| 1. |
- Bonafini, F., et al.
(författare)
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Evaluating indoor and outdoor localization services for LoRaWAN in Smart City applications
- 2019
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728104294 ; , s. 300-305
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Konferensbidrag (refereegranskat)abstract
- Nowadays, wireless technologies penetrate all aspects of our lives. 'Internet of Things' (IoT) and 'Location- Based Services' are the pillars of Smart City concept. The IoT smart objects surrounding us are an integral part of the Internet, thanks to their computational and communication capabilities. In such applications, location information can be exploited in all the layers of the stack, from the application level (e.g., to correctly interpret measurements from sensor nodes deployed on the field), down to the physical level (e.g., for sensing coverage). One of the most viable solutions for Smart City wireless connectivity seems to be the use of long-range, low-power and low-throughput low-power wide area networks (LPWANs). In this work, the authors devise the jointly use of LPWANs with widely-diffused and well-accepted localization techniques, as the Global Positioning Systems (GPS, outdoor) and real-time location systems (RTLS, indoor), for Smart Campus applications. In particular, a LoRaWAN node equipped with both GPS and Ultra Wide Bandbased UWB-RTLS has been developed and tested in real-world scenarios. Experimental results demonstrate the feasibility of the proposed approach; in particular, location errors are in the order of few tens of meters for GPS and in the order of few tens of centimeters for UWB. © 2019 IEEE.
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| 2. |
- Ferrari, P., et al.
(författare)
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Performance evaluation of full-cloud and edge-cloud architectures for Industrial IoT anomaly detection based on deep learning
- 2019
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Ingår i: Proceedings. - : Institute of Electrical and Electronics Engineers Inc.. - 9781728104294 ; , s. 420-425
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Konferensbidrag (refereegranskat)abstract
- One of the most interesting application of data analysis to industry is the real-time detection of anomalies during production. Industrial IoT paradigm includes all the components to realize predictive systems, like the anomaly detection ones. In this case, the goal is to discover patterns, in a given dataset, that do not resemble the 'normal' behavior, to identify faults, malfunctions or the effects of bad maintenance. The use of complex neural networks to implement deep learning algorithm for anomaly detection is very common. The position of the deep learning algorithm is one of the main problem: this kind of algorithm requires both high computational power and data transfer bandwidth, rising serious questions on the system scalability. Data elaboration in the edge domain (i.e. close to the machine) usually reduce data transfer but requires to instantiate expensive physical assets. Cloud computing is usually cheaper but Cloud data transfer is expensive. In this paper a test methodology for the comparison of the two architectures for anomaly detection system is proposed. A real use case is described in order to demonstrate the feasibility. The experimental results show that, by means of the proposed methodology, edge and Cloud solutions implementing deep learning algorithms for industrial applications can be easily evaluated. In details, for the considered use case (with Siemens controller and Microsoft Azure platform) the tradeoff between scalability, communication delay, and bandwidth usage, has been studied. The results show that the full-cloud architecture can outperform the edge-cloud architecture when Cloud computation power is scaled. © 2019 IEEE.
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