Swarm Agent-Based Architecture Suitable for Internet of Things and Smartcities

Chamoso, Pablo; Prieta, Fernando De la; Paz, Francisco de; Corchado, Juan M.

doi:10.1007/978-3-319-19638-1_3

Cited by 24 publications

(14 citation statements)

References 10 publications

(6 reference statements)

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“…PANGEA is better than other multiagent platforms because it provides a number of agents that encapsulate elementary functionality, such as data access, service discovery [25] or rule control, and supports standard interagent communication protocols such as FIPA-ACL [26] PANGEA incorporates agents that manage security at the system level, unlike other types of systems in which it is necessary to develop these types of measures so that the information collected is really the one that is transmitted and analyzed [27].…”

Section: Mas Frameworkmentioning

confidence: 99%

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

et al. 2018

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This article reviews the state-of-the-art developments in Multi-Agent Systems (MASs) and their application to energy optimization problems. This methodology and related tools have contributed to changes in various paradigms used in energy optimization. Behavior and interactions between agents are key elements that must be understood in order to model energy optimization solutions that are robust, scalable and context-aware. The concept of MAS is introduced in this paper and it is compared with traditional approaches in the development of energy optimization solutions. The different types of agent-based architectures are described, the role played by the environment is analysed and we look at how MAS recognizes the characteristics of the environment to adapt to it. Moreover, it is discussed how MAS can be used as tools that simulate the results of different actions aimed at reducing energy consumption. Then, we look at MAS as a tool that makes it easy to model and simulate certain behaviors. This modeling and simulation is easily extrapolated to the energy field, and can even evolve further within this field by using the Internet of Things (IoT) paradigm. Therefore, we can argue that MAS is a widespread approach in the field of energy optimization and that it is commonly used due to its capacity for the communication, coordination, cooperation of agents and the robustness that this methodology gives in assigning different tasks to agents. Finally, this article considers how MASs can be used for various purposes, from capturing sensor data to decision-making. We propose some research perspectives on the development of electrical optimization solutions through their development using MASs. In conclusion, we argue that researchers in the field of energy optimization should use multi-agent systems at those junctures where it is necessary to model energy efficiency solutions that involve a wide range of factors, as well as context independence that they can achieve through the addition of new agents or agent organizations, enabling the development of energy-efficient solutions for smart cities and intelligent buildings.

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Section: Mas Frameworkmentioning

confidence: 99%

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

et al. 2018

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“…The cognitive capacity in this approach is accomplished by incorporating intelligent heavyweight agents that mimic certain human cognitive abilities studied in the area of AI. In applications 2, 3, and 6 summarized in Table 6, which adopt this philosophy to add the intelligence component in IoT, Chamoso et al 58 propose two agent models, one heavy weight and another lightweight (previously described in the category of agents that use probability models). The first of these is based on AI techniques.…”

Section: Iot Agentification and Cognitionmentioning

confidence: 99%

Agentification of the Internet of Things: A systematic literature review

Pico-Valencia

Holgado-Terriza

2018

International Journal of Distributed Sensor Networks

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The Internet of Things is aimed at controlling the physical real world using a global network of heterogeneous smart objects that are interconnected through the Internet. Nonetheless, this emergent paradigm still poses challenges in terms of intelligence, autonomy, security, privacy, and interoperability, all of which must be overcome in order for this approach to be consolidated. The integration of Internet of Things with agent technologies has been proposed as an alternative. In this article, we present a general overview of the main preliminary models, theoretical and experimental studies, and applications currently proposed to carry out the agentification of the Internet of Things through modeling smart objects and networks of Internet of Things objects using software agents. Furthermore, we present an analysis of the main strengths, opportunities, weaknesses, and threats of this approach in terms of computational intelligence. Finally, we discuss the main challenges that must be overcome in order for the agentification process of the Internet of Things to become a consolidated paradigm to build cognitive Internet of Things applications.

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“…This natural and simple algorithmic behaviour has proven to be so successful that it has inspired AI experts for decades in the attempt to implement similar mechanisms into robot interactions or AI systems, creating a research field called computational swarm intelligence (CSI) [2]. However, we can also find SI activity among humans [3,4], and with the evolution towards a society of information, we can also talk about an SI implemented into the Internet of Things (IoT) [5][6][7]. Therefore, we can see that bioinspiration is a successful trend in AI research and that SI provides a reliable framework for the generation and modulation of social behaviours, even at computational and electronically-connected levels.…”

Section: Swarm Intelligence and Systems Intersectionsmentioning

confidence: 99%

Swarm Intelligence via the Internet of Things and the Phenomenological Turn

2017

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Considering the current advancements in biometric sensors and other related technologies, as well as the use of bio-inspired models for AI improvements, we can infer that the swarm intelligence paradigm can be implemented in human daily spheres through the connectivity between user gadgets connected to the Internet of Things. This is a first step towards a real Ambient Intelligence, but also of a Global Intelligence. This unconscious (by the user) connectivity may alter the way by which we feel the world. Besides, with the arrival of new augmented ways of capturing and providing information or radical new ways of expanding our bodies (through synthetic biology or artificial prosthesis like brain-computer connections), we can be very close to a change which may radically affect our experience of ourselves and of the feeling of collectivity. We call it the techno-phenomenological turn. We show social implications, present challenges, and and open questions for the new kind of swarm intelligence-enhanced society, and provide the taxonomy of the field of study. We will also explore the possible roadmaps of this next possible situation.Keywords: Techno-phenomenological turn, consciousness; neuro-interface; radio-transmission; wireless communication; internet of everything; social development Swarm Intelligence and Systems IntersectionsSwarm intelligence (SI) is a concept which describes how complex social behaviours can come from simple non-coordinated individual actions. Ants/bees/termites/wasps colonies, for example, follow this kind of self-organizing and collective intelligence [1]. While no single member of the colony has the power or skills to make decisions about general coordinated actions, the colony behaviour is finally organized efficiently following basic rules. This natural and simple algorithmic behaviour has proven to be so successful that it has inspired AI experts for decades in the attempt to implement similar mechanisms into robot interactions or AI systems, creating a research field called computational swarm intelligence (CSI) [2]. However, we can also find SI activity among humans [3,4], and with the evolution towards a society of information, we can also talk about an SI implemented into the Internet of Things (IoT) [5][6][7]. Therefore, we can see that bioinspiration is a successful trend in AI research and that SI provides a reliable framework for the generation and modulation of social behaviours, even at computational and electronically-connected levels. Taking into account the increment of available smart materials and the technological possibilities of the augmenting/upgrading/enhancing of human bodies, we suggest a future scenario in which SI is part of human behaviour, managing not only unconscious but also conscious actions.

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Swarm Agent-Based Architecture Suitable for Internet of Things and Smartcities

Cited by 24 publications

References 10 publications

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Multi-Agent Systems Applications in Energy Optimization Problems: A State-of-the-Art Review

Agentification of the Internet of Things: A systematic literature review

Swarm Intelligence via the Internet of Things and the Phenomenological Turn

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