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Abstract-The goal of the Internet of Things (IoT) is to transform any thing around us, such as a trash can or a street light, into a smart thing. A smart thing has the ability of sensing, processing, communicating and/or actuating. In order to achieve the goal of a smart IoT application, such as minimizing waste transportation costs or reducing energy consumption, the smart things in the application scenario must cooperate with each other without a centralized control. Inspired by known approaches to design swarm of cooperative and autonomous robots, we modeled our smart things based on the embodied cognition concept. Each smart thing is a physical agent with a body composed of a microcontroller, sensors and actuators, and a brain that is represented by an artificial neural network. This type of agent is commonly called an embodied agent. The behavior of these embodied agents is autonomously configured through an evolutionary algorithm that is triggered according to the application performance. To illustrate, we have designed three homogeneous prototypes for smart street lights based on an evolved network. This application has shown that the proposed approach results in a feasible way of modeling decentralized smart things with self-developed and cooperative capabilities.

Agent-based Internet of Things (IoT) applications have recently emerged as applications that can involve sensors, wireless devices, machines and software that can exchange data and be accessed remotely. Such applications have been proposed in several domains including health care, smart cities and agriculture. However, despite their increased adoption, deploying these applications in specific settings has been very challenging because of the complex static and dynamic variability of the physical devices such as sensors and actuators, the software application behavior and the environment in which the application is embedded. In this paper, we propose a modeling approach for IoT analytics based on learning embodied agents (i.e. situated agents). The approach involves: (i) a variability model of IoT embodied agents; (ii) feedback evaluative machine learning; and (iii) reconfiguration of a group of agents in accordance with environmental context. The proposed approach advances the state of the art in that it facilitates the development of Agent-based IoT applications by explicitly capturing their complex and dynamic variabilities and supporting their self-configuration based on an context-aware and machine learning-based approach.

-The concept of Quantified Self is about connected objects self-monitoring their human owner (e.g., a watch measuring heart rate, etc.). A natural transposition is in self-monitoring arbitrary things, therefore named Quantified Things. In this paper, we present the case of self-monitoring agricultural products. We discuss the rationales for the design of a Quantified Fruit multi-agent architecture for self-monitoring and self-prediction of the maturation of fruits. The architecture includes 6 different types of agents, the 2 more specific ones being respectively, the self-controller equipped with various sensors and the self-prediction module. Our current implementation uses an Arduino microcontroller board with 5 sensors (measuring respectively: temperature, light, humidity, hydrogen and methane). The prediction module uses a neural network. We have implemented the architecture and have conducted various experiments, storing bananas in diverse settings: room, refrigerator, in a box, with other fruits, etc. The paper discusses the architecture, its current implementation, experiments and current results. Future issues (scalability, collaborative prediction, etc.) are also addressed.

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