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The fourth industrial revolution is paving the way for Industrial Internet of Things applications where large number of wireless nodes, equipped with sensors and actuators, monitor the production cycle of industrial goods. This paper proposes and analyses LoRaIN, a network architecture and MAC-layer protocol thought for on-demand monitoring of industrial machines. Our proprietary system is an energy-efficient, reliable and scalable solution, where the protocol is built on top of LoRa at 2.4 GHz. Indeed, the low-power characteristics of LoRa allow to reduce energy consumption, while Wireless Power Transfer is used to recharge batteries, avoiding periodic battery replacement. High reliability is obtained through the joint use of Frequency and Time Division Multiple Access. A dynamic LoRaIN scheduler manages the communication and recharging phases depending on the tasks assigned to the nodes, as well as the number of monitoring devices. Performance is measured in terms of network throughput, energy consumption and latency. Results demonstrate that the proposed solution is suitable for monitoring applications of industry machines.

The LoRa modulation scheme is arousing a growing interest in the Internet of Things community as it is adopted by the emerging LoRaWAN technology. In this paper, we firstly analyse the baseband processing for the generation of LoRa signals at the transmitter side, providing a simple algorithm that leverages digital signal processing techniques to reduce the modulator complexity. Secondly, we analytically investigate the signal demodulation technique. Quite surprisingly, we found that its effectiveness depends on the particular choice of the sampling frequency at the receiver side, which purposely does not meet the sampling theorem requirement. Finally, we consider the actual architecture of digital receivers investigating the trade-off between the selectivity of receive digital filters, which impacts on the required computational effort and power consumption, and the receiver performance.

The article examines the principles of developing wireless networks of autonomous gamma sensors in order to create systems for spatial environmental radiation monitoring. The main task of such systems is to control the level of gamma radiation in areas where potential sources of ionizing radiation are located. An autonomous gamma-ray spectrometer is used as a measuring sensor. The authors propose to apply measuring sensors based on a silicon photomultiplier to create autonomous wireless networks of the industrial Internet for radiation monitoring. To confirm the possibility of using this class of receivers as part of gamma spectrometers, the main structural elements of the system were modeled, and the experimental model of the gamma spectrometer was prototyped. The linearity and energy resolution of the experimental sample were also investigated. To test the model for constructing a gamma spectrometer, a CsI (Tl) scintillation crystal and a Sensl Array-60035-4P photomultiplier were used. The established range of recorded energies is in the range from 121 keV to 1332 keV, the relative energy resolution for the 137Cs peak is 11.07 %, the linearity of the transfer characteristic is 99.91 %. Based on this sensor, the architecture of an automated wireless system for monitoring the spatial distribution of gamma radiation has been developed. The results of the work allow the use of radiation monitoring systems in accordance with the requirements of Industry 4.0.

The trend towards cities and urbanisation, which increases the number of people living in urban areas, requires local authorities to provide services and natural resources more efficiently and effectively and develop some strategies for a sustainable environment. The more effective use of resources, growing awareness of sustainable environment, climate confidence and motivation to make cities more livable is a new concept called Smart City. In this study, the proposed system supports the garbage collection of the city government and works with low budget, low energy, and free radio frequencies. The Internet of Things (IoT) sensor node is assembled, and the network is set up based on the long range wide area network protocol to connect it to the sample garbage bin and collect data. Instant data collection by this network is carried out through the IoT and is designed based on the collected data. The goal is to build an ideal system for smart garbage collection in cities and support sustainability in cities by integrating with the city government's information systems. The data received from the sensor nodes and the efficiency of the system were demonstrated for local governments. The main outcome of this research is to develop a practical smart city application with minimal resources and support local governments in their daily work. Moreover, how a low power wide area network communication network with a frequency of 868 MHz works in Istanbul (Turkey) will be investigated further and which alternative to cellular networks is the most suitable for excellent communication in smart cities will be studied.

While the Internet of Things continues to grow, the LoRaWAN standard is generating special interest due to its open-source nature, ultralow-power consumption and long-range connectivity. Recent works have explored the challenges of implementing LoRaWAN, with scalability being considered one of the major bottlenecks imposed by its Aloha-based medium access control (MAC) layer. Despite much on-going research on LoRaWAN scheduling aimed at alleviating this concern, experimental approaches are rarely found in the literature. In this work, we describe the steps taken and the technical issues overcome to move from a low-overhead synchronization and scheduling concept to its real-world implementation on top of LoRaWAN Class A. Accordingly, an end-to-end architecture was designed and deployed on top of STM32L0 MCUs, which communicate with a central entity responsible for providing synchronization metrics and allocating transmission slots on demand. The clock drift of devices was measured in a temperature-controlled chamber, which served as a basis to define slot lengths in the network. As a result, an operational end-to-end system was implemented and evaluated for different setup scenarios, with 10-ms accuracy being achieved. Our experimental results show significant improvements in packet delivery ratios with respect to Aloha-based setups, especially under high network loads (up to 29% for SF12), thereby demonstrating the feasibility of the presented approach.

LoRa technology has received a lot of attention in the last few years. Numerous success stories about using LoRa technology for the Internet of Things in various implementations. Several studies have found that the use of LoRa technology has the opportunity to be implemented in indoor-based applications. LoRa technology is found more stable and is more resilient to environmental changes. Environmental change of the indoor is a major problem to maintain accuracy in position prediction, especially in the use of Received Signal Strength (RSS) fingerprints as a reference database. The variety of approaches to solving accuracy problems continues to improve as the need for indoor localization applications increases. Deep learning approaches as a solution for the use of fingerprints in indoor localization have been carried out in several studies with various novelties offered. Let’s introduce a combination of the use of LoRa technology's excellence with a deep learning method that uses all variations of measurement results of RSS values at each position as a natural feature of the indoor condition as a fingerprint. All of these features are used for training in-deep learning methods. It is DeepFi-LoRaIn which illustrates a new technique for using the fingerprint data of the LoRa device's RSS device on indoor localization using deep learning methods. This method is used to find out how accurate the model produced by the training process is to predict the position in a dynamic environment. The scenario used to evaluate the model is by giving interference to the RSS value received at each anchor node. The model produced through training was found to have good accuracy in predicting the position even in conditions of interference with several anchor nodes. Based on the test results, DeepFi-LoRaIn Technique can be a solution to cope with changing environmental conditions in indoor localization

The LoRa standard is currently widely employed for low-power long-range wireless sensor networks at sub-GHz frequency bands. The longer wavelengths associated with sub-GHz technology provide excellent radiowave propagation characteristics, yielding a much larger coverage than the higher frequency bands. In the case of wearable sensors, the 868 MHz band can be covered by textile substrate-integrated-waveguide antennas of a convenient size. In body-centric communication systems, front-to-back (F/B) diversity is an important asset to mitigate the shadowing of the antennas by the presence of the human body. This article describes a diversity textile-antenna-based LoRa platform with integrated transceivers. Outdoor measurement campaigns are conducted to assess the performance of the wearable LoRa nodes with F/B diversity in an urban radio propagation environment at walking and cycling speeds. These experiments prove that large ranges of 1.5 km can easily and reliably be achieved for off-body LoRa communication links. The results demonstrate a significant performance improvement in terms of packet loss in NLoS situations when comparing single-receiver performance with different spatial receiver diversity applications. In addition, link budget increases up to 5.5 dB, owing to the realized diversity gain.

Many wireless Internet-of-Things applications require extended battery life ranging from a few months to a few years. Such applications have motivated the recent developments in low power wide area networks, including the rise of Long Range (LoRa) technology. LoRa has a simple modulation scheme designed for extended converge, low battery consumption, and resistance to high interference levels. Thus LoRa is primarily targeted for shared spectrum applications where interference levels are typically higher than controlled spectrum applications where a single operator usually has a dominant control on the quality of service. As a result, it is of paramount importance to carefully design IoT networks while taking into account the impending impacts of interference and propagation environments. This paper presents a novel LoRa network design framework that utilizes a developed open-source emulator to provide a reliable network coverage estimation. The framework is tested in one of the largest open-access IoT network designs in Australia, which enabled the deployment of 294 sensors and 48 gateways. Both the framework and the emulator are implemented using MATLAB scripting, enabling integration with built-in and external radio planning tools. The framework leverages real interference measurements captured using software defined radio that records the spectrotemporal behavior of the existing traffic in the shared band.

The term Internet of Things (IoT) has emerged in recent decades because this network revolutionizes almost every aspect of our daily life, including products such as smartphones and intelligent vehicles, and crucial tasks such as precision agriculture and environmental monitoring. Myriads of communication technologies have been developed to fulfill the two main features of the IoT: long-range transmissions and low power consumption. Long-range (LoRa) has become one of the vital parts of IoT communication. In this study, the real-time deployments of an unmanned aerial vehicle (UAV)-based LoRa communication network are systematically reviewed, with a focus on the communication setup and its reported performance. Importantly, the UAV-based LoRa communication network has a low bit rate connectivity to ensure the high reliability of connections, especially in applications that require long transmission ranges. This study provides recommendations for researchers on what research perspectives need to be explored when implementing UAVs for IoT-based LoRa communication. This study also describes publication trends related to UAV-based LoRa communication networks. A supplementary Excel file that contains the reported publications on UAV-based LoRa communication networks is included to show this publication trend.

Line joint temperature monitoring is used in high-voltage transmission lines to improve the reliability and safety of power lines. However, traditional monitoring methods (e.g., manual inspection, UAV inspection, short-range wireless technology, etc.) cannot realize real-time monitoring of line joint temperature in remote areas. In this paper, we proposed a novel real-time monitoring system, which adopted a communication method combining ZigBee and LPWAN technology (LoRa and NB-IoT) for long-distance transmission. In this system, each power tower was regarded as a communication node that contained a main node and multiple subnodes. First, each node collected temperature data of the transmission line junctions by ZigBee and microprocessor processed and analyzed the data. Second, the LoRa multihop communication technology was used for transmission of the data between adjacent nodes to achieve wider coverage and a longer transmission distance. Finally, the end of the network was a main node, i.e., an LoRa-NB-IoT gateway. The temperature data of all nodes were transmitted to the “USR Cloud” by the NB-IoT technology. By conducting a system test, the system can meet the requirements of long-distance communication outside and reduce power consumption. After calculation, the system communication packet loss rate is approximately 3.3%, and the average power consumption of the system is about 21.19 mA. The PC can monitor the transmission line junction temperature in real time, transmission is stable, and the alarm function can be also realized. Therefore, the system is more suitable for temperature monitoring of line junctions in remote areas, achieving long-distance data acquisition and transmission through low power consumption and high transmission rate.

Internet of Things (IoT) technologies are opening new opportunities and services for different smart grid applications such as advanced meter infrastructures (AMI), distributed energy resources, and electric vehicles. Among these applications, AMIs represent the first step toward future smart grid implementation by enabling the reading and recording of electric power consumption on request or a schedule. In this study, the main focus is designing an IoT-based architecture to support AMIs’ daily reporting and billing in a residential grid. First, the network modeling for AMI is discussed. Long range (LoRa) technology, as one of the promising candidates for long range low power wide area networks (LPWANs), has been studied and discussed. A comprehensive analysis of the LoRa-based architecture is given for a case study of an actual residential grid, Puerto Montt, Chile. The results are analyzed and discussed for packet delivery ratio, energy consumption, throughput, number of collisions, and frequency distribution.

У сферах високоточного землеробства, управління угіддями і рослинництва існують широкі можливості застосування технологій IoT. Зокрема, можливість виконувати моніторинг параметрів ґрунту — його температури, вологості і вологоємності, що необхідно для оптимального поливу і внесення добрив, а також параметрів навколишнього середовища земельних ділянок. Для дистанційного моніторингу та керування цими параметрами необхідно використання безпровідних технологій. В статті були розглянуті різні способи передачі даних, їх переваги та недоліки в енергетичному плані, залежність від зовнішніх факторів, радіус дії та способу передачі інформації. На підставі цього зроблені висновки про найдоцільніші до використання системи передачі даних з теплиць на сервер, які в свою чергу мають менше енергозатрати, та найбільш оптимальні для передачі даних, також розглянуто доцільність використання певних способів підзарядки в залежності від їх переваг., Since the advent of early agriculture, agriculture has sought to increase yields. The continuous development of tools and fertilizers, the cultivation of plants and animals, the accumulation of knowledge about the processes of product growth began more than 10,000 years ago and continues to this day. The modern development of transport has made it possible to achieve the indices of humanity unprecedented before.In recent years, the introduction of Internet technologies, satellite communication and geopositioning, robotics, sensors and automation systems has begun in agriculture. In 2016, GPS and GLONASS have already been implemented and are being actively used in agriculture, which made it possible to improve the accuracy of machinery movement across the field. The transition to robotic transport enabled precise parallel movement. The use of sensitive sensors allows you to monitor the condition of the soil, accurately take into account its composition in small areas and make fertilizers where necessary.Thanks to these technologies, the field of "precision farming" is spreading. UAVs are being used to monitor crops, spray fertilizers and insect repellents. And the implementation of technology is ongoing. It is anticipated that the application of Big Data in agriculture will significantly improve the quality of knowledge about the processes that occur when growing products. Now 2016-2018 is called "experimental" and predicts the rapid development of smart agriculture in 2019-2020 and subsequent years.In the areas of high-precision agriculture, land management and crop production, there are ample opportunities for the use of IoT technologies. In particular, the ability to monitor soil parameters - its temperature, humidity and moisture capacity, which is necessary for optimal irrigation and fertilization, as well as environmental parameters of land. Remote monitoring and control of these parameters requires the use of wireless technologies. Different ways of data transmission, their advantages and disadvantages in the energy plan, dependence on external factors, range and method of information transfer were considered in the article. On this basis, the conclusions about the most appropriate to use the system of data transmission from greenhouses to the server, which in turn have less energy consumption and the most optimal for data transmission, also considered the feasibility of using certain methods of charging, depending on their advantages.

Gateway elimination in a LoRa network could highly reduce the network installation cost. However, LoRa end devices could not overcome many obstacles with only a point-to-point communication. Thus, this research implemented a multi-hop communication in a LoRa network. One or more LoRa end devices are placed between the source node and the destination node to act as relay nodes. A simple routing based on the packet length is configured to determine the packet transmission path. As the results, the designed multi-hop communication could improve packet success rate until 2,47 times in indoor environment. Whereas, the optimum delay time for multi-hop communication is 100 ms for each hop to produce high PRR and lowest RTT.

IoT deployments for smart cities and smart buildings have been multiplying exponentially in recent years, benefiting from a steady rise in the number of new technologies that deal with the underlying networking and application challenges in indoor and outdoor spaces. Due to the overlap in their specifications, we are still trying to figure out which of these technologies fits better to certain application domains, such as building monitoring. In this work, we provide a comparative study between IEEE 802.15.4 and LoRa, based on our experiences from using both wireless networking technologies in the context of indoor deployments aimed at IoT-enabled school buildings in Europe. We provide an apples-to-apples comparison between the two technologies, comparing them in some cases in the same building and application context. Although these two technologies initially might not seem to be competing in the same application space, in practice we found out that both have strengths and weaknesses in the specific application domain we have been using them. Moreover, our LoRa-based networking implementation, on top of Arduino-based hardware, appears to be an option that allows for a robust, reliable and lower overall cost IoT deployment, especially in cases with multi-floor building installations and low bandwidth requirements. We also present a network-level dataset produced from our installations and upon which we based our findings and discussion. We provide data collected from 6 different school buildings, 8 networks and 49 devices, to compare the performance and cost-effectiveness of competing IoT technologies. In that effect, with LoRa we can achieve similar or better link quality to IEEE 802.15.4, with higher data rate and lower costs.

In modern monitoring systems, it is essential to deploy sensor nodes and deliver related data to the information center. Wireless sensor networks (WSNs) usually work in harsh environments with vibration, temperature variations, noise, humidity, and so on. The batteries of sensor nodes are always not replaceable because of difficult access. Most of existing literature tries to prolong network lifetime by improving sleep scheduling strategies and deployment methods, independently or jointly. However, the congenital defects of mesh network can’t be avoided completely. To overcome the technology challenges, this paper develops a LoRaWAN-based WSN and investigates its energy efficient scheduling method. Firstly, the basics and the limits of LoRaWAN are introduced and the feasibility and the considerations of LoRaWAN-based star wireless sensor network are discussed. Secondly, an improved compressed sensing algorithm named ISL0 (improved SL0) is proposed for network data reconstruction and compressed sensing algorithm can reduce the number of LoRa nodes transmitting data packets to avoid collision and latency. Thirdly, a sleep schedule method is proposed to reliably monitor environment data and device operating status. By using the proposed method, not only the abnormal information can be detected on time, but also the overall network data can be recorded termly. Simulation and measurement results verify all nodes have same power level at different times, and the network lifetime is maximized.

Pollution-monitoring systems (PMSs) are used worldwide to sense environmental changes, such as air quality conditions or temperature increases, and to monitor compliance with regulations. However, organizations manage the environmental data collected by such PMSs in a centralized manner, which is why recorded environmental data are vulnerable to manipulation. Moreover, the analysis of pollution data often lacks transparency to outsiders, which may lead to wrong decisions regarding environmental regulations. To address these challenges, we propose a software design for PMSs based on distributed ledger technology (DLT) and the long-range (LoRa) protocol for flexible, transparent, and energy-efficient environment monitoring and data management. To design the PMS, we conducted a comprehensive requirements analysis for PMSs. We benchmarked different consensus mechanisms (e.g., BFT-SMaRt and Raft) and digital signature schemes (e.g., ECDSA and EdDSA) to adequately design the PMS and fulfill the identified requirements. On this basis, we designed and implemented a prototype PMS and evaluated it in the field. The evaluation shows the effectiveness of DLT-based PMSs that include portable low-energy sensor nodes and demonstrates the applicability of the proposed software design for PMSs in contexts other than air pollution.

Traditional waste management system operates based on daily schedule which is highly inefficient and costly. The existing recycle bin has also proved its ineffectiveness in the public as people do not recycle their waste properly. With the development of Internet of Things (IoT) and Artificial Intelligence (AI), the traditional waste management system can be replaced with smart sensors embedded into the system to perform real time monitoring and allow for better waste management. The aim of this research is to develop a smart waste management system using LoRa communication protocol and TensorFlow based deep learning model. LoRa sends the sensor data and Tensorflow performs real time object detection and classification. The bin consists of several compartments to segregate the waste including metal, plastic, paper, and general waste compartment which are controlled by the servo motors. Object detection and waste classification is done in TensorFlow framework with pre-trained object detection model. This object detection model is trained with images of waste to generate a frozen inference graph used for object detection which is done through a camera connected to the Raspberry Pi 3 Model B+ as the main processing unit. Ultrasonic sensor is embedded into each waste compartment to monitor the filling level of the waste. GPS module is integrated to monitor the location and real time of the bin. LoRa communication protocol is used to transmit data about the location, real time and filling level of the bin. RFID module is embedded for the purpose of waste management personnel identification. 2013 IEEE. This work was supported in part by the Ministry of Malaysia under Grant LRGS MRUN/F2/01/2019/1/2, and in part by the Deanship of Scientific Research (DSR), King Abdulaziz University, Jeddah, Saudi Arabia, under Grant RG-39-135-40. Scopus

In this work, a 2-by-2 array of a low-profile EBG ground plane is used as a means of sustaining the radiation performance of a 900-MHz LoRa antenna that could be installed at various construction sites. The proposed semi-circles added mushroom (SAM) structure enables a thin EBG height of 0.02λg on an FR-4 substrate at 900 MHz frequencies. A planar electric meander antenna for the 900-MHz LoRa band is integrated with the proposed 2-by-2 SAM plane. From a simulation and demonstration, it is found that the impedance and radiation properties of the integrated prototype in free space are well maintained with a minimum degradation even when it is placed on a concrete medium.

LoRa-based networks exhibit good flexibility in terms of configurable parameters and adjustable modulation properties. Thanks to this, wireless nodes can be tuned to improve their communication behavior. In fact, optimal network-level transmission configurations ( $\mathrm {C_{opt}}$ ) can be derived in such a way that the global network performance is maximized. To derive this $\mathrm {C_{opt}}$ , one must know the radio-propagation behavior of each node beforehand. Traditionally, this has been pursued by using general, low-precision, propagation models due to the infeasibility (in terms of time and energy) of deriving each individual node propagation behavior. In this work we propose a straightforward bounding technique that reduces up to 73% the energy and time required to obtain the radio-propagation behavior of each individual node in the network, enabling the derivation of network-level optimal transmission configurations. Also, we provide mechanisms to keep this knowledge updated, swiftly reacting to changes in the environment and leading to network performance improvements of 15% when compared to traditional alternatives like LoRaWAN ADR. Furthermore, by means of a testbed we demonstrate that this mechanism can also provide resistance to Denial-of-service attacks. Finally, we incorporate the power consumption into the proposed $\mathrm {C_{opt}}$ formulation and provide a generalizable power-consumption determination methodology. This way we can limit the set of eligible transmission configurations to help extending LoRa network lifetimes more than 40%.

The introduction of low-power wide-area networks (LPWANs) has changed the image of smart systems, due to their wide coverage and low-power characteristics. This category of communication technologies is the perfect candidate to be integrated into smart inverter control architectures for remote microgrid (MG) applications. LoRaWAN is one of the leading LPWAN technologies, with some appealing features such as ease of implementation and the possibility of creating private networks. This study is devoted to analyze and evaluate the aforementioned integration. Initially, the characteristics of different LPWAN technologies are introduced, followed by an in-depth analysis of LoRa and LoRaWAN. Next, the role of communication in MGs with widespread elements is explained. A point-by-point LoRa architecture is proposed to be implemented in the grid-feeding control structure of smart inverters. This architecture is experimentally evaluated in terms of latency analysis and externally generated power setpoint, following smart inverters in different LoRa settings. The results demonstrate the effectiveness of the proposed LoRa architecture, while the settings are optimally configured. Finally, a hybrid communication system is proposed that can be effectively implemented for remote residential MG management.

LoRa Wide Area Networks (LoRaWAN) can provide a connectivity service to Internet of Things (IoT) for an extremely long run-time and with low power consumption. As the LoRaWAN is extensively applied to various IoT scenarios, LoRaWAN solutions face a flexibility issue in terms of inter-operating with various kinds of LoRa modem hardware and protocol scenarios. In this regard, we design a unified protocol architecture for LoRaWAN physical layer, which can flexibly correspond to various deployment and operational cases. The new protocol architecture includes a hardware abstraction sub-layer, which contains generalized handlers for configuring various kinds of the LoRa modem, and a physical procedure sub-layer that structurally models the physical layer procedures of the LoRaWAN based on Finite State Machine(FSM). We illustrate the flexibility of the new protocol architecture by implementing an extensive feature that enhances the packet reception ratio based on the status of preamble detection. For evaluating the new protocol architecture, we implement the LoRaWAN physical layer protocol on real-time embedded systems and conduct experiments. The experimental results show that the proposed protocol robustly transmits and receives packets and generates little amount of additional burden compared with the conventional open source protocol provided by SemTech.

International audience; LoRa is one of the leading technologies for Low-Power Wide Area Networks and the Internet of Things. Collisions in LoRa might cause retransmissions, which negatively impact the network performance and scalability. Several algorithms have been proposed to decode colliding frames under specific conditions. However, there remain indistinguishable frames due to uncertainties in some or all symbols. In this paper, we propose a general algorithm that significantly improves the recovery capabilities of existing algorithms by leveraging the LoRa coding techniques. Simulation results show that our algorithm can significantly reduce the number of the failed decoding of LoRa frames and improve the performance of the network.

Along with the rapid development of sensing systems and wireless transmission technology, the scope of application of the IoT has substantially increased, and research and innovation that integrate artificial intelligence. This study integrated civil engineering and electrical engineering to establish a universal and modularized long-term sensing system. Aiming at positive construction in civil engineering, the campus of National Taipei University of Technology was used as the experimental site as a green campus. This paper focused on the cooling effect of the green roof and the temperature difference of the solar panel to effectively isolate the direct sunlight on the roof of the building. To achieve long-term monitoring, energy consumption must be minimized. Considering that the distance between sensor nodes in the experimental site was over dozens of feet, LoRa transmission technology was selected for data transmission. LoRa only consumes a small amount of energy during data transmission, and it can freely switch between work modes, achieving optimal power utilization efficiency. The greening-related research results indicated that the shade from solar panels on the rooftop could effectively reduce the temperature increase caused by direct sunlight on concrete surfaces. The temperature reduction effect was positively correlated with whether the solar panels provided shade. After 1 week of monitoring, we observed that having plants on the rooftop for greening negatively correlated with temperature reduction efficiency. Permeable pavement on the ground was positively correlated with temperature reduction efficiency. However, its temperature reduction efficiency was inferior to that of solar panel shading. The temperature difference between high-rise buildings and the ground was approximately 1–2 °C. At the same elevation, the temperature difference between buildings with and without greening was approximately 0.8 °C. Regarding the sensing system designed for this site, both hardware and software could be flexibly set according to the research purposes, precision requirements of the sites, and the measurement scope, thereby enabling their application in more fields.

[EN] The LoRa technology enables long distance links with reduced power consumption at low cost, the main limitation being the low bandwidth that it offers. With LoRa, remote locations, like rural areas, can benefit from connectivity based services that would otherwise be impossible. In this work, we describe a LoRa architecture that can include generic external data sources using an MQTT-based interface. We particularly focus on audio sources aiming to two basic services: a voice messaging system that allows users who cannot read or write to send voice notes, and an audio compression service to extract the main features from the audio input to use it for developing intelligent ML-based audio analytics., This work has been partially supported by the Spanish Ministry of Science and Innovation, under the Ramon y Cajal Program (Grant No. RYC2018-025580-I) and under grants RTI2018-096384-B-I00, RTC-2017-6389-5 and RTC2019-007159-5, by the Fundacion Seneca del Centro de Coordinacion de la Investigacion de la Region de Murcia under Project 20813/PI/18, and by the "Conselleria de Educacion, Investigacion, Cultura y Deporte, Direccio General de Ciencia i Investigacio, Proyectos AICO/2020", Spain, under Grant AICO/2020/302.

Seeking to solve problems in the power electric system (PES) related to exacerbated and uncontrolled energy consumption by final consumers such as residences, condominiums, public buildings and industries, electric power companies (EPC) are increasingly seeking new information and communication technologies (ICTs) to transform traditional electric power distribution networks into smart grids (SG). With this implementation, PES will be able to remotely control electric power consumption as well as monitor data generated by smart meters (SM). However, Internet-of-Things (IoT) technologies will enable all this to happen quickly and at low cost, since they are low-cost devices that can be deployed quickly and at scale in these scenarios. With this in mind, this work aimed to study, propose, and implement a hybrid communication infrastructure with LoRaWAN and LoraMesh for the demand-side management as a service (HyDSMaaS) using IoT devices such as long range (LoRa) to provide an advanced metering infrastructure (AMI) capable of performing all these applications as a service offered by EPC to end consumers. Additionally, services such as demand-side management (DSMaaS) can be used in this infrastructure. From the preliminary results it was found that the LoRaWAN network achieved a range of up to 2.35 km distance and the LoRaMESH one of 600 m, thus, the latter is more suitable for scenarios where there is little interference and the SMs are at long distances, while the other is used for scenarios with greater agglomeration of nearby SMs. Considering the hybridized scenario between LoraWAN and LoRaMESH, it can be seen that the implementation possibilities increase, since its range was approximately 3 km considering only one hop, and it can reach 1023 devices present in a mesh network. Thus, it was possible to propose the actual implementation of LoRaWAN and LoRaMESH protocols as well as the hybridization of the two protocols for HyDSMaaS. Additionally, the results obtained are exclusively from Radioenge’s LoRa technology, which can be further improved in the case of using more powerful equipment.

International audience; The increasing number of devices together with uncoordinated transmissions result in a major challenge of scalability in the Internet of things. This paper deals with signal detection in the uplink of a LoRa network through a deep learning-based approach. Two strategies are proposed: regression for bit detection based on a deep feedforward neural network and classification for symbol detection based on a convolutional neural network. These receivers can decode a selected user's signals when multiple users simultaneously transmit over the same frequency band with the same spreading factor. Simulation results show that both receivers outperform the classical LoRa one in the presence of interference. The results show that the introduced approach is relevant to deal with the scalability issue.

Low-power wide-area networks (LPWANs) are probably the most promising radio access technologies for Internet-of-Things (IoT) applications. Amongst these, one of the most auspicious solutions is LoRa, a versatile technology highly compatible with urban environments, enabling long-range communications. Most of the LoRa-based medium access protocols operate under the ALOHA rationale, whose performance is known to be fairly poor. This work targets the medium access in single-channel large-scale LoRa networks, proposing a new protocol, denoted as the LoRa mode adaptive protocol (LoRa-MAP), which manages to maintain the best possible connection between end nodes and the gateway, by adapting the LoRa’s physical layer parameters and making use of control packets for its coordination without violating the duty-cycle constraints of both end nodes and gateway. An analysis on different medium access schemes is conducted, aiming to perceive how different parameters and network layouts influence the coordination process. An energy expenditure analysis is conducted comparing LoRa-MAP to simpler solutions to study the impact of additional transmission/listening periods. The simulation results have shown that the proposed solution increases the LoRa network scalability, deeming it a great candidate for IoT environments.

Oil pipeline monitoring is having a significant role in minimizing the impact on the environment and humans during pipeline accidents. The real-time monitoring of oil pipelines empowers the authorities to have continuous supervision of the oil pipeline. The Internet of Things (IoT) provides an opportunity for realizing the real-time monitoring system by deploying the IoT-enabled end devices on the oil pipeline. In this study, we propose a hybrid architecture based on 2.4 GHz-based Zigbee and LoRa communication for oil pipeline monitoring. Moreover, customized end devices and LoRa based gateway are designed and implemented for sensing the critical parameters of an oil pipeline. Here, we have performed the simulation of ZigBee communication on the OPNET simulator for evaluating the parameters such as packet delivery ratio (PDR), retransmission attempts, throughput, medium access (MAC) queue size, and queue delay. Furthermore, the distinct evaluation metrics of LoRa such as bit rate, link budget, and receiver sensitivity are also included. Finally, a real-time experiment is implemented with customized end devices and a gateway for evaluating the proposed architecture. In the real-time experiment, the devices and gateway are logging the pressure sensory data into the Cayenne cloud.

This paper proposes a novel network architecture integrating a multi-hop Long Range (LoRa)-based thin linear network within a LoRa Wide Area Network (LoRaWAN) infrastructure, with the aim of proposing linear distributed measurement systems forwarding their collected data to a LoRaWAN server by means of a hybrid LoRa-LoRaWAN node. Such device is able to collect LoRa packets coming from the linear network and to encapsulate them in LoRaWAN packets transmitted to the remote server by means of standard LoRaWAN Gateways. The operation of the nodes is regulated by an ad-hoc routing protocol which aims at minimizing their active period, in order to reduce their power consumption increasing the overall system lifetime. Similarly, the synchronization of the nodes aims at increasing the robustness of the network reducing at minimum packet losses. The effectiveness of the proposed network architecture in terms of successful packet deliveries and reduction of active time is tested in different configurations, exploiting 2-node, 3-node and 4-node chains as well as adopting increasingly larger cycle periods. Results show that the proposed configuration ensures a noteworthy robustness in terms of packets delivery while maintaining the duty-cycling at levels that may guarantee long life times and autonomous operation to the overall infrastructure.

This paper presents the study and design of an Integer N synthesizer model for three LoRa ISM bands: 430 MHz 868 MHz and 915 MHz. The proposed topology is composed by two voltage controlled oscillators working in two different bands. The presented model uses the same phase-frequency detector, charge pump and loop filter. This study is focused on dynamic and steady-state analysis in order to infer the synthesizer stability and bandwidth. The performed study shows that the settling time for all bands is less than 40 µs for a bandwidth of 102 kHz.&nbsp

Local area network (LAN) as Bluetooth, WiFi and ZigBee are well established technology. The biggest problem with many LAN is the battery consumption and short ranges link budgets. LoRa is a new, private, unlicensed and spread spectrum modulation technique which allows sending low rates at extremely long ranges with minimal power consumption. More importantly, there is no access fee associated with this type of wireless technology. The main idea behind this work is to conduct performance and capability analysis of a currently available LoRa transceiver. We develop a location monitoring system using LoRa and global positioning system (GPS) module and we analyze the detectable range of its data, its battery consumption as well as received signal strength indicator (RSSI). Our deployment experiment demonstrates that the system is able to detect the transmitted data within 290 meters of distances. Using 6 volts of battery AA, the transmission of data still occurred after 24 hours. This project is emphasized a location monitoring system that provide low power usage but long range.

(1) Background: The scientific development in the field of industrialization demands the automization of electronic shelf labels (ESLs). COVID-19 has limited the manpower responsible for the frequent updating of the ESL system. The current ESL uses QR (quick response) codes, NFC (near-field communication), and RFID (radio-frequency identification). These technologies have a short range or need more manpower. LoRa is one of the prominent contenders in this category as it provides long-range connectivity with less energy harvesting and location tracking. It uses many gateways (GWs) to transmit the same data packet to a node, which causes collision at the receiver side. The restriction of the duty cycle (DC) and dependency of acknowledgment makes it unsuitable for use by the common person. The maximum efficiency of pure ALOHA is 18.4%, while that of slotted ALOHA is 36.8%, which makes LoRa unsuitable for industrial use. It can be used for applications that need a low data rate, i.e., up to approximately 27 Kbps. The ALOHA mechanism can cause inefficiency by not eliminating fast saturation even with the increasing number of gateways. The increasing number of gateways can only improve the global performance for generating packets with Poisson law having a uniform distribution of payload of 1~51 bytes. The maximum expected channel capacity usage is similar to the pure ALOHA throughput. (2) Methods: In this paper, the improved ALOHA mechanism is used, which is based on the orthogonal combination of spreading factor (SF) and bandwidth (BW), to maximize the throughput of LoRa for ESL. The varying distances (D) of the end nodes (ENs) are arranged based on the K-means machine learning algorithm (MLA) using the parameter selection principle of ISM (industrial, scientific and medical) regulation with a 1% DC for transmission to minimize the saturation. (3) Results: The performance of the improved ALOHA degraded with the increasing number of SFs and as well ENs. However, after using K-mapping, the network changes and the different number of gateways had a greater impact on the probability of successful transmission. The saturation decreased from 57% to 1~2% by using MLA. The RSSI (Received Signal Strength Indicator) plays a key role in determining the exact position of the ENs, which helps to improve the possibility of successful transmission and synchronization at higher BW (250 kHz). In addition, a high BW has lower energy consumption than a low BW at the same DC with a double-bit rate and almost half the ToA (time on-air).

This study was conducted using a drone with advanced mobility to develop a unified sensor and communication system as a new platform for in situ atmospheric measurements. As a major cause of air pollution, particulate matter (PM) has been attracting attention globally. We developed a small, lightweight, simple, and cost-effective multi-sensor system for multiple measurements of atmospheric phenomena and related environmental information. For in situ local area measurements, we used a long-range wireless communication module with real-time monitoring and visualizing software applications. Moreover, we developed four prototype brackets with optimal assignment of sensors, devices, and a camera for mounting on a drone as a unified system platform. Results of calibration experiments, when compared to data from two upper-grade PM2.5 sensors, demonstrated that our sensor system followed the overall tendencies and changes. We obtained original datasets after conducting flight measurement experiments at three sites with differing surrounding environments. The experimentally obtained prediction results matched regional PM2.5 trends obtained using long short-term memory (LSTM) networks trained using the respective datasets.

Automatic Dependent Surveillance-Broadcast (ADS-B) is the main communication system currently being used in Air Traffic Control (ATC) around the world. The ADS-B system is planned to be a key component of the Federal Aviation Administration (FAA) NextGen plan, which will manage the increasingly congested airspace in the coming decades. While the benefits of ADS-B are widely known, its lack of security measures and its vulnerability to cyberattacks such as jamming and spoofing is a great concern for flight safety experts. In this paper, we first summarize the cyberattacks and challenges related to ADS-B’s vulnerabilities. Thereafter, we present theoretical and practical methods for implementing an Internet of Things (IoT)-based system as a possible additional safety layer to mitigate the presented cyber-vulnerabilities. Finally, a set of simulations and field experiments is presented to test the expected performance of the suggested IoT flight safety system. We conjecture that the presented system can be implemented in a wide range of civilian airplanes, leading to an improvement in flight safety in cases of cyberattacks or the absence of reliable ADS-B communication.

LoRa, due to its advantage of long-range communication capability, is promising for Internet of Things (IoT) and space-air-ground communications. However, the conventional MAC protocol used with LoRa is classified as an Aloha-based algorithm, which leads to drastic decrease in throughput when a huge amount of end-devices try to access the network. To achieve stable and high throughput of LoRa, we propose a design to combine the distributed queueing (DQ) and in-band-full-duplex (IBFD) technologies. The usage of DQ mechanism is benefit for fast collision resolution, while the IBFD-enabled gateway helps to reduce the heavy control overhead of DQ. The designs of access procedure and frame structure are discussed in detail. The outage probability and average throughput are evaluated under imperfect self-interference cancelation. Also, a mathematical programming method is developed to optimize the spreading factor and code rate. Numerical results show that our proposal gains an extra enhancement of 1.83-fold in throughput.

Intelligent and resilient infrastructure is necessary for smart cities for contributing flexible and smart amenities to the citizens. Concerning the United Nations (UN) estimation, the global population residing in urban cities will reach 68% by 2050. Additionally, the Sustainable Energy Action Plans (SEAP) report suggests implementing energy efficiency technologies in smart cities to meet the rising urban population requirement. Internet of Things (IoT) technology empowers to achieve the goal of energy efficiency by integrating sensors, wireless technology, and renewable energy sources in the lighting system. At present, the IoT-based lighting system in urban cities is implemented with streetlamps and lampposts. In this study, we are focusing on lampposts, as it has the flexibility of establishing and implementing a multitude of applications on a single system. Due to technological advancement, the lamppost is embedded with multiple sensors, communication protocols, and energy distribution infrastructure for delivering smart and affordable amenities to the citizens residing in the smart cities. This motivates us to implement a smart lamppost that provides a multitude of applications such as smart light, digital signs, environmental monitoring conditions, electric vehicle (EV) charging port, wireless fidelity (Wi-Fi) hotspot, etc., on a single lamppost. This study proposed the IoT-assisted fog and edge-based smart lamppost for the smart cities to realize the smart infrastructure. Further, this smart lamppost is integrated with low power and long-range communication, i.e., Long Range (LoRa), enabling the smart lamppost to communicate the sensory data to a long-range. Additionally, LoRa is integrated with a Wi-Fi module for establishing the interconnection between the smart lamppost and IoT server. Generally, the proposed architecture is broad perspective, however, we have developed and implemented the hardware models of three components including lighting system, environmental parameters and image sensing in real time. Lighting system and environmental parameter monitoring are integrated on same hardware model for sensing and logging the real-time values of temperature, humidity, CO and light intensity on the IoT server. The developed image sensing prototype based on ESP 32 controller is also evaluated in real-time scenarios, and the performance of the prototype is efficient. The proposed system delivers reliable performance in terms of sensing and communicating environmental parameters and images to the IoT server. Moreover, in future, we will complete the development of other components of the smart lamppost for enhancing the smarter infrastructure in smart cities.

The last decade has seen multiple research work on the use of LoRaWAN technology in smart agriculture. In open field storage, monitoring is crucial for increasing the logistics efficiency and improving crop quality. As battery maintenance is expensive in such areas, LoRa is a suitable technology that allows for low-power communications. Within the framework of the research, a prototype has been built for tracking goods in open field storage. The battery lifetime has been analysed through calculations and measurements using LoRa communications. Our findings indicate that although sleeping current has the smallest percentage, it has the greatest effect on increasing the battery life, for longer battery life LoRa node must have a low self-discharge battery, and finally, sensors are the main battery depleting factor on the LoRa node.

The Internet of Things (IoT) has the capability to change our lives. In the near future, every device and almost every object will be linked together and connected to the internet. This paper summarizes the work done in developing a Long Range (LoRa) driven home security system for a residential community in a retirement township. Both hardware and software solutions were used to provide emergency alerts. A panic button was developed to generate alarms manually and embedded sensors were implemented to generate alarms automatically. LoRa wireless communication technology was used to connect all hardware devices in the network and to forward all these data to be stored in the server. A mobile application was developed to provide an alternative way of generating manual emergency alerts and to provide a communication channel in the form of a chat room for residents of the same community as well as the security guards. A web application was developed for residential area security management, where administrators can register new residential areas and assign security guards to these residential areas accordingly.

Standard LoRaWANs leverage pure ALOHA at the medium access control layer, which is proved to be a performance bottleneck as the network size scales up. Stimulated by this fact, this paper studies the applicability and the performance of listen before talk (LBT) medium access schemes in the context of LoRaWANs. We consider two different implementations of LBT: physical layer LBT based on energy detection only and MAC layer LBT based on layer 2 frame decoding, and we propose a Markovian framework to evaluate the performance of LoRaWANs under such setting in terms of data extraction rate and average delay experienced by transmitted uplink messages. The proposed framework is also leveraged to assess the performance of “mixed” LoRaWAN scenarios, where some devices access the channel according to the standard-compliant ALOHA protocol, while other devices transmit according to LBT.

In this paper, efficient resource allocation for the uplink transmission of wireless powered IoT networks is investigated. We adopt LoRa technology as an example in the IoT network, but this work is still suitable for other communication technologies. Allocating limited resources, like spectrum and energy resources, among a massive number of users faces critical challenges. We consider grouping wireless powered IoT users into available channels first and then investigate power allocation for users grouped in the same channel to improve the network throughput. Specifically, the user grouping problem is formulated as a many to one matching game. It is achieved by considering IoT users and channels as selfish players which belong to two disjoint sets. Both selfish players focus on maximizing their own utilities. Then we propose an efficient channel allocation algorithm (ECAA) with low complexity for user grouping. Additionally, a Markov Decision Process (MDP) is used to model unpredictable energy arrival and channel conditions uncertainty at each user, and a power allocation algorithm is proposed to maximize the accumulative network throughput over a finite-horizon of time slots. By doing so, we can distribute the channel access and dynamic power allocation local to IoT users. Numerical results demonstrate that our proposed ECAA algorithm achieves near-optimal performance and is superior to random channel assignment, but has much lower computational complexity. Moreover, simulations show that the distributed power allocation policy for each user is obtained with better performance than a centralized offline scheme., 11 pages, 8 figures the accepted version

Recently, LoRaWAN has attracted much attention for the realization of many Internet of Things applications because it offers low-power, long-distance, and low-cost wireless communication. Recent works have shown that the LoRaWAN specification for class A devices comes with scalability limitations due to the ALOHA-like nature of the MAC layer. In this paper, we propose a synchronization and scheduling mechanism for LoRaWAN networks consisting of class A devices. The mechanism runs on top of the LoRaWAN MAC layer. A central network synchronization and scheduling entity will schedule uplink and downlink transmissions. In order to reduce the synchronization packet length, all time slots that are being assigned to an end node are encoded in a probabilistic space-efficient data structure. An end node will check if a time slot is part of the received data structure in order to determine when to transmit. Time slots are assigned based on the traffic needs of the end nodes. We show that in case of a nonsaturated multichannel LoRaWAN network with synchronization being done in a separate channel, the packet delivery ratio (PDR) is easily 7% (for SF7) to 30% (for SF12) higher than in an unsynchronized LoRaWAN network. For saturated networks, the differences in PDR become more profound as nodes are only scheduled as long as they can be accommodated given the remaining capacity of the network. The synchronization process will use less than 3-mAh extra battery capacity per end node during a one year period, for synchronization periods longer than three days. This is less than the battery capacity used to transmit packets that are going to be lost in an unsynchronized network due to collisions.

In this paper, a new low-cost device is proposed for fault location in power distribution system. The device is part of a system that should include several units installed in couples at the two ends of medium voltage (MV) cable lines. Their low cost justifies a widespread installation of these devices, moreover other functionalities could be considered so as to enrich the potential of the system for MV cables monitoring and asset management. The system exploits a two-end traveling wave technique, low complexity, and low-cost solutions both for the analog front end as well as for the communication infrastructure. A LoRa network is indeed considered as wireless modulation technique perfectly suited when communication links with three characteristics are needed: low power, low bit rate, and long range. This paper shows the device that has been set up in Prysmian Electronics with some experimental phases showing the effectiveness of the proposed solution and the further developments expected.

By 2020, more than 50 billion devices will be connected through radio communications. In conjunction with the rapid growth of the Internet of Things (IoT) market, low power wide area networks (LPWAN) have become a popular low-rate long-range radio communication technology. Sigfox, LoRa, and NB-IoT are the three leading LPWAN technologies that compete for large-scale IoT deployment. This paper provides a comprehensive and comparative study of these technologies, which serve as efficient solutions to connect smart, autonomous, and heterogeneous devices. We show that Sigfox and LoRa are advantageous in terms of battery lifetime, capacity, and cost. Meanwhile, NB-IoT offers benefits in terms of latency and quality of service. In addition, we analyze the IoT success factors of these LPWAN technologies, and we consider application scenarios and explain which technology is the best fit for each of these scenarios. Keywords: Internet of Things, LPWAN, LoRa, Sigfox, NB-IoT

In the low earth orbit (LEO) satellite Internet of Things (IoT), the IoT devices can be distributed in remote areas (e.g., desert, ocean, and forest) for some special applications, where they are unable to have direct terrestrial network accesses and can only be covered by satellite. But in the LEO satellite scenario, it has to face the effects of high doppler and fast doppler rate, due to satellite movement at high speed and utilization of higher frequency bands. So fine frequency synchronization and frequency track are necessary to be done, even for LORA. In this paper, we propose a folded chirp-rate shift keying (FCrSK) modulation with strong immunity to doppler effect. Its data information is mapped to different folded chirp-rate waveforms, which have an advantage of the consistency of bandwidth and symbol length among chirp-rates compared to traditional chirp-rate shift keying system. Moreover, we analyze the optimal non-coherent receiver in case of the doppler channel, whose optimal decision depends on signal energy focusability in frequency spectrum after dechirp, so frequency peak average rate adopts as a decision criterion. Furthermore, system parameters’ setting is discussed so as to maximize system modulation efficiency with the minimum interference among chirp-rate. Finally, some simulations are provided to analyze robustness to doppler shift and doppler-rate in the system. And based on universal software radio platform (USRP), some experimental measurements are made to validate its feasibility under the realistic wireless environment and doppler environment.

LoRa is a low-power long-range IoT standard that uses the chirp spread spectrum technique, and we have strived to further extend its coverage by utilizing the direct device-to-device (D2D) links to construct a multi-hop relay network. In LoRa, the spreading factor (SF) is an important parameter, which not only provides great flexibility between the data rate and sensitivity but also presents a new dimension for multiple accesses. Our approach to improving the capacity of a multihop LoRa network is to attempt to off-load the data traffic into several subnets by utilizing this multiple-access dimension. Each subnet rooted at a sink node is allocated a specific SF on the basis of network clustering. This enables packet transmission in parallel with multiple SFs to become feasible. To allow such parallel transmissions, our considerations are: 1) ensuring the connectivity of all subnets; 2) off-loading the traffic according to the number of nodes, data rates, and network topologies of each subnet; and 3) shortening the airtime of each subnet by reducing the hop count. Toward these objectives, we present a tree-based SF clustering algorithm (TSCA) to conduct SF allocation in a multihop LoRa network. The TSCA focuses on balancing the airtime between the subnets while ensuring connectivity. Furthermore, we use simulations to show that our approach can significantly increase the network performance compared with other approaches. We additionally deploy a real-chip experiment to evaluate the feasibility of parallel transmission in practice.

LoRa is a long-range, low power and single-hop wireless technology that has been envisioned for Internet of Things (IoT) applications having battery driven nodes. Nevertheless, increase in number of end devices and varying throughput requirements impair the performance of pure Aloha in LoRaWAN. Considering these limitations, we evaluate the performance of slotted Aloha in LoRaWAN using extensive simulations. We employed packet error rate (PER), throughput, delay, and energy consumption of devices under different payload sizes and varying number of end devices as benchmarks. Moreover, an analytical analysis of backlogged and non-backlogged under slotted Aloha LoRaWAN environment is also performed. The simulation shows promising results in terms of PER and throughput compared to the pure Aloha. However, increase in delay has been observed during experimental evaluation.Finally, we endorse slotted aloha LoRaWAN for Green IoT Environment.

This paper considers newly emerging wireless technologies that can be explored for potential exploitation in the future wireless sensor network solutions for water quality monitoring applications. The reviewed technologies promise to address long-standing issues that confront existing wireless sensor network solutions devoted to the monitoring of water quality parameters. Such issues include energy efficiency and long-range water quality data communication. These issues can be attributed to the shortcomings of legacy communication networks commonly combined with wireless sensor network solutions, due to a discrepancy caused by traditionally powering the sensors nodes using batteries. In particular, some of the legacy communication networks suffer from short communication range, while others are limited by high power consumption. These shortcomings provide scope for the utilization of newly emerging solutions identified in this study to advance the field of a wireless sensor network for water quality monitoring. To achieve this, three key categories of communication networks have been recommended in this paper, including their architectural design and network deployment for water quality monitoring applications. Also, this paper provides future directions on the identified communication networks to enhance their performance for the next-generation of wireless sensor network solutions for water quality monitoring applications.

Long Range (LoRa) has rapidly become one of the most widespread transmission technologies for the development of distributed monitoring systems for a wide range of applications within the Internet of Things (IoT) framework. Indeed, thanks to its robustness and high performances in terms of low power consumption and long transmission range, it has been employed in a wide range of critical application scenarios. In this context, this paper analyses the usability of LoRa as solution for data transmission in icy environments. While the technical feasibility of radio transmission channel in ice has been widely demonstrated, this paper aims at demonstrating the effectiveness of a low cost transmitter developed using off-the-shelf components, when completely immersed in ice. Fields tests were performed by embedding a Low Power Wide Area Network (LoRaWAN) compliant node inside a 10 cm block of dry ice, positioned then in an environmental chamber, at a −10°C temperature. Preliminary tests demonstrate the operation of the transmitter in these operating conditions, while the achieved link margin suggests the usability of this technology even in case of notably thicker layers of ice.

This paper presents a long-range (LoRa)-based Internet of Things (IoT) system that consists of a series of IoT units in the field, as well as several servers for agriculture monitoring and pump control in water bamboo fields. Four types of IoT units were developed in accordance with the application’s need based on LoRa technology. Front- and back-end servers were constructed for data delivery, storage, and visualization, forming a complete solution. Another key feature is making a traditional submersible pump programmable with one IoT unit attached to the magnetic contactor in the electricity distribution box. Moreover, this paper presents design details of the proposed system and field experiment results for function validation. This IoT system is the first step in our project of revealing the relationship between farming and environmental reaction. This makes proposing a new farming procedure possible in the future.

When integrating the Internet of Things (IoT) with Unmanned Aerial Vehicles (UAVs) occurred, tens of applications including smart agriculture have emerged to offer innovative solutions to modernize the farming sector. This paper aims to present a low-cost platform for comprehensive environmental parameter monitoring using flying IoT. This platform is deployed and tested in a real scenario on a farm in Medenine, Tunisia, in the period of March 2020 to March 2021. The experimental work fulfills the requirements of automated and real-time monitoring of the environmental parameters using both under- and aboveground sensors. These IoT sensors are on a farm collecting vast amounts of environmental data, where it is sent to ground gateways every 1 h, after which the obtained data is collected and transmitted by a drone to the cloud for storage and analysis every 12 h. This low-cost platform can help farmers, governmental, or manufacturers to predict environmental data over the geographically large farm field, which leads to enhancement in crop productivity and farm management in a cost-effective, and timely manner. Obtained experimental results infer that automated and human-made sets of actions can be applied and/or suggested, due to the innovative integration between IoT sensors with the drone. These smart actions help in precision agriculture, which, in turn, intensely boost crop productivity, saving natural resources.