compared. The photovoltaic effect in WS₂ or MoS₂ NTs without a junction may exceed the Shockley–Queisser limit for the above-band-gap photovoltage generation. Photoelectrochemical hydrogen evolution is accelerated when monolayer WS₂ or MoS₂ NSs are incorporated into a heterojunction. In addition, the photovoltaic performance of the WSe₂/MoS₂ NSs junction is superior to that of the performance of MoS₂ NSs. The summary of the current research about 1D TMDs can be used in a variety of ways, which assists in the development of new types of nanoscale optoelectronic devices. Finally, it also summarizes the current challenges and prospects.     

Royal Crown Shaped Polarization Insensitive Perfect Metamaterial Absorber for C-, X-, and Ku-Band Applications

This study proposed a new royal crown-shaped polarisation insensitive double negative triple band microwave range electromagnetic metamaterial absorber (MA). The primary purpose of this study is to utilise the exotic characteristics of this perfect metamaterial absorber (PMA) for microwave wireless communications. The fundamental unit cell of the proposed MA consists of two pentagonal-shaped resonators and two inverse C-shaped metallic components surrounded by a split ring resonator (SRR). The bottom thin copper deposit and upper metallic resonator surface are disjoined by an FR-4 dielectric substrate with 1.6 mm thickness. The CST MW studio, a high-frequency electromagnetic simulator has been deployed for numerical simulation of the unit cell in the frequency range of 4 to 14 GHz. In the TE mode, the offered MA structure demonstrated three different absorption peaks at 6.85 GHz (C-band), 8.87 GHz (X-band), and 12.03 GHz (Ku-band), with 96.82%, 99.24%, and 99.43% absorptivity, respectively. The electric field, magnetic field, and surface current distribution were analysed using Maxwell’s-Curl equations, whereas the angle sensitivity was investigated to comprehend the absorption mechanism of the proposed absorber. The numerical results were verified using the Ansys HFSS (high-frequency structure simulator) and ADS (advanced design system) for equivalent circuit models. Moreover, the proposed MA is polarisation and incident angle independent. Hence, the application of this MA can be extended to a great extent, including airborne radar applications, defence, and stealth-coating technology.     

A review of wave-net identical learning &; filling-in in a decomposition space of (JPG-JPEG) sampled images

Continuous flow to send images via encrypted wireless channels may reduce the efficiency of transmission. This is due to the damage or loss of the numerous macro-blocks from these images. Therefore, it is difficult to rebuild these patches from the point of reception. Many algorithms have been proposed in the past decade, particularly error concealment (EC) algorithms. In this paper, we focus on the algorithms that have high efficiency to fill-in the corrupted patches. On the other hand, we also present a new way of detecting the horizontal and vertical gradients especially, in the un-smooth patches. This improves the edge detector filter. Ultimately, a novel scheme for vertical and horizontal interpolation between the corrupted pixels and the non-corrupted adjacent pixels is achieved by improving the efficiency of filling-in. We used a new technique known as the wave-net model. This model combines the wavelet with the neural network classifier (NNC). The neural network was trained in advance to reduce the error extent for the pixels that may occur in the error. The experimental results were convincing and close to the desired. The proposed method is able to enhance image quality in term of both visual perception and the blurriness effects (BE).