Mobile, Secure, and Programmable Networking

Frontmatter

Deep Generative Models for Image Generation: A Practical Comparison Between Variational Autoencoders and Generative Adversarial Networks

Abstract

Deep Learning models can achieve impressive performance in supervised learning but not for unsupervised one. In image generation problem for example, we have no concrete target vector. Generative models have been proven useful for solving this kind of issues. In this paper, we will compare two types of generative models: Generative Adversarial Networks (GANs) and Variational Autoencoders (VAEs). We apply those methods to different data sets to point out their differences and see their capabilities and limits as well. We find that, while VAEs are easier and faster to train, their results are in general more blurry than the images generated by GANs. These last are more realistic but noisy.

Mohamed El-Kaddoury, Abdelhak Mahmoudi, Mohammed Majid Himmi

A Game Stochastic Model for Data Dissemination Between Ferries and Cluster Head in Delay Tolerant Networks Routing Hierarchical Topology

Abstract

Ferries play an important role in data dissemination in delay tolerant networks (DTN). The DTN routing hierarchical topology (DRHT) includes three fundamental concepts: ferries messages, ferries routes and clusters. We use ferries in DRHT to improve network performance. In the DRHT, the intra-cluster routing is managed by the cluster head, while the inter-cluster routing is managed by the ferries messages. In this work, we analyse the behavior of ferries’ problem of data dissemination in the DRHT. More specifically, in order to analyse the inter-cluster communication in the DRHT, we formulate game stochastic which models the behavior of the ferries.

El Arbi Abdellaoui Alaoui, Mustapha El Moudden, Khalid Nassiri, Said Agoujil

Delay-Bandwidth Optimization Method Based on Ant Colony Algorithm Applied to Transport Network Using SDN Paradigm

Abstract

New Transport Technologies are being developed to support the fast growing of IP based services like video and voice, mobile broadband and Cloud. Multiprotocol Label Switching Transport Profile (MPLS-TP) is an emerging transport technology aimed to satisfy requirements of these new technologies. It is characterized by its strong Operation, Administration and Maintenance (OAM) toolbox. MPLS-TP can take advantage of Software Defined Network (SDN) which is an emerging network paradigm allowing network virtualization and offering flexibility for more innovations. Our work is based on existing Ant colony Algorithm which will be modified to fulfil MPLS-TP’s requirements which are different from IP/MPLS technology. The present paper shows how we can take benefit from classical optimization methods in order to optimize resources inside a SDN based transport network using modified Ant Colony Algorithm.

Mounir Azizi, Redouane Benaini, Mouad Ben Mamoun

CCN Context-Naming for Efficient Context-Aware Service Discovery in IoT

Abstract

The emergence of the Internet of Things (IoT) offers great potential for the development of new services and applications connecting the physical world to the virtual world. Due to the rapid growth and heterogeneity of connected objects, it is difficult for users to acquire precise knowledge about IoT services that can interact with. In order to effectively help end-users acquire services and data generated in the IoT, search and discovery mechanisms are crucial.

In this paper, we propose an efficient context-aware service discovery mechanism based on Content Centric Networking (CCN) naming feature which creates high level abstraction access to IoT devices. This solution provides a location-identity binding where content can be accessed quickly and accurately.

Mohamed Labbi, Mohammed Benabdellah

Efficient Distributed Authentication and Access Control System Management for Internet of Things Using Blockchain

Abstract

Internet of things (IoT) enables a huge network of connected devices inter-working and collaborating to provide relevant services and applications. This technology entered the market and is expected to grow in the upcoming years, as the critical questions related to the management and communication security continue to be challenging research problems. Current solutions of access control system management that enables communication between devices depend mainly on the use of digital certificates for authentication. However, such an approach imposes significant overhead on IoT devices since it is computationally demanding and requires validation of the certificate within a limited period. In addition, relying on a central node for deciding on issuing and revoking certificates introduces a single point of failure and could even risk the safety of personal information or physical damages related to IoT services. In this paper, we propose a new distributed authentication and access control system management for IoT by the use of Blockchain technology to keep track of the certificate of each IoT device (valid or revoked) in distributed and immutable records. In essence we replace certificate verification with a lightweight blockchain-based authentication approach. In addition, we propose a fully distributed IoT admission/revocation scheme. We show that our scheme could alleviate the computation overhead and enhance the response time while improving the overall system security.

Hadjer Benhadj Djilali, Djamel Tandjaoui

Design of a New Patch Antenna Using EBG Structures and Superstarte Operating in the Ku-Band for 5G Cellular Networks

Abstract

This paper describes a novel patch antenna operating in Ku-band at 17 GHz for the upcoming fifth generation (5G) cellular communication. A dielectric layer of an Electromagnetic Band Gap (EBG) superstrate is applied above the antenna and four metallic EBGs structures are added for further directivity improvement. AL-shape slot generates the resonance and enhances the antenna performance. The overall size of the antenna is 20 * 20 mm. The antenna is linearly polarized in the band 17 GHz with an impedance bandwidth from 16.59 GHz to 18.19 GHz for |S₁₁|< −10 dB. The proposed antenna using EBGs has a directivity of greater than 9 dBi over frequency band. This proposed antenna is designed with CST Microwave Studio. The simulated results show that the directivity of the antenna is significantly improved. Due to the remarkable performance of the proposed antenna, it can be suitable to be used in 5G network communication.

Sanae Dellaoui, Adel Asselman, Saida Ahyoud, Abdelmoumen Kaabal, Loubna Rmili, Mustapha Elhalaoui

A Data-Filtering Approach for Large-Scale Integrated RFID and Sensor Networks

Abstract

Radio-Frequency identification, referred as RFID, is a technology for storing and retrieving data remotely using radio-labeled tags. Tags are small objects, and can be pasted or incorporated into objects and products or even implanted in living organisms. In the last years, several researches have focused on how to take benefit from this technology to build performing rechargeable sensor networks. The fusion of both technologies can extend widely the network’s lifetime and improve it performances since radio communications are only performed between readers and not between sensor tags. However, the RFID sensor networks present some drawbacks due to the random deployment especially for large scale systems, which can disturb the system’s performances and cause issues such as data duplication, and medium access control (MAC) collisions. In this paper, we deal especially with the redundancy problem by proposing an algorithm that avoids a priori the transmission of duplicated data before sending it into the network. Our approach can be considered as proactive since it predicts duplication by planning a first network discovery phase. Our scheme showed good performances in terms of latency, packet delivery, and computational cost.

Mourad Ouadou, Hajar Sahbani, Ouadoudi Zytoune, Mohamed Ouadou

High Gain Metallic Electromagnetic Band Gap Antenna for WLAN Applications

Abstract

In this paper we presented a Metallic electromagnetic band gap antenna operated on the frequency band 5 GHz which it can be suitable for wireless Local Area Network (WLAN). The proposed antenna contains vertical and horizontal M-EBG printed on a FR4 substrate fed by a 50 Ω feed line. The results exhibit that the proposed antenna shows better performances with M-EBG structures than without M-EBG structures. The antenna design parameters have been optimized by High Frequency Structural Simulator (HFSS), the simulated results shows that the gain of the proposed antenna with metallic electromagnetic band gap had improved and reaches more than 11 dB, the main purpose of this work is to validate the importance and the influence of Metallic electromagnetic band gap.

Loubna Rmili, Adel Asselman, Abdelmoumen Kaabal, Sanae Dellaoui, Mustapha El Halaoui

Toward Design of Advanced System-on-Chip Architecture for Mobile Computing Devices

Abstract

In this paper, we present the design of an advanced system-on-chip architecture for mobile computing devices. The presented architecture facilitates connectivity to an ARM compatible host processor, high-speed intellectual property (IP) cores and slower peripherals using industry standard advanced microcontroller bus architecture. The system consist of standard set of peripherals, such as clock generator, real time clock, a watchdog timer, an interrupt controller, programmable I/O, I²C Host, SPI master, UARTs, trusted platform module, NAND flash controller and USB controllers. Third party 2D/3D graphics engine, audio/video encoder-decoder, wireless network controller IPs are also integrated to provide a complete platform architecture for the development of mobile computing devices.

Mohammed S. BenSaleh, Syed Manzoor Qasim, Abdulfattah M. Obeid

A Multiresolution-Based Fusion Strategy for Improving Speech Emotion Recognition Efficiency

Abstract

This paper presents a multiresolution-based feature extraction for speech emotion recognition in unconstrained environments. In the proposed approach, Mel Frequency Cepstral Coefficients (MFCC) are derived from Discrete Wavelet Transform (DWT) sub-band coefficients. The extracted features are further combined with conventional MFCCs and pitch-based features to form the final feature vector. Linear Discriminant Analysis (LDA) is used to reduce the dimension of the resulting features set prior to Naive Bayes classification. To assess the performance of the proposed approach in unconstrained environments, noisy speech data are generated by adding real world noises to clean speech signals from the Berlin German Emotional Database (EMO-DB) The proposal is also tested through speaker-dependent and speaker-independent experiments. The overall performance results show improvement in speech emotion detection over baselines.

Sara Sekkate, Mohammed Khalil, Abdellah Adib, Sofia Ben Jebara

ECG Beat Classification Based on Stationary Wavelet Transform

Abstract

ECG processing is a non-invasive technique that is frequently used for diagnosis of various cardiac diseases. One of the crucial steps of an ECG diagnosis system is the heartbeat classification. In this work, we propose a new method for QRS complex classification based on Stationary Wavelet Transform (SWT), and two classifiers, which are Support Vector Machine (SVM) and K-Nearest Neighbors (KNN). In our scheme, SWT was used to extract the discriminatory features from the useful frequency sub-bands for each QRS complex class. The extracted features were used as inputs of SVM and KNN in order to classify five types of heartbeats, which are Normal (N), Premature Ventricular Contraction (PVC), Atrium Premature Contraction (APC), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The experimental results obtained on MIT-BIH Arrythmia database (MITDB), show that the proposed system yields acceptable performances with an overall classification accuracy of 98.56% and 98.74% for KNN and SVM classifiers respectively, using the 10-cross validation technique.

Lahcen El Bouny, Mohammed Khalil, Abdellah Adib

Cross-Subject EEG Signal Classification with Deep Neural Networks Applied to Motor Imagery

Abstract

The Brain-Computer Interface (BCI) is a system able to serve as a mean of communication between machine and human where the brainwaves are the control signals acquired by electroencephalography (EEG). One of the most used brainwaves is the sensorimotor rhythm (SMR) which appears for real or imagined motor movement. In general, EEG signals need feature extraction methods and classification algorithms to interpret the raw signals. Deep learning approaches; however, permit the processing of the raw data without any transformation. In this paper, we present a deep learning neural network architecture to classify SMR signals due to its success for some previous works and to visualize the learned features. The architecture is composed of three parts. The first part contains a temporal convolution operation followed by a spatial convolution one. The second part contains recurrent layers. Finally, we use a dense layer to assign the signal to its class. The model is trained with Adam optimizer algorithm. Also, we use various regularization techniques such as dropout to prevent learning problem like overfitting. To evaluate the performance of the proposed architecture, the well known Dataset IIa of the BCI Competition IV is used. As a result, we get equivalent results to those ones of EEGNet.

Mouad Riyad, Mohammed Khalil, Abdellah Adib

Data Communication in Electromagnetic Nano-networks for Healthcare Applications

Abstract

One of the most promising applications of nanotechnology is their use in health care scenarios to monitor, in real-time, several parameters inside the human body such as cancer biomarker detection, glucose level, etc. However, real-time medical parameters communication is constrained by the tiny size of nano-nodes and their extremely limited energy. Ongoing efforts in this area are in their very early stage of development. Therefore further research is required to propose a suitable communication model. In this paper, we study the deployment of nano-networks in a living biological environment, and we focus on communication protocols challenges that must be overcome. We also proposed a multi-hop data dissemination approach that transmits sensed data from nano-nodes moving inside an artery to an outside controller while optimizing energy consumption.

Hanen Ferjani, Haifa Touati

A Distributed Ensemble of Deep Convolutional Neural Networks with Random Forest for Big Data Sentiment Analysis

Abstract

Big data has become an important issue for a large number of research areas. With the advent of social networks, users can express their feelings about the products they bought or the services they used every day. Also, they can share their ideas and interests, discuss current issues. Therefore, Big Data sentiment analysis has become important in decision-making processes. In this paper, we propose a novel distributed ensemble of deep convolutional neural networks with random forest for sentiment analysis, which is tailored to handle large-scale data and improve classification accuracy. Experimental results on two real-world data sets confirm the claim.

Badr Ait Hammou, Ayoub Ait Lahcen, Salma Mouline

Road Safety Against Sybil Attacks Based on RSU Collaboration in VANET Environment

Abstract

Vehicular Ad-hoc Network (VANET) is a special case of an Ad hoc mobile network formed by vehicles communicating directly with each other or via a wireless infrastructure called the Road Side Unit (RSU). Like all similar networks, VANET is more concerned with Sybil attacks where a malicious attacking node sends messages with multiple identities to other nodes in the network. Thus, causing a general malfunction of the network, which gives the illusion of a problem of traffic like traffic jam or a virtual accident. So, honest vehicles change their path or leave the road for the benefit of ‘attacker’. In this paper, we present an effective solution against this severe threat by using a distributed and collaborative technique between RSUs allowing the detection of Sybil vehicles based on their real positions.

Said Benkirane

A New Alert Message Dissemination Protocol for VANETs Based on Leach Routing Protocol and Simulation in Random Waypoint Model Scenario

Abstract

The intelligent Transportation system (ITS), through its derivative system named Vehicle Ad hoc NETwork (VANET) allows vehicles to exchange information relating to safety and comfort of drivers and passengers. Communication equipment fixed in the vehicles makes it possible to send and receive messages. Two categories of VANET communication used: vehicle-to-vehicle (V2V) and vehicle to infrastructure named vehicle-to-road (V2R). In the V2V communication, vehicles act as relay nodes to ensure the exchange of messages between them. In V2R communication, road-side units (RSU) act as relay nodes to transmit information to nearby vehicles and RSUs.

The vehicle knows the roads status by receiving an alert message broadcasted by other vehicles that have detected the event.

The main goal of our work is firstly to inject the alert functionality to one of existing VANETs routing protocols that is the Low-Energy Adaptive Clustering Hierarchy (LEACH). We have used only the basic routing functionalities of LEACH protocol. We have integrated five procedures to the LEACH protocol, which will ensure the following features:

• Procedure 1: Event point detection.
• Procedure 2: Search for close neighbors.
• Procedure 3: Sending of messages to nearby neighbors.
• Procedure 4: Communications From vehicles to Infrastructure.
• Procedure 5: Communications From infrastructure to Vehicles.

The second goal is to simulate our protocol functionality on a random waypoint model and to examine the impact of varying density on the number of sent/received packets and average delay. The results of the number of packets sent/received and of the low average delay prove that our proposed algorithm is reliable.

Hamid Barkouk, El Mokhtar En-Naimi, Mounir Arioua, Aziz Mahboub

Towards Semantic Integration of Heterogeneous Data Based on the Ontologies Modeling

Abstract

The integration of the heterogeneous data is a major problem encountered today by the users of the Web. A typical integration scenario is that two heterogeneous systems A and B are built for different business purposes for different users at different times by different software developers using different information models. The two systems often have heterogeneous semantics, data structures and business rules are different.

It involves in particular the differences between systems infrastructures, the conceptual schematizations of the data and its meanings. Indeed, the ontology specifies its systems of knowledge representation. It allows the modeling of knowledge in an explicit and formal way by concepts and relations between these concepts. The semantic integration comes after syntactic integration and the mechanisms of translation connection.

In this paper, we proceeded to a semantic integration of the heterogeneous data based on the management of the heterogeneousness and the semantic ontology of the knowledge.

Cheikh Ould El Mabrouk, Karim Konaté

A Comparison Between MADM Methods and Utility Functions in the Network Selection Context

Abstract

The vertical handover is the communication switch from a source point of attachment, to a destination one, using a different access typology, for some quality, cost, load balancing or security requirements. In fact, mobile users often walk through wireless multi-access environments and need diverse services. Thus, devices must smartly pick out the best target network to transfer the communications to, from those available anytime. The network selection must consider different parameters together, like the available bandwidth, the energy consumption, the cost, the security, etc. The works in the literature propose many techniques to tackle this decision issue, such as MADM methods and utility functions. These methods can consider multiple attributes together when evaluating the alternatives. This work aims to compare some combinations of these two decision-making techniques in the network selection context. The results show that the compared utility functions outperform MADM methods in some situations, and make steadier decisions to ensure the session continuity.

Mouad Mansouri, Cherkaoui Leghris

A Robust Blind 3-D Mesh Watermarking Technique Based on SCS Quantization and Mesh Saliency for Copyright Protection

Abstract

Due to the recent demand of 3-D meshes in a wide range of applications such as video games, medical imaging, film special effect making, computer-aided design (CAD), among others, the necessity of implementing 3-D mesh watermarking schemes aiming to protect copyright has increased in the last decade. Nowadays, the majority of robust 3-D watermarking approaches have mainly focused on the robustness against attacks while the imperceptibility of these techniques is still a serious challenge. In this context, a blind robust 3-D mesh watermarking method based on mesh saliency and scalar Costa scheme (SCS) for Copyright protection is proposed. The watermark is embedded by quantifying the vertex norms of the 3-D mesh by SCS scheme using the vertex normal norms as synchronizing primitives. The choice of these vertices is based on 3-D mesh saliency to achieve watermark robustness while ensuring high imperceptibility. The experimental results show that in comparison with the alternative methods, the proposed work can achieve a high imperceptibility performance while ensuring a good robustness against several common attacks including similarity transformations, noise addition, quantization, smoothing, elements reordering, etc.

Mohamed Hamidi, Aladine Chetouani, Mohamed El Haziti, Mohammed El Hassouni, Hocine Cherifi

A New Mechanism to Secure IPv6 Networks Using Symmetric Cryptography

Abstract

Traditionally, configuring a network interface of a machine requires manual configuration and it’s often a long and tedious job. With IPv6, this configuration is automated, introducing plug-and-play functionality to the network interface. Automatic configuration means that a machine gets all the information it needs to connect to an IPv6 LAN without any human intervention. The IPv6 address auto-configuration process includes creating a link-local address, verifying its uniqueness, and determining global unicast addresses. Checking the uniqueness of an IPv6 address is done by running an algorithm called DAD (Duplicate Address Detection) by the new node. This algorithm uses the multicast communications namely the messages neighbor solicitation and neighbor advertisement. However, this mechanism is not secure. In our paper, we propose a new algorithm to secure these multicast communications using symmetric cryptography. Our method shows its efficiency in terms of execution time and security level.

Ali El Ksimi, Cherkaoui Leghirs

Deep Neural Networks for Indoor Localization Using WiFi Fingerprints

Abstract

In this paper, we propose a novel Wi-Fi positioning method based on Deep Learning. More specifically, we investigate a Stacked AutoEncoder-based model for global location recognition from WiFi fingerprinting data. Stacked AutoEncoder works very well in learning useful high-level features for better representation of input raw data. For our proposed model, two trained unsupervised autoencoders were stacked, then the whole network was trained globally by adding a Softmax output layer for classification. The experimental results show that our Deep Learning based model performs better than SVM and KNN machine learning approaches in a large multi-floor building composed of 162 rooms. Our model achieves an accuracy of \(85.58\%\) and a test time that does not exceed 0.26 s.

Souad BelMannoubi, Haifa Touati

A New Secure Cellular Automata Cryptosystem for Embedded Devices

Abstract

Cryptography is one of the essential methods used to warrant the security of sensitive data stored into devices or exchanged between different entities. Many cryptosystems have been defined; those designed for embedded systems must take in consideration the resource-limited devices. In this paper, we propose a new secret key encryption algorithm that supports 64-bits block size with 128-bits keys size. We utilize irreversible Elementary Cellular Automata to generate the sub keys and two-dimensional reversible Cellular Automata to realize substitutions and permutations. The implemented encryption algorithm is analyzed using diffusion and confusion tests. The acquired results show that the proposed cryptosystem exhibits significant high avalanche effect which improves the security level. The paper gives a comparison of avalanche effect, CPU time and memory usage for our proposed cryptosystem and AES-128.

Youssef Sbaytri, Saiida Lazaar, Hafssa Benaboud, Said Bouchkaren

An ElGamal-Like Digital Signature Based on Elliptic Curves

Abstract

In this work, we present a new variant of the ElGamal digital signature scheme. We ameliorated the signature equation to make it more secure against current attacks. The method is based on the elliptic curves notion. We analyze the complexity and security of the protocol.

Leila Zahhafi, Omar Khadir

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