Massive M2M Access with Reliability Guarantees in LTE Systems

Machine-to-Machine (M2M) communications are one of the major drivers of the cellular network evolution towards 5G systems. One of the key challenges is on how to provide reliability guarantees to each accessing device in a situation in which there is a massive number of almost-simultaneous arrivals from a large set of M2M devices. The existing solutions take a reactive approach in dealing with massive arrivals, such as non-selective barring when a massive arrival event occurs, which implies that the devices cannot get individual reliability guarantees. In this paper we propose a proactive approach, based on a standard operation of the cellular access. The access procedure is divided into two phases, an estimation phase and a serving phase. In the estimation phase the number of arrivals is estimated and this information is used to tune the amount of resources allocated in the serving phase. Our results show that the proactive approach is instrumental in delivering high access reliability to the M2M devices.Comment: Accepted for presentation in ICC 201

Discovering Network Control Vulnerabilities and Policies in Evolving Networks

The range and number of new applications and services are growing at an unprecedented rate. Computer networks need to be able to provide connectivity for these services and meet their constantly changing demands. This requires not only support of new network protocols and security requirements, but often architectural redesigns for long-term improvements to efficiency, speed, throughput, cost, and security. Networks are now facing a drastic increase in size and are required to carry a constantly growing amount of heterogeneous traffic. Unfortunately such dynamism greatly complicates security of not only the end nodes in the network, but also of the nodes of the network itself. To make matters worse, just as applications are being developed at faster and faster rates, attacks are becoming more pervasive and complex. Networks need to be able to understand the impact of these attacks and protect against them. Network control devices, such as routers, firewalls, censorship devices, and base stations, are elements of the network that make decisions on how traffic is handled. Although network control devices are expected to act according to specifications, there can be various reasons why they do not in practice. Protocols could be flawed, ambiguous or incomplete, developers could introduce unintended bugs, or attackers may find vulnerabilities in the devices and exploit them. Malfunction could intentionally or unintentionally threaten the confidentiality, integrity, and availability of end nodes and the data that passes through the network. It can also impact the availability and performance of the control devices themselves and the security policies of the network. The fast-paced evolution and scalability of current and future networks create a dynamic environment for which it is difficult to develop automated tools for testing new protocols and components. At the same time, they make the function of such tools vital for discovering implementation flaws and protocol vulnerabilities as networks become larger and more complex, and as new and potentially unrefined architectures become adopted. This thesis will present the design, implementation, and evaluation of a set of tools designed for understanding implementation of network control nodes and how they react to changes in traffic characteristics as networks evolve. We will first introduce Firecycle, a test bed for analyzing the impact of large-scale attacks and Machine-to-Machine (M2M) traffic on the Long Term Evolution (LTE) network. We will then discuss Autosonda, a tool for automatically discovering rule implementation and finding triggering traffic features in censorship devices. This thesis provides the following contributions: 1. The design, implementation, and evaluation of two tools to discover models of network control nodes in two scenarios of evolving networks, mobile network and censored internet 2. First existing test bed for analysis of large-scale attacks and impact of traffic scalability on LTE mobile networks 3. First existing test bed for LTE networks that can be scaled to arbitrary size and that deploys traffic models based on real traffic traces taken from a tier-1 operator 4. An analysis of traffic models of various categories of Internet of Things (IoT) devices 5. First study demonstrating the impact of M2M scalability and signaling overload on the packet core of LTE mobile networks 6. A specification for modeling of censorship device decision models 7. A means for automating the discovery of features utilized in censorship device decision models, comparison of these models, and their rule discover

Soft-Defined Heterogeneous Vehicular Network: Architecture and Challenges

Heterogeneous Vehicular NETworks (HetVNETs) can meet various quality-of-service (QoS) requirements for intelligent transport system (ITS) services by integrating different access networks coherently. However, the current network architecture for HetVNET cannot efficiently deal with the increasing demands of rapidly changing network landscape. Thanks to the centralization and flexibility of the cloud radio access network (Cloud-RAN), soft-defined networking (SDN) can conveniently be applied to support the dynamic nature of future HetVNET functions and various applications while reducing the operating costs. In this paper, we first propose the multi-layer Cloud RAN architecture for implementing the new network, where the multi-domain resources can be exploited as needed for vehicle users. Then, the high-level design of soft-defined HetVNET is presented in detail. Finally, we briefly discuss key challenges and solutions for this new network, corroborating its feasibility in the emerging fifth-generation (5G) era