@conference {18447,
	title = {Change Detection in Multivariate Datastreams: Likelihood and  Detectability Loss},
	booktitle = {25th International Joint Conference on Artificial Intelligence (IJCAI-16)},
	year = {2016},
	month = {07/2016},
	address = { New York, USA},
	abstract = {We address the problem of detecting changes in multivariate datastreams, and we investigate the intrinsic difficulty that change-detection methods have to face when the data-dimension scales. In particular, we consider the general approach that detects changes by comparing the distribution of the log-likelihood of the datastream over different time windows. Despite the fact that this approach constitutes the frame for several change-detection methods, its effectiveness when the dimension of data scales has never been investigated, which is indeed the goal of our paper. We show that the magnitude of the change can be naturally measured by the symmetric Kullback-Leibler divergence between the pre- and post-change distributions, and that the detectability of a change of a given magnitude worsens when the data-dimension increases. This structural problem, which we refer to as detectability loss, is due to the linear relationship existing between the variance of the log-likelihood and the data dimension, and reveals to be harmful even at low data-dimensions (say, 10). We analytically derive the detectability loss on Gaussian-distributed datastreams, and empirically demonstrate that this problem holds also on real-world datasets.},
	author = {Alippi, Cesare and Boracchi, Giacomo and Carrera, Diego and Roveri, Manuel}
}
@article {18511,
	title = {A Cloud to the Ground: The New Frontier of Intelligent and Autonomous Networks of Things},
	journal = {IEEE Communication Magazine},
	volume = {54},
	issue = {12},
	year = {2016},
	month = {11/2016},
	pages = {14 - 20},
	chapter = {14},
	abstract = {The Internet-of-Things (IoT) paradigm is supporting -and will support- an ever-increasing number of services and applications impacting on almost every aspect of our everyday life. The current trend is forecasting IoT to connect tens of billion objects by 2020 yielding a very-high volume of data to be acquired, transmitted and processed. IoT typically relies on Cloud Computing to process, analyze and store the data acquired by IoT entities. Unfortunately, the need to transmit all data from the information producing objects to the Cloud for a subsequent processing/analysis phase would require a large bandwidth and increase the latency in the {\textquotedblleft}decision-making process{\textquotedblright} whenever decisions/reactions must be promptly taken by the IoT units.
The Fog Computing (FC) paradigm aims at addressing these problems by extending Cloud Computing towards the edge of the network. In this direction, this paper introduces a novel FC-IoT paradigm designed
to move computing, storage and applications/services close to IoT objects so as to reduce communication bandwidth and energy consumption as well as {\textquotedblleft}decision-making{\textquotedblright} latency. The proposed IoT-based solution has been designed to have intelligent and autonomous IoT objects that are integrated with a FC and Fog Networking approach. The distinguishing features of the intelligent FC-IoT platform are low-latency, self-adaptation, low energy consumption and spectrum efficiency.
},
	doi = { 10.1109/MCOM.2016.1600541CM},
	author = {Alippi, Cesare and Fantacci, Romano and Marabissi, Dania and Roveri, Manuel}
}
