Mobile Network Optimization
Finally, my first paper @ IMDEA is out. It will be presented at European Wireless 2014 in Barcelona this May. It is a joint work with Foivos and Joerg, that we needed as both a state of the art survey and to define some concepts for the next papers. Here is the abstract:
In this paper we propose a stochastic model to predict user throughput in mobile networks. In particular, the model accounts for uncertainty such as random phenomena (e.g., fast fading) or inexact information (e.g., user location) to derive the statistical distribution of the user throughput. Such a model is highly useful for aiding scheduling and resource allocation decisions. In addition, we provide a taxonomy of prediction techniques to investigate error sources and the main characteristics of prediction accuracy. Finally, we show the versatility of the model by analyzing LTE user throughput for the case where knowledge of either the user's actual position or the congestion level in the cell is inexact.
Internet of Things
Before joining IMDEA my first interest was Internet of Things (and earlier Wireless Sensor and Actuator Networks) and a few of my latest works are just coming out these days. The latest has just been published on the first number of the first issue of the IEEE Internet of Things Journal and describe a successfull implementation of the Smart City concept using IoT technology in Padova, Italy. Earlier this year MDPI Sensors published our compression and classification algorithm, RAZOR. In what follows you can read the abstracts of the two papers.
Internet of Things for Smart Cities
The Internet of Things (IoT) shall be able to incorporate transparently and seamlessly a large number of different and heterogeneous end systems, while providing open access to selected subsets of data for the development of a plethora of digital services. Building a general architecture for the IoT is hence a very complex task, mainly because of the extremely large variety of devices, link layer technologies, and services that may be involved in such a system. In this paper we focus specifically to an urban IoT systems that, while still being quite a broad category, are characterized by their specific application domain. Urban IoTs, in fact, are designed to support the Smart City vision, which aims at exploiting the most advanced communication technologies to support added-value services for the administration of the city and for the citizens. This paper hence provides a comprehensive survey of the enabling technologies, protocols and architecture for an urban IoT. Furthermore, the paper will present and discuss the technical solutions and best-practice guidelines adopted in the Padova Smart City project, a proof of concept deployment of an IoT island in the city of Padova, Italy, performed in collaboration with the city municipality.
RAZOR: A Compression and Classification Solution for the Internet of Things
The Internet of Things is expected to increase the amount of data produced and exchanged in the network, due to the huge number of smart objects that will interact with one another. The related information management and transmission costs are increasing and becoming an almost unbearable burden, due to the unprecedented number of data sources and the intrinsic vastness and variety of the datasets. In this paper, we propose RAZOR, a novel lightweight algorithm for data compression and classification, which is expected to alleviate both aspects by leveraging the advantages offered by data mining methods for optimizing communications and by enhancing information transmission to simplify data classification. In particular, RAZOR leverages the concept of motifs, recurrent features used for signal categorization, in order to compress data streams: in such a way, it is possible to achieve compression levels of up to an order of magnitude, while maintaining the signal distortion within acceptable bounds and allowing for simple lightweight distributed classification. In addition, RAZOR is designed to keep the computational complexity low, in order to allow its implementation in the most constrained devices. The paper provides results about the algorithm configuration and a performance comparison against state-of-the-art signal processing techniques.