Broadcasting methods in mobile ad hoc networks: Taxonomy and current state of the art

Flooding also known as broadcasting is one of the most primitive methodologies that focus on investigating searches concerning mobile ad hoc networking due to poorer network procedures which is a main feature in the concept of broadcasting which provides implications to superior applications that includes routing. Broadcasting means in conventional ways transmitting messages from a given branch to all other branches present in a network. The whole grid of the network is manned to ensure that the transmitted data is uniformly ported to the remaining nodes in a decentralized type of network setup. The two issues that renders nodes out of reach all the time are limited radio range and their immovability which assists in concluding that te issue of data transmission covering all networks is assumed to be a multi-objective issue that aims at increasing the count of number of nodules and also decreasing the time taken to reach the specified nodules and also reducing the network overhead which is a crucial characteristic because of the fact that this may direct to congestion also known as broadcast storm issue. This article aims at giving an insight of the taxonomy of transmitting methodologies in MANETS and current state of the art

Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks

Future wireless networks have a substantial potential in terms of supporting a broad range of complex compelling applications both in military and civilian fields, where the users are able to enjoy high-rate, low-latency, low-cost and reliable information services. Achieving this ambitious goal requires new radio techniques for adaptive learning and intelligent decision making because of the complex heterogeneous nature of the network structures and wireless services. Machine learning (ML) algorithms have great success in supporting big data analytics, efficient parameter estimation and interactive decision making. Hence, in this article, we review the thirty-year history of ML by elaborating on supervised learning, unsupervised learning, reinforcement learning and deep learning. Furthermore, we investigate their employment in the compelling applications of wireless networks, including heterogeneous networks (HetNets), cognitive radios (CR), Internet of things (IoT), machine to machine networks (M2M), and so on. This article aims for assisting the readers in clarifying the motivation and methodology of the various ML algorithms, so as to invoke them for hitherto unexplored services as well as scenarios of future wireless networks.Comment: 46 pages, 22 fig

Design and evaluation of wireless dense networks : application to in-flight entertainment systems

Le réseau sans fil est l'un des domaines de réseautage les plus prometteurs avec des caractéristiques uniques qui peuvent fournir la connectivité dans les situations où il est difficile d'utiliser un réseau filaire, ou lorsque la mobilité des nœuds est nécessaire. Cependant, le milieu de travail impose généralement diverses contraintes, où les appareils sans fil font face à différents défis lors du partage des moyens de communication. De plus, le problème s'aggrave avec l'augmentation du nombre de nœuds. Différentes solutions ont été introduites pour faire face aux réseaux très denses. D'autre part, un nœud avec une densité très faible peut créer un problème de connectivité et peut conduire à l'optension de nœuds isolés et non connectes au réseau. La densité d'un réseau est définit en fonction du nombre de nœuds voisins directs au sein de la portée de transmission du nœud. Cependant, nous croyons que ces métriques ne sont pas suffisants et nous proposons une nouvelle mesure qui considère le nombre de voisins directs et la performance du réseau. Ainsi, la réponse du réseau, respectant l'augmentation du nombre de nœuds, est considérée lors du choix du niveau de la densité. Nous avons défini deux termes: l'auto-organisation et l'auto-configuration, qui sont généralement utilisés de façon interchangeable dans la littérature en mettant en relief la différence entre eux. Nous estimons qu'une définition claire de la terminologie peut éliminer beaucoup d'ambiguïté et aider à présenter les concepts de recherche plus clairement. Certaines applications, telles que Ies systèmes "In-Flight Entertainment (IFE)" qui se trouvent à l'intérieur des cabines d'avions, peuveut être considérées comme des systèmes sans fil de haute densité, même si peu de nœuds sont relativement présents. Pour résoudre ce problème, nous proposons une architecture hétérogène de différentes technologies à fin de surmonter les contraintes spécifiques de l'intérieur de la cabine. Chaque technologie vise à résoudre une partie du problème. Nous avons réalisé diverses expérimentations et simulations pour montrer la faisabilité de l'architecture proposée. Nous avons introduit un nouveau protocole d'auto-organisation qui utilise des antennes intelligentes pour aider certains composants du système IFE; à savoir les unités d'affichage et leurs systèmes de commande, à s'identifier les uns les autres sans aucune configuration préliminaire. Le protocole a été conçu et vérifié en utilisant le langage UML, puis, un module de NS2 a été créé pour tester les différents scénarios.Wireless networking is one of the most challenging networking domains with unique features that can provide connectivity in situations where it is difficult to use wired networking, or when ! node mobility is required. However, the working environment us! ually im poses various constrains, where wireless devices face various challenges when sharing the communication media. Furthermore, the problem becomes worse when the number of nodes increase. Different solutions were introduced to cope with highly dense networks. On the other hand, a very low density can create a poor connectivity problem and may lead to have isolated nodes with no connection to the network. It is common to define network density according to the number of direct neighboring nodes within the node transmission range. However, we believe that such metric is not enough. Thus, we propose a new metric that encompasses the number of direct neighbors and the network performance. In this way, the network response, due to the increasing number of nodes, is considered when deciding the density level. Moreover, we defined two terms, self-organization and self-configuration, which are usually used interchangeably in the literature through highlighting the difference ! between them. We believe that having a clear definition for terminology can eliminate a lot of ambiguity and help to present the research concepts more clearly. Some applications, such as In-Flight Entertainment (IFE) systems inside the aircraft cabin, can be considered as wirelessly high dense even if relatively few nodes are present. To solve this problem, we propose a heterogeneous architecture of different technologies to overcome the inherited constrains inside the cabin. Each technology aims at solving a part of the problem. We held various experimentation and simulations to show the feasibility of the proposed architecture

Performance of Routing Protocol in MANET with Combined Scalable Video Coding

Development of wireless network technology to support various types of information services continues to increase especially in video streaming services. Video transmission especially in Mobile Ad Hoc Network (MANET) environment used in this research, particularly in the Combined Scalable Video Coding (CSVC) scheme which is a development of H.264 / MPEG-4. The contribution of this research focuses on the analysis of the performance of AODV, DSDV and DSR routing protocols in the MANET environment for CSVC, which is a new scheme in the development of H.264 / MPEG-4  video schemes. Performance evaluation are measured using the NS-2 simulation that supports MANET environment. As test metrics in this study are the end-to- end delay and PSNR. The test result shows the end-to-end delay in AODV is 0.60 seconds; this is lower than results on DSDV and DSR. On PSNR, DSR simulation results show 16.2 dB. This result exceed those in AODV and DSDV. The results of this research evaluation are influenced by channels in wireless networks, the larger the wireless network channel the more streaming video frames can be accepted by the destination node.