The most important standard in wireless local area networks (WLANs) is IEEE 802.11. For this reason, much of the research work for the enhancement of WLANs is generally based on the behaviour of the IEEE 802.11 standard. This standard is divided into several layers. One of the important layers is the medium access control (MAC) layer. It plays an important role in accessing the transmission medium and data transmission of wireless stations. However, it still presents many challenges related to the performance metrics of quality of service (QoS), such as system throughput and access delay. Modelling and performance analysis of the MAC layer are also extremely important. Thus, the performance modelling and analysis have become very important in the design and enhancement of wireless networks. Therefore, this research work is devoted to evaluate and enhance the performance modelling of IEEE 802.11 MAC-distributed coordination function (DCF), which can lead to the improvement of the performance metrics of QoS. In order to more accurately evaluate the system performance for IEEE 802.11 DCF, a new analytical model to compute a packet transmission probability for IEEE 802.11 DCF has been proposed based on difference probabilities in transmission mechanism. The performance saturated throughput is then evaluated with the proposed analytical model. In addition, a new analytical model for estimating the MAC layer packet delay distribution of IEEE 802.11 DCF is also proposed. The performance results highlight the importance of considering the different probabilities between events in transmission mechanism for an accurate performance evaluation model of IEEE 802.11 DCF in terms of throughput and delay. To enhance the effectiveness of IEEE 802.11 DCF, a new dynamic control backoff time algorithm to enhance both the delay and throughput performances of the IEEE 802.11 DCF is proposed. This algorithm considers the distinction between high and low traffic loads in order to deal with unsaturated traffic load conditions. In particular, the equilibrium point analysis (EPA) model is used to represent the algorithm under various traffic load conditions. Results of extensive simulation experiments illustrate that the proposed algorithm yields better performance throughput and a better average transmission packet delay than related algorithms.

Along with the rapid growth in consumer adoption of modern portable devices, video streaming is expected to dominate a large share of the global Internet traffic in the near future. Today user experience is becoming a reliable indicator for video service providers and telecommunication operators to convey overall end-to-end system functioning. Towards this, there is a profound need for an efficient Quality of Experience (QoE) monitoring and prediction. QoE is a subjective metric, which deals with user perception and can vary due to the user expectation and context. However, available QoE measurement techniques that adopt a full reference method are impractical in real-time transmission since they require the original video sequence to be available at the receiver’s end. QoE prediction, however, requires a firm understanding of those Quality of Service (QoS) factors that are the most influential on QoE. The main aim of this thesis work is the development of novel and efficient models for video quality prediction in a non-intrusive way and to demonstrate their application in QoE-enabled optimisation schemes for video delivery. In this thesis, the correlation between QoS and QoE is utilized to objectively estimate the QoE. For this, both objective and subjective methods were used to create datasets that represent the correlation between QoS parameters and measured QoE. Firstly, the impact of selected QoS parameters from both encoding and network levels on video QoE is investigated. The obtained QoS/QoE correlation is backed by thorough statistical analysis. Secondly, the development of two novel hybrid non-reference models for predicting video quality using fuzzy logic inference systems (FIS) as a learning-based technique. Finally, attention was move onto demonstrating two applications of the developed FIS prediction model to show how QoE is used to optimise video delivery.

Highly dynamic and complex computing systems are increasingly needed and are relied upon in daily life. One such system is the dynamic network, particularly in communication, in which it has widespread applications, such as: Internet, peer-to-peer networks, mobile networks and wireless networks. Dynamic networks consist of nodes and edges whose operating status may change over time; the edges may be unreliable and operate intermittently. Message-passing in such networks is inherently difficult and reasoning about the behaviour of message-passing algorithms is also difficult and hard to analyse. Their behaviour and correctness are hard to formulate and establish. To undertake formal reasoning about such systems, abstract models are essential in order to separate the general reasoning about message routing and the updating of routing tables from the details of how these are implemented in particular networks. This thesis proposes a new approach to modelling and reasoning about dynamic networks as follows. It develops a series of abstract models which makes it possible to focus on the correctness of routing methods. It models the dynamic network as a “demonic” process which runs concurrently with routing updates and message-passing, to express dynamic networks as concurrent systems. This allows the use of temporal logic and fairness constraints to reason about dynamic networks. To do so, it introduces a modal logic and formulates concepts of fairness which capture network properties. The correctness of dynamic networks means that under certain conditions, all messages will eventually be delivered. Formulating networks as concurrent systems means can establish the correctness for networks that never cease to change. Modelling at that one level of abstraction means being able to prove the properties of networks independently of the mechanisms in actual networks. Therefore, it provides “a factorisation” of proofs of correctness for actual dynamic networks. The models are implemented as multi-threaded programs, and then adopted an experimental runtime verification tool called RULER to test whether model instances satisfy the modal correctness for message delivery.

The area of wireless embedded nodes has attracted significant research interest, primarily with respect to the utilisation of this technology in a number of applications domains. Under this context, the main topic of this thesis pertains to the design of a framework for real-time, range-only target tracking utilizing low power wireless embedded nodes. The proposed tracking system is designed to operate solely on range measurements which are obtained without the need for additional hardware incorporated on the embedded nodes. The core objective of this research was to present a target tracking system that can be applied to real-world applications, incorporating support for effectively tracking manoeuvring targets facilitated by the ability to obtain accurate range readings from low-power embedded nodes and finally the ability to achieve real-time system operation. The contribution of the work presented in this thesis is threefold. The tracking problem is theoretically formulated as a dynamical system with the objective being, the real-time estimation of the target’s kinematic variables based on range observations. To address the need for effective tracking of manoeuvring targets an adaptive multiple-model approach was developed. The resulting system is non-linear, due to the non-linearity between the range observations and the kinematic variables. To solve this system, a novel adaptive multiple-model Particle Filter tracking algorithm is proposed. Secondly, to achieve accurate enough ranging between embedded nodes a Time-of-Flight ranging scheme is adopted as part of the proposed tracking system. The final contribution of this work pertains to the real-time operation of the tracking system. The tracking algorithms were evaluated on a simulation environment under realistic experimental conditions. The ranging method was implemented on embedded nodes and tested in terms of accuracy in various environments. Ultimately, the entire system was implemented on hardware and tested in outdoor experiments. In the experiments carried out one mobile wireless node was used as the target and a set of anchor nodes attempted to infer the target’s kinematic variables. A total of 25 experiments are presented in this thesis. An average accuracy of approximately 2.6m for position and 1.9m/s for velocity was attained in a 15m x 15m square area. Such performance, which is confirmed from the simulation results reveal the potential of the proposed range-only system in application domains where real-time tracking of mobile targets is a demand.

With increasing computational capabilities and advances in robotics, technology is at the verge of the next industrial revolution. An growing number of tasks can be performed by artificial intelligence and agile robots. This impacts almost every part of the economy, including agriculture, transportation, industrial manufacturing and even social interactions. In all applications of automated machines, communication is a critical component to enable cooperation between machines and exchange of sensor and control signals. The mobility and scale at which these automated machines are deployed also challenges todays communication systems. These complex cyber-physical systems consisting of up to hundreds of mobile machines require highly reliable connectivity to operate safely and efficiently. Current automation systems use wired communication to guarantee low latency connectivity. But wired connections cannot be used to connect mobile robots and are also problematic to deploy at scale. Therefore, wireless connectivity is a necessity. On the other hand, it is subject to many external influences and cannot reach the same level of reliability as the wired communication systems. This thesis aims to address this problem by proposing methods to combine multiple unreliable wireless connections to a stable channel. The foundation for this work is Caterpillar Random Linear Network Coding (CRLNC), a new variant of network code designed to achieve low latency. CRLNC performs similar to block codes in recovery of lost packets, but with a significantly decreased latency. CRLNC with Feedback (CRLNC-FB) integrates a Selective-Repeat ARQ (SR-ARQ) to optimize the tradeoff between delay and throughput of reliable communication. The proposed protocol allows to slightly increase the overhead to reduce the packet delay at the receiver. With CRLNC, delay can be reduced by more than 50 % with only a 10 % reduction in throughput. Finally, CRLNC is combined with a statistical multipath scheduler to optimize the reliability and service availability in wireless network with multiple unreliable paths. This multipath CRLNC scheme improves the reliability of a fixed-rate packet stream by 10 % in a system model based on real-world measurements of LTE and WiFi. All the proposed protocols have been implemented in the software library NCKernel. With NCKernel, these protocols could be evaluated in simulated and emulated networks, and were also deployed in several real-world testbeds and demonstrators.:Abstract 2 Acknowledgements 6 1 Introduction 7 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2 Use Cases and Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 9 1.3 Opportunities of Multipath . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15 1.4 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2 State of the Art of Multipath Communication 19 2.1 Physical Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2 Data Link Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.3 Network Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 21 2.4 Transport Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.5 Application Layer and Session Layer . . . . . . . . . . . . . . . . . . . . . . . . . . . 23 2.6 Research Gap . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3 NCKernel: Network Coding Protocol Framework 27 3.1 Theory that matters! . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27 3.2 Requirements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 28 3.3 Implementation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.1 Socket Buffers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29 3.3.2 En-/Re-/Decoder API . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30 3.3.3 Configuration . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.4 Timers . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 3.3.5 Tracing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.4 Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 32 3.5 Protocols . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33 4 Low-Latency Network Coding 35 4.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35 4.2 Random Linear Network Coding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36 4.3 Low Latency Network Codes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 4.4 CRLNC: Caterpillar Random Linear Network Coding . . . . . . . . . . . . . . . . . . 38 4.4.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 38 4.4.2 Decoding . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.4.3 Computational Complexity . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43 4.5.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44 4.5.3 Packet Loss Probability . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45 4.5.4 Delay . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 46 4.5.5 Window Size Considerations . . . . . . . . . . . . . . . . . . . . . . . . . . . 48 4.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 5 Delay-Throughput Tradeoff 55 5.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.2 Network Coding with ARQ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55 5.3 CRLNC-FB: CRLNC with Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . 56 5.3.1 Encoding and Packet Format . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.2 Decoding and Feedback . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57 5.3.3 Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59 5.4 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.1 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 5.4.2 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.3 Systematic Retransmissions . . . . . . . . . . . . . . . . . . . . . . . . . . . 63 5.4.4 Coded Packet Memory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 5.4.5 Comparison with other Protocols . . . . . . . . . . . . . . . . . . . . . . . . 67 5.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 6 Multipath for Reliable Low-Latency Packet Streams 73 6.1 Problem Statement . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73 6.2 Related Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74 6.3 System Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.1 Traffic Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.2 Network Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75 6.3.3 Channel Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.3.4 Reliability Definition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 76 6.4 Multipath CRLNC . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77 6.4.1 Window Size for Heterogeneous Paths . . . . . . . . . . . . . . . . . . . . . 77 6.4.2 Packet Scheduling . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.1 Simulator . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79 6.5.2 Preliminary Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81 6.5.3 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82 6.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 7 Conclusion 94 7.1 Results and Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94 7.2 Future Research Topics . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95 Acronyms 99 Publications 101 Bibliography 103

Mobile Ad hoc Network of Networks (MANoN) are a group of large autonomous wireless nodes communicating on a peer-to-peer basis in a heterogeneous environment with no pre-defined infrastructure. In fact, each node by itself is an ad hoc network with its own management. MANoNs are evolvable systems, which mean each ad hoc network has the ability to perform separately under its own policies and management without affecting the main system; therefore, new ad hoc networks can emerge and disconnect from the MANoN without conflicting with the policies of other networks. The unique characteristics of MANoN makes such networks highly vulnerable to security attacks compared with wired networks or even normal mobile ad hoc networks. This thesis presents a novel security-management system based upon the Recommendation ITU-T M.3400, which is used to evaluate, report on the behaviour of our MANoN and then support complex services our system might need to accomplish. Our security management will concentrate on three essential components: Security Administration, Prevention and Detection and Containment and Recovery. In any system, providing one of those components is a problem; consequently, dealing with an infrastructure-less MANoN will be a dilemma, yet we approached each set group of these essentials independently, providing unusual solutions for each one of them but concentrating mainly on the prevention and detection category. The contributions of this research are threefold. First, we defined MANoN Security Architecture based upon the ITU-T Recommendations: X.800 and X.805. This security architecture provides a comprehensive, end-to-end security solution for MANoN that could be applied to every wireless network that satisfies a similar scenario, using such networks in order to predict, detect and correct security vulnerabilities. The security architecture identifies the security requirements needed, their objectives and the means by which they could be applied to every part of the MANoN, taking into consideration the different security attacks it could face. Second, realising the prevention component by implementing some of the security requirements identified in the Security Architecture, such as authentication, authorisation, availability, data confidentiality, data integrity and non-repudiation has been proposed by means of defining a novel Security Access Control Mechanism based on Threshold Cryptography Digital Certificates in MANoN. Network Simulator (NS-2) is a real network environment simulator, which is used to test the performance of the proposed security mechanism and demonstrate its effectiveness. Our ACM-MANoN results provide a fully distributed security protocol that provides a high level of secure, available, scalable, flexible and efficient management services for MANoN. The third contribution is realising the detection component, which is represented by providing a Behavioural Detection Mechanism based on nodes behavioural observation engaged with policies. This behaviour mechanism will be used to detect malicious nodes acting to bring the system down. This approach has been validated using an attacks case study in an unknown military environment to cope with misbehaving nodes.

New event detection, also known as first story detection (FSD), has become very popular in recent years. The task consists of finding previously unseen events from a stream of documents. Despite the apparent simplicity, FSD is very challenging and has applications anywhere where timely access to fresh information is crucial: from journalism to stock market trading, homeland security, or emergency response. With the rise of user generated content and citizen journalism we have entered an era of big and noisy data, yet traditional approaches for solving FSD are not designed to deal with this new type of data. The amount of information that is being generated today exceeds by many orders of magnitude previously available datasets, making traditional approaches obsolete for modern event detection. In this thesis, we propose a modern approach to event detection that scales to unbounded streams of text, without sacrificing accuracy. This is a crucial property that enables us to detect events from large streams like Twitter, which none of the previous approaches were able to do. One of the major problems in detecting new events is vocabulary mismatch, also known as lexical variation. This problem is characterized by different authors using different words to describe the same event, and it is inherent to human language. We show how to mitigate this problem in FSD by using paraphrases. Our approach that uses paraphrases achieves state-of-the-art results on the FSD task, while still maintaining efficiency and being able to process unbounded streams. Another important property of user generated content is the high level of noise, and Twitter is no exception. This is another problem that traditional approaches were not designed to deal with, and here we investigate different methods of reducing the amount of noise. We show that by using information from Wikipedia, it is possible to significantly reduce the amount of spurious events detected in Twitter, while maintaining a very small latency in detection. A question is often raised as to whether Twitter is at all useful, especially if one has access to a high-quality stream such as the newswire, or if it should be considered as sort of a poor man’s newswire. In our comparison of these two streams we find that Twitter contains events not present in the newswire, and that it also breaks some events sooner, showing that it is useful for event detection, even in the presence of newswire.

Natural Language Processing systems crucially depend on the availability of lexical and conceptual knowledge representations. They need to be able to disambiguate word senses and detect synonyms. In order to draw inferences, they require access to hierarchical relations between concepts (dog isAn animal) as well as non-hierarchical ones (gasoline fuels car). Knowledge resources such as lexical databases, semantic networks and ontologies explicitly encode such conceptual knowledge. However, traditionally, these have been manually created, which is expensive and time consuming for large re- sources, and cannot provide adequate coverage in specialised domains. In order to alleviate this acquisition bottleneck, statistical methods have been created to acquire lexical and conceptual knowledge automatically from text. In particular, unsupervised techniques have the advantage that they can be easily adapted to any domain, given some corpus on the topic. However, due to sparseness issues, they often require very large corpora to achieve high quality results. The spectrum of resources and statistical methods has a crucial gap in situations when manually cre- ated resources do not provide the necessary coverage and only limited corpora are available. This is the case for real-world domain applications such as an NLP system for processing technical information based on a limited amount of company documentation. We provide a large-scale demonstration that this gap can be filled through the use of automatically generated networks. The corpus is automatically transformed into a network representing the terms or concepts which occur in the text and their relations, based entirely on linguistic tools. The net- works structurally lie in between the unstructured corpus and the highly structured manually created resources. We show that they can be useful in situations for which neither existing approach is ap- plicable. In contrast to manually created resources, our networks can be generated quickly and on demand. Conversely, they make it possible to achieve higher quality representations from less text than corpus-based methods, relieving the requirement of very large scale corpora. We devise scaleable frameworks for building networks from plain text and Wikipedia with varying levels of expressiveness. This work creates concrete networks from the entire British National Corpus covering 1.2m terms and 21m relations and a Wikipedia network covering 2.7m concepts. We develop a network-based semantic space model and evaluate it on the task of measuring semantic relatedness. In addition, noun compound paraphrasing is tackled to demonstrate the quality of the indirect paths in the network for concept relation description. On both evaluations we achieve results competitive to the state of the art. In particular, our network-based methods outperform corpus-based methods, demonstrating the gain created by leveraging the network structure.

Modern data centres provide large aggregate capacity in the backbone of networks so that servers can theoretically communicate with each other at their maximum rates. However, the Transport Control Protocol (TCP) cannot efficiently use this large capacity even if Equal-Cost Multi-Path (ECMP) routing is enabled to exploit the existence of parallel paths. MultiPath TCP (MPTCP) can effectively use the network resources of such topologies by performing fast distributed load balancing. MPTCP is an appealing technique for data centres that are very dynamic in nature. However, it is ill-suited for handling short flows since it increases their flow completion time. To mitigate these problems, we propose Maximum MultiPath TCP (MMPTCP), a novel transport protocol for modern data centres. Unlike MPTCP, it provides high performance for all network flows. It also decreases the bursty nature of data centres, which is essentially rooted in traffic patterns of short flows. MMPTCP achieves these nice features by randomising a flow's packets via all parallel paths to a destination during the initial phase of data transmission until a certain amount of data is delivered. It then switches to MPTCP with several subflows in which data transmission is governed by MPTCP congestion control. In this way, short flows are delivered very fast via the initial phase only, and long flows are delivered by MPTCP with several subflows. We evaluate MMPTCP in a FatTree topology under various network conditions. We found that MMPTCP decreases the loss rate of all the links throughout the network and helps competing flows to achieve a better performance. Unlike MPTCP with a fixed number of subflows, MMPTCP offers high burst tolerance and low-latency for short flows while it maintains high overall network utilisation. MMPTCP is incrementally deployable in existing data centres because it does not require any modification to the network and application layers.

Mobile wireless ad hoc networks (MANETs) have become of increasing interest in view of their promise to extend connectivity beyond traditional fixed infrastructure networks. In MANETs, the task of routing is distributed among network nodes which act as both end points and routers in a wireless multi-hop network environment. To discover a route to a specific destination node, existing on-demand routing protocols employ a broadcast scheme referred to as simple flooding whereby a route request packet (RREQ) originating from a source node is blindly disseminated to the rest of the network nodes. This can lead to excessive redundant retransmissions, causing high channel contention and packet collisions in the network, a phenomenon called a broadcast storm. To reduce the deleterious impact of flooding RREQ packets, a number of route discovery algorithms have been suggested over the past few years based on, for example, location, zoning or clustering. Most such approaches however involve considerably increased complexity requiring additional hardware or the maintenance of complex state information. This research argues that such requirements can be largely alleviated without sacrificing performance gains through the use of probabilistic broadcast methods, where an intermediate node rebroadcasts RREQ packets based on some suitable forwarding probability rather than in the traditional deterministic manner. Although several probabilistic broadcast algorithms have been suggested for MANETs in the past, most of these have focused on “pure” broadcast scenarios with relatively little investigation of the performance impact on specific applications such as route discovery. As a consequence, there has been so far very little study of the performance of probabilistic route discovery applied to the well-established MANET routing protocols. In an effort to fill this gap, the first part of this thesis evaluates the performance of the routing protocols Ad hoc On demand Distance Vector (AODV) and Dynamic Source Routing (DSR) augmented with probabilistic route discovery, taking into account parameters such as network density, traffic density and nodal mobility. The results reveal encouraging benefits in overall routing control overhead but also show that network operating conditions have a critical impact on the optimality of the forwarding probabilities. In most existing probabilistic broadcast algorithms, including the one used here for preliminary investigations, each forwarding node is allowed to rebroadcast a received packet with a fixed forwarding probability regardless of its relative location with respect to the locations of the source and destination pairs. However, in a route discovery operation, if the location of the destination node is known, the dissemination of the RREQ packets can be directed towards this location. Motivated by this, the second part of the research proposes a probabilistic route discovery approach that aims to reduce further the routing overhead by limiting the dissemination of the RREQ packets towards the anticipated location of the destination. This approach combines elements of the fixed probabilistic and flooding-based route discovery approaches. The results indicate that in a relatively dense network, these combined effects can reduce the routing overhead very significantly when compared with that of the fixed probabilistic route discovery. Typically in a MANET there are regions of varying node density. Under such conditions, fixed probabilistic route discovery can suffer from a degree of inflexibility, since every node is assigned the same forwarding probability regardless of local conditions. Ideally, the forwarding probability should be high for a node located in a sparse region of the network while relatively lower for a node located in a denser region of the network. As a result, it can be helpful to identify and categorise mobile nodes in the various regions of the network and appropriately adjust their forwarding probabilities. To this end the research examines probabilistic route discovery methods that dynamically adjust the forwarding probability at a node, based on local node density, which is estimated using number of neighbours as a parameter. Results from this study return significantly superior performance measures compared with fixed probabilistic variants. Although the probabilistic route discovery methods suggested above can significantly reduce the routing control overhead without degrading the overall network throughput, there remains the problem of how to select efficiently forwarding probabilities that will optimize the performance of a broadcast under any given conditions. In an attempt to address this issue, the final part of this thesis proposes and evaluates the feasibility of a node estimating its own forwarding probability dynamically based on locally collected information. The technique examined involves each node piggybacking a list of its 1-hop neighbours in its transmitted RREQ packets. Based on this list, relay nodes can determine the number of neighbours that have been already covered by a broadcast and thus compute the forwarding probabilities most suited to individual circumstances.