Mobility Management in beyond 3G-Environments

Beyond 3G-environments are typically defined as environments that integrate different wireless and fixed access network technologies. In this paper, we address IP based Mobility Management (MM) in beyond 3G-environments with a focus on wireless access networks, motivated by the current trend of WiFi, GPRS, and UMTS networks. The GPRS and UMTS networks provide countrywide network access, while the WiFi networks provide network access in local areas such as city centres and airports. As a result, mobile end-users can be always on-line and connected to their preferred network(s), these network preferences are typically stored in a user profile. For example, an end-user who wishes to be connected with highest bandwidth could be connected to a WiFi network when available and fall back to GPRS when moving outside the hotspot area.\ud In this paper, we consider a combination of MM for legacy services (like web browsing, telnet, etc.) using Mobile IP and multimedia services using SIP. We assume that the end-user makes use of multi-interface terminals with the capability of selecting one or more types of access networks\ud based on preferences. For multimedia sessions, like VoIP or streaming video, we distinguish between changes in network access when the end-user is in a session or not in a session. If the end-user is not in a session, he or she needs to be able to start new sessions and receive invitations for new sessions. If the end-user is in a session, the session needs to be handed over to the new access network as seamless as possible from the perspective of the end-user. We propose an integrated but flexible solution to these problems that facilitates MM with a customizable transparency to applications and end-users

Jointly Optimizing Placement and Inference for Beacon-based Localization

The ability of robots to estimate their location is crucial for a wide variety of autonomous operations. In settings where GPS is unavailable, measurements of transmissions from fixed beacons provide an effective means of estimating a robot's location as it navigates. The accuracy of such a beacon-based localization system depends both on how beacons are distributed in the environment, and how the robot's location is inferred based on noisy and potentially ambiguous measurements. We propose an approach for making these design decisions automatically and without expert supervision, by explicitly searching for the placement and inference strategies that, together, are optimal for a given environment. Since this search is computationally expensive, our approach encodes beacon placement as a differential neural layer that interfaces with a neural network for inference. This formulation allows us to employ standard techniques for training neural networks to carry out the joint optimization. We evaluate this approach on a variety of environments and settings, and find that it is able to discover designs that enable high localization accuracy.Comment: Appeared at 2017 International Conference on Intelligent Robots and Systems (IROS

Deep Q-Learning for Self-Organizing Networks Fault Management and Radio Performance Improvement

We propose an algorithm to automate fault management in an outdoor cellular network using deep reinforcement learning (RL) against wireless impairments. This algorithm enables the cellular network cluster to self-heal by allowing RL to learn how to improve the downlink signal to interference plus noise ratio through exploration and exploitation of various alarm corrective actions. The main contributions of this paper are to 1) introduce a deep RL-based fault handling algorithm which self-organizing networks can implement in a polynomial runtime and 2) show that this fault management method can improve the radio link performance in a realistic network setup. Simulation results show that our proposed algorithm learns an action sequence to clear alarms and improve the performance in the cellular cluster better than existing algorithms, even against the randomness of the network fault occurrences and user movements.Comment: (c) 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other work

Robotic ubiquitous cognitive ecology for smart homes

Robotic ecologies are networks of heterogeneous robotic devices pervasively embedded in everyday environments, where they cooperate to perform complex tasks. While their potential makes them increasingly popular, one fundamental problem is how to make them both autonomous and adaptive, so as to reduce the amount of preparation, pre-programming and human supervision that they require in real world applications. The project RUBICON develops learning solutions which yield cheaper, adaptive and efficient coordination of robotic ecologies. The approach we pursue builds upon a unique combination of methods from cognitive robotics, machine learning, planning and agent- based control, and wireless sensor networks. This paper illustrates the innovations advanced by RUBICON in each of these fronts before describing how the resulting techniques have been integrated and applied to a smart home scenario. The resulting system is able to provide useful services and pro-actively assist the users in their activities. RUBICON learns through an incremental and progressive approach driven by the feed- back received from its own activities and from the user, while also self-organizing the manner in which it uses available sensors, actuators and other functional components in the process. This paper summarises some of the lessons learned by adopting such an approach and outlines promising directions for future work

Multinet : enabler for next generation enterprise wireless services

Wireless communications are currently experiencing a fast migration toward the beyond third-generation (B3G)/fourth generation (4G) era. This represents a generational change in wireless systems: new capabilities related to mobility and new services support is required and new concepts as individual-centric, user-centric or ambient-aware communications are included. One of the main restrictions associated to wireless technology is mobility management, this feature was not considered in the design phase; for this reason, a complete solution is not already found, although different solutions are proposed and are being proposed. In MULTINET project, features as mobility and multihoming are applied to wireless network to provide the necessary network and application functionality enhancements for seamless data communication mobility considering end-user scenario and preferences. The aim of this paper is to show the benefits of these functionalities from the Service Providers and final User point of view