Enhanced Forwarding Strategies in Information Centric Networking

Content Centric Networking (CCN), a Clean Slate architecture to Information Centric Networking (ICN) , uses new approaches to routing named content, achieving scalability, security and performance. This thesis proposes a design of an effective multi-path forwarding strategy and performs an evaluation of this strategy in a set of scenarios that consider large scale deployments. The evaluations show improved performance in terms of user application throughput, delays, adoptability and scalability against adverse conditions (such as differing background loads and mobility) compared to the originally proposed forwarding strategies. Secondly, this thesis proposes an analytical model based on Markov Modulated Rate Process (MMRP) to characterize multi-path data transfers in CCN. The results show a close resemblance in performance between the analytical model and the simulation model

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

Large scale probabilistic available bandwidth estimation

The common utilization-based definition of available bandwidth and many of the existing tools to estimate it suffer from several important weaknesses: i) most tools report a point estimate of average available bandwidth over a measurement interval and do not provide a confidence interval; ii) the commonly adopted models used to relate the available bandwidth metric to the measured data are invalid in almost all practical scenarios; iii) existing tools do not scale well and are not suited to the task of multi-path estimation in large-scale networks; iv) almost all tools use ad-hoc techniques to address measurement noise; and v) tools do not provide enough flexibility in terms of accuracy, overhead, latency and reliability to adapt to the requirements of various applications. In this paper we propose a new definition for available bandwidth and a novel framework that addresses these issues. We define probabilistic available bandwidth (PAB) as the largest input rate at which we can send a traffic flow along a path while achieving, with specified probability, an output rate that is almost as large as the input rate. PAB is expressed directly in terms of the measurable output rate and includes adjustable parameters that allow the user to adapt to different application requirements. Our probabilistic framework to estimate network-wide probabilistic available bandwidth is based on packet trains, Bayesian inference, factor graphs and active sampling. We deploy our tool on the PlanetLab network and our results show that we can obtain accurate estimates with a much smaller measurement overhead compared to existing approaches.Comment: Submitted to Computer Network

Resource Allocation in Ad Hoc Networks

Unlike the centralized network, the ad hoc network does not have any central administrations and energy is constrained, e.g. battery, so the resource allocation plays a very important role in efficiently managing the limited energy in ad hoc networks. This thesis focuses on the resource allocation in ad hoc networks and aims to develop novel techniques that will improve the network performance from different network layers, such as the physical layer, Medium Access Control (MAC) layer and network layer. This thesis examines the energy utilization in High Speed Downlink Packet Access (HSDPA) systems at the physical layer. Two resource allocation techniques, known as channel adaptive HSDPA and two-group HSDPA, are developed to improve the performance of an ad hoc radio system through reducing the residual energy, which in turn, should improve the data rate in HSDPA systems. The channel adaptive HSDPA removes the constraint on the number of channels used for transmissions. The two-group allocation minimizes the residual energy in HSDPA systems and therefore enhances the physical data rates in transmissions due to adaptive modulations. These proposed approaches provide better data rate than rates achieved with the current HSDPA type of algorithm. By considering both physical transmission power and data rates for defining the cost function of the routing scheme, an energy-aware routing scheme is proposed in order to find the routing path with the least energy consumption. By focusing on the routing paths with low energy consumption, computational complexity is significantly reduced. The data rate enhancement achieved by two-group resource allocation further reduces the required amount of energy per bit for each path. With a novel load balancing technique, the information bits can be allocated to each path in such that a way the overall amount of energy consumed is minimized. After loading bits to multiple routing paths, an end-to-end delay minimization solution along a routing path is developed through studying MAC distributed coordination function (DCF) service time. Furthermore, the overhead effect and the related throughput reduction are studied. In order to enhance the network throughput at the MAC layer, two MAC DCF-based adaptive payload allocation approaches are developed through introducing Lagrange optimization and studying equal data transmission period