D3M: multicast listener mobility support mechanisms over distributed mobility anchoring architectures

The explosion in mobile data traffic is a driver for future network operator technologies, given its large potential to affect both network performance and generated revenue. The concept of distributed mobility management (DMM) has emerged in order to overcome efficiency-wise limitations in centralized mobility approaches, proposing not only the distribution of anchoring functions but also dynamic mobility activation sensitive to the applications needs. Nevertheless, there is not an acceptable solution for IP multicast in DMM environments, as the first proposals based on MLD Proxy are prone to tunnel replication problem or service disruption. We propose the application of PIM-SM in mobility entities as an alternative solution for multicast support in DMM, and introduce an architecture enabling mobile multicast listeners support over distributed anchoring frameworks in a network-efficient way. The architecture aims at providing operators with flexible options to provide multicast mobility, supporting three modes: the first one introduces basic IP multicast support in DMM; the second improves subscription time through extensions to the mobility protocol, obliterating the dependence on MLD protocol; and the third enables fast listener mobility by avoiding potentially slow multicast tree convergence latency in larger infrastructures, by benefiting from mobility tunnels. The different modes were evaluated by mathematical analysis regarding disruption time and packet loss during handoff against several parameters, total and tunneling packet delivery cost, and regarding packet and signaling overhead.

IP Mobility in Wireless Operator Networks

Wireless network access is gaining increased heterogeneity in terms of the types of IP capable access technologies. The access network heterogeneity is an outcome of incremental and evolutionary approach of building new infrastructure. The recent success of multi-radio terminals drives both building a new infrastructure and implicit deployment of heterogeneous access networks. Typically there is no economical reason to replace the existing infrastructure when building a new one. The gradual migration phase usually takes several years. IP-based mobility across different access networks may involve both horizontal and vertical handovers. Depending on the networking environment, the mobile terminal may be attached to the network through multiple access technologies. Consequently, the terminal may send and receive packets through multiple networks simultaneously. This dissertation addresses the introduction of IP Mobility paradigm into the existing mobile operator network infrastructure that have not originally been designed for multi-access and IP Mobility. We propose a model for the future wireless networking and roaming architecture that does not require revolutionary technology changes and can be deployed without unnecessary complexity. The model proposes a clear separation of operator roles: (i) access operator, (ii) service operator, and (iii) inter-connection and roaming provider. The separation allows each type of an operator to have their own development path and business models without artificial bindings with each other. We also propose minimum requirements for the new model. We present the state of the art of IP Mobility. We also present results of standardization efforts in IP-based wireless architectures. Finally, we present experimentation results of IP-level mobility in various wireless operator deployments.

Decentralizing IP mobility management in future networks

The massive adoption of sophisticated mobile devices and applications led to the increase of mobile data in the last decade, which it is expected to continue. This increase of mobile data negatively impacts the network planning and dimension, since core networks are heavy centralized. Mobile operators are investigating atten network architectures that distribute the responsibility of providing connectivity and mobility, in order to improve the network scalability and performance. Moreover, service providers are moving the content servers closer to the user, in order to ensure high availability and performance of content delivery. Besides the e orts to overcome the explosion of mobile data, current mobility management models are heavy centralized to ensure reachability and session continuity to the users connected to the network. Nowadays, deployed architectures have a small number of centralized mobility anchors managing the mobile data and the mobility context of millions of users, which introduces issues related to performance and scalability that require costly network mechanisms. The mobility management needs to be rethought out-of-the box to cope with atten network architectures and distributed content servers closer to the user, which is the purpose of the work developed in this Thesis. The Thesis starts with a characterization of mobility management into well-de ned functional blocks, their interaction and potential grouping. The decentralized mobility management is studied through analytical models and simulations, in which di erent mobility approaches distinctly distribute the mobility management functionalities through the network. The outcome of this study showed that decentralized mobility management brings advantages. Hence, it was proposed a novel distributed and dynamic mobility management approach, which is exhaustively evaluated through analytical models, simulations and testbed experiments. The proposed approach is also integrated with seamless horizontal handover mechanisms, as well as evaluated in vehicular environments. The mobility mechanisms are also speci ed for multihomed scenarios, in order to provide data o oading with IP mobility from cellular to other access networks. In the pursuing of the optimized mobile routing path, a novel network-based strategy for localized mobility is addressed, in which a replication binding system is deployed in the mobility anchors distributed through the access routers and gateways. Finally, we go further in the mobility anchoring subject, presenting a context-aware adaptive IP mobility anchoring model that dynamically assigns the mobility anchors that provide the optimized routing path to a session, based on the user and network context. The integration of dynamic and distributed concepts in the mobility management, such as context-aware adaptive mobility anchoring and dynamic mobility support, allow the optimization of network resources and the improvement of user experience. The overall outcome demonstrates that decentralized mobility management is a promising direction, hence, its ideas should be taken into account by mobile operators in the deployment of future networks.

Efficient Content-based Routing, Mobility-aware Topologies, and Temporal Subspace Matching

Event-based systems are seen as good candidates for supporting distributed applications in dynamic and ubiquitous environments because they support decoupled and asynchronous many-to-many information dissemination. Event systems are widely used, because asynchronous messaging provides a flexible alternative to RPC (Remote Procedure Call). They are typically implemented using an overlay network of routers. A content-based router forwards event messages based on filters that are installed by subscribers and other routers. The filters are organized into a routing table in order to forward incoming events to proper subscribers and neighbouring routers. This thesis addresses the optimization of content-based routing tables organized using the covering relation and presents novel data structures and configurations for improving local and distributed operation. Data structures are needed for organizing filters into a routing table that supports efficient matching and runtime operation. We present novel results on dynamic filter merging and the integration of filter merging with content-based routing tables. In addition, the thesis examines the cost of client mobility using different protocols and routing topologies. We also present a new matching technique called temporal subspace matching. The technique combines two new features. The first feature, temporal operation, supports notifications, or content profiles, that persist in time. The second feature, subspace matching, allows more expressive semantics, because notifications may contain intervals and be defined as subspaces of the content space. We also present an application of temporal subspace matching pertaining to metadata-based continuous collection and object tracking.

Neural network-based detection of virtual environment anomalies

The increasingly widespread use of large-scale 3D virtual environments has translated into an increasing effort required from designers, developers and testers. While considerable research has been conducted into assisting the design of virtual world content and mechanics, to date, only limited contributions have been made regarding the automatic testing of the underpinning graphics software and hardware. In the work presented in this paper, two novel neural network-based approaches are presented to predict the correct visualization of 3D content. Multilayer perceptrons and self-organizing maps are trained to learn the normal geometric and color appearance of objects from validated frames and then used to detect novel or anomalous renderings in new images. Our approach is general, for the appearance of the object is learned rather than explicitly represented. Experiments were conducted on a game engine to determine the applicability and effectiveness of our algorithms. The results show that the neural network technology can be effectively used to address the problem of automatic and reliable visual testing of 3D virtual environments.

A New DER coordination in LV network based on the concept of distributed control

Given the paradigm of smart grid as the promising backbone for future network, this paper uses this paradigm to propose a new coordination approach for LV network based on distributed control algorithm. This approach divides the LV network into hierarchical communities where each community is controlled by a control agent. Different level of communication has been proposed for this structure to control the network in different operation modes.

A neural-network based flight controller for UASs

This paper presents a nonlinear gust-attenuation controller based on constrained neural-network (NN) theory. The controller aims to achieve sufficient stability and handling quality for a fixed-wing unmanned aerial system (UAS) in a gusty environment when control inputs are subjected to constraints. Constraints in inputs emulate situations where aircraft actuators fail requiring the aircraft to be operated with fail-safe capability. The proposed controller enables gust-attenuation property and stabilizes the aircraft dynamics in a gusty environment. The proposed flight controller is obtained by solving the Hamilton-Jacobi-Isaacs (HJI) equations based on an policy iteration (PI) approach. Performance of the controller is evaluated using a high-fidelity six degree-of-freedom Shadow UAS model. Simulations show that our controller demonstrates great performance improvement in a gusty environment, especially in angle-of-attack (AOA), pitch and pitch rate. Comparative studies are conducted with the proportional-integral-derivative (PID) controllers, justifying the efficiency of our controller and verifying its suitability for integration into the design of flight control systems for forced landing of UASs.

Sensor Network Based AUV Localisation

The operation of Autonomous Underwater Vehicles (AUVs) within underwater sensor network fields provides an opportunity to reuse the network infrastructure for long baseline localisation of the AUV. Computationally efficient localisation can be accomplished using off-the-shelf hardware that is comparatively inexpensive and which could already be deployed in the environment for monitoring purposes. This paper describes the development of a particle filter based localisation system which is implemented onboard an AUV in real-time using ranging information obtained from an ad-hoc underwater sensor network. An experimental demonstration of this approach was conducted in a lake with results presented illustrating network communication and localisation performance.

University of Glasgow (qirdcsuog) at TREC Crowdsourcing 2011 : TurkRank-network-based worker ranking in crowdsourcing

For TREC Crowdsourcing 2011 (Stage 2) we propose a networkbased approach for assigning an indicative measure of worker trustworthiness in crowdsourced labelling tasks. Workers, the gold standard and worker/gold standard agreements are modelled as a network. For the purpose of worker trustworthiness assignment, a variant of the PageRank algorithm, named TurkRank, is used to adaptively combine evidence that suggests worker trustworthiness, i.e., agreement with other trustworthy co-workers and agreement with the gold standard. A single parameter controls the importance of co-worker agreement versus gold standard agreement. The TurkRank score calculated for each worker is incorporated with a worker-weighted mean label aggregation.

A gray-box neural network-based model identification and fault estimation scheme for nonlinear dynamic systems

A novel gray-box neural network model (GBNNM), including multi-layer perception (MLP) neural network (NN) and integrators, is proposed for a model identification and fault estimation (MIFE) scheme. With the GBNNM, both the nonlinearity and dynamics of a class of nonlinear dynamic systems can be approximated. Unlike previous NN-based model identification methods, the GBNNM directly inherits system dynamics and separately models system nonlinearities. This model corresponds well with the object system and is easy to build. The GBNNM is embedded online as a normal model reference to obtain the quantitative residual between the object system output and the GBNNM output. This residual can accurately indicate the fault offset value, so it is suitable for differing fault severities. To further estimate the fault parameters (FPs), an improved extended state observer (ESO) using the same NNs (IESONN) from the GBNNM is proposed to avoid requiring the knowledge of ESO nonlinearity. Then, the proposed MIFE scheme is applied for reaction wheels (RW) in a satellite attitude control system (SACS). The scheme using the GBNNM is compared with other NNs in the same fault scenario, and several partial loss of effect (LOE) faults with different severities are considered to validate the effectiveness of the FP estimation and its superiority.

Convolutional Neural Network-based place recognition

Recently Convolutional Neural Networks (CNNs) have been shown to achieve state-of-the-art performance on various classification tasks. In this paper, we present for the first time a place recognition technique based on CNN models, by combining the powerful features learnt by CNNs with a spatial and sequential filter. Applying the system to a 70 km benchmark place recognition dataset we achieve a 75% increase in recall at 100% precision, significantly outperforming all previous state of the art techniques. We also conduct a comprehensive performance comparison of the utility of features from all 21 layers for place recognition, both for the benchmark dataset and for a second dataset with more significant viewpoint changes.

Artificial Neural Network Based Automatic Emergency Landing Site Selection for UAVs and Highly Automated Aircraft

In this report an artificial neural network (ANN) based automated emergency landing site selection system for unmanned aerial vehicle (UAV) and general aviation (GA) is described. The system aims increase safety of UAV operation by emulating pilot decision making in emergency landing scenarios using an ANN to select a safe landing site from available candidates. The strength of an ANN to model complex input relationships makes it a perfect system to handle the multicriteria decision making (MCDM) process of emergency landing site selection. The ANN operates by identifying the more favorable of two landing sites when provided with an input vector derived from both landing site's parameters, the aircraft's current state and wind measurements. The system consists of a feed forward ANN, a pre-processor class which produces ANN input vectors and a class in charge of creating a ranking of landing site candidates using the ANN. The system was successfully implemented in C++ using the FANN C++ library and ROS. Results obtained from ANN training and simulations using randomly generated landing sites by a site detection simulator data verify the feasibility of an ANN based automated emergency landing site selection system.