Julien Bruneau

DiaSim: A parameterized simulator for pervasive computing applications

Pervasive computing applications involve both software concerns, like any software system, and integration concerns, for the constituent networked devices of the pervasive computing environment. This situation is problematic for testing because it requires acquiring, testing and interfacing a variety of software and hardware entities. This process can rapidly become costly and time-consuming when the target environment involves many entities.

Toward a Tool-Based Development Methodology for Pervasive Computing Applications

Despite much progress, developing a pervasive computing application remains a challenge because of a lack of conceptual frameworks and supporting tools. This challenge involves coping with heterogeneous devices, overcoming the intricacies of distributed systems technologies, working out an architecture for the application, encoding it in a program, writing specific code to test the application, and finally deploying it. This paper presents a design language and a tool suite covering the development life-cycle of a pervasive computing application. The design language allows us to define a taxonomy of area-specific building-blocks, abstracting over their heterogeneity. This language also includes a layer to define the architecture of an application, following an architectural pattern commonly used in the pervasive computing domain. Our underlying methodology assigns roles to the stakeholders, providing separation of concerns. Our tool suite includes a compiler that takes design artifacts written in our language as input and generates a programming framework that supports the subsequent development stages, namely, implementation, testing, and deployment. Our methodology has been applied on a wide spectrum of areas. Based on these experiments, we assess our approach through three criteria: expressiveness, usability, and productivity.

DiaSuite: A tool suite to develop Sense/Compute/Control applications

We present DiaSuite, a tool suite that uses a software design approach to drive the development process. DiaSuite focuses on a specific domain, namely Sense/Compute/Control (SCC) applications. It comprises a domain-specific design language, a compiler producing a Java programming framework, a 2D-renderer to simulate an application, and a deployment framework. We have validated our tool suite on a variety of concrete applications in areas including telecommunications, building automation, robotics and avionics.

Going Through, Going Around: A Study on Individual Avoidance of Groups

When avoiding a group, a walker has two possibilities: either he goes through it or around it. Going through very dense groups or around huge ones would not seem natural and could break any sense of presence in a virtual environment. This paper aims to enable crowd simulators to handle such situations correctly. To this end, we need to understand how real humans decide to go through or around groups. As a first hypothesis, we apply the Principle of Minimum Energy (PME) on different group sizes and density. According to this principle, a walker should go around small and dense groups whereas he should go through large and sparse groups. Such principle has already been used for crowd simulation; the novelty here is to apply it to decide on a global avoidance strategy instead of local adaptations only. Our study quantifies decision thresholds. However, PME leaves some inconclusive situations for which the two solutions paths have similar energetic costs. In a second part, we propose an experiment to corroborate PME decisions thresholds with real observations. As controlling the factors of an experiment with many people is extremely hard, we propose to use Virtual Reality as a new method to observe human behavior. This work represents the first crowd simulation algorithm component directly designed from a VR-based study. We also consider the role of secondary factors in inconclusive situations. We show the influence of the group appearance and direction of relative motion in the decision process. Finally, we draw some guidelines to integrate our conclusions to existing crowd simulators and show an example of such integration. We evaluate the achieved improvements.

A tool suite to prototype pervasive computing applications

Despite much progress, developing a pervasive computing application remains a challenge because of a lack of conceptual frameworks and supporting tools. This challenge involves coping with heterogeneous entities, overcoming the intricacies of distributed systems technologies, working out an architecture for the application, encoding it in a program, writing specific code to test the application, and finally deploying it.

Virtual Testing for Smart Buildings

Smart buildings promise to revolutionize the way we live. Applications ranging from climate control to fire management can have significant impact on the quality and cost of these services. However, smart buildings and any technology with direct effect on human safety and life must undergo extensive testing. Virtual testing by means of computer simulation can significantly reduce the cost of testing and, as a result, accelerate the development of novel applications. Unfortunately, building physically-accurate simulation codes can be labor intensive. To address this problem, we propose a framework for rapid, physically-accurate virtual testing. The proposed framework supports analytical modeling of both a discrete distributed system as well as the physical environment that hosts it. The discrete models supported are accurate enough to allow the automatic generation of a dedicated programming framework that will help the developer in the implementation of these systems. The physical environment models supported are equational specifications that are accurate enough to produce running simulation codes. Combined, these two frameworks enable simulating both active systems and physical environments. These simulations can be used to monitor the behavior and gather statistics about the performance of an application in the context of precise virtual experiments. To illustrate the approach, we present models of Heating, Ventilating and Air-Conditioning (HVAC) systems. Using these models, we construct virtual experiments that illustrate how the approach can be used to optimize energy and cost of climate control for a building.

Energy-efficient mid-term strategies for collision avoidance in crowd simulation

When navigating in crowds, humans are able to move efficiently between people. They look ahead to know which path would reduce the complexity of their interactions with others. Current navigation systems for virtual agents consider the long-term planning to find a path in the static environment and the short term reaction to avoid collision with close obstacles. Recently some mid-term considerations have been added to avoid high density areas. However, there is no mid-term planning among static and dynamic obstacles that would enable the agent to look ahead and avoid difficult paths or find easy ones as human do. In this paper we present a system for such mid-term planning. This system is added to the navigation process between the path finding and the local avoidance to improve the navigation of virtual agents. We show the capacities of such system on several case studies. Finally we use an energy criterion to compare trajectories computed with and without the mid-term planning.

Walking with Virtual People: Evaluation of Locomotion Interfaces in Dynamic Environments

Navigating in virtual environments requires using some locomotion interfaces, especially when the dimensions of the environment exceed the ones of the Virtual Reality system. Locomotion interfaces induce some biases both in the perception of the self-motion or in the formation of virtual locomotion trajectories. These biases have been mostly evaluated in the context of static environments, and studies need to be revisited in the new context of populated environments where users interact with virtual characters. We focus on a situation of collision avoidance between a real participant and a virtual character, and compared it to previous studies on real walkers. Our results show that, as in reality, the risk of future collision is accurately anticipated by participants, however with delay. We also show that collision avoidance trajectories formed in VR have common properties with real ones, with some quantitative differences in avoidance distances. More generally, our evaluation demonstrates that reliable results can be obtained for qualitative analysis of small scale interactions in VR. We discuss these results in the perspective of a VR platform for large scale interaction applications, such as in a crowd, for which real data are difficult to gather.

Group Modeling: A Unified Velocity-Based Approach

Crowd simulators are commonly used to populate movie or game scenes in the entertainment industry. Even though it is crucial to consider the presence of groups for the believability of a virtual crowd, most crowd simulations only take into account individual characters or a limited set of group behaviors. We introduce a unified solution that allows for simulations of crowds that have diverse group properties such as social groups, marches, tourists and guides, etc. We extend the Velocity Obstacle approach for agent‐based crowd simulations by introducing Velocity Connection; the set of velocities that keep agents moving together while avoiding collisions and achieving goals. We demonstrate our approach to be robust, controllable, and able to cover a large set of group behaviors.

Following behaviors: a model for computing following distances based on prediction

In this paper, we present a new model to simulate following behavior. This model is based on a dynamic following distance that changes according to the followers speed and to the leaders motion. The following distance is associated with a prediction of the leaders future position to give a following ideal position. We show the resulting following trajectory and detail the importance of the distance variation in different situations. The model is evaluated using real data. We demonstrate the capacity of our model to reproduce macroscopic patterns and show that it is also able to synthesize trajectories similar to real ones. Finally, we compare our results with other following models and point out the improvements.