3HA: Hybrid Hole Healing Algorithm in a Wireless Sensor Networks

In wireless sensor networks (WSNs), the appearance of coverage holes over a large target field is mostly possible. Those holes reduce network performance and may affect the network efficiency. Several approaches were proposed to heal coverage holes in WSNs, but they still suffer from some weaknesses. In this paper we suggest a distributed algorithm, named hybrid hole healing algorithm (3HA), to find the minimum effective patching positions to deploy additional nodes to cover the holes. A hole manager node of each hole is responsible for operating the 3HA algorithm which requires two phases. The first phase finds all candidate patching positions using a Voronoi diagram. It takes all Voronoi vertices within the hole as the initial patching positions list. The second phase reduces as much as possible this list based on integer linear programming and on a probabilistic sensor model. The 3HA algorithm repeats the above phases in rounds, until all Voronoi vertices are covered. Simulation results show that our solution offers a high coverage ratio for various forms and sizes of holes and reduces the number of additional sensors when compared to some algorithms like the Perimeter-based, the Delaunay triangulation-based, the Voronoi-based, and the Trees-based coverage hole healing methods.

     

A New Criteria Decision Applied on the Primary Synchronization in the DS-WCDMA Interface of the UMTS System

In DS-WCDMA mobile systems such the UMTS, asynchronous cell site operation,assigning different long spreading code to each cell, yields the advantageof flexible system deployment. We can design an indoor system basedon an outdoor one. However, in general, much longer search time isrequired in asynchronous operation than in synchronous. This paperproposes three techniques to take decisions about synchronizationbased on observation of correlated signals. Classical decision criterialike maximum and threshold criterion are presented. A new decisioncriteria that we call Threshold&Max combined decision criteriais analyzed. The results of this new introduced technique is comparedwith the classical ones.     

Electrical properties of tri-n-butyl tin acrylate-methylmethacrylate copolymers

The copolymerization of tri-n-butyl tin acrylate (TBTA) with methylmethacrylate (MMA) has been investigated in dioxane. The composition of these copolymers was determined quantitatively by ¹H nuclear magnetic resonance (NMR) spectroscopy. The tin contents were estimated by gravimetric as well as thermogravimetric techniques (TGA). The reactivity ratio of such copolymers was estimated by application of the Kelen-Tudos method. The dielectric properties of the copolymers have been studied over a frequency range of 100–50 kHz at different temperatures from 20 to 70°C. The electrical conductivity for such copolymers was also measured. The results are interpreted in terms of the tin content of the copolymers.     

Virtual-water content of agricultural production and food trade balance of Tunisia

This article is devoted to the assessment of Tunisian agricultural production and food trade balance water-equivalent. A linear regression model relating annual rainfall to crop yields is developed to estimate the agricultural production water-equivalent. Its implementation is based on national data for crop and animal production, leading to food demand water-equivalent quantification. Results highlight the relationship between agricultural and water policies and provide a picture of food security in the country in relation to local agricultural production, and to virtual water fluxes related to foodstuffs trade balance.     

Data Security Model for Cloud Computing

From the perspective of data security, which has always been an important aspect of quality of service, cloud computing focuses a new challenging security threats. Therefore, a data security model must solve the most challenges of cloud computing security. The proposed data security model provides a single default gateway as a platform. It used to secure sensitive user data across multiple public and private cloud applications, including Salesforce, Chatter, Gmail, and Amazon Web Services, without influencing functionality or performance. Default gateway platform encrypts sensitive data automatically in a real time before sending to the cloud storage without breaking cloud application. It did not effect on user functionality and visibility. If an unauthorized person gets data from cloud storage, he only sees encrypted data. If authorized person accesses successfully in his cloud, the data is decrypted in real time for your use. The default gateway platform must contain strong and fast encryption algorithm, file integrity, malware detection, firewall, tokenization and more. This paper interested about authentication, stronger and faster encryption algorithm, and file integrity.     

First-Principle Study of Electronic and Half-Metallic Ferromagnetic Properties of Vanadium (V)-Doped Cubic BP and InP

In this study, we use the first-principle calculations of density functional theory with gradient generalized approximation of Wu–Cohen to investigate the doping effect of vanadium impurity on structural, electronic and magnetic properties of In_1?x V _x P and B_1?x V _x P alloys at various concentrations x = 0.0625, 0.125 and 0.25. Owing to the metallic nature of majority spin and semiconducting minority spin, the In_1?x V _x P compounds exhibit a half-metallic character with total magnetic moments of 2 μ _B, while the B_1?x V _x P has metallic nature for all concentrations. The results of exchange parameters revealed that exchange coupling between vanadium atoms and the conduction band is ferromagnetic, confirming the magnetic feature of In_1?x V _x P and B_1?x V _x P. From our findings, we have predicted that the In_1?x V _x P alloys seem to be potential materials for spintronics.     

Optimal Deep Learning Model Enabled Secure UAV Classification forIndustry 4.0

Emerging technologies such as edge computing, Internet of Things (IoT), 5G networks, big data, Artificial Intelligence (AI), and Unmanned Aerial Vehicles (UAVs) empower, Industry 4.0, with a progressive production methodology that shows attention to the interaction between machine and human beings. In the literature, various authors have focused on resolving security problems in UAV communication to provide safety for vital applications. The current research article presents a Circle Search Optimization with Deep Learning Enabled Secure UAV Classification (CSODL-SUAVC) model for Industry 4.0 environment. The suggested CSODL-SUAVC methodology is aimed at accomplishing two core objectives such as secure communication via image steganography and image classification. Primarily, the proposed CSODL-SUAVC method involves the following methods such as Multi-Level Discrete Wavelet Transformation (ML-DWT), CSO-related Optimal Pixel Selection (CSO-OPS), and signcryption-based encryption. The proposed model deploys the CSO-OPS technique to select the optimal pixel points in cover images. The secret images, encrypted by signcryption technique, are embedded into cover images. Besides, the image classification process includes three components namely, Super-Resolution using Convolution Neural Network (SRCNN), Adam optimizer, and softmax classifier. The integration of the CSO-OPS algorithm and Adam optimizer helps in achieving the maximum performance upon UAV communication. The proposed CSODL-SUAVC model was experimentally validated using benchmark datasets and the outcomes were evaluated under distinct aspects. The simulation outcomes established the supreme better performance of the CSODL-SUAVC model over recent approaches.