An agent program in an IoT system to recommend activities to minimize childhood obesity problems

Souza, Jerffeson; Campos, Gustavo Augusto Lima de; Rocha, Cleilton; Werbet, Eriko; Costa, Lílian Moreira; Melo, Ricardo; Alves, Lucas Vieira

doi:10.1145/3341105.3373927

Cited by 10 publications

(5 citation statements)

References 5 publications

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“…Furthermore, in [42], the authors aim to investigate the influence of the parameters associated with the agent called Child and the real consequences for performance. This research direction aims to optimize the performance of AI agents in IoT systems.…”

Section: Safety and Performance Of Communication And Computing Resour...mentioning

confidence: 99%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

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A Multiagent System (MAS) refers to a network of agents collaborating to achieve same objective. This system comprises numerous individual programs or hardware components (agents) that are simpler to construct and manage. Additionally, these agents can dynamically and swiftly adapt to changes in their environment. The MAS proves advantageous in addressing intricate issues by employing the divide-and-conquer approach. It finds application in diverse fields where the emphasis is on distributed computing and control, enabling the development of resilient, adaptable, and scalable systems. The Multiagent System (MAS) is not a substitute or rival for Artificial Intelligence (AI) methods. Instead, AI techniques can be integrated within the agents to enhance their computational and decision-making capabilities. The diversity or uniformity of goals, actions, domain knowledge, sensor inputs, and outputs among the agents in the MAS can determine whether each agent is heterogeneous or homogeneous. The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologies that have long been applied to the development of smart systems. These systems cover various areas, such as smart cities, energy management, autonomous cars, etc. Intelligence, autonomy, and real-time monitoring are the fundamental elements that characterize these application areas. The convergence of artificial intelligence (AI) and IoT, known as AIOT, Allows those electronic devices to make intelligent, autonomous and more automatic decisions. This integration leverages the power of MAS to enable intelligent communication and collaboration among various entities, while IoT provides a vast network of interconnected sensors and devices that collect and transmit real-time data. On the other hand, AI algorithms process and analyze this data to derive valuable insights and make informed decisions. The authors devoted efforts on the critical analysis of AIOT research, highlighting specific areas with insufficient solutions and pointing out gaps for future advances. Essentially, the authors contribution is in the formulation of innovative research directions, outlining a clear guide for researchers and professionals in the expansion of knowledge in AIOT integration. Research results in a significant contribution to the continuous advance of the area, enriching the understanding of challenges and boosting the development of solutions and strategies in this technological convergence. Eleven research questions are considered at the beginning of the review, including typical research topics and application domains. From the SLR results the research directions are: (i) Development of a methodology that shows how to integrate the different applications independent of the scenarios that are deployed in. Additionally, elaboration of the tools used in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology and Internet of Things (IoT) devices (processors, controllers, sensors, and actuators).

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Section: Safety and Performance Of Communication And Computing Resour...mentioning

confidence: 99%

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Luzolo,

Elrawashdeh,

Outay

et al. 2024

Preprint

View full text Add to dashboard Cite

show abstract

“…Furthermore, in [50], the authors aim to investigate the influence of the parameters associated with the agent called Child and the real consequences for performance. This research direction aims to optimize the performance of AI agents in IoT systems.…”

Section: Safety and Performance Of Communication And Computing Resour...mentioning

confidence: 99%

“…As can be seen in Table 10, the raspberry pi microprocessor is the most used with 29%, of a total of scientific articles after fine granulation. Out of the 94 articles surveyed, 58 (61.05%) do not mention the type of controller to be used Raspberry PI [19,23,24,40,52,57,60,61,63,64,66,71,72,75,84,92,98,101,106,107,119] Arduino [19, 20, 47, 50, 56, 57, 60-64, 67, 72, 75, 93-95, 98, 100, 106, 119] ESP [20,47,50,55,58,59,62,65,93,95,100,111] for deploying AI on IoT devices. This indicates a lack of emphasis on the importance of choosing the appropriate hardware controller for the intended use case.…”

Section: Controller and Computing Resourcesmentioning

confidence: 99%

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

Luzolo

Galland

Elrawashdeh

et al. 2023

Preprint

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The Internet of Things (IoT) and Artificial Intelligence (AI) are two technologiesthat have long been applied to the development of smart systems. These systemscover various areas, such as smart cities, energy management, autonomous cars,etc. Intelligence, autonomy, and real-time monitoring are the fundamental ele-ments that characterize these application areas. The convergence of multi-agentsystems (MAS), IoT, and AI has opened up new possibilities for creating smarterbuildings and cities. This integration leverages the power of MAS to enable intelli-gent communication and collaboration among various entities, while IoT providesa vast network of interconnected sensors and devices that collect and transmitreal-time data. On the other hand, AI algorithms process and analyze this data toderive valuable insights and make informed decisions. Multi-agent systems tech-nology is one of the most used for the implementation of such intelligent systems.This article aims to focus on multi-agent systems, although it also covers otherrelated topics by carrying out a Systematic Literature Review (SLR) on relevantresearch works in the previous ten years. Eleven research questions are consideredat the beginning of the review, including typical research topics and applicationdomains. From the SLR results the research directions are: (i) Development ofa methodology that shows how to integrate the different applications indepen-dent of the scenarios that are deployed in. Additionally, elaboration of the toolsused in the integration process; (ii) Deployment of an agent in a microprocessor; (iii) How to implement and connect Multi-agent systems (MAS) technology andInternet of Things (IoT) devices (processors, controllers, sensors, and actuators).

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“…However, the system has only been evaluated in a theoretical case study using the UKDALE dataset. Authors in [91] proposed a recommender system-based multi-agent approach, which focuses on the eating and physical activities of children who suffer from obesity. The system consists of two agents: a child agent that processes data about the child and a health advisor who receives the data from the child agent to produce a suitable recommendation.…”

Section: Robustnessmentioning

confidence: 99%

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

Altulyan

Yao

Wang

et al. 2021

The Computer Journal

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Recommendation is a critical tool for developing and promoting the benefits of the Internet of Things (IoT). In recent years, recommender systems have attracted considerable attention in many IoT-related fields such as smart health, smart home, smart tourism and smart marketing. However, traditional recommender system approaches fail to exploit ever-growing, dynamic and heterogeneous IoT data in building recommender systems for the IoT (RSIoT). This article aims to provide a comprehensive review of state-of-the-art RSIoT, including the related techniques, applications and a discussion on the limitations of applying recommendation systems to IoT. Finally, we propose a reference framework for comparing existing studies to guide future research and practices.

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An agent program in an IoT system to recommend activities to minimize childhood obesity problems

Cited by 10 publications

References 5 publications

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Combining Multi-Agent Systems and ArtificialIntelligence of Things: Technical Challenges andGains

Combining Multiagent Systems with IoT forSmarter Buildings and Cities: a literature review

A Survey on Recommender Systems for Internet of Things: Techniques, Applications and Future Directions

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