Firefighters are equipped with an immobility detector device also called the Personal Alert Safety System (PASS) that is integrated into the user's Self-Contained Breathing Apparatus (SCBA). If a firefighter remains motionless for a certain period of time, a loud audible alert is triggered to notify the Firefighter Assist and Search Team (FAST) deployed in the area of intervention that the wearer of the PASS device is in trouble and in need of rescue. However, this device is not reliable enough since it triggers frequently false positives which lead to developing a tolerance for sounding alarms among the crew. As a consequence, they do not seem to be concerned about it as they should and the alarms are just ignored sometimes. In this paper, we propose a PERsonal LPWAN sYstem (PERLY) prototype for state assessment and localization of Firefighters. The latter's specifications were set by personnel from the fire and emergency response department of the Doubs brigade. The aim was to make the system more reliable compared to the PASS, to add additional important functionalities, and to minimize the false positive alarms.

In condition-based maintenance, real-time observations are crucial for on-line health assessment. When the monitoring system is a wireless sensor network (WSN), data loss becomes highly probable and this affects the quality of the remaining useful life prediction. In this paper, we present a fully distributed algorithm that ensures fault tolerance and recovers data loss in WSNs. We first theoretically analyze the algorithm and give correctness proofs, then provide simulation results and show that the algorithm is (i) able to ensure data recovery with a low failure rate and (ii) preserves the overall energy for dense networks.

The Internet of Things (IoT) has gained worldwide attention in recent years. It transforms the everyday objects that surround us into proactive actors of the Internet, generating and consuming information. An important issue related to the appearance of such a large-scale self-coordinating IoT is the reliability and the collaboration between the objects in the presence of environmental hazards. High failure rates lead to significant loss of data. Therefore, data survivability is a main challenge of the IoT. In this article, we have developed a compartmental e-Epidemic SIR (Susceptible-Infectious-Recovered) model to save the data in the network and let it survive after attacks. Furthermore, our model takes into account the dynamic topology of the network where natural death (crashing nodes) and birth are defined and analyzed. Theoretical methods and simulations are employed to solve and simulate the system of equations developed and to analyze the model.

Variations in mitochondrial genes are usually considered to infer phylogenies. However some of these genes are lesser constraint than other ones, and thus may blur the phylogenetic signals shared by the majority of the mitochondrial DNA sequences. To investigate such effects, in this research work, the molecular phylogeny of the genus Taenia is studied using 14 coding sequences extracted from mitochondrial genomes of 17 species. We constructed 16,384 trees, using a combination of 1 up to 14 genes. We obtained 131 topologies, and we showed that only four particular instances were relevant. Using further statistical investigations, we then extracted a particular topology, which displays more robustness properties.

In computational biology and bioinformatics, the manner to understand evolution processes within various related organisms paid a lot of attention these last decades. However, accurate methodologies are still needed to discover genes content evolution. In a previous work, two novel approaches based on sequence similarities and genes features have been proposed. More precisely, we proposed to use genes names, sequence similarities, or both, insured either from NCBI or from DOGMA annotation tools. Dogma has the advantage to be an up-to-date accurate automatic tool specifically designed for chloroplasts, whereas NCBI possesses high quality human curated genes (together with wrongly annotated ones). The key idea of the former proposal was to take the best from these two tools. However, the first proposal was limited by name variations and spelling errors on the NCBI side, leading to core trees of low quality. In this paper, these flaws are fixed by improving the comparison of NCBI and DOGMA results, and by relaxing constraints on gene names while adding a stage of post-validation on gene sequences. The two stages of similarity measures, on names and sequences, are thus proposed for sequence clustering. This improves results that can be obtained using either NCBI or DOGMA alone. Results obtained with this “quality control test” are further investigated and compared with previously released ones, on both computational and biological aspects, considering a set of 99 chloroplastic genomes.

Wireless sensor networks are often deployed in public or otherwise untrusted and even hostile environments, which prompts a number of security issues. Although security is a necessity in other types of networks, it is much more so in sensor networks due to the resource-constraint, susceptibility to physical capture, and wireless nature. In this work we emphasize two security issues: (1) secure communication infrastructure and (2) secure nodes scheduling algorithm. Due to resource constraints, specific strategies are often necessary to preserve the network's lifetime and its quality of service. For instance, to reduce communication costs nodes can go to sleep mode periodically (nodes scheduling). These strategies must be proven as secure, but protocols used to guarantee this security must be compatible with the resource preservation requirement. To achieve this goal, secure communications in such networks will be defined, together with the notions of secure scheduling. Finally, some of these security properties will be evaluated in concrete case studies.

This paper presents an energy-efficient technique for data aggregation in periodic sensor networks. We investigate the problem of finding all pairs of nodes generating similar data sets such that similarity between each pair of sets is above a threshold t. We provide a frequency filtering approach to solve this problem. Our experiments demonstrate that our algorithm outperforms existing prefix filtering methods in reducing energy consumption.

This research work presents a new class of non-blind information hiding algorithms that are stego-secure and robust. They are based on some finite domains iterations having the Devaney's topological chaos property. Owing to a complete formalization of the approach, we prove security against watermark-only attacks of a large class of steganographic algorithms. Finally a complete study of robustness is given in frequency DWT and DCT domains.

In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. To achieve/ensure the convergence of node states to the average of the initial measurements of the network even when sensor nodes are subject to failures, two algorithms are presented. We introduce first an efficient fault-tolerant scheme capable of supporting faults due to battery depletion. Next, we derive a more complex solution to resist to frequent and unexpected fail-silent/fail-stop node failures. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

Lifetime optimization becomes a commonplace feature of wireless sensor networks. In addition, as the scale is expanding, node reliabilities gradually become dynamically heterogeneous over time even if sensor nodes are symmetric by design. At the same time, as sensor nodes get smaller and approach technological limits, they suffer from increased susceptibility to wear-out which becomes a real problem as complex sensor networks with many nodes operate in unsafe and harsh environments for long times. Con sequently, there is an increasing need for developing techniques to achieve more reliability, i.e., increase the net work's operational time. In this paper, we introduce an efficient distributed failure-aware strategy (on-line solution) using probabilistic Weibull distribution to resist to frequent and unexpected fail-silent/fail-stop node failures. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed solution.

As protein folding is a NP-complete problem, artificial intelligence tools like neural networks and genetic algorithms are used to attempt to predict the 3D shape of an amino acids sequence. Underlying these attempts, it is supposed that this folding process is predictable. However, to the best of our knowledge, this important assumption has been neither proven, nor studied. In this paper the topological dynamic of protein folding is evaluated. It is mathematically established that protein folding in 2D hydrophobic-hydrophilic (HP) square lattice model is chaotic as defined by Devaney. Consequences for both structure prediction and biology are then outlined.

Energy is a major constraint in wireless sensor networks. Data Aggregation constitutes a fundamental mechanism for energy optimization. The idea is to minimize redundancy from the raw data captured by the sensors, minimizing the number of transmissions to the sink and thus saving energy. Since the data is often captured on a periodic basis, and sensor nodes detect common phenomena, a periodic based protocol that manages collected data sets can help to preserve the scarce energy. This paper proposes a new filtering technique for identifying duplicate sets of periodically captured data. We suggest a data aggregation model based on set joins similarity functions that conserves data integration while eliminating inherited redundancy. We show through the result that our approach offers significant data reduction by eliminating in-network redundancy and sending only necessary information to the sink.

This paper introduces a new notion of chaotic algorithms. These algorithms are iterative and are based on so-called chaotic iterations. Contrary to all existing studies on chaotic iterations, we are not interested in stable states of such iterations but in their possible unpredictable behaviors. By establishing a link between chaotic iterations and the notion of Devaney's topological chaos, we give conditions ensuring that these kind of algorithms produce topological chaos. This leads to algorithms that are highly unpredictable. After presenting the theoretical foundations of our approach, we are interested in its practical aspects. We show how the theoretical algorithms give rise to computer programs that produce true topological chaos, then we propose applications in the area of information security.

Being one of the important factors of evaluation in peer-to-peer streaming networks, availability of data is the point of our focus. Efficient data sharing in global peer-to-peer system is complicated by node failure, unreliable network connectivity and limited bandwidth. Using a caching system can improve availability of data. Yet, determining when and where to put the data in the cache to met performance goals in system with many users and files, dynamic network characteristics and changing users behavior, is difficult. We propose an approach to face these problems using a probabilistic model queue M/M/1/K and an optimization method called "Pareto method of dominance and optimality" to maximize the availability and access of data in the network. We evaluate the accuracy and performance of the proposed approach using simulations which showing the effectiveness of our approach.

In this paper, we deal with the problem of distributed data fusion in unsafe large-scale sensor networks. Data fusion application is the phase of processing the collected data by sensor nodes before sending it the end user. During this phase, resource failures are more likely to occur and can have an adverse effect on the application. Hence, we introduce first an efficient self-stabilizing algorithm to achieve/ensure the convergence of node states to the average of the initial measurements of the network. Next, we present a fault tolerant scheme to resist to frequent and unexpected not concomitant fail-silent/fail-stop node failures. The major contribution of this paper is the design of an analytical expression (an upper bound) of the actual number of moves/iterations required by the algorithm. We provide a comprehensive set of experimental results, that fully demonstrate the usefulness of the proposed schemes.

This paper brings a correction to the formulation of the basins of fixed-point states of fully asynchronous discrete-time discrete-state dynamic networks presented in our paper that appeared in the IEEE Transactions on Neural Networks, vol. 17, no. 2, pp. 397-408, March 2006. In our subsequent works on totally asynchronous systems, we have discovered that the formulation given in that previous paper lacks an additional condition. We present in this paper why the previous formulation is incomplete and give the correct formulation.

The aim of this paper is to study the behaviors of the well known conjugate gradient (CG) algorithm and the multisplitting algorithm in a grid context. We focus on the CG implementation used in the NAS benchmark and on the multisplitting approach which produces similar results (from a numerical point of view). By grid context we mean an architecture composed of several heterogeneous clusters geographically distributed and the use of a software environment able to tackle the heterogeneity of the nodes. Under these hypothesis, we performed a set of experiments on the Grid'5000 platform using the pure Java Jace V2 environment. We show that, by drastically reducing global synchronizations, the asynchronous multi-splitting method outperforms the NAS CG implementation, with distant sites, whatever the communication protocol used.

Viewing multimedia contents through P2P streaming systems is gaining to be an emerging practice. In such systems, the steaming mode for multimedia content is used rather than the downloading mode. The advantages of this mode are numerous, especially in terms of time latency. Nonetheless, P2P streaming systems suffer from data availability problem resulting from the unpredictable peer's departure. In this paper, we present a novel approach based on the use of an embedded distributed virtual cache amongst the peers in theP2P network. An efficient allocation policy, that balances the streaming load over peers, is developed to distribute the cached data over the resulting distributed virtual cache. Additionally, we present an adaptive cache management policy, based on a closed-loop feed back control system that automatically adjusts the size of the cached data to the system workload. The simulation results we obtained show clearly the effectiveness of our approach.

This paper introduces a parallel algorithm to solve large stiff ODE systems in a geographically distant cluster environment. This algorithm is based on the coupling of the waveform relaxation concept and the CVODE algorithm. With respect to the standard PVODE algorithm, it allows to drastically reduce the number of messages exchanged between nodes. It is a coarse grained algorithm well suited for distant grid environments connected via high latency networks. In this paper our work consists in analyzing the execution times taken by the PVODE solver and our algorithm and in explaining the benefits brought by this work.

This paper focuses on large scale experiments with Java and asynchronous iterative applications. In those applications, tasks are dependent and the use of distant clusters may be difficult, for example, because of latencies, heterogeneity, and synchronizations. Experiments have been conducted on the Grid'5000 platform using a new version of the Jace environment. We study the behavior of an application (the Poisson problem) with the following experimentation conditions: one and several sites, large number of processors (from 80 to 500), different communication protocols (RMI, sockets and NIO), synchronous and asynchronous model. The results we obtained, demonstrate both the scalability of the Jace environment and its ability to support wide-area deployments and the robustness of asynchronous iterative algorithms in a large scale context.

In this paper, first order diffusion load balancing algorithms for totally dynamic networks are investigated. Totally dynamic networks are networks in which the topology may change dynamically. Some edges or nodes can appear, disappear or move during the time. In our previous works on dynamic networks, the dynamism was limited to the edges. The main result of this study consists in proving that the load balancing algorithms reduce the unbalance on arbitrary dynamic networks. Notice that the hypotheses of our result are realistic and that for example the network does not have to be maintained connected. To study the behavior of these algorithms, we compare the load evolution by several simulations.

Using Peer-to-Peer (P2P) networks is a way to federate a large amount of processors in order to solve large scale scientific problems. Those networks are decentralized, highly dynamic and composed of heterogeneous machines. The goals of our work is to compute large scale scientific iterative applications on P2P networks. We propose JaceP2P, a multi-threaded Java based library designed to build asynchronous parallel iterative applications. Using this library, it is possible to run such applications on a set of dynamic and heterogeneous machines organized in a decentralized and P2P fashion.

Java is often criticized for its poor performances compared to native codes. Nevertheless, this language provides lots of interesting functionalities to easily implement scientific applications on a widely distributed architecture (i.e. grid). The context of this paper is that of iterative algorithms. In order to increase the efficiency of the code, we suggest to use a special class of algorithms called AIACs (asynchronous iterations, asynchronous computations). This paper presents new results on our works to combine Java and asynchronism within a programming/execution environment called JACE. New functionalities have been added and interesting comparisons with C/MPI and on the impact of overlap techniques are given

This paper gives a formulation of the basins of fixed point states of fully asynchronous discrete-time discrete-state dynamic networks. That formulation provides two advantages. The first one is to point out the different behaviors between synchronous and asynchronous modes and the second one is to allow us to easily deduce an algorithm which determines the behavior of a network for a given initialization. In the context of this study, we consider networks of a large number of neurons (or units, processors, etc.), whose dynamic is fully asynchronous with overlapping updates. We suppose that the neurons take a finite number of discrete states and that the updating scheme is discrete in time. We make no hypothesis on the activation functions of the nodes, so that the dynamic of the network may have multiple cycles and/or basins. Our results are illustrated on a simple example of a fully asynchronous Hopfield neural network.

In a previous paper, we have shown the very high power of asynchronism for parallel iterative algorithms in a global context of grid computing. In this article, we study the interest of coupling load balancing with asynchronism in such algorithms. After proposing a noncentralized version of dynamic load balancing which is best suited to asynchronism, we verify its efficiency by some experiments on a general partial differential equation (PDE) problem. Finally, we give some general conditions for the use of load balancing to obtain good results with this kind of algorithm and discuss the choice of the residual as an efficient load estimator.

We introduce a theoretical algorithm and its practical version to perform a decentralized detection of the global convergence of parallel asynchronous iterative algorithms. We prove that, even if the algorithm is completely decentralized, the detection of global convergence is achieved on one processor under the classical conditions. The proposed algorithm is very useful in the context of grid computing in which the processors are distributed and in which detecting the convergence on a master processor may be penalizing or even impossible as in peer to peer computation frameworks. Finally, the efficiency of the practical algorithm is illustrated in a typical experiment.

Summary form only given. AIAC algorithms (Asynchronous Iterations Asynchronous Communications) are a particular class of parallel iterative algorithms. Their asynchronous nature makes them more efficient than their synchronous counterparts in numerous cases as has already been shown in previous works. The first goal is to compare several parallel programming environments in order to see if there is one of them which is best suited to efficiently implement AIAC algorithms. The main criterion for this comparison consists in the performances achieved in a global context of grid computing for two classical scientific problems. Nevertheless, we also take into account two secondary criteria, which are the ease of programming and the ease of deployment. The second goal is to extract from this comparison the important features that a parallel programming environment must have in order to be suited for the implementation of AIAC algorithms.

Summary form only given. Nowadays, distributed computing over large networks is a popular and good alternative to dedicated parallel machines or clusters. Nevertheless, grid computing often suffers poor performances due to the heterogeneity of the machines and bottlenecks on the network. This is especially true for iterative computations when a lot of iterations are needed to converge to the problem solution. We evaluate Jace, a Java environment we have developed to easily implement and execute asynchronous iterations-asynchronous communications algorithms on the grid. Our evaluation is based on a typical iterative algorithm : the power method.

We consider networks of a large number of neurons (or units, processors, ...), whose dynamics are fully asynchronous with overlapping updating. We suppose that the neurons take a finite number of states (discrete states), and that the updating scheme is discrete in time. We make no hypotheses on the activation function of the neurons; the networks may have multiple cycles and basins. We derive conditions on the initialization of the networks, which ensures convergence to fixed points only. Application to a fully asynchronous Hopfield neural network allows us to validate our study.

Association rule mining (ARM) is one of the data mining problems receiving a great deal of attention in the database community. The main computation step in an ARM algorithm is frequent itemset discovery. In this paper, a frequent itemset discovery algorithm based on the Hopfield model is presented.