In this work, we focus on the issue of diffusion in dynamic networks. We begin by proposing a generic framework that merges two essential aspects of the problem: diffusion and network dynamics. Then, our framework is used for comparing effects of two aspects of the human behavior, sociability and network dynamics. On the one hand the dynamics of the network is related to the movements of individuals in a social space that lead ephemeral social proximity contacts. On the other hand the diffusion phenomenon is studied through the epidemic paradigm. The results obtained by simulations provide original insight about how and why the "social dynamics" of individuals, related to the proximity in a social space and its evolutions, influences diffusion phenomena through a population.

This paper investigates an application of wireless sensor networks to highlight existing communities within bird populations by collecting and analyzing behavioral and social data. We propose a solution based on fixed sensors fitted with microphone, a method more suited to the context of birds. This kind of sensor provides a good mean to enable the automatic detection of bird songs and thus to elicit social interactions between individuals by analyzing the occurrence and location of songs into given regions. The underlying sensor network architecture we propose allows each item to capture individual and collective patterns, by measuring the frequency of interactions among birds and between birds and regions. In this paper, we discuss our motivation to design a new architecture, and advantages and limitations of the proposed method. We demonstrate the efficiency of our solution by experimentations conducted by our simulation tool called Lypus.

The increasing development of world trade has promoted the spread of diseases. Recent events, like the H1N1outbreak and spread, have confirmed this tendency. Everyone could observe that this kind of infectious disease often has the ability to cross countries borders and spread rapidly. Preventing, monitoring and controlling the outbreak of these diseases have appeared to be a public health problem of primary importance for years. However, although numerous works have been conducted in order to understand and study epidemics, this subject remains a topical issue because of the complexity of the phenomena. In this paper, we present new opportunities offered by the field of social networks for understanding the spread of infectious diseases. This work focuses both on techniques already used in epidemiology, and very recent works in social network analysis that open new perspectives. We give an overview of methods currently used to model and study the spread of diseases and future directions that the social network approach allows to glimpse.

A key issue for any project that aims to study endangered animals is the counting process. In this work, we propose an algorithm for automatic counting of singing birds in their habitat by using wireless sensors fitted with microphone. Sensors are used to record audio samples of bird songs and samples are analyzed to extract fingerprints that permit to recognize bird species. This paper details the counting algorithm that uses graph theory and audio inputs comparison. Finally, we demonstrate our scheme efficiency through experimentations.

The aim of this study is to suggest an automated technique for distributed decision. It is based on population protocol deployed on a Wireless Sensor Networks (WSN). This approach could be used by any medical staff to detect earlier than usual some diseases on patients. Moreover, It could be easily adapted to many other fields (fire monitoring, environment supervising, ...) In this context, a patient is equipped with health parameter transducers which are connected to a WSN. This WSN will perform some computations and will raise an alarm when some diseases are suspected. The algorithm is distributed, i.e., the decision may be done by any mote dealing with the disease detection. The experimentations show promising results.

More and more animal species are endangered every day on earth. In order to study their adaptation to world and climate change and their chances of survival, numerous initiatives have been taken that mostly need human intrusion into animal communities. Today mobile devices enable researchers to go beyond this limit. In this paper, we propose an original solution that consists on a new framework for detecting individual songs in a bird population and identifying remotely by this way their collective behavior in movements without human interaction. Movement patterns are elicited by analyzing data collected via wireless sensors fitted with microphone. Whereas similar methods use mobile devices fitted on some specimens, we rather propose fixed sensors. We demonstrate that this solution provides a good answer to technical constraints assessed by the context and we discuss results of experimental simulations that allow to define optimized parameters for the architecture to be set up on the ground. Experimental results are provided and show the relative impact of different parameters such as the number of sensors or the population size on the detection rate.

The aim of this study is to suggest two automated techniques able to help medical staff to detect earlier than usual some diseases using wireless sensor networks (WSNs). In this context, a patient is equipped with physical sensors which sense health parameters. This WSN will perform some computations and will run an alarm when a disease is suspected. The first technique uses a population protocol to handle data exchanged between motes and provides an efficient algorithm to suggest that a disease is diagnosed on a patient. The algorithm is distributed, i.e., the decision may be done by any sensor dealing with the disease detection. The second technique uses a token algorithm where, some motes are denoted as masters. Each of them is in charge of deciding if a specific disease occurs. This technique is not totally distributed but enhances the network efficiency regarding to the energy consumption, the time execution and the number of exchanged messages.

Many initiatives have been taken to observe the adaptation of endangered animal species to various environmental changes they face and their chances of surviving. In this paper, we propose an original solution that consists on a new framework based on wireless sensor technology that enables to track individual movements and to collect spatio-temporal data to be analyzed without any harmful contact with animals. We discuss network configuration parameters what may optimize the elicitation of two specific kinds of collective motion behavior from such recorded data.

We present a study on the optimasation of the number of messages for medical diagnosis over wireless sensors networks (WSNs). We suggest distributed algorithms to diagnosis some diseases while monitoring a patient. Then, we compare the number of messages needed to make a decision between the algorithms. In our approach, each sensor will make some computations in order to participate to the decision. Indeed, some computation and collaboration tasks are performed over the sensors and the final diagnosis is performed in a distributed way. During the experimentation of our algorithms, we used a real data flow extracted from hospital monitoring.

This paper proposes several methods to estimate the number of endangered birds in their habitat. Our solutions use passive unknown networks sensors to get birds positions and gather data to a central base which runs our algorithms to estimate the number of singing birds. A first scheme derives the motes networks and the data in an altered unit disk graph to give its estimation on the number of birds. The second scheme, a linear algorithm, uses aggregated information from the motes to count the birds. Finally, we confirm our schemes efficiency through experimentation.

This paper presents a study on power management optimisation over a sensor network. We have proposed a set of management rules which are executed by each sensor of the network. These rules adapt themselves to sensors depending on their energy status and on the network energy. Periodically, each sensor computes its rules and decides how to behave (sleeping during a long period or not). An experimentation of these rules has been implemented, we used a distributed sort algorithm on the whole network. We have observed the network behaviour and noticed that the use of these rules allows to increase the average energy life up to 20 %.

In this paper we study how to detect anomalies in a distributed manner by using wireless sensor networks (WSNs). We focus on a medical context, where existing works generally provide large environments to monitor patients using sensors as simple transducers. Those devices forward sensed health parameters to a main base station. This station collects received data and may perform some computations. In this paper, we perform some distributed tasks on the sensors. We propose a distributed algorithm which allows to raise alarms under some initial rules to alert efficiently medical staff in case of critical situations without needless warnings.

In real-time collaborative systems, replicated objects, shared by users, are subject to concurrency constraints. In order to satisfy these, various algorithms, qualified as op?timistic, [3, 5, 13, 17, 14, 15, 18], have been proposed that exploit the semantic properties of operations to serialize concurrent operations and achieve copy con?vergence of replicated objects. Their drawback is that they either re?quire a condition on user's operations which is hard to verify when possible to ensure, or they need undo?ing then redoing operations in some situations. The main purpose of this paper is to present two new algorithms that over?come these drawbacks. They are based upon the imple?mentation of a continuous global order which enables that condition to be released, and simplifies the operation inte?gration process. In the second algorithm, thanks to de?ferred broadcast of operations to other sites, this process becomes even more simplified.