13 research outputs found
A multi-agent system for on-the-fly web map generation and spatial conflict resolution
RĂ©sumĂ© Internet est devenu un moyen de diffusion de lâinformation gĂ©ographique par excellence. Il offre de plus en plus de services cartographiques accessibles par des milliers dâinternautes Ă travers le monde. Cependant, la qualitĂ© de ces services doit ĂȘtre amĂ©liorĂ©e, principalement en matiĂšre de personnalisation. A cette fin, il est important que la carte gĂ©nĂ©rĂ©e corresponde autant que possible aux besoins, aux prĂ©fĂ©rences et au contexte de lâutilisateur. Ce but peut ĂȘtre atteint en appliquant les transformations appropriĂ©es, en temps rĂ©el, aux objets de lâespace Ă chaque cycle de gĂ©nĂ©ration de la carte. Lâun des dĂ©fis majeurs de la gĂ©nĂ©ration dâune carte Ă la volĂ©e est la rĂ©solution des conflits spatiaux qui apparaissent entre les objets, essentiellement Ă cause de lâespace rĂ©duit des Ă©crans dâaffichage. Dans cette thĂšse, nous proposons une nouvelle approche basĂ©e sur la mise en Ćuvre dâun systĂšme multiagent pour la gĂ©nĂ©ration Ă la volĂ©e des cartes et la rĂ©solution des conflits spatiaux. Cette approche est basĂ©e sur lâutilisation de la reprĂ©sentation multiple et la gĂ©nĂ©ralisation cartographique. Elle rĂ©sout les conflits spatiaux et gĂ©nĂšre les cartes demandĂ©es selon une stratĂ©gie innovatrice : la gĂ©nĂ©ration progressive des cartes par couches dâintĂ©rĂȘt. Chaque couche dâintĂ©rĂȘt contient tous les objets ayant le mĂȘme degrĂ© dâimportance pour lâutilisateur. Ce contenu est dĂ©terminĂ© Ă la volĂ©e au dĂ©but du processus de gĂ©nĂ©ration de la carte demandĂ©e. Notre approche multiagent gĂ©nĂšre et transfĂšre cette carte suivant un mode parallĂšle. En effet, une fois une couche dâintĂ©rĂȘt gĂ©nĂ©rĂ©e, elle est transmise Ă lâutilisateur. Dans le but de rĂ©soudre les conflits spatiaux, et par la mĂȘme occasion gĂ©nĂ©rer la carte demandĂ©e, nous affectons un agent logiciel Ă chaque objet de lâespace. Les agents entrent ensuite en compĂ©tition pour lâoccupation de lâespace disponible. Cette compĂ©tition est basĂ©e sur un ensemble de prioritĂ©s qui correspondent aux diffĂ©rents degrĂ©s dâimportance des objets pour lâutilisateur. Durant la rĂ©solution des conflits, les agents prennent en considĂ©ration les besoins et les prĂ©fĂ©rences de lâutilisateur afin dâamĂ©liorer la personnalisation de la carte. Ils amĂ©liorent la lisibilitĂ© des objets importants et utilisent des symboles qui pourraient aider lâutilisateur Ă mieux comprendre lâespace gĂ©ographique. Le processus de gĂ©nĂ©ration de la carte peut ĂȘtre interrompu en tout temps par lâutilisateur lorsque les donnĂ©es dĂ©jĂ transmises rĂ©pondent Ă ses besoins. Dans ce cas, son temps dâattente est rĂ©duit, Ă©tant donnĂ© quâil nâa pas Ă attendre la gĂ©nĂ©ration du reste de la carte. Afin dâillustrer notre approche, nous lâappliquons au contexte de la cartographie sur le web ainsi quâau contexte de la cartographie mobile. Dans ces deux contextes, nous catĂ©gorisons nos donnĂ©es, qui concernent la ville de QuĂ©bec, en quatre couches dâintĂ©rĂȘt contenant les objets explicitement demandĂ©s par lâutilisateur, les objets repĂšres, le rĂ©seau routier et les objets ordinaires qui nâont aucune importance particuliĂšre pour lâutilisateur. Notre systĂšme multiagent vise Ă rĂ©soudre certains problĂšmes liĂ©s Ă la gĂ©nĂ©ration Ă la volĂ©e des cartes web. Ces problĂšmes sont les suivants : 1. Comment adapter le contenu des cartes, Ă la volĂ©e, aux besoins des utilisateurs ? 2. Comment rĂ©soudre les conflits spatiaux de maniĂšre Ă amĂ©liorer la lisibilitĂ© de la carte tout en prenant en considĂ©ration les besoins de lâutilisateur ? 3. Comment accĂ©lĂ©rer la gĂ©nĂ©ration et le transfert des donnĂ©es aux utilisateurs ? Les principales contributions de cette thĂšse sont : 1. La rĂ©solution des conflits spatiaux en utilisant les systĂšmes multiagent, la gĂ©nĂ©ralisation cartographique et la reprĂ©sentation multiple. 2. La gĂ©nĂ©ration des cartes dans un contexte web et dans un contexte mobile, Ă la volĂ©e, en utilisant les systĂšmes multiagent, la gĂ©nĂ©ralisation cartographique et la reprĂ©sentation multiple. 3. Lâadaptation des contenus des cartes, en temps rĂ©el, aux besoins de lâutilisateur Ă la source (durant la premiĂšre gĂ©nĂ©ration de la carte). 4. Une nouvelle modĂ©lisation de lâespace gĂ©ographique basĂ©e sur une architecture multi-couches du systĂšme multiagent. 5. Une approche de gĂ©nĂ©ration progressive des cartes basĂ©e sur les couches dâintĂ©rĂȘt. 6. La gĂ©nĂ©ration et le transfert, en parallĂšle, des cartes aux utilisateurs, dans les contextes web et mobile.Abstract Internet is a fast growing medium to get and disseminate geospatial information. It provides more and more web mapping services accessible by thousands of users worldwide. However, the quality of these services needs to be improved, especially in term of personalization. In order to increase map flexibility, it is important that the map corresponds as much as possible to the userâs needs, preferences and context. This may be possible by applying the suitable transformations, in real-time, to spatial objects at each map generation cycle. An underlying challenge of such on-the-fly map generation is to solve spatial conflicts that may appear between objects especially due to lack of space on display screens. In this dissertation, we propose a multiagent-based approach to address the problems of on-the-fly web map generation and spatial conflict resolution. The approach is based upon the use of multiple representation and cartographic generalization. It solves conflicts and generates maps according to our innovative progressive map generation by layers of interest approach. A layer of interest contains objects that have the same importance to the user. This content, which depends on the userâs needs and the mapâs context of use, is determined on-the-fly. Our multiagent-based approach generates and transfers data of the required map in parallel. As soon as a given layer of interest is generated, it is transmitted to the user. In order to generate a given map and solve spatial conflicts, we assign a software agent to every spatial object. Then, the agents compete for space occupation. This competition is driven by a set of priorities corresponding to the importance of objects for the user. During processing, agents take into account usersâ needs and preferences in order to improve the personalization of the final map. They emphasize important objects by improving their legibility and using symbols in order to help the user to better understand the geographic space. Since the user can stop the map generation process whenever he finds the required information from the amount of data already transferred, his waiting delays are reduced. In order to illustrate our approach, we apply it to the context of tourist web and mobile mapping applications. In these contexts, we propose to categorize data into four layers of interest containing: explicitly required objects, landmark objects, road network and ordinary objects which do not have any specific importance for the user. In this dissertation, our multiagent system aims at solving the following problems related to on-the-fly web mapping applications: 1. How can we adapt the contents of maps to usersâ needs on-the-fly? 2. How can we solve spatial conflicts in order to improve the legibility of maps while taking into account usersâ needs? 3. How can we speed up data generation and transfer to users? The main contributions of this thesis are: 1. The resolution of spatial conflicts using multiagent systems, cartographic generalization and multiple representation. 2. The generation of web and mobile maps, on-the-fly, using multiagent systems, cartographic generalization and multiple representation. 3. The real-time adaptation of mapsâ contents to usersâ needs at the source (during the first generation of the map). 4. A new modeling of the geographic space based upon a multi-layers multiagent system architecture. 5. A progressive map generation approach by layers of interest. 6. The generation and transfer of web and mobile maps at the same time to users
FROM INTELLIGENT WEB OF THINGS TO SOCIAL WEB OF THINGS
Numerous challenges, including limited resources, random mobility, and lack of standardized communication protocols, are currently preventing a myriad of heterogeneous devices to interact and provide Web services within the context of the Web of Things (WoT). We argue in this paper that these devices should be augmented with artificial intelligence techniques for an enhanced management of their resources and an easier construction of Web applications integrating Real World Things (RWT). To this end, we present a new classification of the WoT challenges and highlight the opportunities of embedding smartness into RWT. We also present our vision of Intelligent WoT by proposing a multiagent system-based architecture for intelligent Web service composition. In addition, we discuss the shift of the WoT toward a Social WoT (SWoT) and debate our ideas within two important scenarios, namely the Intelligent VANET-WoT and smart logistics
Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems
Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs
Cyber-physical system design with sensor networking technologies
This book describes how wireless sensor networking technologies can help in establishing and maintaining seamless communications between the physical and cyber systems to enable efficient, secure, reliable acquisition, management, and routing of data
Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems
Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs
ABAMA: An Agent-based Architecture for Mapping Natural Ecosystems onto Wireless Sensor Networks
AbstractWireless Sensor Networks (WSNs) comprise of hundreds of spatially distributed sensors which may collaborate, compete, and self-organize in order to solve complex tasks which are beyond their individual capabilities. The efficiency of these actions is commonly restricted by the limited energy, the environmental context, and the processing capabilities of the sensors. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and strong communication mechanisms used by living organisms. While mapping these organisms onto sensors and ecosystems onto WSNs, we identify the similarities of both parties in terms of structure, active entities, topology, goals, communications, and functions and highlight shortcomings that would prevent WSNs from matching the behavior of ecosystems. We then propose an agent-based architecture that migrates the complex processing loads outside the physical sensor network while incorporating missing characteristics such as autonomy, intelligence, and context awareness to the WSN. In contrast to existing works, we use software agents to bridge the gap between WSNs and natural ecosystems and achieve an optimal mapping between both systems. Our ultimate goal is to enhance the capabilities of WSNs to take advantage of ecology-inspired algorithms
Enabling Cyber Physical Systems with Wireless Sensor Networking Technologies, Multiagent System Paradigm, and Natural Ecosystems
Wireless sensor networks (WSNs) are key components in the emergent cyber physical systems (CPSs). They may include hundreds of spatially distributed sensors which interact to solve complex tasks going beyond their individual capabilities. Due to the limited capabilities of sensors, sensor actions cannot meet CPS requirements while controlling and coordinating the operations of physical and engineered systems. To overcome these constraints, we explore the ecosystem metaphor for WSNs with the aim of taking advantage of the efficient adaptation behavior and communication mechanisms of living organisms. By mapping these organisms onto sensors and ecosystems onto WSNs, we highlight shortcomings that prevent WSNs from delivering the capabilities of ecosystems at several levels, including structure, topology, goals, communications, and functions. We then propose an agent-based architecture that migrates complex processing tasks outside the physical sensor network while incorporating missing characteristics of autonomy, intelligence, and context awareness to the WSN. Unlike existing works, we use software agents to map WSNs to natural ecosystems and enhance WSN capabilities to take advantage of bioinspired algorithms. We extend our architecture and propose a new intelligent CPS framework where several control levels are embedded in the physical system, thereby allowing agents to support WSNs technologies in enabling CPSs
Real-Time Traffic Data Smoothing from GPS Sparse Measures Using Fuzzy Switching Linear Models
International audienceTraffic is one of the urban phenomena that have been attracting substantial interest in different scientific and industrial communities since many decades. Indeed, traffic congestions can have severe negative effects on people's safety, daily activities and quality of life, resulting into economical, environmental and health burden for both governments and organizations. Traffic monitoring has become a hot multidisciplinary research topic that aims to minimize traffic's negative effects by developing intelligent techniques for accurate traffic states' estimation, control and prediction. In this paper, we propose a novel algorithm for traffic state estimation from GPS data and using fuzzy switching linear models. The use of fuzzy switches allows the representation of intermediate traffic states, which provides more accurate traffic estimation compared to the traditional hard switching models, and consequently enables making better proactive and in-time decisions. The proposed algorithm has been tested on open traffic datasets collected in England, 2014. The results of the experiments are promising, with a maximum absolute relative error equal to 9.04%