Luca Canzian

Trajectories of depression: unobtrusive monitoring of depressive states by means of smartphone mobility traces analysis

One of the most interesting applications of mobile sensing is monitoring of individual behavior, especially in the area of mental health care. Most existing systems require an interaction with the device, for example they may require the user to input his/her mood state at regular intervals. In this paper we seek to answer whether mobile phones can be used to unobtrusively monitor individuals affected by depressive mood disorders by analyzing only their mobility patterns from GPS traces. In order to get ground-truth measurements, we have developed a smartphone application that periodically collects the locations of the users and the answers to daily questionnaires that quantify their depressive mood. We demonstrate that there exists a significant correlation between mobility trace characteristics and the depressive moods. Finally, we present the design of models that are able to successfully predict changes in the depressive mood of individuals by analyzing their movements.

Inter-Network Cooperation Exploiting Game Theory and Bayesian Networks

Relay sharing has been recently investigated to increase the performance of coexisting wireless multi-hop networks. In this paper, we analyze a scenario where two wireless ad hoc networks are willing to share some of their nodes, acting as relays, in order to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian network analysis is exploited to compute the probabilistic relationships between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through the use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

A characterization of resource allocation in LTE systems aimed at game theoretical approaches

This paper proposes a novel approach, based on game theory, for radio resource allocation in the downlink of cellular networks using Orthogonal Frequency Division Multiple Access. The reference technology is the Long Term Evolution of the 3GPP UTRAN. The main contribution is to identify a model for the allocation objectives, and how to approach them in a tunable manner. The resource management issue is framed in the context of spectrum sharing, where multiple entities agree on utilizing the radio access channel simultaneously. A trade-off between sum-rate throughput and fairness among the users is identified and addressed through game theory, i.e., moving the operation of the system towards a stable Pareto efficient point. Such a methodology can be implemented with low complexity while ensuring logical modularity of the overall system. Numerical results are also shown, to exemplify the validity of the proposed approach.

Anti-spoofing and open GNSS signal authentication with signal authentication sequences

Global Navigation Satellite System (GNSS) signal authentication is a requirement for a number of applications. GNSS authentication has been proposed with aiding techniques that can be applied to the existing GPS and as a new security function for future GNSS. The paper proposes a concept of a new authentication scheme based on signal authentication sequences that can be integrated in GNSS. The method works on systems that provide an open and encrypted service on the same frequency. The scheme would require minimum impact to the system. The architecture is explained in the different components of ground, space and user segment. A simulation of the architecture has been implemented in Matlab and performances and test results are shown. The paper concludes with suggestions of optimal parameters for an hypothetical implementation, explaining the future research steps.

Ensemble of Distributed Learners for Online Classification of Dynamic Data Streams

We present a distributed online learning scheme to classify data captured from distributed and dynamic data sources. Our scheme consists of multiple distributed local learners, which analyze different streams of data that are correlated to a common event that needs to be classified. Each learner uses a local classifier to make a local prediction. The local predictions are then collected by each learner and combined using a weighted majority rule to output the final prediction. We propose a novel online ensemble learning algorithm to update the aggregation rule in order to adapt to the underlying data dynamics. We rigorously determine an upper bound for the worst-case mis-classification probability of our algorithm, which tends asymptotically to 0 if the misclassification probability of the best (unknown) static aggregation rule is 0. Then we extend our algorithm to address challenges specific to the distributed implementation and prove new bounds that apply to these settings. Finally, we test our scheme by performing an evaluation study on several data sets.

Promoting Cooperation in Wireless Relay Networks Through Stackelberg Dynamic Scheduling

This paper discusses a new perspective for the application of game theory to wireless relay networks, namely, how to employ it not only as an analytical evaluation instrument, but also in constructively deriving practical network management policies. We focus on the problem of medium sharing in wireless networks, which is often seen as a case where game theory just proves the inefficiency of distributed access, without proposing any remedy. Instead, we show how, by properly modeling the agents involved in such a scenario, and enabling simple but effective incentives towards cooperation for the users, we obtain a resource allocation scheme which is meaningful from both perspectives of game theory and network engineering. Such a result is achieved by introducing throughput redistribution as a way to transfer utilities, which enables cooperation among the users. Finally, a Stackelberg formulation is proposed, involving the network access point as a further player. Our approach is also able to take into account power consumption of the terminals, still without treating it as an insurmountable hurdle to cooperation, and at the same time to drive the network allocation towards an efficient cooperation level.

Using game theory and Bayesian networks to optimize cooperation in ad hoc wireless networks

Infrastructure sharing has been recently investigated as a viable solution to increase the performance of coexisting wireless networks. In this paper, we analyze a scenario where two wireless networks are willing to share some of their nodes to gain benefits in terms of lower packet delivery delay and reduced loss probability. Bayesian Network analysis is exploited to compute the correlation between local parameters and overall performance, whereas the selection of the nodes to share is made by means of a game theoretic approach. Our results are then validated through use of a system level simulator, which shows that an accurate selection of the shared nodes can significantly increase the performance gain with respect to a random selection scheme.

Context-adaptive big data stream mining

Emerging stream mining applications require classification of large data streams generated by single or multiple heterogeneous sources. Different classifiers can be used to produce predictions. However, in many practical scenarios the distribution over data and labels (and hence the accuracies of the classifiers) may be unknown a priori and may change in unpredictable ways over time. We consider data streams that are characterized by their context information which can be used as meta-data to choose which classifier should be used to make a specific prediction. Since the context information can be high dimensional, learning the best classifiers to make predictions using contexts suffers from the curse of dimensionality. In this paper, we propose a context-adaptive learning algorithm which learns online what is the best context, learner, and classifier to use to process a data stream. Then, we theoretically bound the regret of the proposed algorithm and show that its time order is independent of the dimension of the context space. Our numerical results illustrate that our algorithm outperforms most prior online learning algorithms, for which such online performance bounds have not been proven.

Real-time stream mining: online knowledge extraction using classifier networks

The world is increasingly information-driven. Vast amounts of data are being produced by different sources and in diverse formats. It is becoming critical to endow assessment systems with the ability to process streaming information from sensors in real time in order to better manage physical systems, derive informed decisions, tweak production processes, and optimize logistics choices. This article first surveys the works dealing with building, adapting, and managing networks of classifiers, then describes the challenges and limitations of the current approaches, discusses possible directions to deal with these limitations, and presents some open research questions that need to be investigated.

Timely Event Detection by Networked Learners

We consider a set of distributed learners that are interconnected via an exogenously-determined network. The learners observe different data streams that are related to common events of interest, which need to be detected in a timely manner. Each learner is equipped with a set of local classifiers, which generate local predictions about the common event based on the locally observed data streams. In this work, we address the following key questions: (1) Can the learners improve their detection accuracy by exchanging and aggregating information? (2) Can the learners improve the timeliness of their detections by forming clusters, i.e., by collecting information only from surrounding learners? (3) Given a specific tradeoff between detection accuracy and detection delay, is it desirable to aggregate a large amount of information, or is it better to focus on the most recent and relevant information? To address these questions, we propose a cooperative online learning scheme in which each learner maintains a set of weight vectors (one for each possible cluster), selects a cluster and the corresponding weight vector, generates a local prediction, disseminates it through the network, and combines all the received local predictions from the learners belonging to the selected cluster by using a weighted majority rule. The optimal cluster and weight vector that a learner should adopt depend on the specific network topology, on the location of the learner in the network, and on the characteristics of the data streams. To learn such optimal values, we propose a general online learning rule that exploits only the feedbacks that the learners receive. We determine an upper bound for the worst-case mis-detection probability and for the worst-case prediction delay of our scheme in the realizable case. Numerical simulations show that the proposed scheme is able to successfully adapt to the unknown characteristics of the data streams and can achieve substantial performance gains with respect to a scheme in which the learners act individually or a scheme in which the learners always aggregate all available local predictions. We numerically evaluate the impact that different network topologies have on the final performance. Finally, we discuss several surprising existing trade-offs.