Distributed topology discovery in resource-constrained IoT: a Directed Flooding approach

As IoT networks become larger and more diversified, service discovery becomes a cumbersome task. Nodes need to be polled and the upkeep of routing tables might not be possible in low-power devices as this comes with a considerable energy cost and added complexity. For these reasons, flooding is a very well-known strategy for building large peer networks with low-energy protocols. But flooding requires blindly spreading information until the destinations have been reached and is poorly suited to unicast traffic. In this paper we explore a fully distributed topology discovery in low-energy networks, we show that flooding can be a great strategy for early communication while a topology is built. We investigate which additional information should be added to a data packet in order to build the topology quickly and reliably and what is the trade-off in terms of communication overhead. Furthermore, we show that not all nodes need to be queried for a reliable topology discovery but traditional centrality measures can be exploited and specific nodes can be targeted based on the partial topology each node is able to build over time

Wireless Network Analytics for Next Generation Spectrum Awareness

The importance of networks, in their broad sense, is rapidly and massively growing in modern-day society thanks to unprecedented communication capabilities offered by technology. In this context, the radio spectrum will be a primary resource to be preserved and not wasted. Therefore, the need for intelligent and automatic systems for in-depth spectrum analysis and monitoring will pave the way for a new set of opportunities and potential challenges. This thesis proposes a novel framework for automatic spectrum patrolling and the extraction of wireless network analytics. It aims to enhance the physical layer security of next generation wireless networks through the extraction and the analysis of dedicated analytical features. The framework consists of a spectrum sensing phase, carried out by a patrol composed of numerous radio-frequency (RF) sensing devices, followed by the extraction of a set of wireless network analytics. The methodology developed is blind, allowing spectrum sensing and analytics extraction of a network whose key features (i.e., number of nodes, physical layer signals, medium access protocol (MAC) and routing protocols) are unknown. Because of the wireless medium, over-the-air signals captured by the sensors are mixed; therefore, blind source separation (BSS) and measurement association are used to estimate the number of sources and separate the traffic patterns. After the separation, we put together a set of methodologies for extracting useful features of the wireless network, i.e., its logical topology, the application-level traffic patterns generated by the nodes, and their position. The whole framework is validated on an ad-hoc wireless network accounting for MAC protocol, packet collisions, nodes mobility, the spatial density of sensors, and channel impairments, such as path-loss, shadowing, and noise. The numerical results obtained by extensive and exhaustive simulations show that the proposed framework is consistent and can achieve the required performance