Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities

The future Intelligent Transport System "ITS" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle “V2V”), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network “VANET”, which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding “GF” mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced “GFA”, to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic

Enhancing OLSR Protocol by an Advanced Greedy Forwarding Mechanism for VANET in Smart Cities

The future Intelligent Transport System "ITS" is one of the major challenges of the smart city. It requires fast and efficient communication between vehicles (vehicle-to-vehicle “V2V”), to ensure information exchange in order to improve safety, which reduces accidents and consequently save lives, hence the need of the Vehicular Ad Hoc Network “VANET”, which makes possible the inter-vehicle communication. This network is characterized by a variable topology. Therefore, MANET (Mobile Adhoc NETwork) routing protocols need a few tweaks to be available for the vehicle environment. In this paper, we start by exposing some works related to the evaluation of the most well-known protocols. After a comparative study, we deduce that the OLSR (Optimized Link State Routing) protocol outperforms other routing protocols in terms of End-to-End Delay (EED) and Packet Delivery Ratio (PDR). In addition, we note that the Greedy forwarding “GF” mechanism is suited for the VANET environment, which has been improved and called Greedy forwarding Advanced “GFA”, to overcome the stationary node problem. Our approach improves the OLSR protocol to be more suitable and efficient for VANET by introducing the GFA mechanism. Moreover, we compare our approach to the OLSR classic version. In this work, we use a realistic scenario from Open Street Map (OSM), and simulations are performed using SUMO (Simulation of Urban MObility). The trace files generated from SUMO are used for further simulation in NS-3 (Network Simulator) to validate our proposition. The simulation results are analyzed and discussed. Our approach performs best compared to OLSR in terms of EED and PDR, especially for dense traffic

Moving towards Smart Cities: A Selection of Middleware for Fog-to-Cloud Services

Smart cities aim at integrating various IoT (Internet of Things) technologies by providing many opportunities for the development, governance, and management of user services. One of the ways to support this idea is to use cloud and edge computing techniques to reduce costs, manage resource consumption, enhance performance, and connect the IoT devices more effectively. However, the selection of services remains a significant research question since there are currently different strategies towards cloud computing, including services for central remote computing (traditional cloud model) as well as distributed local computing (edge computing). In this paper, we offer an integrated view of these two directions and the selection among the edge technologies based on MCDA (Multiple Criteria Decision Analysis) algorithms. To this end, we propose a foglet as a middleware that aims at achieving satisfactory levels of customer services by using fuzzy similarity and TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) to facilitate the rating and selection of services in the fog-to-cloud environment. Then, we describe the selection process with a numerical example, and conclude our work with an outline of future perspectives

Moving to the Edge-Cloud-of-Things: Recent Advances and Future Research Directions

Cloud computing has significantly enhanced the growth of the Internet of Things (IoT) by ensuring and supporting the Quality of Service (QoS) of IoT applications. However, cloud services are still far from IoT devices. Notably, the transmission of IoT data experiences network issues, such as high latency. In this case, the cloud platforms cannot satisfy the IoT applications that require real-time response. Yet, the location of cloud services is one of the challenges encountered in the evolution of the IoT paradigm. Recently, edge cloud computing has been proposed to bring cloud services closer to the IoT end-users, becoming a promising paradigm whose pitfalls and challenges are not yet well understood. This paper aims at presenting the leading-edge computing concerning the movement of services from centralized cloud platforms to decentralized platforms, and examines the issues and challenges introduced by these highly distributed environments, to support engineers and researchers who might benefit from this transition

An Assistive Technology for Braille Users to Support Mathematical Learning: A Semantic Retrieval System

Mathematical learning from digital libraries and the web is a challenging problem for people with visual impairments and blindness. In this paper, we focus on developing the mathematical learning skills of braille users with a new assistive technology developed to retrieve semantically mathematical information from the web. This kind of research is still in the study phase. This paper presents an overview of assistive technologies for braille users followed by a description of the proposed system, which works in four phases. In the first phase, we translate a query math formula in braille into MathML code, and then we extract the structural and semantic meaning from the MathML expressions using multilevel presentation. In the classification phase, we choose a multilevel similarity measure based on K-Nearest Neighbors to evaluate the relevance between expressions. Finally, the query result is converted to braille math expressions. Experiments based on our database show that the new system provides more efficient results in responding to user queries

Collaborating filtering using unsupervised learning for image reconstruction from missing data

Abstract In the image acquisition process, important information in an image can be lost due to noise, occlusion, or even faulty image sensors. Therefore, we often have images with missing and/or corrupted pixels. In this work, we address the problem of image completion using a matrix completion approach that minimizes the nuclear norm to recover missing pixels in the image. The image matrix has a low rank. The proposed approach uses the nuclear norm function as a surrogate of the rank function in the aim to resolve the problem of rank minimization that is known as an NP-hard problem. It is an adaptation of the collaborating filtering approach used for users’ profile construction. The main advantage of this approach is that it uses a learning process to classify pixels into clusters and exploits them to run a predictive method in the aim to recover the missing or unknown data. For performance evaluation, the proposed approach and the existing matrix completion methods are compared for image reconstruction according to the PSNR measure. These methods are applied on a dataset composed of standard images used for image processing. All the recovered images obtained during experimentation are also dressed to compare them visually. Simulation results verify that the proposed approach achieves better performances than the existing matrix completion methods used for image reconstruction from missing data

An improved SIMPLEC scheme for fluid registration

The image registration is always a strongly ill-posed problem, a stable numerical approach is then desired to better approximate the deformation vectors. This paper introduces an efficient numerical implementation of the Navier Stokes equation in the fluid image registration context. Although fluid registration approaches have succeeded in handling large image deformations, the numerical results are sometimes inconsistent and unexpected. This is related, in fact, to the used numerical scheme which does not take into consideration the different properties of the continuous operators. To take into account these properties, we use a robust numerical scheme based on finite volume with pressure correction. This scheme, which is called by the Semi-Implicit Method for Pressure-Linked Equation-Consistent (SIMPLEC), is known for its stability and consistency in fluid dynamics context. The experimental results demonstrate that the proposed method is more efficient and stable, visually and quantitatively, compared to some classical registration methods

Multi-Criteria Decision Analysis Methods in the Mobile Cloud Offloading Paradigm

Mobile cloud computing (MCC) is becoming a popular mobile technology that aims to augment local resources of mobile devices, such as energy, computing, and storage, by using available cloud services and functionalities. The offloading process is one of the techniques used in MCC to enhance the capabilities of mobile devices by moving mobile data and computation-intensive operations to cloud platforms. Several techniques have been proposed to perform and improve the efficiency and effectiveness of the offloading process, such as multi-criteria decision analysis (MCDA). MCDA is a well-known concept that aims to select the best solution among several alternatives by evaluating multiple conflicting criteria, explicitly in decision making. However, as there are a variety of platforms and technologies in mobile cloud computing, it is still challenging for the offloading process to reach a satisfactory quality of service from the perspective of customers’ computational service requests. Thus, in this paper, we conduct a literature review that leads to a better understanding of the usability of the MCDA methods in the offloading operation that is strongly reliant on the mobile environment, network operators, and cloud services. Furthermore, we discuss the challenges and opportunities of these MCDA techniques for offloading research in mobile cloud computing. Finally, we recommend a set of future research directions in MCDA used for the mobile cloud offloading process

Energy Efficient Strategy for WSN Technology Using Modified HGAF Technique

Due to the rapid growth in technologies has led to the development of sensor nodes. As we know that wireless sensor network is collection of small and large number of sensor node. These sensor nodes are used for different domain like environmental research, health care, monitor, military, and record the physical activity. These nodes communicate with each other and forward the message to base station. For communication of these node different algorithms used Geographical adaptive fidelity (GAF) is one of them. Dropping energy utilization in wireless sensor network directly affects the network lifetime. HGAF is one of the important multiple location based on routing system algorithm. The main function of HGAF technique is to turn-off the unnecessary nodes in the network without interrupting the other connected node. In this paper we proposed a technique known as modified HGAF and it design as a power saving method. In the proposed technique the size of cell structure in grid changed and communication method improve due to those changes.  Based on the result the proposed technique increase 25% in dead node ratio and also increase the network