Abstract
Billions of resources, such as cars, clothes, household appliances and even food are being connected to the Internet forming the Internet of Things (IoT). Subsets of these resources can work together to create new self-regulating IoT applications such as smart health, smart communities and smart homes. However, several challenging issues need to be addressed before this vision of applications based on IoT concepts becomes a reality. Because many IoT applications will be distributed over a large number of interacting devices, centralized control will not be possible and so open problems will need to be solved that relate to building locally operating self-organizing and self-adaptive systems. As an initial step in creating IoT applications with these features, this paper presents a Framework for IoT (FIoT). The approach is based on Multi-Agent Systems (MAS) and Machine Learning Techniques, such as neural networks and evolutionary algorithms. To illustrate the use of FIoT, the paper contains two different IoT applications: (i) Quantified Things and (ii) Smart traffic control. We show how flexible points of our framework are instantiated to generate these IoT application.
Original language | English (US) |
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Pages (from-to) | 161-176 |
Number of pages | 16 |
Journal | Information Sciences |
Volume | 378 |
DOIs | |
State | Published - Feb 1 2017 |
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Software
- Control and Systems Engineering
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence