Giorgio Quer

Sensing, Compression, and Recovery for WSNs: Sparse Signal Modeling and Monitoring Framework

We address the problem of compressing large and distributed signals monitored by a Wireless Sensor Network (WSN) and recovering them through the collection of a small number of samples. We propose a sparsity model that allows the use of Compressive Sensing (CS) for the online recovery of large data sets in real WSN scenarios, exploiting Principal Component Analysis (PCA) to capture the spatial and temporal characteristics of real signals. Bayesian analysis is utilized to approximate the statistical distribution of the principal components and to show that the Laplacian distribution provides an accurate representation of the statistics of real data. This combined CS and PCA technique is subsequently integrated into a novel framework, namely, SCoRe1: Sensing, Compression and Recovery through ON-line Estimation for WSNs. SCoRe1 is able to effectively self-adapt to unpredictable changes in the signal statistics thanks to a feedback control loop that estimates, in real time, the signal reconstruction error. We also propose an extensive validation of the framework used in conjunction with CS as well as with standard interpolation techniques, testing its performance for real world signals. The results in this paper have the merit of shedding new light on the performance limits of CS when used as a recovery tool in WSNs.

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.

On the Exploitation of the Android OS for the Design of a Wireless Mesh Network Testbed

Wireless devices running the Android operating system offer a novel opportunity to study network behaviors and to observe and modify in real time key networking parameters. This opens up an unprecedented opportunity to study, test and evaluate the performance of techniques operating at different layers of the protocol stack and adopting the cognitive networking paradigm. In this paper, we describe our novel IEEE 802.11 mesh network testbed that integrates Android based devices. The aim is to build a flexible testbed to observe in-stack and out-stack parameters of interest, that can be used to test many networking techniques in both civilian and tactical and hostile scenarios. We provide the implementation details to create an ad hoc network among these inexpensive commercial devices, and specify how to observe and modify the networking parameters at different layers of the protocol stack. Through some examples we show the stability of the network and discuss the time evolution of some parameters of interest.

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.

Caching strategies in heterogeneous networks with D2D, small BS and macro BS communications

The increase in wireless data traffic is encouraging a shift from standard cellular networks, with one base station providing wireless connectivity to all the users in the cell, to heterogeneous networks, where several small base stations can assist the macro base station in providing service. Furthermore, with device-to-device communications the users can also share local content without multiple requests to the base station. In this complex network scenario, we propose a proactive caching policy to exploit all these communication opportunities and reduce congestion at the backhaul link. The goal is to minimize the system cost, in terms of energy or bandwidth wastage. We provide a closed form expression for the average system cost in the case in which we consider user mobility and different classes of users interests. We present a robust optimization framework, and we show significant performance gains compared to a static caching policy.

On the effects of cognitive mobility prediction in wireless multi-hop ad hoc networks

In this paper, we address an important problem in mobile ad hoc networks, namely, the intrinsic inefficiency of the standard transmission control protocol (TCP), which has not been designed to work in these types of networks. After an initial training phase, we predict the mobility status of the network through a probabilistic approach, and we propose a series of ad hoc strategies to counteract the TCP inefficiency based on this prediction. Via simulation, we show the performance improvements in various wireless scenarios, in terms of increased average throughput and decreased length of the outage intervals. The significant performance improvements shown here will be verified in a future work by implementing our approach in a real testbed.

Bayesian and neural network schemes for call admission control in LTE systems

Cognitive networking paradigms may help meet the challenges of operating complex wireless communications networks. In this paper, we contrast the neural network (NN) and the Bayesian network (BN) models to extract information from real-time observations and optimize network performance. In particular, we apply these two models to the problem of call admission control (CAC) for a long term evolution (LTE) system. We simulate a realistic LTE scenario with mobility in ns-3 and we select the most relevant features that can be observed by the base station. Then, we design two new CAC schemes that autonomously learn the network behavior from the observation of the selected features. Furthermore, we propose a performance comparison among these two schemes and a state-of-the-art CAC scheme, showing that the NN and the BN schemes are very promising solutions for CAC in LTE systems.

Wavelet coherence reveals entrainment of heart rate variability among people involved in group activities

The study of group dynamics is of primary importance in psychology and medicine because group dynamics affects the state of each member in a group. We study the RR interval time series, a heart rate measure, for a group of individuals involved in Kundalini meditation sessions. We analyze individual signals and study the wavelet coherence among the heart rate variability of different individuals. For specific activities, we found a high degree of coherence among all the people in the group. We also propose a novel method to detect temporally varying connections among the individuals based on the coherence of their heart rate.

Wireless SDN mobile ad hoc network: From theory to practice

A promising approach for dealing with the increasing demand of data traffic is the use of device-to-device (D2D) technologies, in particular when the destination can be reached directly, or though few retransmissions by peer devices. Thus, the cellular network can offload local traffic that is transmitted by an ad hoc network, e.g., a mobile ad hoc network (MANET), or a vehicular ad hoc network (VANET). The cellular base station can help coordinate all the devices in the ad hoc network by reusing the software tools developed for software-defined networks (SDNs), which divide the control and the data messages, transmitted in two separate interfaces. In this paper, we present a practical implementation of an SDN MANET, describe in detail the software components that we adopted, and provide a repository for all the new components that we developed. This work can be a starting point for the wireless networking community to design new testbeds with SDN capabilities that can have the advantages of D2D data transmissions and the flexibility of a centralized network management. In order to prove the feasibility of such a network, we also showcase the performance of the proposed network implemented in real devices, as compared to a distributed ad hoc network.

A Bayesian model of heart rate to reveal real-time physiological information

The human heart rate is influenced by different internal systems of the body and can reveal valuable information about health and disease conditions. In this paper, we analyze the instantaneous heart rate signal using a Bayesian method, inferring in real time a probabilistic distribution that approximates the real distribution of this signal. The best model is chosen after an experimental analysis of real data collected within our framework. The parameters of this distribution can reveal interesting insights on the influences of the sympathetic and parasympathetic divisions of the autonomic nervous system (ANS) in real time.