14,868 research outputs found
Dynamic learning of the environment for eco-citizen behavior
Le développement de villes intelligentes et durables nécessite le déploiement des technologies de l'information et de la communication (ITC) pour garantir de meilleurs services et informations disponibles à tout moment et partout. Comme les dispositifs IoT devenant plus puissants et moins coûteux, la mise en place d'un réseau de capteurs dans un contexte urbain peut être coûteuse. Cette thèse propose une technique pour estimer les informations environnementales manquantes dans des environnements à large échelle. Notre technique permet de fournir des informations alors que les dispositifs ne sont pas disponibles dans une zone de l'environnement non couverte par des capteurs. La contribution de notre proposition est résumée dans les points suivants : - limiter le nombre de dispositifs de détection à déployer dans un environnement urbain ; - l'exploitation de données hétérogènes acquises par des dispositifs intermittents ; - le traitement en temps réel des informations ; - l'auto-calibration du système. Notre proposition utilise l'approche AMAS (Adaptive Multi-Agent System) pour résoudre le problème de l'indisponibilité des informations. Dans cette approche, une exception est considérée comme une situation non coopérative (NCS) qui doit être résolue localement et de manière coopérative. HybridIoT exploite à la fois des informations homogènes (informations du même type) et hétérogènes (informations de différents types ou unités) acquises à partir d'un capteur disponible pour fournir des estimations précises au point de l'environnement où un capteur n'est pas disponible. La technique proposée permet d'estimer des informations environnementales précises dans des conditions de variabilité résultant du contexte d'application urbaine dans lequel le projet est situé, et qui n'ont pas été explorées par les solutions de l'état de l'art : - ouverture : les capteurs peuvent entrer ou sortir du système à tout moment sans qu'aucune configuration particulière soit nécessaire ; - large échelle : le système peut être déployé dans un
contexte urbain Ă large Ă©chelle et assurer un fonctionnement correct avec un nombre significatif de dispositifs ;
- hétérogénéité : le système traite différents types d'informations sans aucune configuration a priori. Notre proposition ne nécessite aucun paramètre d'entrée ni aucune reconfiguration. Le système peut fonctionner dans des environnements ouverts et dynamiques tels que les villes, où un grand nombre de capteurs peuvent apparaître ou disparaître à tout moment et sans aucun préavis. Nous avons fait différentes expérimentations pour comparer les résultats obtenus à plusieurs techniques standard afin d'évaluer la validité de notre proposition. Nous avons également développé un ensemble de techniques standard pour produire des résultats de base qui seront comparés à ceux obtenus par notre proposition multi-agents.The development of sustainable smart cities requires the deployment of Information and Communication Technology (ICT) to ensure better services and available information at any time and everywhere. As IoT devices become more powerful and low-cost, the implementation of an extensive sensor network for an urban context can be expensive. This thesis proposes a technique for estimating missing environmental information in large scale environments. Our technique enables providing
information whereas devices are not available for an area of the environment not covered by sensing devices. The contribution of our proposal is summarized in the following points: * limiting the number of sensing devices to be
deployed in an urban environment; * the exploitation of heterogeneous data acquired from intermittent devices; * real-time processing of information; * self-calibration of the system. Our proposal uses the Adaptive Multi-Agent System (AMAS) approach to solve the problem of information unavailability. In this approach, an exception is considered as a Non-Cooperative Situation (NCS) that has to be solved locally and cooperatively. HybridIoT exploits both homogeneous (information of the same type) and heterogeneous information (information of different types or units) acquired from some available sensing device to provide accurate estimates in the point of the environment where a sensing device is not available. The proposed technique enables estimating accurate environmental information under conditions of uncertainty arising from the urban application context in which the project is situated, and which have not been explored by the state-of-the-art solutions: * openness: sensors can enter or leave the system at any time without the need for any reconfiguration; * large scale: the system can be deployed in a large, urban context and ensure correct operation with a significative number of devices; * heterogeneity: the system handles different types of information without any a priori configuration. Our proposal does not require any input parameters or reconfiguration. The system can operate in open, dynamic environments such as cities, where a large number of sensing devices can appear or disappear at any time and without any prior notification. We carried out different experiments to compare the obtained results to various standard techniques to assess the validity of our proposal. We also developed a pipeline of standard techniques to produce baseline results that will be compared to those obtained by our multi-agent proposal
Thirty Years of Machine Learning: The Road to Pareto-Optimal Wireless Networks
Future wireless networks have a substantial potential in terms of supporting
a broad range of complex compelling applications both in military and civilian
fields, where the users are able to enjoy high-rate, low-latency, low-cost and
reliable information services. Achieving this ambitious goal requires new radio
techniques for adaptive learning and intelligent decision making because of the
complex heterogeneous nature of the network structures and wireless services.
Machine learning (ML) algorithms have great success in supporting big data
analytics, efficient parameter estimation and interactive decision making.
Hence, in this article, we review the thirty-year history of ML by elaborating
on supervised learning, unsupervised learning, reinforcement learning and deep
learning. Furthermore, we investigate their employment in the compelling
applications of wireless networks, including heterogeneous networks (HetNets),
cognitive radios (CR), Internet of things (IoT), machine to machine networks
(M2M), and so on. This article aims for assisting the readers in clarifying the
motivation and methodology of the various ML algorithms, so as to invoke them
for hitherto unexplored services as well as scenarios of future wireless
networks.Comment: 46 pages, 22 fig
Trust and Risk Relationship Analysis on a Workflow Basis: A Use Case
Trust and risk are often seen in proportion to each other; as such, high trust may induce low risk and vice versa. However, recent research argues that trust and risk relationship is implicit rather than proportional. Considering that trust and risk are implicit, this paper proposes for the first time a novel approach to view trust and risk on a basis of a W3C PROV provenance data model applied in a healthcare domain. We argue that high trust in healthcare domain can be placed in data despite of its high risk, and low trust data can have low risk depending on data quality attributes and its provenance. This is demonstrated by our trust and risk models applied to the BII case study data. The proposed theoretical approach first calculates risk values at each workflow step considering PROV concepts and second, aggregates the final risk score for the whole provenance chain. Different from risk model, trust of a workflow is derived by applying DS/AHP method. The results prove our assumption that trust and risk relationship is implicit
Distributed Hybrid Simulation of the Internet of Things and Smart Territories
This paper deals with the use of hybrid simulation to build and compose
heterogeneous simulation scenarios that can be proficiently exploited to model
and represent the Internet of Things (IoT). Hybrid simulation is a methodology
that combines multiple modalities of modeling/simulation. Complex scenarios are
decomposed into simpler ones, each one being simulated through a specific
simulation strategy. All these simulation building blocks are then synchronized
and coordinated. This simulation methodology is an ideal one to represent IoT
setups, which are usually very demanding, due to the heterogeneity of possible
scenarios arising from the massive deployment of an enormous amount of sensors
and devices. We present a use case concerned with the distributed simulation of
smart territories, a novel view of decentralized geographical spaces that,
thanks to the use of IoT, builds ICT services to manage resources in a way that
is sustainable and not harmful to the environment. Three different simulation
models are combined together, namely, an adaptive agent-based parallel and
distributed simulator, an OMNeT++ based discrete event simulator and a
script-language simulator based on MATLAB. Results from a performance analysis
confirm the viability of using hybrid simulation to model complex IoT
scenarios.Comment: arXiv admin note: substantial text overlap with arXiv:1605.0487
Big data analytics:Computational intelligence techniques and application areas
Big Data has significant impact in developing functional smart cities and supporting modern societies. In this paper, we investigate the importance of Big Data in modern life and economy, and discuss challenges arising from Big Data utilization. Different computational intelligence techniques have been considered as tools for Big Data analytics. We also explore the powerful combination of Big Data and Computational Intelligence (CI) and identify a number of areas, where novel applications in real world smart city problems can be developed by utilizing these powerful tools and techniques. We present a case study for intelligent transportation in the context of a smart city, and a novel data modelling methodology based on a biologically inspired universal generative modelling approach called Hierarchical Spatial-Temporal State Machine (HSTSM). We further discuss various implications of policy, protection, valuation and commercialization related to Big Data, its applications and deployment
Smart and Intelligent Automation for Industry 4.0 using Millimeter-Wave and Deep Reinforcement Learning
Innovations in communication systems, compute hardware, and deep learning algorithms have led to the advancement of smart industry automation. Smart automation includes industrial sectors such as intelligent warehouse management, smart infrastructure for first responders, and smart monitoring systems. Automation aims to maximize efficiency, safety, and reliability. Autonomous forklifts can significantly increase productivity, reduce safety-related accidents, and improve operation speed to enhance the efficiency of a warehouse. Forklifts or robotic agents are required to perform different tasks such as position estimation, mapping, and dispatching. Each of the tasks involves different requirements and design constraints. Smart infrastructure for first responder applications requires robotic agents like Unmanned Aerial Vehicles (UAVs) to provide situation awareness surrounding an emergency. An immediate and efficient response to a safety-critical situation is crucial, as a better first response significantly impacts the safety and recovery of parties involved. But these UAVs lack the computational power required to run Deep Neural Networks (DNNs) that are used to provide the necessary intelligence. In this dissertation, we focus on two applications in smart industry automation. In the first part, we target smart warehouse automation for Intelligent Material Handling (IMH), where we design an accurate and robust Machine Learning (ML) based indoor localization system for robotic agents working in a warehouse. The localization system utilizes millimeter-wave (mmWave) wireless sensors to provide feature information in the form of a radio map which the ML model uses to learn indoor positioning. In the second part, we target smart infrastructure for first responders, where we present a computationally efficient adaptive exit strategy in multi-exit Deep Neural Networks using Deep Reinforcement Learning (DRL). The proposed adaptive exit strategy provides faster inference time and significantly reduces computations
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