Parameterized Verification of Safety Properties in Ad Hoc Network Protocols

We summarize the main results proved in recent work on the parameterized verification of safety properties for ad hoc network protocols. We consider a model in which the communication topology of a network is represented as a graph. Nodes represent states of individual processes. Adjacent nodes represent single-hop neighbors. Processes are finite state automata that communicate via selective broadcast messages. Reception of a broadcast is restricted to single-hop neighbors. For this model we consider a decision problem that can be expressed as the verification of the existence of an initial topology in which the execution of the protocol can lead to a configuration with at least one node in a certain state. The decision problem is parametric both on the size and on the form of the communication topology of the initial configurations. We draw a complete picture of the decidability and complexity boundaries of this problem according to various assumptions on the possible topologies.Comment: In Proceedings PACO 2011, arXiv:1108.145

Early and reliable event detection using proximity space representation

Conference of 33rd International Conference on Machine Learning, ICML 2016 ; Conference Date: 19 June 2016 Through 24 June 2016; Conference Code:124527International audienceLet us consider a specific action or situation (called event) that takes place within a time series. The objective in early detection is to build a decision function that is able to go off as soon as possible from the onset of an occurrence of this event. This implies making a decision with an incomplete information. This paper proposes a novel framework that i) guarantees that a de-tection made with a partial observation will also occur at full observation of the time-series; ii) incorporates in a consistent manner the lack of knowledge about the minimal amount of information needed to make a decision. The proposed detector is based on mapping the temporal sequences to a landmarking space thanks to appropriately designed similarity functions. As a by-product, the framework benefits from a scalable training algorithm and a theoretical guarantee concerning its generalization ability. We also discuss an important improvement of our framework in which decision function can still be made reliable while being more expressive. Our experimental studies provide compelling results on toy data, presenting the trade-off that occurs when aiming at accuracy, earliness and reliability. Results on real physiological and video datasets show that our proposed approach is as accurate and early as state-of-the-art algorithm, while ensuring reliability and being far more efficient to learn

On the Power of Cliques in the Parameterized Verification of Ad Hoc Networks

We study decision problems for parameterized verification of protocols for ad hoc networks. The problem we consider is control state reachability for networks of arbitrary size. We restrict our analysis to topologies that approximate the notion of cluster (graphs with bounded diameter) often used in ad hoc networks for optimizing broadcast communication. In particular we are interested in classes of graphs that include at least cliques of arbitrary order. We show that, although decidable, control state reachability over cliques is already Ackermann-hard and study more sophisticated topologies for which the problem remains decidable

Parameterized Verification of Broadcast Networks of Register Automata

none3noneG. Delzanno; A. Sangnier; R. TraversoDelzanno, Giorgio; A., Sangnier; R., Travers

Filter bank Kernel Learning for nonstationary signal classification

Conference of 2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 ; Conference Date: 26 May 2013 Through 31 May 2013; Conference Code:101421International audienceThis paper addresses the problem of automatic feature extraction for signal classification. In order to handle non-stationarity, features are designed in the time-frequency domain using a Filter Bank as the mapping function, which enables an easy interpretation for practitioners. The strategy adopted is to jointly learn a Filter Bank with a Support Vector Machine by casting the optimization program as a Multiple Kernel Learning problem. This solves the program for a finite set of filters. Thus, in order to handle an infinite number of filters, a novel active constraint algorithm is proposed based on the latest breakthroughs. Our method has been tested on a toy dataset and compared to classical methods with competitive results

Verification of Ad Hoc Networks with Node and Communication Failures

We investigate the impact of node and communication failures on the decidability and complexity of parametric verification of a formal model of ad hoc networks. We start by considering three possible types of node failures: intermittence, restart, and crash. Then we move to three cases of communication failures: nondeterministic message loss, message loss due to conflicting emissions, and detectable conflicts. Interestingly, we prove that the considered decision problem (reachability of a control state) is decidable for node intermittence and message loss (either nondeterministic or due to conflicts) while it turns out to be undecidable for node restart/crash, and conflict detection