In this paper we consider a network of agents that can evaluate each other according to an interaction graph modeling some physical interconnection or social relationship. Each agent provides a score for its (out-)neighboring agents in the interaction graph. The goal is to design a distributed protocol, run by the agents themselves, to group the network nodes into two classes (binary classification) on the basis of the evaluation outcomes. We propose a hierarchical Bayesian framework in which the agents' belonging to one of the two classes is assumed to be a probabilistic event with unknown parameter. Exploiting such a hierarchical framework, we are able to design a distributed classification scheme in which nodes cooperatively classify their own state. We characterize the solution for a fault-diagnosis context in cyber-physical systems, and for an opinion-classification/community-discovery setup in social networks.

Third-generation cellular networks are exposed to novel forms of denial-of-service attacks that only recently have started to be recognized and documented by the scientific community. In this contribution, we review some recently published attack models specific for cellular networks. We review them collectively in order to identify the main system-design aspects that are ultimately responsible for the exposure to the attack. The goal of this contribution is to build awareness about the intrinsic weaknesses of 3G networks from a system-design perspective. In doing that we hope to inform the design practice of future generation networks, motivating the adoption of randomization, adaptation and prioritization as central ingredients of robust system design.

Mobile users in cellular networks produce calls, initiate connections and send packets. Such events have a binary outcome — success or failure. The term “failure” is used here in a broad sense: it can take different meanings depending on the type of event, from packet loss or late delivery to call rejection. The Mean Failure Probability (MFP) provides a simple summary indicator of network-wide performance — i.e., a Key Performance Indicator (KPI) — that is an important input for the network operation process. However, the robust estimation of the MFP is not trivial. The most common approach is to take the ratio of the total number of failures to the total number of requests. Such simplistic approach suffers from the presence of heavy-users, and therefore does not work well when the distribution of traffic (i.e., requests) across users is heavy-tailed — a typical case in real networks. This motivates the exploration of more robust methods for MFP estimation. In a previous work [1] we derived a simple but robust sub-optimal estimator, called EPWR, based on the weighted average of individual (per-user) failure probabilities. In this follow-up work we tackle the problem from a different angle and formalize the problem following a Bayesian approach, deriving two variants of non-parametric optimal estimators. We apply these estimators to a real dataset collected from a real 3G network. Our results confirm the goodness of the proposed estimators and show that EPWR, despite its simplicity, yields near-optimum performance.

Current Radio-Frequency Identification (RFID) technology involves two types of physical devices: tags and reader. The reader combines in a single physical device transmission (to the tags) and reception (from the tags) functions. In this paper we discuss an alternative approach, where receive functions are performed by a separate device called "RFID listener". This allows distributed tag-sensing schemes where one transmitter coexists with multiple listeners. We discuss pros and cons of both approaches and present our implementation of a passive RFID listener on GNU Radio. Our implementation is a basis for experimenting with future distributed listener-based systems, but it can be also used as a cheap and flexible protocol analyzer for currently available commercial RFID readers.

In this paper we consider a network of monitors that can count the occurrences of binary events of interest. The aim is to estimate both the local event probabilities and some global features of the system as, e.g., the mean probability. This scenario is motivated by several applications in cyber-physical systems and social networks. We propose a hierarchical Bayesian approach in which the individual event probabilities are treated as random variables with an emph{a priori} density function. Following the empirical Bayes approach, the prior is chosen in a family of distributions parameterized by suitable unknown hyperparameters. We develop a distributed optimization algorithm, as a variant of a standard distributed dual decomposition scheme, to obtain locally the Maximum Likelihood estimates of the hyperparameters. These estimates allow each monitor to gain accuracy in both the local and global estimation tasks. This approach is particularly well suited in scenarios in which the number of samples at each node are allowed to be highly inhomogeneous.

In this paper we consider a network of agents monitoring a spatially distributed traffic process. Each node measures the number of arrivals seen at its monitoring point in a given time-interval. We propose an asynchronous distributed approach based on a hierarchical Bayes model with unknown hyperparameter, which allows each node to compute the minimum mean square error (MMSE) estimator of the local arrival rate by suitably fusing the information from the whole network. Simulation results show that the distributed scheme improves the estimation accuracy compared to a purely decentralized setup and is reliable even in presence of limited local data. An ad-hoc algorithm with reduced complexity is also proposed, which performs very closely to the optimal MMSE estimator.

In this paper we present a Maximum Likelihood (ML) trajectory estimation of a mobile node from Received Signal Strength (RSS) measurements. The reference scenario includes a number of nodes in fixed and known positions (anchors) and a target node (blind) in motion whose instantaneous position is unknown. We first consider the dynamic estimation of the channel parameters from anchor-to-anchor measurements, statistically modeled according to the well-known Path-Loss propagation model. Then, we address the ML estimation problem for the position and velocity of the blind node based on a set of blind-to-anchor RSS measurements. We compare also the algorithm with a ML-based single-point localization algorithm, and discuss the applicability of both methods for slowly moving nodes. We present simulation results to assess the accuracy of the proposed solution in terms of localization error and velocity estimation (modulus and angle). The distribution of the localization error on the initial and final point is analyzed and closed-form expressions are derived.

We consider the problem of RSS-based indoor localization with Maximum Likelihood (ML) estimation techniques in low-cost Wireless Sensor Networks (WSN). In the perspective of fully automated methods, we consider the problem of channel and position estimation as coupled problems. We compare via simulations the approaches of separate and joint ML estimation, plus a third method based on multi-lateration. We find that channel estimation via simple linear regression combined with ML localization has the potential to achieve good accuracy while keeping a very low level of computational and implementation complexity. We also find that in 3D localization the vertical error on the z-axis is considerably larger than the horizontal error on the xy-plane. This is due to the limited vertical offset that can be imposed to anchor beacons in “flat” buildings where the height is considerably smaller than the horizontal dimensions.

In this work, a basic resource allocation (RA) problem is considered, where a fixed capacity must be shared among a set of users. The RAtask can be formulated as an optimization problem, with a set of simple constraints and an objective function to be minimized. A fundamental relation between the RA optimization problem and the notion of max–min fairness is established. A sufficient condition on the objective function that ensures the optimal solution is max–min fairness is provided. Notably, some important objective functions like least squares and maximum entropy fall in this case. Finally, an application of max–min fairness for overload protection in 3G networks is considered.

We present a statistical analysis of the laser ranging data of LAGEOS and LAGEOS 2 satellites which have provided a test of dragging of inertial frames, or frame-dragging, by rotating mass as predicted by General Relativity. As a result, the analysis of the residuals, after filtering out the periodic effects, is consistent with a Gaussian model, with a nonzero mean in agreement with the one predicted by Lense–Thirring effect.

Abstract—In this work we describe a methodology to estimate one-way packet loss from IPFIX or NetFlow flow records collected at two monitoring points. The proposed method does not require tight synchronization between the two monitoring points, nor it relies upon external routing information. It can run online or offline, and can work on legacy IPFIX/NetFlow traces which were not collected for the specific purpose of loss estimation. In this preliminary work we describe the estimation procedure and present early validation results from a real testbed.