Design and Experimental Validation of a LoRaWAN Fog Computing Based Architecture for IoT Enabled Smart Campus Applications

Abstract

A smart campus is an intelligent infrastructure where smart sensors and actuators collaborate to collect information and interact with the machines, tools, and users of a university campus. As in a smart city, a smart campus represents a challenging scenario for Internet of Things (IoT) networks, especially in terms of cost, coverage, availability, latency, power consumption, and scalability. The technologies employed so far to cope with such a scenario are not yet able to manage simultaneously all the previously mentioned demanding requirements. Nevertheless, recent paradigms such as fog computing, which extends cloud computing to the edge of a network, make possible low-latency and location-aware IoT applications. Moreover, technologies such as Low-Power Wide-Area Networks (LPWANs) have emerged as a promising solution to provide low-cost and low-power consumption connectivity to nodes spread throughout a wide area. Specifically, the Long-Range Wide-Area Network (LoRaWAN) standard is one of the most recent developments, receiving attention both from industry and academia. In this article, the use of a LoRaWAN fog computing-based architecture is proposed for providing connectivity to IoT nodes deployed in a campus of the University of A Coruña (UDC), Spain. To validate the proposed system, the smart campus has been recreated realistically through an in-house developed 3D Ray-Launching radio-planning simulator that is able to take into consideration even small details, such as traffic lights, vehicles, people, buildings, urban furniture, or vegetation. The developed tool can provide accurate radio propagation estimations within the smart campus scenario in terms of coverage, capacity, and energy efficiency of the network. The results obtained with the planning simulator can then be compared with empirical measurements to assess the operating conditions and the system accuracy. Specifically, this article presents experiments that show the accurate results obtained by the planning simulator in the largest scenario ever built for it (a campus that covers an area of 26,000 m 2 ), which are corroborated with empirical measurements. Then, how the tool can be used to design the deployment of LoRaWAN infrastructure for three smart campus outdoor applications is explained: a mobility pattern detection system, a smart irrigation solution, and a smart traffic-monitoring deployment. Consequently, the presented results provide guidelines to smart campus designers and developers, and for easing LoRaWAN network deployment and research in other smart campuses and large environments such as smart cities.

Towards Next Generation Teaching, Learning, and Context-Aware Applications for Higher Education: A Review on Blockchain, IoT, Fog and Edge Computing Enabled Smart Campuses and Universities

“…LoRaWAN defines the communication protocol and the system architecture for the network and uses LoRa as the physical layer. Although there are several recent works on the application of LoRa/LoRaWAN to multiple scenarios [150], only a few of them are focused on the deployment of smart campuses services [66,70,[151][152][153]. For example, the authors of [152] analyze the indoor and outdoor performance of LoRaWAN on a French smart campus.…”

“…In addition, other researchers [153] proposed a smart campus air quality system whose communications were carried out by LoRaWAN nodes. Another example can be found in [70], where the authors make use of a radio planning simulator to determine the optimal location of LoRaWaN nodes that provide smart campus services in outdoor applications.…”

Aggregator to Electric Vehicle LoRaWAN Based Communication Analysis in Vehicle-to-Grid Systems in Smart Cities

“…In order to reduce computational complexity, the simulation code has been optimized in order to include hybrid simulation options, such as neural network-based interpolators, diffraction estimation based on electromagnetic diffusion equation or collaborative filtering data base extraction. A full description of the code is available at [53][54][55][56][57], which has been tested and validated in different use cases and applications, including vehicular communications LoRaWAN technology and 5G [58][59][60].…”

Design and Empirical Validation of a LoRaWAN IoT Smart Irrigation System

“…Since the typical sensitivity of a LoRaWAN device goes from -130 dBm to -148 dBm, a first approximation regarding the gateway optimum location can be easily extracted from the simulation results. To validate the simulation results, they were compared with the empirical results obtained during a previous measurement campaign [6]. As it can be observed in Figure 3, the simulation and empirical results are really close, obtaining a mean error of 0.53 dB and a standard deviation of 3.39 dB.…”

“…The use of wireless communications in the field of precision agriculture, specially in urban environments, presents several technical challenges (e.g., the need for long-range communications, long battery life, high network capacity, cost-effectiveness) that can be in part overcome with Low-Power Wide Area Networks (LPWAN) technologies like Sigfox, Ingenu, NB-IoT, DASH7, Weightless or LoRaWAN (Long-Range Wide Area Network). Specifically, in the last years, the LoRaWAN standard has received attention both from industry and academia in the so-called smart scenarios [6].…”