Design and implementation of an ultra-low power wireless sensor network for indoor environment monitoring

Liang, Yan; Meng, Xiangyan; Hu, Yifan; Zhang, Kun

doi:10.1109/icct.2017.8359772

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…From this digital environment, the IoT (Internet of Things) has emerged as a platform for interconnecting sets of autonomous digital systems to society, and collecting and providing information between end users [1]- [5]. The integration of heterogeneous sensors (sources of information) with data management systems has created intelligent environments capable of responding to all types of imminent risk [6]- [8]. Big cities are host sites of this new integration [9], [10].…”

Section: Introductionmentioning

confidence: 99%

An Intelligent Transportation System: the Quito City Case Study

Zambrano

Ortiz

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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Managing traffic in a large city has become a topic of great interest in both politics and science. The costs of poor traffic management have been quantified as losses equal to millions of dollars, not counting the unquantifiable value of the time that a person loses in traffic jams. Intelligent transport systems (ITS) offer a set of innovative solutions specific to the management of different modes of transport. This article focuses on the development of an ITS for the city of Quito that allows smart decisionmaking to direct heavy haul transporters that want to enter the city via one of its main access routes. Technologies such as Sensor Web Enablement (SWE), in association with the Message Queuing Telemetry Transport (MQTT) communication protocol, facilitate the development of a vehicular management platform/system capable of sending notifications in real-time and issuing instructions to drivers regarding traffic delays along routes, average speeds, etc. The system supports a network of heterogeneous sensors accessible through the web. It can integrate any device that uses HTTP protocol. Time interval and location range testing have been undertaken to refine the accuracy of the system and make it adaptable to any geographic situation. The system allows communicate with the server through MQTT or through web services, using technologies such as: MongoDB and GeoJSON. One of the most relevant results is that the degree of accuracy of the system is within appropriate ranges when compared to commercial applications such as Google Maps and Waze.

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Section: Introductionmentioning

confidence: 99%

An Intelligent Transportation System: the Quito City Case Study

Zambrano

Ortiz

et al. 2020

Int. J. Adv. Sci. Eng. Inf. Technol.

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“…Wireless sensor network (WSN) plays a very important role in the infrastructure of IoT. It has been applied to the fields of smart cities [Mitton et al, 2012], street light control [Sunehra et al, 2017], smart building management [Ghayvat et al, 2015;Liang et al, 2017;Minoli et al, 2017;Zanella et al, 2014], environmental and air pollution monitoring [Nagaraj et al, 2017;Ray 2015], energy monitoring [Grosso et al, 2018;Liu et al, 2012;Ray 2016], health monitoring [Chauhan et al, 2016;Garrido et al, 2019;Othman et al, 2014], and intelligence & precision agriculture [Calderón-Córdova et al, 2018;Davcev et al, 2018;Granda-Cantuña et al, 2018;Ma et al, 2018;Medeiros et al, 2018;Nooriman et al, 2018;Onibonoje et al, 2017;Sriploy et al, 2018]. Monteiro et al (2018) proposed a forecast model for indoor temperature prediction in an IoT scenario composed of a home appliance.…”

Section: Introductionmentioning

confidence: 99%

Smart Building Temperature Monitoring System using Matplotlib

Wei¹,

Zhang²,

Lu³

2019

JCSIT

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Wireless sensor network (WSN) has been widely adopted by applications of Internet of Things (IoT). In this paper, we present a practical design of real time temperature monitoring system for smart building by using XBee based mesh WSN. The proposed system collects temperature data from wireless sensors and dynamically displays those data using Matplotlib with Python. The data was recorded by the SQLite3 database simultaneously for cloud usage. Moreover, we evaluated the link quality of the wireless transmission in our system. Experimental results demonstrated that the proposed real time smart building temperature monitoring system is a cross-platform and low cost system for IoT applications. It could be easily applied to single board computer systems such as Raspberry Pi, Jetson Nano, or LatteePanda to further reduce the cost.

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