TY - GEN
T1 - Distributing and Processing Data from the Edge. A Case Study with Ultrasound Sensor Modules
AU - Poza-Lujan, Jose Luis
AU - Uribe-Chavert, Pedro
AU - Sáenz-Peñafiel, Juan José
AU - Posadas-Yagüe, Juan Luis
N1 - Publisher Copyright:
© 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.
PY - 2022
Y1 - 2022
N2 - Currently, the proliferation of interconnected smart devices is related to smart urban management. These devices can process sensor data in order to obtain significant information. This information can be provided to the upper layers but also can be used by the devices to take some smart actions. This article shows the change from the classic hierarchical devices and data paradigm to a paradigm based on distributed intelligent devices. The distributed model has been used to create a system architecture with Arduino-based Control Nodes interconnected by means of an I2C-bus. Each module can read the distance to each vehicle, and process this data to provide the vehicle speed and length. A case has experimented where modules share raw data and another case where modules share processed data. Results show that it is possible to reduce processing load up to 22% in the case of sharing processed information instead of raw data.
AB - Currently, the proliferation of interconnected smart devices is related to smart urban management. These devices can process sensor data in order to obtain significant information. This information can be provided to the upper layers but also can be used by the devices to take some smart actions. This article shows the change from the classic hierarchical devices and data paradigm to a paradigm based on distributed intelligent devices. The distributed model has been used to create a system architecture with Arduino-based Control Nodes interconnected by means of an I2C-bus. Each module can read the distance to each vehicle, and process this data to provide the vehicle speed and length. A case has experimented where modules share raw data and another case where modules share processed data. Results show that it is possible to reduce processing load up to 22% in the case of sharing processed information instead of raw data.
UR - https://www.scopus.com/pages/publications/85115254105
U2 - 10.1007/978-3-030-86261-9_19
DO - 10.1007/978-3-030-86261-9_19
M3 - Contribución a la conferencia
AN - SCOPUS:85115254105
SN - 9783030862602
T3 - Lecture Notes in Networks and Systems
SP - 190
EP - 199
BT - Distributed Computing and Artificial Intelligence, Volume 1
A2 - Matsui, Kenji
A2 - Omatu, Sigeru
A2 - Yigitcanlar, Tan
A2 - González, Sara Rodríguez
PB - Springer Science and Business Media Deutschland GmbH
T2 - 18th International Symposium on Distributed Computing and Artificial Intelligence, DCAI 2021
Y2 - 6 October 2021 through 8 October 2021
ER -