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Most IoT networks implement one-way messages from the sensor nodes to the “application host server” via a gateway. Messages from any sensor node in the network are sent when its sensor is triggered or at regular intervals as dictated by the application, such as a Smart-City deployment of LoRaWAN traps/sensors for rat detection. However, these traps can, due to the nature of this application, be moved out of signal range from their original location, or obstructed by objects, resulting in under 69% of the messages reaching the gateway. Therefore, applications of this type would benefit from control messages from the “application host server” back to the sensor nodes for enhancing their performance/connectivity. This paper has implemented a cloud-based AI engine, as part of the “application host server”, that dynamically analyses all received messages from the sensor nodes and exchanges data/enhancement back and forth with them, when necessary. Hundreds of sensor nodes in various blocked/obstructed IoT network connectivity scenarios are used to test our DXN solution. We achieved 100% reporting success if access to any blocked sensor node was possible via a neighbouring node. DXN is based on DNN and Time Series models.

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.

Maintaining hand hygiene has been an essential preventive measure for reducing disease transmission in public facilities, particularly during the COVID-19 pandemic. The large number of sanitizer stations deployed within public facilities, such as on university campuses, brings challenges for effective facility management. This paper proposes an IoT sensor network for tracking sanitizer usage in public facilities and supporting facility management using a data-driven approach. Specifically, the system integrates low-cost wireless sensors, LoRaWAN, and cloud-based computing techniques to realize data capture, communication, and analysis. The proposed approach was validated through field experiments in a large building on a university campus to assess the network signal coverage and effectiveness of sensor operation for facility monitoring. The results show that a LoRaWAN created from a single gateway can successfully connect to sensors distributed throughout the entire building, with the sensor nodes recording and transmitting events across the network for further analysis. Overall, this paper demonstrates the potential of leveraging the IoT-based Sanitizer Station Network to track public health mitigation methods in a large facility, which ultimately contributes to reducing the burden of maintaining public health during and post-pandemic.

Nowadays, due to modernization or advancement in the Internet of Things (IoT) especially in the Healthcare area, we want to take care of our elders with some monitoring equipment, and the Internet of Things can play a significant role in it. The motivation of writing this paper is to collect the information of various existing Internet of Things Architecture and Communication Techniques used in Healthcare Monitoring System to observe that how efficiently, different researchers have used it. So we have studied different real-time health monitoring system based on diseases which are common in elderly people like diabetes, blood pressure, heart disease, sleep apnea, and cancer, etc. In this real-time health monitoring system, researchers introduced many new measures, communication techniques like ZigBee, Long-Range Wide Area Network (LoRawan), Radio Frequency Identification (RFID). Apart from this, it was also observed that remote monitoring system in Healthcare is incomplete without data processing and early prediction in such diseases. Though, Machine learning provides efficient techniques to extract knowledge from diagnostic medical datasets collected from the patients. That is why we highlighted the current role of various Machine Learning algorithms like Support Vector Machine, K-Nearest Neighbor, Random Forest, etc., for processing of Healthcare data and also helpful to predict the output more precisely.

International audience; Internet of things (IoT) technology is based on connecting each real object to the internet. Every single object is uniquely recognized and reachable over the network. IoT last mile connectivity is based on different communication technologies and protocols, where the majority is categorized as short-range networks that operate in ISM bands like Zigbee, Wi-Fi, and Bluetooth. Short-range technologies were successfully tested and deployed in different industrial sectors. However, in the energy sectors, the deployment is challenging in certain hard to reach areas where reliable last-mile connectivity is required between the home area network (HAN) smart meters and the meter data management system (MDMS). Therefore recently, Low Power Wide Area Network (LPWAN) technology implementations, which offer long-range connectivity, has emerged as a promising alternative for IoT in Energy applications. Within LPWAN, a variety of technology implementations exist and operate in licensed and unlicensed spectrum respectively like NB-IoT, LoRaWAN, and Sigfox. In this article, we discuss both the performance of LoRaWAN in a real-world environment and its deployment as a low-cost, long-range, and reliable last-mile solution for energy smart metering in urban area scenario where a short-range solution may not be the optimum one. Furthermore, a prototype that is adapted to the existing Lebanese traditional energy sector was developed to test LoRaWAN's usefulness in Lebanon.

Light or sunlight can be converted into electricity using solar panel technology. The measurement process is needed on current and voltage parameters to determine whether a solar panel is working correctly or not. The application of the Internet of Things (IoT) is a suitable method for monitoring efficiency measurements on solar panel performance in real-time by combining several computational components, protocols, and sensors to interact more quickly and help all activities become more efficient. One IoT technology that can work efficiently is the Long Range Wide Area Network (LoRaWAN). LoRa communicates using radio frequencies with a wide coverage range and has a low power consumption level. In this study, the implementation of LoRaWAN technology as a communication protocol between three series of nodes and one gateway in building a solar panel system is then visualized on a web that can monitor currents and voltages in the form of graphs and numbers. Displays notifications when there is a change in the condition of the large voltage from the solar panel can control the state of turning on or off the lights and turning off the whole node, and displaying a history of current and voltage readings. The functionality of the system will later be tested using black-box testing. There was also a distribution of questionnaires to 35 respondents to measure the level of agreement that the system designed was running well.

Owing to the recent research and development on the Internet-of-Things (IoT) and machine-to-machine (M2M) communication, wireless sensor networks have attracted considerable attention. Among these networks, low power wide area networks (LPWANs), which realize low power, low data rate, and wide communication area, are most commonly used for long-range communication. These networks adopt asynchronous random-access protocols, such as the pure ALOHA protocol in the medium access control (MAC) layer. Thus, there is a high possibility that multiple nodes transmit packets simultaneously on the same frequency channel, resulting in packet collisions. Carrier-sense multiple access/collision avoidance (CSMA/CA) and centralized resource allocation are effective for avoiding packet collisions. However, these schemes increase the energy consumption of battery-powered LPWAN nodes. In addition, LPWAN has a large coverage area; hence, there is a high possibility that the carrier sense will not work successfully. Thus, this paper proposes a simple but effective machine-learning-based scheme that tackles the packet collision problem by offsetting the transmission timings and avoiding unnecessary packet transmission in an autonomous decentralized manner. Each LPWAN node adjusts the transmission probability and timing using the Q-learning technique. The proposed scheme provides effective packet collision avoidance for LPWAN nodes without the need for an additional control signal. The computer simulation results show that the proposed scheme can improve the average packet delivery ratio (PDR) by 60% compared to the pure ALOHA protocol.

There are surplus applications in modern smart cities where localization of indoor environments is critical ranging from surveillance and trailing in smart structures to the localized wireless distribution of advertising content in shopping malls. These applications are only successful if a robust and cost-effective real-time system is developed for precise localization. Another aspect considered for indoor localization is power consumption. Recent wireless standards such as Bluetooth Low Energy (BLE) and LoRa consume less power which makes them a perfect candidate for indoor localization. This work aims to carry out an experimental evaluation which would help to decide which wireless standard i.e., Wi-Fi, Bluetooth Low Energy (BLE), and LoRa are most suitable for indoor localization. Experiments are carried out using trilateration in three multiple environments. RSSI is used to calculate the coordinates of a sensor node. Results obtained from the experiment show that Wi-Fi is most accurate with an average error of 0.54 m. LoRa is second most accurate with an average error of 0.62 m and BLE is the least accurate with an average error of 0.82 m. These results can be used to decide which wireless standard is best suited for indoor localization.

The article discusses IoT based solutions to improve waste collection and monitoring in public waste containers. The paper examines whether the monitoring system used in the measurement process can be used to monitor the filling of containers. The system consists of a wireless module connected to an ultrasonic sensor. The data sent makes it possible to calculate the empty space in the waste containers. The values are obtained by measuring the distance from the top of the container to the surface of the waste. Low-power long-distance broadband network (LoRaWAN) transmitters were used for data transmission. The aim of the research: to test an inexpensive monitoring system and to describe a new system of sensors and transmitter modules. The system is characterized by extremely low power consumption and a wide range of sensors used. Measurements were performed in Vilnius. Containers of two types of sizes were analyzed, measurements were performed at five points of horizontal cross section of the containers. The study was performed at two different levels of container filling. The results show that an ultrasonic sensor can representatively measure waste filling depths at different levels of container filling. Based on the results, this sensor can be recommended in the intelligent waste management market. Such a system can provide the necessary data for optimizing waste collection processes in cities and avoid container overcrowding problems. Article in Lithuanian. Atliekų konteinerių užpildymo lygio matavimas naudojant daiktų interneto ir „LoRaWAN“ technologijos mažos galios jutiklius Santrauka Straipsnyje aptariami daiktų interneto jutikliais pagrįsti sprendimai, gerinantys atliekų surinkimą ir stebėjimą viešuose atliekų konteineriuose. Straipsnyje nagrinėta, ar matavimo procese naudojama stebėjimo sistema gali būti pritaikyta konteinerių užpildymui stebėti. Sistemą sudaro belaidis modulis, sujungtas su ultragarsiniu jutikliu. Siunčiami duomenys leidžia apskaičiuoti tuščią vietą atliekų konteineriuose. Vertės gaunamos matuojant atstumą nuo konteinerio viršaus iki atliekų paviršiaus. Duomenims perduoti panaudoti mažaenergiai tolimojo plačiajuosčio tinklo (LoRaWAN) siųstuvai. Tyrimo tikslas – išbandyti nebrangią stebėjimo sistemą bei apibūdinti naują jutiklių ir siųstuvų modulių sistemą. Sistemos vartojamos elektros energijos sąnaudos yra itin mažos, naudojamų jutiklių spektras gana platus. Matavimai atlikti Vilniaus mieste. Analizuoti dviejų dydžių tipų konteineriai, matavimai atlikti penkiuose konteinerių horizontalaus pjūvio taškuose. Tyrimas atliktas esant dviem skirtingiems konteinerių užpildymo lygiams. Rezultatai rodo, kad ultragarso jutiklis gali reprezentatyviai matuoti atliekų užpildymo gylius, esant skirtingam konteinerių užpildymo lygiui. Atsižvelgiant į rezultatus, galima rekomenduoti šį jutiklį intelektualiojoje atliekų tvarkymo rinkoje. Tokia sistema gali suteikti reikiamų duomenų atliekų surinkimo procesui optimizuoti miestuose ir išvengti konteinerių perpildymo problemų. Reikšminiai žodžiai: konteineriai, atliekų tvarkymas, „LoRaWAN“, daiktų interneto jutikliai.

This paper discusses how to address and overcome some of the problems related to smart data generation and visualization, such as the poor autonomy of wireless sensor devices and the flexibility of the data management platform. We described the implementation and field experiment of a modular IoT application for Smart-Farming, in which the sensor devices are powered by an on-board battery and the data management system is based on a highly flexible software stack, capable of displaying time series graphs and processing millions of data per second. The experiment shown that the power consumption of the sensor devices depends on many factors and that the lifecycle of the devices can reach years using ultra-low power processors, low power wide area network (LPWAN) such as LoRaWAN and a mix of energy saving techniques. The data management and visualization platform shown to be able to display many types of time-series graphs, deal with a wide variety of data-sources and effectively manage a large amount of data.

In recent years, Internet of Things (IoT) as an essential infrastructure for industrial development, environmental protection and human life enhancement has attracted researchers' attention. Currently, there are four hot research topics in IoT fields, including sensor design, communication scheme, secure transmission, and data mining. The LoRaWAN, an unlicensed band based long range wide area network specification, is very suitable for the activities or operations in an IoT environment due to its low power and long range communication. In the LoRaWAN, star-of-stars topology, asynchronous communication, and three communication modes are used to reduce its power consumption. In order to enhance the security of network communication, the LoRaWAN adopts the 128-bit Advanced Encryption Standard (AES-128) and utilizes two session keys: network session key and application session key, for encrypting/decrypting data between end devices and network/application servers. However, according to the LoRaWAN Backend Interfaces 1.0 Specification announced by LoRa Alliance in 2017, the application layer communication securities between two arbitrary servers (including network servers, join server, and application servers) are out of the specification's scope. That is to say that the important data transmitted from one server to another may be attacked, falsified, or stolen easily. In this paper, a session key generation method is proposed to generate session keys with which two servers can securely communicate with each other, especially enhancing the application layer communication securities undefined in the LoRaWAN Specification. By integrating elliptic curve cryptography and AES-128, the session keys for different pairs of servers are created. The security discussion shows that the proposed method provides the features of mutual authentication, confidentiality and message integrity. Besides, it can also help to resist replay and eavesdropping attacks.

Internet of Things (IoT) deployments are on the rise globally with Low Power Wide Area Networks (LPWAN) providing the wireless networks needed for this expansion. One of these technologies namely Long Range Wide Area Network (LoRaWAN) has proven to be a very popular choice. The LoRaWAN protocol allows for confirmed traffic from the end device to the gateway (uplink) and the reverse (downlink), increasing the number of IoT use cases that it can support. However, this comes at a cost as downlink traffic severely impacts scalability due to in part a gateway's duty cycle restrictions. This paper highlights some of the use cases that require confirmed traffic, examines the recent works focused on LoRaWAN confirmed traffic and discusses the mechanism with which is implemented. It was found that confirmed traffic is viable in small networks, especially when data transfer is infrequent. Additionally, the following aspects negatively impact the viability of confirmed traffic in large networks: the duty cycle restrictions placed on gateways, the use of spreading factor 12 for receive window 2 transmissions, a high maximum number of transmissions (NbTrans) and the ACK_TIMEOUT transmission backoff interval. The paper also raises and suggests solutions to open research challenges that must be overcome to increase the viability of confirmed traffic.

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.

With the greater availability and reduced dimensions of complex electronics, an Internet of Things (IoT) will emerge in the future, in which machines use sensors to collect and evaluate information about the physical world and share it with other machines without having to rely on human interaction. Blockchain technology encompasses features such as trustworthiness and decentralization and these features can be advantageous in the IoT context, as they can help to ensure data integrity in an Internet of Things unmonitored by humans. In the course of this paper, we investigate to what extent the use of blockchain technology is feasible for autonomous devices in IoT. For this purpose, a prototypical client blockchain component is built using the single-board computer Raspberry Pi and a simulated Ethereum blockchain. This prototype will be examined for aspects like proper functionality, response times and performance.

The long-range wide area network (LoRaWAN) has recently emerged as one of the most potential technologies for the mobile Internet of Things (IoT) applications. In a LoRaWAN, the network-managed adaptive data rate (ADR) method is responsible for managing resource allocation (i.e., spreading factor (SF) and transmit power (TP)) to end-devices (EDs) through ADR commands. When an ED is mobile and receives a new configuration (i.e., SF and TP) from the network server, the propagation scenario could have been dramatically changed. Hence, the SF and the link budget will no longer be valid, which results in packet loss and massive retransmissions. Therefore, we propose a mobility-aware resource (SF) assignment scheme (M-ASFA), which aims to allocate the best SF to IoT-enabled mobile EDs at each uplink transmission by considering the strength of the signals that a gateway receives from the EDs. The simulation results demonstrate that, in our proposed M-ASFA solution, the SF is assigned to mobile EDs by proactively responding to the mobility of the EDs. This proactive behavior of the proposed scheme enhances the packet success ratio by significantly reducing the impact of interference, retransmissions, and packet loss when compared with the LoRaWAN-based ADR.

Long-range wide-area network (LoRaWAN) is an energy-efficient and inexpensive networking technology that is rapidly being adopted for many Internet-of-Things applications. In this study, we perform extensive measurements on a new LoRaWAN deployment to characterise the spatio-temporal properties of the LoRaWAN channel. Our experiments reveal that LoRaWAN frames are mostly lost due to the channel effects, which are adverse when the end-devices are mobile. The frame losses are up to 70 percent, which can be bursty for both mobile and stationary scenarios. Frame losses result in data losses since the frames are transmitted only once in the basic configuration. To reduce data losses in LoRaWAN, we design a novel coding scheme for data recovery called DaRe that works on the application layer. DaRe combines techniques from convolutional and fountain codes. By implementing DaRe, we show that 99 percent of the data can be recovered with a code rate of 1/2 when the frame loss is up to 40 percent. Compared to the repetition coding scheme, DaRe provides 21 percent higher data recovery and can save up to 42 percent of the energy consumed on a transmission for 10-byte data units. We also show that DaRe provides better resilience to bursty frame losses.

LoRaWAN deployments follow an ad-hoc deployment model that has organically led to overlapping communication networks, sharing the wireless spectrum, and completely unaware of each other. LoRaWAN uses ALOHA-style communication where it is almost impossible to schedule transmission between networks belonging to different owners properly. The inability to schedule overlapping networks will cause inter-network interference, which will increase node-to-gateway message losses and gateway-to-node acknowledgement failures. This problem is likely to get worse as the number of LoRaWAN networks increase. In response to this problem, we propose IRONWAN, a wireless overlay network that shares communication resources without modifications to underlying protocols. It utilises the broadcast nature of radio communication and enables gateway-to-gateway communication to facilitate the search for failed messages and transmit failed acknowledgements already received and cached in overlapping network's gateways. IRONWAN uses two novel algorithms, a Real-time Message Inter-arrival Predictor, to highlight when a server has not received an expected uplink message. The Interference Predictor ensures that extra gateway-to-gateway communication does not negatively impact communication bandwidth. We evaluate IRONWAN on a 1000-node simulator with up to ten gateways and a 10-node testbed with 2-gateways. Results show that IRONWAN can achieve up to 12\% higher packet delivery ratio (PDR) and total messages received per node while increasing the minimum PDR by up to 28\%. These improvements save up to 50\% node's energy. Finally, we demonstrate that IRONWAN has comparable performance to an optimal solution (wired, centralised) but with 2-32 times lower communication costs. IRONWAN also has up to 14\% better PDR when compared to FLIP, a wired-distributed gateway-to-gateway protocol in certain scenarios., Accepted for publication at IEEE Internet of Things Journal

The aim of this paper is to present the viability of an energy-harvesting system prototype, based on thermoelectric generators (TEGs), to be embedded in a Long-Range Wide Area Network (LoRaWAN)-based wireless sensor node, allowing continuous quasi-real-time data transmission even for low temperature gradients and for frequent transmissions. To this end, an RFM95x LoRa module is used in the system. The energy management of the entire node is achieved by exploiting a nano power boost charger buck converter integrated circuit, which allows power extraction from the energy-harvesting source and, at the same time, regulates the charging/discharging process of a Li-Po battery that supplies the wireless node. The first phase of the project was dedicated to understanding the electrical characteristics of the TEG. A series of tests were performed to study the open circuit voltage, the current and the power generated by the TEG at different temperature gradients. Following this first phase, tests were then set up to study the charging/discharging process of the battery by changing two crucial parameters: the temperature between the faces of the TEG and the frequency of the transmissions performed by the transceiver. Experimental results show a positive balance for the battery charging at different conditions, which suggests two important conclusions: first of all, with high temperature gradients, it is possible to set relatively high transmission frequencies for the LoRaWAN module without discharging the battery. The second important consideration concerns the operation of the system at extremely low temperature gradients, with a minimum of 5 °C reached during one of the measurements. This suggests the usability of thermoelectric energy-harvesting systems in a wide range of possible applications even in conditions of low temperature gradients.

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.

International audience; Interest and need for Wireless Body Area Networks (WBANs) havesignicantly increased recently. WBAN consists of miniaturized sensors de-signed to collect and transmit data through wireless network, enabling medicalspecialists to monitor patients during their normal daily life and providing realtime opinions for medical diagnosis. Many wireless technologies have provedthemselves in WBAN applications, while others are still under investigations.The choice of the technology to adopt may depend on the disease to moni-tor and the performance requirements, i.e. reliability, latency and data rate.In addition, the suitable sensor is essential when seeking to extract the datarelated to a medical measure.This paper aims at surveying the wireless technologies used in WBAN sys-tems. In addition to a detailed survey on the existing technologies, the use ofthe emerging Low Power Wide Area Network (LPWAN) technologies, and thefuture 5G, B5G and 6G is investigated, where the suitability of these technolo-gies to WBAN applications is studied from several perspectives. Furthermore,medical applications of WBAN are discussed by presenting their methodolo-gies, the adopted wireless technologies and the used sensors. Given that eachmedical application requires the appropriate sensor to extract the data, wehighlight a wide range of the sensors used in the market for medical systems.Recent and future challenges in WBAN systems are given related to the powerconsumption, the emergence of the Internet of Things (IoT) technologies inWBAN and others.

In this paper the idea of functioning of Building Management Systems and Object Management Systems in intelligent buildings is presented. New functionalities of intelligent buildings resulting from the introduction of microgeneration are described. Low-Power Wide-Area Networks (LPWAN) are characterized and compared. The selected Long-Range (LoRaWAN) technology is tested for its use for communication with energy meters and monitoring the power supply network in intelligent buildings. In the paper a new system for reading and monitoring the network is proposed, consisting of hardware, communication, and application layers. A key element of the system is a specially developed converter, which has been designed and tested in a real urban environment. Using our solution in practice could allow to change the architecture of a measurement data acquisition system to much more flexible and efficient.

Large-scale deployments of the Internet of Things (IoT) are adopted for performance improvement and cost reduction in several application domains. The four main IoT application domains covered throughout this article are smart cities, smart transportation, smart healthcare, and smart manufacturing. To increase IoT applicability, data generated by the IoT devices need to be time-stamped and spatially contextualized. LPWANs have become an attractive solution for outdoor localization and received significant attention from the research community due to low-power, low-cost, and long-range communication. In addition, its signals can be used for communication and localization simultaneously. There are different proposed localization methods to obtain the IoT relative location. Each category of these proposed methods has pros and cons that make them useful for specific IoT systems. Nevertheless, there are some limitations in proposed localization methods that need to be eliminated to meet the IoT ecosystem needs completely. This has motivated this work and provided the following contributions: (1) definition of the main requirements and limitations of outdoor localization techniques for the IoT ecosystem, (2) description of the most relevant GNSS-free outdoor localization methods with a focus on LPWAN technologies, (3) survey the most relevant methods used within the IoT ecosystem for improving GNSS-free localization accuracy, and (4) discussion covering the open challenges and future directions within the field. Some of the important open issues that have different requirements in different IoT systems include energy consumption, security and privacy, accuracy, and scalability. This paper provides an overview of research works that have been published between 2018 to July 2021 and made available through the Google Scholar database.

Irrigation installations in cities or agricultural operations use large amounts of water and electrical energy in their activity. Therefore, optimising these resources is essential nowadays. Wireless networks offer ideal support for such applications. The long-range wide-area network (LoRaWAN) used in this research offers a large coverage of up to 5 km, has low power consumption and does not need additional hardware such as repeaters or signal amplifiers. This research develops a control and monitoring system for irrigation systems. For this purpose, an irrigation algorithm is designed that uses rainfall probability data to regulate the irrigation of the installation. The algorithm is complemented by checking the sending and receiving of information in the LoRa network to reduce the loss of information packets. In addition, two temperature and humidity measurement devices for LoRaWAN (THMDLs) and an electrovalve control device for LoRaWAN (ECDLs) were developed. The hardware and software were also designed, and prototypes were built with the development of the electronic board. The wide coverage of the LoRaWAN allows the covering of small to large irrigation areas.

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.

Accurate localization for wireless sensor end devices is critical, particularly for Internet of Things (IoT) location-based applications such as remote healthcare, where there is a need for quick response to emergency or maintenance services. Global Positioning Systems (GPS) are widely known for outdoor localization services, however, high-power consumption and hardware cost become a significant hindrance to dense wireless sensor networks in large-scale urban areas. Therefore, wireless technologies such as Long-Range Wide-Area Networks (LoRaWAN) are being investigated in different location-aware IoT applications due to having more advantages with low-cost, long-range, and low-power characteristics. Furthermore, various localization methods, including fingerprint localization techniques, are present in the literature but with different limitations. This study uses LoRaWAN Received Signal Strength Indicator (RSSI) values to predict the unknown X and Y position coordinates on a publicly available LoRaWAN dataset for Antwerp in Belgium using Random Neural Networks (RNN). The proposed localization system achieves an improved high-level accuracy for outdoor dense urban areas and outperforms the present conventional LoRa-based localization systems in other work, with a minimum mean localization error of 0.29 m.

Fluvial flooding occurs when a river overspills its banks due to excessive rainfall, and it is the most common flood event. In urban areas, the increment of urbanization makes communities more susceptible to fluvial flooding since the excess of impervious surfaces reduced the natural permeable areas. As flood prevention strategies, early warning systems (EWS) are used to reduce damage and protect people, but key elements need to be selected. This manuscript proposes the monitoring instruments, communication protocols, and media to forecast and disseminate EWS alerts efficiently during fluvial floods in urban areas. First, we conducted a systematic review of different EWS architectures for fluvial floods in urban areas and identified that not all projects monitor the most important variables related to the formation of fluvial floods and most use communication protocols with high-energy consumption.ZigBee and LoRaWAN are the communication protocols with lower power consumption from the review, and to determine which technology has better performance in urban areas, two wireless sensor networks were deployed and simulated in two urban areas susceptible to fluvial floods using Radio Mobile software. The results showed that although Zigbee technology has better-received signal strength, the difference with LoRAWAN is lower than 2dBm, but LoRaWAN has a better signal-to-noise ratio, power consumption, coverage, and deployment cost.

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.

© 2021 by the authors; licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) LoRaWAN has become a popular technology for the Internet of Things (IoT) device connectivity. One of the expected properties of LoRaWAN is high network scalability. However, LoRaWAN network performance may be compromised when even a relatively small number of devices use link-layer reliability. After failed frame delivery, such devices typically tend to reduce their physical layer bit rate by increasing their spreading factor (SF). This reaction increases channel utilization, which may further degrade network performance, even into congestion collapse. When this problem arises, all the devices performing reliable frame transmission end up using SF12 (i.e., the highest SF in LoRaWAN). In this paper, we identify and characterize the described network condition, which we call the SF12 Well, in a range of scenarios and by means of extensive simulations. The results show that by using alternative SF-management techniques it is possible to avoid the problem, while achieving a packet delivery ratio increase of up to a factor of 4.7.

Most Internet of Things (IoT) communication technologies rely on terrestrial network infrastructure. When such infrastructure is not available or does not provide sufficient coverage, satellite communication offers an alternative IoT connectivity solution. Satellite-enabled IoT devices are typically powered by a limited energy source. However, as of this writing, and to our best knowledge, the energy performance of satellite IoT technology has not been investigated. In this paper, we model and evaluate the energy performance of Iridium satellite technology for IoT devices. Our work is based on real hardware measurements. We provide average current consumption, device lifetime, and energy cost of data delivery results as a function of different parameters. Results show, among others, that an Iridium-enabled IoT device, running on a 2400 mAh battery and sending a 100-byte message every 100 min, may achieve a lifetime of 0.95 years. However, Iridium device energy performance decreases significantly with message rate. This work was supported in part by the Spanish Government through project PID2019- 106808RA-I00, AEI/FEDER, EU, and by Secretaria d’Universitats i Recerca del Departament d’Empresa i Coneixement de la Generalitat de Catalunya through project 2017 SGR 376.

A Long-Range Wide Area Network (LoRaWAN) is one of the most efficient technologies and is widely adopted for the Internet of Things (IoT) applications. The IoT consists of massive End Devices (EDs) deployed over large geographical areas, forming a large environment. LoRaWAN uses an Adaptive Data Rate (ADR), targeting static EDs. However, the ADR is affected when the channel conditions between ED and Gateway (GW) are unstable due to shadowing, fading, and mobility. Such a condition causes massive packet loss, which increases the convergence time of the ADR. Therefore, we address the convergence time issue and propose a novel ADR at the network side to lower packet losses. The proposed ADR is evaluated through extensive simulation. The results show an enhanced convergence time compared to the state-of-the-art ADR method by reducing the packet losses and retransmission under dynamic mobile LoRaWAN network.

The remarkable evolution of the IoT raised the need for an efficient way to update the device’s firmware. Recently, a new process was released summarizing the steps for firmware updates over the air (FUOTA) on top of the LoRaWAN protocol. The FUOTA process needs to be completed quickly to reduce the systems’ interruption and, at the same time, to update the maximum number of devices with the lowest power consumption. However, as the literature showed, a single gateway cannot optimize the FUOTA procedure and offer the above mentioned goals since various trade-offs arise. In this paper, we conducted extensive experiments via simulation to investigate the impact of multiple gateways during the firmware update process. To achieve that, we extended the FUOTAsim simulation tool to support multiple gateways. The results revealed that several gateways could eliminate the trade-offs that appeared using a single gateway

The massive deployment of Internet of Things (IoT) architectures and applications, in many fields over the last decade, has accelerated research efforts on low-power wireless connectivity protocols. Moreover, many standards have been introduced, highlighting the need to make data flow among different communication protocols and network interfaces feasible. To this end, devices like Gateways (GWs) play a crucial role in many IoT applications and will impact future developments and possibilities. In this paper, the design and deployment of a new modular and scalable GW architecture solution, suitable for a wide plethora of use case scenarios and useful as a starting point for many possible improvements and applications, is proposed. Experimental performance results are discussed, showing the roles of different interfaces in specific use cases in which the proposed this solution may be applied.

[EN] The adoption of LoRaWAN as a technology for wireless deployments in many applications, such as smart cities or industry 4.0, still presents challenges such as energy consumption, robustness, or reduced throughput in harsh, noisy scenarios. Class B is a MAC mode that allows better performance in downlink traffic but has difficulties regarding scalability and its response to channel interference. This article introduces, via simulation software, the possibility of testing deployments, adding interference sources that model industrial scenarios, and proposes an adaptive data rate (ADR) mechanism to enhance the operation for downlink and class B devices, called DROB (downlink rate optimization for class B) to study the impact of these conditions in a network with detailed event characterization., The research leading to these results received funding from the Horizon 2020 Program of the European Commission under Grant Agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP)". It was also partially supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00).

This paper aims at demonstrating the feasibility of a LoRaWAN-based sensor node for temperature monitoring, autonomously powered by a polycrystalline silicon photovoltaic module with possible applications within the Internet of Things (loT) domain in the horticulture field. The commercial solar cell was characterized under two light sources: a conventional white 4000 K Light Emitting Diode (LED) and a red and far red (R:FR) lamp peaked at 655 nm and 730 nm. The sensor node is equipped with a RFM95x LoRa transceiver which proved to be a valid technology in those application scenarios where robustness and low power consumption are required. The energy harvesting features are performed by a nano-power boost charger buck converter which deals with the power extraction from the photovoltaic module, the LiPo battery charging/discharging management and the supply of the sensor node. Field tests demonstrate that under R:fr light source, the energy self-sufficiency of the system is achieved: a positive balance between the battery charge and discharge is measured, sufficient both for the node working operation and for the battery charging.

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.

The penetration of wearable devices in our daily lives is unstoppable. Although they are very popular, so far, these elements provide a limited range of services that are mostly focused on monitoring tasks such as fitness, activity, or health tracking. Besides, given their hardware and power constraints, wearable units are dependent on a master device, e.g., a smartphone, to make decisions or send the collected data to the cloud. However, a new wave of both communication and artificial intelligence (AI)-based technologies fuels the evolution of wearables to an upper level. Concretely, they are the low-power wide-area network (LPWAN) and tiny machine-learning (TinyML) technologies. This paper reviews and discusses these solutions, and explores the major implications and challenges of this technological transformation. Finally, the results of an experimental study are presented, analyzing (i) the long-range connectivity gained by a wearable device in a university campus scenario, thanks to the integration of LPWAN communications, and (ii) how complex the intelligence embedded in this wearable unit can be. This study shows the interesting characteristics brought by these state-of-the-art paradigms, concluding that a wide variety of novel services and applications will be supported by the next generation of wearables.

The rapid development and widespread use of information and telecommunication technologies do not mitigate, in many situations, information exclusion, nor the physical isolation of people—mainly that of the elderly living in remote locations, whose mobile network coverage is deficient or non-existent, preventing them from accessing health care, be it routine follow-up procedures or emergencies. Addressing this, we raise the question that guides our study: how can we monitor the elderly’s residence and health conditions, detect falls, and track their movement in the vicinity of their homes in a non-intrusive manner? To answer this question, we present a system prototype that uses affordable, low-cost, and low-energy equipment with media and data processing, supported by LoRa (Long Range) and ESP32 microcontrollers, coupling several sensors. As a result, it is possible to monitor sensors that predict and detect falls or other risk events for the user, e.g., fire, with authorized persons and entities, family members, civil protection, and security forces accessing the gathered data, assuring their security. We conclude that the system could decisively improve people’s quality of life, particularly those of the elderly who live in remote places with greater vulnerability.

International audience; Long Range Wide Area Network (LoRaWAN) introduces the Adaptive Data rate (ADR) mechanism [1] aiming to maximize both battery life of the end-devices and overall network capacity. ADR performs adaptive tuning of radio configurations of the nodes by adjusting bandwidth, spreading factor, coding rate and transmission power parameters whenever the signal quality changes. The ADR algorithm was established for stable radio channel environments and is not efficient when conditions dramatically change (e.g. mobility). So, we have previously proposed an Enhanced-ADR (E-ADR) [2] that deals mobile nodes in case of predefined mobility patterns. However, several Internet Of Thing (IoT) applications, such as smart cattle ranching in smart farms [3], require sensors travelling with unknown or undefined trajectories. So, this paper extends E-ADR to unknown mobility pattern. This E-ADR extension, called VHMM-based E-ADR, is based on a Variable order Hidden Markov Model (VHMM) to predict the node trajectory. It has been implemented on Waspmote SX1272 hardware platform. Experimental results show its high efficiency in terms of the packet loss rate (PLR) and the energy consumption.

Automation of greenhouse environment using simple timer-based actuators or by means of conventional control algorithms that require feedbacks from offline sensors for switching devices are not efficient solutions in large-scale modern greenhouses. Wireless instruments that are integrated with artificial intelligence (AI) algorithms and knowledge-based decision support systems have attracted growers’ attention due to their implementation flexibility, contribution to energy reduction, and yield predictability. Sustainable production of fruits and vegetables under greenhouse environments with reduced energy inputs entails proper integration of the existing climate control systems with IoT automation in order to incorporate real-time data transfer from multiple sensors into AI algorithms and crop growth models using cloud-based streaming systems. This chapter provides an overview of such an automation workflow in greenhouse environments by means of distributed wireless nodes that are custom-designed based on the powerful dual-core 32-bit microcontroller with LoRa modulation at 868 MHz. Sample results from commercial and research greenhouse experiments with the IoT hardware and software have been provided to show connection stability, robustness, and reliability. The presented setup allows deployment of AI on embedded hardware units such as CPUs and GPUs, or on cloud-based streaming systems that collect precise measurements from multiple sensors in different locations inside greenhouse environments. Published version Yes (Peer reviewed?)

This paper focuses on the study of IoT network deployments, in both unlicensed and licensed bands, considering LoRaWAN and NB-IoT standards, respectively. The objective is to develop a comprehensive and detailed network planning and coverage dimensioning methodology for assessing key metrics related to the achieved throughput and capacity for specific requirements in order to identify tradeoffs and key issues that are related to the applicability of IoT access technologies for representative use case types. This paper will provide a concise overview of key characteristics of IoT representative IoT access network standards that are considered for being deployed in unlicensed and licensed bands and will present a methodology for modeling the characteristics of both access network technologies in order to assess their coverage and capacity considering different parameters.

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.

Low-power wide-area networks are continuously gaining momentum as enablers of massive machine-type communication, thanks to long-range wireless technologies operating either on unlicensed spectrum like, e.g., Long-Range (LoRa) and Sigfox, or on licensed spectrum, as Narrowband-IoT. For the former category of technologies, stringent requirements in terms of channel utilization and radiated power must be fulfilled, thus algorithms and protocols for enhanced radio-resource management need to be investigated. In this paper, we propose a disruptive approach for LoRa systems, exploiting a novel operational mode at the gateways side called time-power multiplexing, which fully exploits the degrees of freedom of the unlicensed bands regulations. The simulation results show that the proposed solution boosts the number of users that a LoRa network can support with respect to the default operational mode.

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.

The Low Power Wide Area Networks (LPWANs) have been proposed as viable wireless connection method for the implementation of the Internet of Things (IoT), thanks to their wide coverage, low complexity and low power consumption. In this scenario, LoRaWAN emerged as a de-facto leading technology, because of its openness and the current availability of many devices (like sensors and gateways). Several implementations of the LoRaWAN specifications have appeared in the market, each one showing support for different hardware, different software architectures, and different operating systems. Thus, the need of test procedures for comparing them is evident. In this paper, a test methodology for the experimental assessment of architectural delays of LoRaWAN implementations is presented. The new approach is composed of: a mapping phase, needed for highlighting the LoRaWAN blocks inside the implementation under test; followed by a measurement phase, in which the relevant timestamps are taken along the information path from LoRaWAN node to LoRaWAN customer application. The use and the effectiveness of the proposed methodology are shown by means of use cases that involve different LoRaWAN implementations and several hardware platforms. The experiments described in the paper are not intended to evaluate particular implementations but they are aimed to assess the suitability of the proposed methodology. The results demonstrate that the proposed approach can be used to compare the performance of entire LoRaWAN systems, helping the owner of the infrastructure and the user in making choices and optimizations.

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 recent years, the Internet of Things (IoT) has significantly increased the number of Internet connections to a large number of objects in different domains, including industry, homes and transportation. The LoRaWAN developed by the LoRa Alliance is a long-range wide area network specification suitable for an IoT environment due to its low-power communication. It provides a star-of-stars topology, well-defined MAC layer protocol and three communication modes to further lower its power consumption and employs the Advanced Encryption Standard (AES) cryptography and several session keys to increase its network security. However, for battery powered IoT end nodes, the AES encryption process consumes some amounts of power owing to involving multiple cycles of repetition. To solve this problem, in this study, we propose a low power consumed AES encryption architecture, named Low-Power AES Data Encryption Architecture (LPADA), which reduces the power consumed by the AES for data encryption by using low power SBox, power gating technique and power management method. A key updating procedure is also proposed to increase the security of the session-key renewal. The system is simulated using the Synopsys library with five different supply voltages. The experimental results show that 62.0% of dynamic power reduction and 88.5% of leakage power lowering have been achieved compared to the power consumed by traditional AES data encryption. The security analysis also shows that the key updating procedure for the LPADA enables mutual authentication between end nodes and application servers, and resists replay attacks and eavesdropping attacks from hackers.

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.

International audience; Low-power Wide Area Networks (LPWANs) constitute a type of networks which is used to connect things to the Internet from a wide variety of sectors. These types of technologies provide the Internet of Things (IoT) devices with the ability to transmit few bytes of data for long ranges, taking into consideration minimum power consumption. In parallel, IoT applications will cover a wide range of human and life needs from smart environments (cities, home, transportation etc.) to health and quality of life. Among these popular LPWANs technologies, we have identified the unlicensed frequency band (LoRa, DASH7, SigFox, Wi-SUN, etc.), and the licensed frequency band standards (NB-IoT, LTE Cat-M, EC-GSM-IoT, etc.). In general, both types of standards only consider fixed interconnected things, and less attention has been provided to the mobility of the things or devices. In this paper, we address the mobility of the things and the connectivity in each of the three LPWAN standards: LoRaWAN, DASH7, and NB-IoT. In particular, we show how the mobility of things can be achieved when transmitting and receiving data. Then, we provide a general and technical comparison for the three standards. Finally, we illustrate several application scenarios where the mobility is required, and we show how to select the most suited standard. We also discuss the research challenges and perspectives.