JANUS: A Framework for Distributed Management of Wireless Mesh Networks

Abstract — Wireless Mesh Networks (WMNs) are emerging as a potentially attractive access architecture for metropolitan-scale networks. While research on WMNs has been up to a large extent confined to the study of efficient routing protocols, there is a clear need to envision new network management tools, able to sufficiently exploit the peculiarities of WMNs. In particular, a new generation of middleware tools for network monitoring and profiling must be introduced in order to speed up development and testing of novel protocol architectures. Currently, manage-ment functionalities are developed using conventional central-ized approaches. The distributed and self-organizing nature of WMNs suggest a transition from network monitoring to network sensing. In this work, we propose JANUS, a novel framework for distributed monitoring of WMNs. We describe the JANUS architecture, present a possible implementation based on open-source software and report some experimental measurements carried out on a small-scale testbed. Index Terms — wireless mesh networks, network management, distributed hash table, overlay networks, publish-subscribe sys-tems I

Media handling for conferencing in MANETs

Mobile Ad hoc NETworks (MANETs) are formed by devices set up temporarily to communicate without using a pre-existing network infrastructure. Devices in these networks are disparate in terms of resource capabilities (e.g. processing power, battery energy). Multihop Cellular Networks (MCNs) incorporate multihop mobile ad-hoc paradigms into 3G conventional single-hop cellular networks. Conferencing, an essential category of applications in MANETs and MCNs, includes popular applications such as audio/video conferencing. It is defined as an interactive multimedia service comprising online exchange of multimedia content among several users. Conferencing requires two sessions: a call signaling session and a media handling session. Call signaling is used to set up, modify, and tear down conference sessions. Media handling deals with aspects such as media transportation, media mixing, and transcoding. In this thesis, we are concerned with media handling for conferencing in MANETs and MCNs. We propose an architecture based on two overlay networks: one for mixing and one for control. The first overlay is composed of nodes acting as mixers. Each node in the network has a media connection with one mixer in the first overlay. A novel distributed mixing architecture that minimizes the number of mixers in end-to-end paths is proposed as an architectural solution for this first overlay. A sub-network of nodes, called controllers, composes the second overlay. Each controller controls a set of mixers, and collectively, they manage and control the two-overlay network. The management and control tasks are assured by a media signaling architecture based on an extended version of Megaco/H.L248. The two-overlay network is self-organizing, and thus automatically assigns users to mixers, controls mixers and controllers, and recovers the network from failures. We propose a novel self-organizing scheme that has three components: self-growing, self-shrinking and self-healing. Self-growing and self-shrinking use novel workload balancing schemes that make decisions to enable and disable mixers and controllers. The workload balancing schemes use resources efficiently by balancing the load among the nodes according to their capabilities. Self-healing detects failed nodes and recovers the network when failures of nodes with responsibilities (mixers and controllers) occur. Detection of failed nodes is based on a novel application-level failure detection architecture. A novel architecture for media handling in MCNs is proposed. We use mediator concepts to connect the media handling entities of a MANET with the media entities of a 3G cellular network. A media mediator assures signaling and media connectivity between the two networks and acts as a translator of the different media handling protocols

A Framework for the Self-Configuration of Wireless Mesh Networks

The use of wireless radio technology is well established for narrowband access systems, but its use for broadband access is relatively new. Wireless mesh architecture is a first step towards providing high-bandwidth wireless network coverage, spectral efficiency, and economic advantage. However, the widespread adoption and use of Wireless Mesh Networks (WMN) as a backbone for large wireless access networks and for last-mile subscriber access is heavily dependent on the technology’s ease of deployment. In order for WMNs to be regarded as mainstream technology, it needs to gain a competitive edge compared to wireline technologies such as DSL and cable. To achieve this, a broadband wireless network must be self-configuring, self-healing and self-organizing. In this thesis, we address these challenges. First, we propose a four-stage scheme (power-up, bootstrapping, network registration, and network optimization). We develop algorithms for each of these stages, taking advantage of the inherent properties of WMNs to determine the network’s topology. The novel part of our scheme is in the de-coupling of the subscriber’s credentials from the network hardware. This is a key part of our architecture as it helps ensure quick network enrolment, management and portability. It also helps, in our opinion, make the concept of widespread deployment using commodity hardware feasible

Reinforcement Learning in Self Organizing Cellular Networks

Self-organization is a key feature as cellular networks densify and become more heterogeneous, through the additional small cells such as pico and femtocells. Self- organizing networks (SONs) can perform self-configuration, self-optimization, and self-healing. These operations can cover basic tasks such as the configuration of a newly installed base station, resource management, and fault management in the network. In other words, SONs attempt to minimize human intervention where they use measurements from the network to minimize the cost of installation, configuration, and maintenance of the network. In fact, SONs aim to bring two main factors in play: intelligence and autonomous adaptability. One of the main requirements for achieving such goals is to learn from sensory data and signal measurements in networks. Therefore, machine learning techniques can play a major role in processing underutilized sensory data to enhance the performance of SONs. In the first part of this dissertation, we focus on reinforcement learning as a viable approach for learning from signal measurements. We develop a general framework in heterogeneous cellular networks agnostic to the learning approach. We design multiple reward functions and study different effects of the reward function, Markov state model, learning rate, and cooperation methods on the performance of reinforcement learning in cellular networks. Further, we look into the optimality of reinforcement learning solutions and provide insights into how to achieve optimal solutions. In the second part of the dissertation, we propose a novel architecture based on spatial indexing for system-evaluation of heterogeneous 5G cellular networks. We develop an open-source platform based on the proposed architecture that can be used to study large scale directional cellular networks. The proposed platform is used for generating training data sets of accurate signal-to-interference-plus-noise-ratio (SINR) values in millimeter-wave communications for machine learning purposes. Then, with taking advantage of the developed platform, we look into dense millimeter-wave networks as one of the key technologies in 5G cellular networks. We focus on topology management of millimeter-wave backhaul networks and study and provide multiple insights on the evaluation and selection of proper performance metrics in dense millimeter-wave networks. Finally, we finish this part by proposing a self-organizing solution to achieve k-connectivity via reinforcement learning in the topology management of wireless networks

Self-organization and management of wireless sensor networks

Wireless sensor networks (WSNs) are a newly deployed networking technology consisting of multifunctional sensor nodes that are small in size and communicate over short distances. These sensor nodes are mainly in large numbers and are densely deployed either inside the phenomenon or very close to it. They can be used for various application areas (e.g. health, military, home). WSNs provide several advantages over traditional networks, such as large-scale deployment, highresolution sensed data, and application adaptive mechanisms. However, due to their unique characteristics (having dynamic topology, ad-hoc and unattended deployment, huge amount of data generation and traffic flow, limited bandwidth and energy), WSNs pose considerable challenges for network management and make application development nontrivial. Management of wireless sensor networks is extremely important in order to keep the whole network and application work properly and continuously. Despite the importance of sensor network management, there is no generalize solution available for managing and controlling these resource constrained WSNs. In network management of WSNs, energy-efficient network selforganization is one of the main challenging issues. Self-organization is the property which the sensor nodes must have to organize themselves to form the network. Selforganization of WSNs is challenging because of the tight constraints on the bandwidth and energy resources available in these networks. A self organized sensor network can be clustered or grouped into an easily manageable network. However, existing clustering schemes offer various limitations. For example, existing clustering schemes consume too much energy in cluster formation and re-formation. This thesis presents a novel cellular self-organizing hierarchical architecture for wireless sensor networks. The cellular architecture extends the network life time by efficiently utilizing nodes energy and support the scalability of the system. We have analyzed the performance of the architecture analytically and by simulations. The results obtained from simulation have shown that our cellular architecture is more energy efficient and achieves better energy consumption distribution. The cellular architecture is then mapped into a management framework to support the network management system for resource constraints WSNs. The management framework is self-managing and robust to changes in the network. It is application-co-operative and optimizes itself to support the unique requirements of each application. The management framework consists of three core functional areas i.e., configuration management, fault management, and mobility management. For configuration management, we have developed a re-configuration algorithm to support sensor networks to energy-efficiently re-form the network topology due to network dynamics i.e. node dying, node power on and off, new node joining the network and cells merging. In the area of fault management we have developed a new fault management mechanism to detect failing nodes and recover the connectivity in WSNs. For mobility management, we have developed a two phase sensor relocation solution: redundant mobile sensors are first identified and then relocated to the target location to deal with coverage holes. All the three functional areas have been evaluated and compared against existing solutions. Evaluation results show a significant improvement in terms of re-configuration, failure detection and recovery, and sensors relocation

A cell outage management framework for dense heterogeneous networks

In this paper, we present a novel cell outage management (COM) framework for heterogeneous networks with split control and data planes-a candidate architecture for meeting future capacity, quality-of-service, and energy efficiency demands. In such an architecture, the control and data functionalities are not necessarily handled by the same node. The control base stations (BSs) manage the transmission of control information and user equipment (UE) mobility, whereas the data BSs handle UE data. An implication of this split architecture is that an outage to a BS in one plane has to be compensated by other BSs in the same plane. Our COM framework addresses this challenge by incorporating two distinct cell outage detection (COD) algorithms to cope with the idiosyncrasies of both data and control planes. The COD algorithm for control cells leverages the relatively larger number of UEs in the control cell to gather large-scale minimization-of-drive-test report data and detects an outage by applying machine learning and anomaly detection techniques. To improve outage detection accuracy, we also investigate and compare the performance of two anomaly-detecting algorithms, i.e., k-nearest-neighbor- and local-outlier-factor-based anomaly detectors, within the control COD. On the other hand, for data cell COD, we propose a heuristic Grey-prediction-based approach, which can work with the small number of UE in the data cell, by exploiting the fact that the control BS manages UE-data BS connectivity and by receiving a periodic update of the received signal reference power statistic between the UEs and data BSs in its coverage. The detection accuracy of the heuristic data COD algorithm is further improved by exploiting the Fourier series of the residual error that is inherent to a Grey prediction model. Our COM framework integrates these two COD algorithms with a cell outage compensation (COC) algorithm that can be applied to both planes. Our COC solution utilizes an actor-critic-based reinforcement learning algorithm, which optimizes the capacity and coverage of the identified outage zone in a plane, by adjusting the antenna gain and transmission power of the surrounding BSs in that plane. The simulation results show that the proposed framework can detect both data and control cell outage and compensate for the detected outage in a reliable manner

Introducing mobile edge computing capabilities through distributed 5G Cloud Enabled Small Cells

Current trends in broadband mobile networks are addressed towards the placement of different capabilities at the edge of the mobile network in a centralised way. On one hand, the split of the eNB between baseband processing units and remote radio headers makes it possible to process some of the protocols in centralised premises, likely with virtualised resources. On the other hand, mobile edge computing makes use of processing and storage capabilities close to the air interface in order to deploy optimised services with minimum delay. The confluence of both trends is a hot topic in the definition of future 5G networks. The full centralisation of both technologies in cloud data centres imposes stringent requirements to the fronthaul connections in terms of throughput and latency. Therefore, all those cells with limited network access would not be able to offer these types of services. This paper proposes a solution for these cases, based on the placement of processing and storage capabilities close to the remote units, which is especially well suited for the deployment of clusters of small cells. The proposed cloud-enabled small cells include a highly efficient microserver with a limited set of virtualised resources offered to the cluster of small cells. As a result, a light data centre is created and commonly used for deploying centralised eNB and mobile edge computing functionalities. The paper covers the proposed architecture, with special focus on the integration of both aspects, and possible scenarios of application.Peer ReviewedPostprint (author's final draft