Swarm Intelligence and IoT-Based Smart Cities: A Review

Zedadra, Ouarda; Guerrieri, Antonio; Jouandeau, Nicolas; Spezzano, Giandomenico; Séridi, Hamid; Fortino, Giancarlo

doi:10.1007/978-3-319-96550-5_8

Cited by 35 publications

(19 citation statements)

References 62 publications

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“…However, this work did not report on using advanced analytics such as machine learning to derive further value from the gathered data streams. Recent and advanced works which deployed EI for smart cities and ASIoTs can be found in the works by Singh et al 68 and Zedadra et al 69 Singh et al 68 proposed a novel smart city IoT architecture combining deep learning, SDN, and blockchain technologies. The figure in Singh et al 68 shows a schematic of their smart city and IoT architecture.…”

Section: Smart Cities and Asiotsmentioning

confidence: 99%

Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Ang

Seng

2022

International Journal of Distributed Sensor Networks

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The advances and convergence in sensor technology, information and communication technology, and intelligent analytics have given rise to the Internet of Things or also known as the Internet of Everything or the Industrial Internet. The research and development works for the Internet of Things can be seen to have progressed in two main phases: (1) In the first phase, the earlier works for the Internet of Things focused on developing the building blocks and enabling technologies such as the sensors and RFID technologies, communications and wireless protocols, machine-to-machine interfaces, energy efficiency of nodes, and energy harvesting technologies, and (2) in the second phase, the latter and recent works focused on the addition of, and embedding value to application-specific Internet of Things using technologies for smart environments and applications such as intelligent analytics and machine learning, embedded vision and image processing, augmented reality, and autonomous systems. We associate the term of embedded intelligence and analytics with the data-driven future for application-specific Internet of Things. In this article, we give an introduction and review recent developments of embedded intelligence for the Internet of Things; the various embedded intelligence computational frameworks such as edge, fog, and cloud for the application-specific Internet of Things; and highlight the techniques, challenges, and opportunities for effective deployment of application-specific Internet of Things technology to address complex problems for various smart environments and applications.

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Section: Smart Cities and Asiotsmentioning

confidence: 99%

Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Ang

Seng

2022

International Journal of Distributed Sensor Networks

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“…Evolutionary Computation (EC) involves combinatorial optimization mechanisms and is inspired by biological evolution, whereas Swarm Intelligence (SI) is based on the collective behavior of decentralized and self-organized systems. These techniques solve problems in an implicit way through collective behavior and cooperation between the actors in a system, and several recent examples of successful applications of these techniques can be found in the literature, alone or using hybrid approaches where two or more CI techniques are combined, regarding resource allocation [35], bioinformatics [36], energy management optimization [37], the Internet of Things [38], scheduling and routing [39], and social community network analysis [40], among others.…”

Section: Computational Intelligence Algorithmsmentioning

confidence: 99%

Analysis of Nature-Inspired Algorithms for Long-Term Digital Preservation

El-Fakdi

Rosa

2021

Mathematics

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Digital preservation is a research area devoted to keeping digital assets preserved and usable for many years. Out of the many approaches to digital preservation, the present research article follows a new object-centered digital preservation paradigm where digital objects share part of the responsibility for preservation: they can move, replicate, and evolve to a higher-quality format inside a digital ecosystem. In the new framework, the behavior of digital objects needs to be modeled in order to obtain the best preservation strategy. Thus, digital objects are programmed with the mission of their own long-term self-preservation, which entails being accessible and reproducible by users at any time in the future regardless of frequent technological changes due to software and hardware upgrades. Three nature-inspired computational intelligence algorithms, based on the collective behavior of decentralized and self-organized systems, were selected for the modeling approach: multipopulation genetic algorithm, ant colony optimization, and a virus-based algorithm. TiM, a simulated environment for running distributed digital ecosystems, was used to perform the experiments. The results map the relation between the models and the expected object diversity obtained in short- and mid-term digital preservation scenarios. Comparing the results, the best performance corresponded to the multipopulation genetic algorithm. The article aims to be a first step in the digital self-preservation field. Building nature-inspired model behaviors is a good approach and opens the door to future tests with other AI-based methods.

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“…For example, in IoT-based systems, the SI algorithm has been used for task scheduling [ 4 ]. In IoT-based smart cities, SI algorithms have been used due to its population-based feature to make the system flexible and scalable [ 70 ].…”

Section: Real-world Applicationsmentioning

confidence: 99%

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

Yang

Shen

et al. 2020

Lecture Notes in Computer Science

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The Swarm Intelligence (SI) algorithms have been proved to be a comprehensive method to solve complex optimization problems by simulating the emergence behaviors of biological swarms. Nowadays, data science is getting more and more attention, which needs quick management and analysis of massive data. Most traditional methods can only be applied to continuous and differentiable functions. As a set of population-based approaches, it is proven by some recent research works that the SI algorithms have great potential for relevant tasks in this field. In order to gather better insight into the utilization of these methods in data science and to provide a further reference for future researches, this paper focuses on the relationship between data science and swarm intelligence. After introducing the mainstream swarm intelligence algorithms and their common characteristics, both the theoretical and real-world applications in the literature which utilize the swarm intelligence to the related domains of data analytics are reviewed. Based on the summary of the existing works, this paper also analyzes the opportunities and challenges in this field, which attempts to shed some light on designing more effective algorithms to solve the problems in data science for real-world applications.

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Swarm Intelligence and IoT-Based Smart Cities: A Review

Cited by 35 publications

References 62 publications

Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Embedded intelligence and the data-driven future of application-specific Internet of Things for smart environments

Analysis of Nature-Inspired Algorithms for Long-Term Digital Preservation

Swarm Intelligence in Data Science: Applications, Opportunities and Challenges

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