Efficient packet delivery in modern communication networks

Modern communication networks are often designed for diverse applications, such as voice, data and video. Packet-switching is often adapted in today’s networks to transmit multiple types of traffic. In packet-switching networks, network performance is directly affected by how the networks handle their packets. This work addresses the packet-handling issues from the following two aspects: Quality of Service (QoS) and network coding. QoS has been a well-addressed issue in the study of IP-based networks. Generally, nodes in a network need to be informed of the state of each communication link in order to make intelligent decisions to route packets according to their QoS demands. The link state can, however, change rapidly in a network; therefore, nodes would have to receive frequent link state updates in order to maintain the latest link state information at all times. Frequent link state updating is resource-consuming and hence impractical in network design. Therefore, there is a trade-off between the link state updating frequency and the QoS routing performance. It is necessary to design a link state update algorithm that utilizes less frequent link state updates to achieve a high degree of satisfaction in QoS performance. The first part of this work addresses this link state update problem and provides two solutions: ROSE and Smart Packet Marking. ROSE is a class-based link state update algorithm, in which the class boundaries are designed based on the statistical data of users’ QoS requests. By doing so, link state update is triggered only when certain necessary conditions are met. For example, if the available bandwidth of a link is fluctuating within a range that is higher than the highest possible bandwidth request, there is no need to update the state of this link. Smart Packet Marking utilizes a similar concept like ROSE, except that the link state information is carried in the probing packet sent in conjunction with each connection request instead of through link state updates. The second part of this work addresses the packet-handling issue by means of network coding. Instead of the traditional store-and-forward approach, network coding allows intermediate nodes in a multi-hop path to code multiple packets into one in order to reduce bandwidth consumption. The coded packet can later be decoded by its recipients to retrieve the original plain packet. Network coding is found to be beneficial in many network applications. This dissertation makes contributions in network coding in two areas: peer-to-peer file sharing and wireless ad-hoc networks. The benefit of network coding in peer-to-peer file sharing networks is analyzed, and a network coding algorithm – Downloader-Initiated Random Linear Network Coding (DRLNC) – is proposed. DLRNC shifts the coding decision from the seeders to the leechers; by doing so it solves the “collision” problem without increasing the field size. In wireless network coding, this work addresses the implementation difficulty pertaining to MAC layer scheduling. To achieve the ideal network coding gain in wireless networks, it requires perfect MAC layer scheduling. This dissertation first provides an algorithm to solve the ideal-case MAC layer scheduling problem. Since the ideal MAC layer schedule is often difficult to realize, a practical approach is then proposed to increase the network coding performance by modifying the ACK packets in the 802.11 MAC

Opportunistic routing in wireless mesh networks

Advances in communication and networking technologies are rapidly making ubiquitous network connectivity a reality. In recent years, Wireless Mesh Networks (WMNs) have already become very popular and been receiving an increasing amount of attention by the research community. Basically, a WMN consists of simple mesh routers and mesh clients, where mesh routers form the backbone of WMN. Due to the limited transmission range of the radio, many pairs of nodes in WMN may not be able to communicate directly, hence they need other intermediate nodes to forward packets for them. Routing in such networks is an important issue and it poses great challenges. Opportunistic Routing (OR) has been investigated in recent years as a way to increase the performance of WMNs by exploiting its broadcast nature. In OR, in contrast to traditional routing, instead of pre-selecting a single specic node to be the next-hop as a forwarder for a packet, an ordered set of nodes (referred to as candidates) is selected as the potential next-hop forwarders. Thus, the source can use multiple potential paths to deliver the packets to the destination. More specically, when the current node transmits a packet, all the candidates that successfully receive it will coordinate with each other to determine which one will actually forward it, while the others will simply discard the packet. This dissertation studies the properties, performance, maximum gain, candidate selection algorithms and multicast delivery issues about Opportunistic Routing in WMNs. Firstly, we focus on the performance analysis of OR by proposing a Discrete Time Markov Chain (DTMC). This model can be used to evaluate OR in terms of expected number of transmissions from the source to the destination. Secondly, we apply our Markov model to compare relevant candidate selection algorithms that have been proposed in the literature. They range from non-optimum, but simple, to optimum, but with a high computational cost. Thirdly, the set of candidates which a node uses and priority order of them have a signicant impact on the performance of OR. Therefore, using a good metric and algorithm to select and order the candidates are key factors in designing an OR protocol. As the next contribution we propose a new metric that measures the expected distance progress of sending a packet using a set of candidates. Based on this metric we propose a candidate selection algorithm which its performance is very close to the optimum algorithm although our algorithm runs much faster. Fourthly, we have investigated the maximum gain that can be obtained using OR. We have obtained some equations that yield the distances of the candidates in OR such that the per transmission progress towards the destination is maximized. Based on these equations we have proposed a novel candidate selection algorithm. Our new algorithm only needs the geographical location of nodes. The performance of our proposal is very close to the optimum candidate selection algorithm although our algorithm runs much faster. Finally, using OR to support multicast is an other issue that we have investigated in this thesis. We do so by proposing a new multicast protocol which uses OR. Unlike traditional multicast protocols, there is no designated next-hop forwarder for each destination in our protocol, thus the delivery ratio is maximized by taking advantage of spatial diversity

A performance model of a telecommunications network structured according to intelligent network principles

The Intelligent Network (IN) is an telecommunications services architecture which enables the rapid creation and deployment of supplementary telecommunications services. However, this flexibility makes the management of the network performance critical in ensuring that customers receive their expected Quality of Service. This thesis describes a model which has been developed to predict the delays in the network due to IN-specific service processing. The model proposed is a queuing network which builds on the existing state of the art as follows. The characteristics of the flows between the IN physical entities are considered and general service time distributions are assumed at each entity. Additionally, the model allows the reservation of capacities as the SCP for each service type. An analytic formulation was developed using the decomposition approximate method. The model was also simulated in order to ascertain its’ accuracy. The results of the analytic solution and the simulation were compared for different scenarios and the results are presented in this thesis. The analytic approximation was found to be a very good solution for situations where network utilisation was low or medium. As the utilisation of the network increases to higher utilisation factors, the accuracy of the analytic solution decreases