The Experts below are selected from a list of 25104 Experts worldwide ranked by ideXlab platform
Giancarlo Fortino - One of the best experts on this subject based on the ideXlab platform.
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Swarm Intelligence and IoT-Based Smart Cities: A Review
The Internet of Things for Smart Urban Ecosystems, 2019Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giandomenico Spezzano, Antonio Guerrieri, Giancarlo FortinoAbstract:Smart cities are complex and large distributed systems characterized by their heterogeneity, security, and reliability challenges. In addition, they are required to take into account several scalability, efficiency, safety, real-time responses, and smartness issues. All of this means that building smart city applications is extremely complex. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviors to deal with dynamic and fast changing systems. The Intelligence of cities can be modeled as a Swarm of digital telecommunication networks (the nerves), ubiquitously embedded Intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence). In this chapter, Swarm Intelligence-based algorithms and existing Swarm Intelligence-based smart city solutions will be analyzed. Moreover, a Swarm-based framework for smart cities will be presented. Then, a set of trends on how to use Swarm Intelligence in smart cities, in order to make them flexible and scalable, will be investigated.
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Swarm Intelligence-based algorithms within IoT-based systems: A review
Journal of Parallel and Distributed Computing, 2018Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Giandomenico Spezzano, Antonio Guerrieri, Seridi Hamid, Giancarlo FortinoAbstract:IoT-based systems are complex and dynamic aggregations of entities (Smart Objects) which usually lack decentralized control. Swarm Intelligence systems are decentralized, self-organized algorithms used to resolve complex problems with dynamic properties, incomplete information, and limited computation capabilities. This study provides an initial understanding of the technical aspects of Swarm Intelligence algorithms and their potential use in IoT-based applications. We present the existing Swarm Intelligence-based algorithms with their main applications, then we present existing IoT-based systems that use SI-based algorithms. Finally, we discuss trends to bring together Swarm Intelligence and IoT-based systems. This review will pave the path for future studies to easily choose the appropriate SI-based algorithm for IoT-based systems.
Ouarda Zedadra - One of the best experts on this subject based on the ideXlab platform.
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Swarm Intelligence and IoT-Based Smart Cities: A Review
The Internet of Things for Smart Urban Ecosystems, 2019Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giandomenico Spezzano, Antonio Guerrieri, Giancarlo FortinoAbstract:Smart cities are complex and large distributed systems characterized by their heterogeneity, security, and reliability challenges. In addition, they are required to take into account several scalability, efficiency, safety, real-time responses, and smartness issues. All of this means that building smart city applications is extremely complex. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviors to deal with dynamic and fast changing systems. The Intelligence of cities can be modeled as a Swarm of digital telecommunication networks (the nerves), ubiquitously embedded Intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence). In this chapter, Swarm Intelligence-based algorithms and existing Swarm Intelligence-based smart city solutions will be analyzed. Moreover, a Swarm-based framework for smart cities will be presented. Then, a set of trends on how to use Swarm Intelligence in smart cities, in order to make them flexible and scalable, will be investigated.
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Swarm Intelligence-based algorithms within IoT-based systems: A review
Journal of Parallel and Distributed Computing, 2018Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Giandomenico Spezzano, Antonio Guerrieri, Seridi Hamid, Giancarlo FortinoAbstract:IoT-based systems are complex and dynamic aggregations of entities (Smart Objects) which usually lack decentralized control. Swarm Intelligence systems are decentralized, self-organized algorithms used to resolve complex problems with dynamic properties, incomplete information, and limited computation capabilities. This study provides an initial understanding of the technical aspects of Swarm Intelligence algorithms and their potential use in IoT-based applications. We present the existing Swarm Intelligence-based algorithms with their main applications, then we present existing IoT-based systems that use SI-based algorithms. Finally, we discuss trends to bring together Swarm Intelligence and IoT-based systems. This review will pave the path for future studies to easily choose the appropriate SI-based algorithm for IoT-based systems.
Satchidananda Dehuri - One of the best experts on this subject based on the ideXlab platform.
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Multi-objective Swarm Intelligence - Multi-objective Swarm Intelligence
Studies in Computational Intelligence, 2015Co-Authors: Satchidananda Dehuri, Alok Kumar Jagadev, Mrutyunjaya PandaAbstract:The aim of this book is to understand the state-of-the-art theoretical and practical advances of Swarm Intelligence. It comprises seven contemporary relevant chapters. In chapter 1, a review of Bacteria Foraging Optimization (BFO) techniques for both single and multiple criterions problem is presented. A survey on Swarm Intelligence for multiple and many objectives optimization is presented in chapter 2 along with a topical study on EEG signal analysis. Without compromising the extensive simulation study, a comparative study of variants of MOPSO is provided in chapter 3. Intractable problems like subset and job scheduling problems are discussed in chapters 4 and 7 by different hybrid Swarm Intelligence techniques. An attempt to study image enhancement by ant colony optimization is made in chapter 5. Finally, chapter 7 covers the aspect of uncertainty in data by hybrid PSO.
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Innovations in Swarm Intelligence - Innovations in Swarm Intelligence
Studies in Computational Intelligence, 2009Co-Authors: Chee Peng Lim, Lakhmi C. Jain, Satchidananda DehuriAbstract:Over the past two decades, Swarm Intelligence has emerged as a powerful approach to solving optimization as well as other complex problems. Swarm Intelligence models are inspired by social behaviours of simple agents interacting among themselves as well as with the environment, e.g., flocking of birds, schooling of fish, foraging of bees and ants. The collective behaviours that emerge out of the interactions at the colony level are useful in achieving complex goals. The main aim of this research book is to present a sample of recent innovations and advances in techniques and applications of Swarm Intelligence. Among the topics covered in this book include: particle Swarm optimization and hybrid methods, ant colony optimization and hybrid methods, bee colony optimization, glowworm Swarm optimization, and complex social Swarms, application of various Swarm Intelligence models to operational planning of energy plants, modeling and control of nanorobots, classification of documents, identification of disease biomarkers, and prediction of gene signals. The book is directed to researchers, practicing professionals, and undergraduate as well as graduate students of all disciplines who are interested in enhancing their knowledge in techniques and applications of Swarm Intelligence.
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Swarm Intelligence for Multi-objective Problems in Data Mining - Swarm Intelligence for Multi-objective Problems in Data Mining
Studies in Computational Intelligence, 2009Co-Authors: Carlos A. Coello Coello, Satchidananda Dehuri, Susmita GhoshAbstract:The purpose of this book is to collect contributions that are at the intersection of multi-objective optimization, Swarm Intelligence (specifically, particle Swarm optimization and ant colony optimization) and data mining. Such a collection intends to illustrate the potential of multi-objective Swarm Intelligence techniques in data mining, with the aim of motivating more researchers in evolutionary computation and machine learning to do research in this field. This volume consists of eleven chapters, including an introduction that provides the basic concepts of Swarm Intelligence techniques and a discussion of their use in data mining. Some of the research challenges that must be faced when using Swarm Intelligence techniques in data mining are also addressed. The rest of the chapters were contributed by leading researchers, and were organized according to the steps normally followed in Knowledge Discovery in Databases (KDD) (i.e., data preprocessing, data mining, and post processing). We hope that this book becomes a valuable reference for those wishing to do research on the use of multi-objective Swarm Intelligence techniques in data mining and knowledge discovery in databases.
Nicolas Jouandeau - One of the best experts on this subject based on the ideXlab platform.
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Swarm Intelligence and IoT-Based Smart Cities: A Review
The Internet of Things for Smart Urban Ecosystems, 2019Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giandomenico Spezzano, Antonio Guerrieri, Giancarlo FortinoAbstract:Smart cities are complex and large distributed systems characterized by their heterogeneity, security, and reliability challenges. In addition, they are required to take into account several scalability, efficiency, safety, real-time responses, and smartness issues. All of this means that building smart city applications is extremely complex. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviors to deal with dynamic and fast changing systems. The Intelligence of cities can be modeled as a Swarm of digital telecommunication networks (the nerves), ubiquitously embedded Intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence). In this chapter, Swarm Intelligence-based algorithms and existing Swarm Intelligence-based smart city solutions will be analyzed. Moreover, a Swarm-based framework for smart cities will be presented. Then, a set of trends on how to use Swarm Intelligence in smart cities, in order to make them flexible and scalable, will be investigated.
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Swarm Intelligence-based algorithms within IoT-based systems: A review
Journal of Parallel and Distributed Computing, 2018Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Giandomenico Spezzano, Antonio Guerrieri, Seridi Hamid, Giancarlo FortinoAbstract:IoT-based systems are complex and dynamic aggregations of entities (Smart Objects) which usually lack decentralized control. Swarm Intelligence systems are decentralized, self-organized algorithms used to resolve complex problems with dynamic properties, incomplete information, and limited computation capabilities. This study provides an initial understanding of the technical aspects of Swarm Intelligence algorithms and their potential use in IoT-based applications. We present the existing Swarm Intelligence-based algorithms with their main applications, then we present existing IoT-based systems that use SI-based algorithms. Finally, we discuss trends to bring together Swarm Intelligence and IoT-based systems. This review will pave the path for future studies to easily choose the appropriate SI-based algorithm for IoT-based systems.
Antonio Guerrieri - One of the best experts on this subject based on the ideXlab platform.
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Swarm Intelligence and IoT-Based Smart Cities: A Review
The Internet of Things for Smart Urban Ecosystems, 2019Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Hamid Seridi, Giandomenico Spezzano, Antonio Guerrieri, Giancarlo FortinoAbstract:Smart cities are complex and large distributed systems characterized by their heterogeneity, security, and reliability challenges. In addition, they are required to take into account several scalability, efficiency, safety, real-time responses, and smartness issues. All of this means that building smart city applications is extremely complex. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviors to deal with dynamic and fast changing systems. The Intelligence of cities can be modeled as a Swarm of digital telecommunication networks (the nerves), ubiquitously embedded Intelligence (the brains), sensors and tags (the sensory organs), and software (the knowledge and cognitive competence). In this chapter, Swarm Intelligence-based algorithms and existing Swarm Intelligence-based smart city solutions will be analyzed. Moreover, a Swarm-based framework for smart cities will be presented. Then, a set of trends on how to use Swarm Intelligence in smart cities, in order to make them flexible and scalable, will be investigated.
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Swarm Intelligence-based algorithms within IoT-based systems: A review
Journal of Parallel and Distributed Computing, 2018Co-Authors: Ouarda Zedadra, Nicolas Jouandeau, Giandomenico Spezzano, Antonio Guerrieri, Seridi Hamid, Giancarlo FortinoAbstract:IoT-based systems are complex and dynamic aggregations of entities (Smart Objects) which usually lack decentralized control. Swarm Intelligence systems are decentralized, self-organized algorithms used to resolve complex problems with dynamic properties, incomplete information, and limited computation capabilities. This study provides an initial understanding of the technical aspects of Swarm Intelligence algorithms and their potential use in IoT-based applications. We present the existing Swarm Intelligence-based algorithms with their main applications, then we present existing IoT-based systems that use SI-based algorithms. Finally, we discuss trends to bring together Swarm Intelligence and IoT-based systems. This review will pave the path for future studies to easily choose the appropriate SI-based algorithm for IoT-based systems.
