This paper presents the complete design, the hardware implementation, and the experimental validation of a distributed intelligence Internet-of-Things (IoT) system, based on Heterogeneous Event-Driven Edge Mesh Architecture (HED-EMA). The latter differentiates from most common computational schemes (i.e. Cloud computing) since it distributes intelligence at local level. The proposed IoT system has a bottom-up structure composed by three main layers: Sensing/Actuation, Edge, and Cloud. It exploits commercial devices, specifically programmed and configured in order to operate within the IoT paradigm. Aiming to validate the proposed system, experimental data coming from two specific setups (home-setup and automotive-setup) will be presented and discussed. Data are extracted by a distributed sensors network, real-time collected, and processed. The lower response time compared to standard Cloud architectures and the whole system power consumption reduction are then demonstrated.
Rescati, M., Scapini, E., Dematteis, M., Schettini, R., Pau, D., Paganoni, M., et al. (2019). HAEMS: Implementation of an Intelligent Event-Driven Edge Mesh IoT Architecture. In 5th International Forum on Research and Technologies for Society and Industry: Innovation to Shape the Future, RTSI 2019 - Proceedings (pp.39-44). Institute of Electrical and Electronics Engineers Inc. [10.1109/RTSI.2019.8895581].
HAEMS: Implementation of an Intelligent Event-Driven Edge Mesh IoT Architecture
Dematteis M.;Schettini R.;Paganoni M.;Baschirotto A.
2019
Abstract
This paper presents the complete design, the hardware implementation, and the experimental validation of a distributed intelligence Internet-of-Things (IoT) system, based on Heterogeneous Event-Driven Edge Mesh Architecture (HED-EMA). The latter differentiates from most common computational schemes (i.e. Cloud computing) since it distributes intelligence at local level. The proposed IoT system has a bottom-up structure composed by three main layers: Sensing/Actuation, Edge, and Cloud. It exploits commercial devices, specifically programmed and configured in order to operate within the IoT paradigm. Aiming to validate the proposed system, experimental data coming from two specific setups (home-setup and automotive-setup) will be presented and discussed. Data are extracted by a distributed sensors network, real-time collected, and processed. The lower response time compared to standard Cloud architectures and the whole system power consumption reduction are then demonstrated.
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