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Enhancing intelligence in IOTS: approaches and applications in sensors, UAVs and smartphones

Grant number: 15/21642-6
Support type:Regular Research Grants
Duration: February 01, 2016 - January 31, 2018
Field of knowledge:Physical Sciences and Mathematics - Computer Science - Computer Systems
Principal Investigator:Jó Ueyama
Grantee:Jó Ueyama
Home Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Assoc. researchers:André Carlos Ponce de Leon Ferreira de Carvalho ; Gustavo Pessin ; Isvani Inocencio Frías Blanco

Abstract

Resource constrained devices, such as the Internet of Things (IoT) are increasingly ubiquitous and widespread. They are often collecting data for improving and optimizing the daily life in environments like a smart city. The IoT usually consists of sensors and objects that interact and work together, while being connected to the Internet. The IoT is considered as part of the Future Internet and incorporates devices such as smartphones, cameras, cars, UAVs and or any home appliances. As more objects and sensors are connected together, a higher volume of data (including voice, video and image) is generated and transported, thus demanding greater intelligence to handle properly such data. However, the biggest problem is that much of the IoT device as sensors and home appliances are devices that have low memory and processing power. As a consequence, this research project aims at providing a greater degree of intelligence in these devices with limited resources. The key challenge is to provide such intelligence on the devices themselves, despite the limited resources of memory and processing power that these devices have. Appropriate techniques should be investigated and employed, so that we can optimize the use of the few available resources. This project involves studying the techniques of evolution and artificial intelligence in the literature. In addition, the proposal also includes investigating the centralized or distributed intelligence approach in the nodes (i.e. centralize all intelligence into one single central node or distribute it between nodes in the network). There are several applications to be explored in this project and one of them is the use of artificial intelligence to evaluate the emotional state of smartphone users. Another application to be considered is to embed intelligence in sensors to predict floods in urban rivers in the context of a smart city. Other scenarios involve the use of UAVs for smart spraying of chemical pesticides with an adjusted route (or "evolved") at runtime. This takes into account the imposed climate conditions, such as changes in direction and wind speed. Finally, it is noteworthy that the first results of our previous research were published in the FAPESP Research Magazine issued in January 2015. We wish to continue these promising results through this new proposed project. (AU)

Scientific publications (6)
(References retrieved automatically from Web of Science and SciELO through information on FAPESP grants and their corresponding numbers as mentioned in the publications by the authors)
MANO, LEANDRO Y.; MAZZO, ALESSANDRA; NETO, JOSE R. T.; MESKA, MATEUS H. G.; GIANCRISTOFARO, GABRIEL T.; UEYAMA, JO; JUNIOR, GERSON A. P. Using emotion recognition to assess simulation-based learning. NURSE EDUCATION IN PRACTICE, v. 36, p. 13-19, MAR 2019. Web of Science Citations: 0.
DE ASSIS, LUIZ FERNANDO F. G.; HORITA, FLAVIO E. A.; DE FREITAS, EDISON P.; UEYAMA, JO; DE ALBUQUERQUE, JOAO PORTO. A Service-Oriented Middleware for Integrated Management of Crowdsourced and Sensor Data Streams in Disaster Management. SENSORS, v. 18, n. 6 JUN 2018. Web of Science Citations: 3.
FILHO, GERALDO P. R.; VILLAS, LEANDRO A.; FREITAS, HEITOR; VALEJO, ALAN; GUIDONI, DANIEL L.; UEYAMA, JO. ResiDI: Towards a smarter smart home system for decision-making using wireless sensors and actuators. Computer Networks, v. 135, p. 54-69, APR 22 2018. Web of Science Citations: 5.
FURQUIM, GUSTAVO; FILHO, GERALDO P. R.; JALALI, ROOZBEH; PESSIN, GUSTAVO; PAZZI, RICHARD W.; UEYAMA, JO. How to Improve Fault Tolerance in Disaster Predictions: A Case Study about Flash Floods Using IoT, ML and Real Data. SENSORS, v. 18, n. 3 MAR 2018. Web of Science Citations: 4.
FAICAL, BRUNO S.; FREITAS, HEITOR; GOMES, PEDRO H.; MANO, LEANDRO Y.; PESSIN, GUSTAVO; DE CARVALHO, ANDRE C. P. L. F.; KRISHNAMACHARI, BHASKAR; UEYAMA, JO. An adaptive approach for UAV-based pesticide spraying in dynamic environments. COMPUTERS AND ELECTRONICS IN AGRICULTURE, v. 138, p. 210-223, JUN 1 2017. Web of Science Citations: 18.
GIUNTINI, FELIPE TALIAR; BEDER, DELANO MEDEIROS; UEYAMA, JO. Exploiting self-organization and fault tolerance in wireless sensor networks: A case study on wildfire detection application. International Journal of Distributed Sensor Networks, v. 13, n. 4 APR 12 2017. Web of Science Citations: 1.

Please report errors in scientific publications list by writing to: cdi@fapesp.br.