Distributed network of meteostations with LoRa

Současná práce analyzuje možnosti nasazení distribuované sítě meteostanic v městském prostředí. Cílem této práce je návrh a implementace zařízení s důrazem na jednoduchost instalace, minimální spotřebu, bezdrátový přenos dat a využití alternativního zdroje energie. V rámci této práce byl také implementován algoritmus založený na architektuře neuronové sítě LSTM, schopný generovat předpověď měřených parametrů. Kromě toho na hostingu Amazon byla nasazena infrastruktura, která kombinuje centralizovaný sběr dat ze všech zařízení, předpovídání měřených parametrů, sdílení dat s komunitními projekty monitorování počasí a navíc bylo poskytnuto webové rozhraní pro zobrazování měřených a předpovězených dat. Vyvinutý systém byl úspěšně otestován v reálných klimatických podmínkách. Nakonec byla provedena srovnávací analýza vyvinutého zařízení a komerčních analogů ze stejné a vyšší cenové kategorie. Výsledkem této práce je systém, který má komerční potenciál a je schopen konkurovat populárním stávajícím řešením.The present work analyzes the possibilities of deploying a distributed network of meteostations in an urban environment. The aim of this work is the design and implementation of a device with an emphasis on the simplest possible installation, minimum power consumption, wireless data transmission and the use of alternative power source. Also, within the framework of this work, an algorithm based on the LSTM neural network architecture has been implemented, capable of generating a forecast of the measured parameters. In addition, an infrastructure was deployed on Amazon hosting, combining both centralized data collection from all devices, predicting measured parameters, sharing data with community weather monitoring projects, and, moreover, the web interface was implemented displaying both device data along with measured and predicted parameters. The developed system has been successfully tested in real climatic conditions. Finally, a comparative analysis of the developed device and commercial counterparts from the same and premium price segments was carried out. The result of the present work is a system with commercial potential and the ability to compete with popular existing solutions

Relaying in the Internet of Things (IoT): A Survey

The deployment of relays between Internet of Things (IoT) end devices and gateways can improve link quality. In cellular-based IoT, relays have the potential to reduce base station overload. The energy expended in single-hop long-range communication can be reduced if relays listen to transmissions of end devices and forward these observations to gateways. However, incorporating relays into IoT networks faces some challenges. IoT end devices are designed primarily for uplink communication of small-sized observations toward the network; hence, opportunistically using end devices as relays needs a redesign of both the medium access control (MAC) layer protocol of such end devices and possible addition of new communication interfaces. Additionally, the wake-up time of IoT end devices needs to be synchronized with that of the relays. For cellular-based IoT, the possibility of using infrastructure relays exists, and noncellular IoT networks can leverage the presence of mobile devices for relaying, for example, in remote healthcare. However, the latter presents problems of incentivizing relay participation and managing the mobility of relays. Furthermore, although relays can increase the lifetime of IoT networks, deploying relays implies the need for additional batteries to power them. This can erode the energy efficiency gain that relays offer. Therefore, designing relay-assisted IoT networks that provide acceptable trade-offs is key, and this goes beyond adding an extra transmit RF chain to a relay-enabled IoT end device. There has been increasing research interest in IoT relaying, as demonstrated in the available literature. Works that consider these issues are surveyed in this paper to provide insight into the state of the art, provide design insights for network designers and motivate future research directions

Internet of Things (IoT) in Buildings: A Learning Factory

Advances towards smart ecosystems showcase Internet of Things (IoT) as a transversal strategy to improve energy efficiency in buildings, enhance their comfort and environmental conditions, and increase knowledge about building behavior, its relationships with users and the interconnections among themselves and the environmental and ecological context. EU estimates that 75% of the building stock is inefficient and more than 40 years old. Although many buildings have some type of system for regulating the indoor temperature, only a small subset provides integrated heating, ventilation, and air conditioning (HVAC) systems. Within that subset, only a small percentage includes smart sensors, and only a slight portion of that percentage integrates those sensors into IoT ecosystems. This work pursues two objectives. The first is to understand the built environment as a set of interconnected systems constituting a complex framework in which IoT ecosystems are key enabling technologies for improving energy efficiency and indoor air quality (IAQ) by filling the gap between theoretical simulations and real measurements. The second is to understand IoT ecosystems as cost-effective solutions for acquiring data through connected sensors, analyzing information in real time, and building knowledge to make data-driven decisions. The dataset is publicly available for third-party use to assist the scientific community in its research studies. This paper details the functional scheme of the IoT ecosystem following a three-level methodology for (1) identifying buildings (with regard to their use patterns, thermal variation, geographical orientation, etc.) to analyze their performance; (2) selecting representative spaces (according to their location, orientation, use, size, occupancy, etc.) to monitor their behavior; and (3) deploying and configuring an infrastructure with +200 geolocated wireless sensors in +100 representative spaces, collecting a dataset of +10,000 measurements every hour. The results obtained through real installations with IoT as a learning factory include several learned lessons about building complexity, energy consumption, costs, savings, IAQ and health improvement. A proof of concept of building performance prediction based on neural networks (applied to CO2 and temperature) is proposed. This first learning shows that IAQ measurements meet recommended levels around 90% of the time and that an IoT-managed HVAC system can achieve energy-consumption savings of between 10 and 15%. In summary, in a real context involving economic restrictions, complexity, high energy costs, social vulnerability, and climate change, IoT-based strategies, as proposed in this work, offer a modular and interoperable approach, moving towards smart communities (buildings, cities, regions, etc.) by improving energy efficiency and environmental quality (indoor and outdoor) at low cost, with quick implementation, and low impact on users. Great challenges remain for growth and interconnection in IoT use, especially challenges posed by climate change and sustainability

A Systematic Review of LPWAN and Short-Range Network using AI to Enhance Internet of Things

Artificial intelligence (AI) has recently been used frequently, especially concerning the Internet of Things (IoT). However, IoT devices cannot work alone, assisted by Low Power Wide Area Network (LPWAN) for long-distance communication and Short-Range Network for a short distance. However, few reviews about AI can help LPWAN and Short-Range Network. Therefore, the author took the opportunity to do this review. This study aims to review LPWAN and Short-Range Networks AI papers in systematically enhancing IoT performance. Reviews are also used to systematically maximize LPWAN systems and Short-Range networks to enhance IoT quality and discuss results that can be applied to a specific scope. The author utilizes selected reporting items for systematic review and meta-analysis (PRISMA). The authors conducted a systematic review of all study results in support of the authors' objectives. Also, the authors identify development and related study opportunities. The author found 79 suitable papers in this systematic review, so a discussion of the presented papers was carried out. Several technologies are widely used, such as LPWAN in general, with several papers originating from China. Many reports from conferences last year and papers related to this matter were from 2020-2021. The study is expected to inspire experimental studies in finding relevant scientific papers and become another review