Orion Routing Protocol for Delay-Tolerant Networks

In this paper, we address the problem of efficient routing in delay tolerant network. We propose a new routing protocol dubbed as ORION. In ORION, only a single copy of a data packet is kept in the network and transmitted, contact by contact, towards the destination. The aim of the ORION routing protocol is twofold: on one hand, it enhances the delivery ratio in networks where an end-to-end path does not necessarily exist, and on the other hand, it minimizes the routing delay and the network overhead to achieve better performance. In ORION, nodes are aware of their neighborhood by the mean of actual and statistical estimation of new contacts. ORION makes use of autoregressive moving average (ARMA) stochastic processes for best contact prediction and geographical coordinates for optimal greedy data packet forwarding. Simulation results have demonstrated that ORION outperforms other existing DTN routing protocols such as PRoPHET in terms of end-to-end delay, packet delivery ratio, hop count and first packet arrival

Temporal Modeling of Link Characteristic in Mobile Ad hoc Network

Ad hoc network consists of a set of identical nodes that move freely and independently and communicate among themselves via wireless links. The most interesting feature of this network is that they do not require any existing infrastructure of central administration and hence is very suitable for temporary communication links in an emergency situation. This flexibility, however, is achieved at a price of communication uncertainty induced due to frequent topology changes. In this article, we have tried to identify the system dynamics using the proven concepts of time series modeling. Here, we have analyzed variation of link utilization between any two particular nodes over a fixed area for differentmobility patterns under different routing algorithm. We have considered four different mobility models – (i) Gauss-Markov mobility model, (ii) Manhattan Grid Mobility model and (iii) Random Way Point mobility model and (iv) Reference Point Group mobility model. The routing protocols under which, we carried out our experiments are (i) Ad hoc On demand Distance Vector routing (AODV), (ii) Destination Sequenced Distance Vector routing (DSDV) and (iii) Dynamic Source Routing (DSR). The value of link load between two particular nodes behaves as a random variable for any mobility pattern under a routing algorithm. The pattern of link load for every combination of mobility model and for every routing protocol can be well modeled as an autoregressive model of order p i.e. AR(p). The order of p is estimated and it is found that most of them are of order 1 only

Temporal Modeling of Node Mobility in Mobile Ad hoc Network

Ad-hoc network consists of a set of identical nodes that move freely and independently and communicate via wireless links. The most interesting feature of this network is that it does not require any predefined infrastructure or central administration and hence it is very suitable for establishing temporary communication links in emergency situations. This flexibility however is achieved at the price of communication link uncertainties due to frequent topology changes. In this article we describe the system dynamics using the proven concept of time series modeling. Specifically, we analyze variations of the number of neighbor nodes of a particular node over a geographical area and for given total number of nodes assuming different values of (i) the speeds of nodes, (ii) the transmission powers, (iii) sampling periods and (iv) different mobility patterns. We consider three different mobility models: (i) Gaussian mobility model, (ii) random walk mobility model and (iii) random way point mobility model. The number of neighbor nodes of a particular node behaves as a random variable for any mobility pattern. Through our analysis we find that the variation of the number of neibhbor nodes can be well modeled by an autoregressive AR$(p)$ model. The values of $p$ evaluated for different scenarios are found to be in the range between $1$ and $5$. Moreover, we also investigate the relationship between the speed and the time of measurements, and the transmission range of a specific node under various mobility patterns

On backoff mechanisms for wireless Mobile Ad Hoc Networks

Since their emergence within the past decade, which has seen wireless networks being adapted to enable mobility, wireless networks have become increasingly popular in the world of computer research. A Mobile Ad hoc Network (MANET) is a collection of mobile nodes dynamically forming a temporary network without the use of any existing network infrastructure. MANETs have received significant attention in recent years due to their easiness to setup and to their potential applications in many domains. Such networks can be useful in situations where there is not enough time or resource to configure a wired network. Ad hoc networks are also used in military operations where the units are randomly mobile and a central unit cannot be used for synchronization. The shared media used by wireless networks, grant exclusive rights for a node to transmit a packet. Access to this media is controlled by the Media Access Control (MAC) protocol. The Backoff mechanism is a basic part of a MAC protocol. Since only one transmitting node uses the channel at any given time, the MAC protocol must suspend other nodes while the media is busy. In order to decide the length of node suspension, a backoff mechanism is installed in the MAC protocol. The choice of backoff mechanism should consider generating backoff timers which allow adequate time for current transmissions to finish and, at the same time, avoid unneeded idle time that leads to redundant delay in the network. Moreover, the backoff mechanism used should decide the suitable action to be taken in case of repeated failures of a node to attain the media. Further, the mechanism decides the action needed after a successful transmission since this action affects the next time backoff is needed. The Binary exponential Backoff (BEB) is the backoff mechanisms that MANETs have adopted from Ethernet. Similar to Ethernet, MANETs use a shared media. Therefore, the standard MAC protocol used for MANETs uses the standard BEB backoff algorithms. The first part of this work, presented as Chapter 3 of this thesis, studies the effects of changing the backoff behaviour upon a transmission failure or after a successful transmission. The investigation has revealed that using different behaviours directly affects both network throughput and average packet delay. This result indicates that BEB is not the optimal backoff mechanism for MANETs. Up until this research started, no research activity has focused on studying the major parameters of MANETs. These parameters are the speed at which nodes travel inside the network area, the number of nodes in the network and the data size generated per second. These are referred to as mobility speed, network size and traffic load respectively. The investigation has reported that changes made to these parameters values have a major effect on network performance. Existing research on backoff algorithms for MANETs mainly focuses on using external information, as opposed to information available from within the node, to decide the length of backoff timers. Such information includes network traffic load, transmission failures of other nodes and the total number of nodes in the network. In a mobile network, acquiring such information is not feasible at all times. To address this point, the second part of this thesis proposes new backoff algorithms to use with MANETs. These algorithms use internal information only to make their decisions. This part has revealed that it is possible to achieve higher network throughput and less average packet delay under different values of the parameters mentioned above without the use of any external information. This work proposes two new backoff algorithms. The Optimistic Linear-Exponential Backoff, (OLEB), and the Pessimistic Linear-Exponential Backoff (PLEB). In OLEB, the exponential backoff is combined with linear increment behaviour in order to reduce redundant long backoff times, during which the media is available and the node is still on backoff status, by implementing less dramatic increments in the early backoff stages. PLEB is also a combination of exponential and linear increment behaviours. However, the order in which linear and exponential behaviours are used is the reverse of that in OLEB. The two algorithms have been compared with existing work. Results of this research report that PLEB achieves higher network throughput for large numbers of nodes (e.g. 50 nodes and over). Moreover, PLEB achieves higher network throughput with low mobility speed. As for average packet delay, PLEB significantly improves average packet delay for large network sizes especially when combined with high traffic rate and mobility speed. On the other hand, the measurements of network throughput have revealed that for small networks of 10 nodes, OLEB has higher throughput than existing work at high traffic rates. For a medium network size of 50 nodes, OLEB also achieves higher throughput. Finally, at a large network size of 100 nodes, OLEB reaches higher throughput at low mobility speed. Moreover, OLEB produces lower average packet delay than the existing algorithms at low mobility speed for a network size of 50 nodes. Finally, this work has studied the effect of choosing the behaviour changing point between linear and exponential increments in OLEB and PLEB. Results have shown that increasing the number of times in which the linear increment is used increases network throughput. Moreover, using larger linear increments increase network throughput

Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks

Hoy en día existen varias tecnologías que coexisten en una misma zona formando un sistema heterogéneo. Además, este hecho se espera que se vuelva más acentuado con todas las nuevas tecnologías que se están estandarizando actualmente. Hasta ahora, generalmente son los usuarios los que eligen la tecnología a la que se van a conectar, ya sea configurando sus terminales o usando terminales distintos. Sin embargo, esta solución es incapaz de aprovechar al máximo todos los recursos. Para ello es necesario un nuevo conjunto de estrategias. Estas estrategias deben gestionar los recursos radioeléctricos conjuntamente y asegurar la satisfacción de la calidad de servicio de los usuarios. Siguiendo esta idea, esta Tesis propone dos nuevos algoritmos. El primero es un algoritmo de asignación dinámica de recusos conjunto (JDRA) capaz de asignar recursos a usuarios y de distribuir usuarios entre tecnologías al mismo tiempo. El algoritmo está formulado en términos de un problema de optimización multi-objetivo que se resuelve usando redes neuronales de Hopfield (HNNs). Las HNNs son interesantes ya que se supone que pueden alcanzar soluciones sub-óptimas en cortos periodos de tiempo. Sin embargo, implementaciones reales de las HNNs en ordenadores pierden esta rápida respuesta. Por ello, en esta Tesis se analizan las causas y se estudian posibles mejoras. El segundo algoritmo es un algoritmo de control de admisión conjunto (JCAC) que admite y rechaza usuarios teniendo en cuenta todas las tecnologías al mismo tiempo. La principal diferencia con otros algorimos propuestos es que éstos últimos toman las dicisiones de admisión en cada tecnología por separado. Por ello, se necesita de algún mecanismo para seleccionar la tecnología a la que los usuarios se van a conectar. Por el contrario, la técnica propuesta en esta Tesis es capaz de tomar decisiones en todo el sistema heterogéneo. Por lo tanto, los usuarios no se enlazan con ninguna tecnología antes de ser admitidos.Calabuig Soler, D. (2010). Common Radio Resource Management Strategies for Quality of Service Support in Heterogeneous Wireless Networks [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/7348Palanci

Traffic pattern prediction in cellular networks.

PhDIncreasing numbers of users together with a more use of high bit-rate services complicate radio resource management in 3G systems. In order to improve the system capacity and guarantee the QoS, a large amount of research had been carried out on radio resource management. One viable approach reported is to use semi-smart antennas to dynamically change the radiation pattern of target cells to reduce congestion. One key factor of the semi-smart antenna techniques is the algorithm to adjust the beam pattern to cooperatively control the size and shape of each radio cell. Methods described in the literature determine the optimum radiation patterns according to the current observed congestion. By using machine learning methods, it is possible to detect the upcoming change of the traffic patterns at an early stage and then carry out beamforming optimization to alleviate the reduction in network performance. Inspired from the research carried out in the vehicle mobility prediction field, this work learns the movement patterns of mobile users with three different learning models by analysing the movement patterns captured locally. Three different mobility models are introduced to mimic the real-life movement of mobile users and provide analysable data for learning. The simulation results shows that the error rates of predictions on the geographic distribution of mobile users are low and it is feasible to use the proposed learning models to predict future traffic patterns. Being able to predict these patterns mean that the optimized beam patterns could be calculated according to the predicted traffic patterns and loaded to the relevant base stations in advance