LoRa Network Research Articles

Distributed localization using normalized quad lateration in LoRa networks: application for tourist position estimation

<span lang="EN-US">The tourism sector has been growing rapidly along with the decline of COVID-19. This sector should pay attention to safety and security, which is by tracking the position of tourists. In this work, we propose a tourist positioning tracking system. A node containing a microcontroller with LoRa wireless communication called unknown node (UN) is assembled as a wearable device. We use the received signal strength indicator (RSSI)-based localization technique with an additional normalization method to correct the error rate in position estimation. This method estimates the distance between the anchor node (AN) and UN to approximate the actual distance value. Three-dimensional position tracking (longitude, latitude, altitude) of five UN surrounded by four AN was performed using the quadlateration method. The estimation process was carried out by the UN whereas receiving information is transmitted from the four AN. The communication between AN and UN used a scheduling algorithm on all AN. The position estimation error of the five UNs was measured using mean square error (MSE) yields 20.73 m, 50.32 m, 32.92 m, 21.40 m, and 16.89 m respectively. The accuracy of the estimated distance obtained by the proposed normalization quadlateration method is increased up to 15.22%.</span>

Decentralizing Online Food Delivery Services: A Blockchain and IoT Model for Smart Cities

With the worldwide pandemic outbreak, the style of restaurant food consumption underwent a major shift towards online delivery services. This poses an urgent need in trust between main stakeholders of the process: requiring restaurants to correspond to the quality declared, providing high quality and safe to consume meals, and obliging delivery entities to deliver food carefully, scrupulously following the delivery conditions. In this research, we explore a novelty approach that combines IoT, Blockchain technology, and city LoRa network to create a new trusted, decentralized approach for food distribution process in the context of a Smart City. This approach allows controlling the food delivery process using sensors data to control live location, temperature, vibrations, and shakings during the transportation process. We also suggest a fresh perspective on a rating system of delivery entities, where reputation points will be provided both from the side of the restaurant and the final consumer. This will create more trust towards the delivery entity since information will be tamper-proof and immutable due to the nature of Blockchain. This novel system proposal allows rethinking the online food delivery process in the context of Smart City, using the city’s LoRa LPWAN radio frequency technology and Blockchain decentralized solution.

Joint load-balancing and power control strategy to maximize the data extraction rate of LoRaWAN networks

LPWAN enabled networks have a dizzying growth and continue to meet an essential need in the Internet of Things market due to their ability to provide low-cost wireless access to geographically spread-out devices. Consequently, an efficient allocation of wireless resources in order to support numerous devices becomes a major concern. In this paper, we propose an SF assignment approach in LoRa networks, paying attention on the traffic load both per Spreading Factor and over the channels. Indeed, our strategy consists in finding a better distribution of the end-devices on the SF by orchestrating an effective load balancing. Moreover, the performance of our solution is evaluated under diverse network configurations, taking into account the capture effect and the non-orthogonality of SFs. Furthermore, we extended the solution by a transmission power control strategy for overall system energy-efficiency. In addition, we validated some assumptions by full-scale experiments like for the 3GPP path loss model, which is used for the first time in LoRa simulations. Our results suggest that Load Shifting leads to better performance in terms of Date Extraction Rate (DER) while guaranteeing good scalability on the network size and density.

The MauMe network - A LoRa multi-hop collaborative protocol and low-cost implementation example

Low-cost low-power long-distance LoRa radio devices recently made available on the market allow the emerging of a new generation of networking paradigms on the Internet of Things. While the LoRaWan offers an efficient way of connecting LoRa end-point devices to the internet, LoRaWan nodes do not act as peers, but rather as terminals connected to gateways. As such, the current state of the art does not offer a public protocol for easily taking advantage of the fact that individuals and professionals wishing to aggregate data using LoRa devices are likely to run each a “base-node” on a continuous basis. Such permanently-powered base-nodes could potentially offer an increased cumulated area coverage to other nodes if they would behave as relays. In the present study, an Epidemic Delay Tolerant Network messaging protocol is proposed, which allows multi-hops between peer LoRa relay-nodes through a raw-LoRa controlled-flooding approach. This protocol has been implemented on low-cost LoRa-chip-equipped microcontrollers in the form of an open-source library acting as a network transport-layer. The design analysis concluded that MauMe is better suited for networks with reduced connectivity (≤ 9), and that a 4-connectivity redundant network could allow each user to send several tens of messages several tens of hops away. The proposed approach was tested on three different LoRa network topologies, and the totality of the packets and acknowledgments reached their destination. Furthermore, the network's activity ceased after delivery, confirming the ability of the protocol to return the network to rest after the flooding. The proposed library also offers to users a free emergency-messaging system implemented as an http server embedded in each node and accessible via Wi-fi. End-users can readily use our solution to easily deploy a network of sensors at a much lower cost than when using multiple gateways. The MauMe protocol is a first step towards the creation of collaborative and application-friendly extended-range LoRa-peers networks, and is intended for use in remote regions with scattered housings such as in the islands and atolls of Polynesia.

LMAC: Efficient Carrier-Sense Multiple Access for LoRa

Current LoRa networks including those following the LoRaWAN specification use the primitive ALOHA mechanism for media access control due to LoRa’s lack of carrier sense capability. From our extensive measurements, the channel activity detection feature that was recently introduced to LoRa for energy-efficiently detecting preamble chirps can also detect payload chirps reliably. This sheds light on an efficient carrier-sense multiple access protocol that we refer to as LMAC for LoRa networks. This article presents the designs of three advancing versions of LMAC that respectively implement carrier-sense multiple access, and balance the communication loads among the channels defined by frequencies and spreading factors based on the end nodes’ local information and then additionally the gateway’s global information. Experiments on a 50-node lab testbed and a 16-node university deployment show that, compared with ALOHA, LMAC brings up to 2.2× goodput improvement and 2.4× reduction of radio energy per successfully delivered frame. Thus, should LoRaWAN’s ALOHA be replaced with LMAC, network performance boosts can be realized.

LoRa-Based IoT Network Assessment in Rural and Urban Scenarios.

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.

Development and implementation of a PQ analyser to monitoring public lighting installations with a LoRa wireless system

In smart cities, public lighting installations have a significant consumption of electrical energy. In order to measure this consumption, it is necessary to install sensors capable of obtaining electrical parameters in real time. In this way, the installations are monitored and energy saving and efficiency measures can be implemented. In this sense, this research has developed a PQ analyser meter with Long Range wireless communications technology (LoRa). This technology allows coverage of up to 5 km, which makes it ideal for public lighting applications. PQ Analyser with LoRa (PQAL) has been developed to achieve a fully functional prototype. PQAL comes in two versions for single-phase and three-phase street lighting lines, so that it adapts to any type of installation. PQAL uses the open-source Arduino platform which allows it to adapt to the monitoring needs of every installation. PQAL can monitor installations up to 100 A and 23 kW per phase. The use of non-invasive current sensors allows this device to be installed without modifying the installation to be monitored. Due to the long range of the LoRa network, it can be installed in locations far from the gateway without the need for additional cabling or Internet connection.

LoRa and IoT tools usage possibility in the agricultural sectors

In this paper, I present a LoRa spread spectrum networking device that provides significant efficiency improvements over previously used techniques through digital techniques. Subsequently, the LoRaWAN network is presented, which can communicate with the network server available on the Internet through gateways. Their data rate and transmission power can be optimised depending on the distance from the base stations and the time required for communication can be measured in tens of seconds.
 In the second major part of the article, Internet of Things devices that also communicate with each other using the LoRa network will be presented. It allows smart devices used in our everyday life to communicate, which are supported by sensors and transmitters to record states and perform actions.
 IoT devices are also becoming increasingly common in agriculture. Farmers who are already using the technology can better monitor their livestock and crops. IoT can also be used effectively in many areas of agriculture, including dairy, vegetable, fruit and greenhouse operations.

An artificial intelligence‐based spectrum sensing methodology for LoRa and cognitive radio networks

SummaryThe artificial intelligence‐based spectrum sensing approach is extremely important in terms of effective bandwidth utilization for low power wide area networks (LPWANs) based on cognitive radio networks (CRNs). Most studies perform spectrum detection with CRNs using optimization or deep neural network methods. However, optimization‐based spectrum detection approaches based on current LPWANs are scarce. For this purpose, in this study, a hybrid optimization methodology integrated with CRNs is proposed for LoRa, which is one of the most compatible LPWAN technologies in the Internet of Things (IoTs) recently. In the particle swarm optimization (PSO) part of this hybrid methodology, agent users are created so that secondary users (SUs) could use the licensed band of primary users (PUs) in cognitive radio. On the genetic algorithm side, LoRa error rates are minimized in order to further improve the performance of the proposed method. In this way, effective spectrum sensing is performed in the LoRa network. Various LoRa‐CRN experiments have been carried out in the simulation environment, and the probability of detection and false alarm performances have been compared with both theoretical and proposed approaches in terms of quality estimation parameters. It is clear from the results that the proposed methods give successful results for the LoRa‐CRNs.

Natural Disaster Detection for Smart IoT Communication using LoRA model

There is annual financial loss, mental pain, bodily injury, and loss of life due to natural and artificial disasters. Unfortunately, natural disasters are becoming much deadlier due to climate change. Consequently, IoT-based catastrophe detection and response systems have been developed to improve the handling of catastrophic disasters and other times of extreme urgency. As a consequence, information gathered from Internet-connected devices is utilized to aid in the categorization of several types of disasters, both natural and artificial. A determination of the nature of the crisis and notification of the relevant command center is accomplished using preexisting methods. We have shown how to modify an existing system into a particular early warning system for natural disasters using two Internet of Things (IoT) devices: the Arduino Uno and the Nodemcu. Using this data, we can pinpoint the exact position of every person whose phone is within range of the disaster and send them warnings before the situation worsens. The botmasters have shifted their paradigm away from IRC and toward an HTTP-based CC server due to the widespread use of HTTP services. Like HTTP bots, IRC bots have a single point of failure. HTTP bots, however, are harder to stop. It is also challenging to detect HTTP botnets while keeping the false positive rate low since every service on the Internet utilizes the HTTP protocol. This chapter provides a host-based HTTP botnet detection approach that uses Hidden semi-Markov Model (HsMM) variables and the Simple Network Management Protocol-Management Information Base (SNMP-MIB). The device operates following the specifications established by the LoRa network. In this project, we used a device called Nodemcu, which was made to be configured explicitly on the receiving end to identify the users at the place where the catastrophe was detected. At that point, everyone connected to the gadget would receive a geolocation-based alert. MQTT is used to notify the right people when an issue arises. We saw better and more beneficial results from the IoT project after including LoRa.

Requirements, Deployments, and Challenges of LoRa Technology: A Survey.

LoRa is an ISM-band based LPWAN communication protocol. Despite their wide network penetration of approximately 20 kilometers or higher using lower than 14 decibels transmitting power, it has been extensively documented and used in academia and industry. Although LoRa connectivity defines a public platform and enables users to create independent low-power wireless connections while relying on external architecture, it has gained considerable interest from scholars and the market. The two fundamental components of this platform are LoRaWAN and LoRa PHY. The consumer LoRaWAN component of the technology describes the network model, connectivity procedures, ability to operate the frequency range, and the types of interlinked gadgets. In contrast, the LoRa PHY component is patentable and provides information on the modulation strategy which is being utilized and its attributes. There are now several LoRa platforms available. To create usable LoRa systems, there are presently several technical difficulties to be overcome, such as connection management, allocation of resources, consistent communications, and security. This study presents a thorough overview of LoRa networking, covering the technological difficulties in setting up LoRa infrastructures and current solutions. Several outstanding challenges of LoRa communication are presented depending on our thorough research of the available solutions. The research report aims to stimulate additional research toward enhancing the LoRa Network capacity and allowing more realistic installations.

Delay Optimization in LoRaWAN by Employing Adaptive Scheduling Algorithm With Unsupervised Learning

Low Power Wide Area Network (LPWAN) technologies have been exponentially growing because of the tremendous growth of the Internet of Things (IoT) devices across the globe. Several LPWAN technologies have been utilized by the researchers to address certain issues like increased number of collisions, retransmissions, delay, and energy consumption. However, Long Range Wide Area Network (LoRaWAN) is the most suitable and attractive technology in terms of delay optimization, low cost and efficient energy consumption. The main issue which arises in LoRaWAN is because of its high packet drop rate due to collision. The reason behind this packet drop rate is the MAC scheme known as Pure Aloha used by LoRaWAN for the transmission of the frames. Long Range (LoRa) End Devices (EDs) initiate communication with Pure Aloha that leads to a high number of retransmissions. These retransmissions further enhance the delay in LoRa networks. This paper aims to optimize the delay in LoRaWAN by using an Adaptive Scheduling Algorithm (ASA) with an unsupervised probabilistic approach called Gaussian Mixture Model (GMM). By using ASA with GMM, the retransmissions are reduced which optimizes the delay in LoRaWAN. The results show that in our approach, Packet Collision Rate (PCR) is reduced by 39% as compared to conventional LoRaWAN. In addition, the Packet Success Ratio (PSR) is also increased by 39% as compared to the conventional LoRaWAN and Dynamic Priority Scheduling Technique (PST). Further, the delay is optimized by 91% and 79%. This research could be effective for the environments where the critical data of patients need to be sent with optimised retransmissions and minimum delay towards gateways.

Concurrent Transmission and Multiuser Detection of LoRa Signals

This article investigates a new model to improve the scalability of low-power long-range (LoRa) networks by allowing a group of multiple end devices (EDs) to communicate with multiple multi-antenna gateways simultaneously (i.e., in the same time slot) on the same frequency band and using the same spreading factor. The maximum-likelihood (ML) decision rule is first derived for noncoherent detection of information bits transmitted by multiple devices in a group. To overcome the high complexity of the ML detection, we propose a suboptimal two-stage detection algorithm to balance the computational complexity and error performance. In the first stage, we identify transmitted chirps (without knowing which EDs transmit them). In the second stage, we determine the EDs that transmit the specific chirps identified from the first stage. To improve the detection performance in the second stage, we also optimize the transmit powers of EDs to minimize the similarity, measured by the Jaccard coefficient, between the received powers of any pair of EDs in the same group. As the power control optimization problem is nonconvex, we use concepts from successive convex approximation to transform it to an approximate convex optimization problem that can be solved iteratively and guaranteed to reach a suboptimal solution. Simulation results demonstrate and justify the tradeoff between transmit power penalties and network scalability of the proposed LoRa network model. In particular, by grouping two or three EDs in each group for concurrent transmission, the uplink capacity of the proposed network can be doubled or tripled over that of a conventional LoRa network, albeit at the expense of additional 3.0 or 4.7 dB transmit power.

Machine learning approaches for LoRa networks: a survey

Machine learning approaches for LoRa networks: a survey

LoRa network performance analysis for IoT systems

Low power wide area networks (LPWANs) are a new generation of wireless communication technology designed for the Internet of Things to provide low energy and long range connectivity. LoRa is one of the promising technology. In this paper, we examine LoRa system performance and analyze the scalability of this technology considering packet payload size, spreading factor, number of nodes and number of gateways.

Signal Denoising and Detection for Uplink in LoRa Networks Based on Bayesian-Optimized Deep Neural Networks

Long-range and low-power communications are suitable technologies for the Internet of things networks. The long-range implies a very low signal-to-noise ratio at the receiver. In addition, low power consumption requires reduced signaling, hence the use of less complex protocols, such as ALOHA, so reduced communication coordination. Therefore, the increase of objects using this technology will automatically lead to an increase in interference. In this letter, we propose a detector for Long Range (LoRa) networks based on an autoencoder for denoising and dealing with the interference, followed by a convolutional neural network for symbol detection. Simulation results demonstrate that the proposed approach outperforms both the convolutional neural network-based detector and the classical LoRa detector in the presence of interference from other LoRa users. The proposed detector shows around 3 dB gain for a target Symbol Error Rate (SER) of 10−4.

Machine learning approaches for LoRa networks:a survey

Machine learning approaches for LoRa networks:a survey

Review of LoRAWAN and the protocol suitability for low bandwidth wireless sensor networks over 5G

LoRaWAN is currently the best among the LPWANs protocols when it comes to IoT or machine-to-machine (M2M) communication such as in the deployment of wireless sensor networks. LoRaWAN is an RF technology operating in ISM bands. Its strength comes from the capability of Long-range communication between remote sensors and the gateways, deep indoor penetration, and public and private network deployment. All these traits are possible because of the LoRa network modulation technique. This paper provides an overview of the LoRaWAN protocol and the 5G cellular network. Also, the paper gives the benefits of using LoRaWAN over the other technologies for the deployment of the IoT module communication links.

MRT-LoRa: A multi-hop real-time communication protocol for industrial IoT applications over LoRa networks

Recent literature proposed MAC protocols running on top of LoRa that are able to offer real-time communications for Industrial Internet of Things (IIoT) applications. However, such solutions work over single-hop star topologies and therefore, to reach a wide coverage through a single hop, they require to configure the LoRa radio parameters in a way that reduces the maximum supported bit rate, thus increasing the message time on air. Consequently, to comply with the maximum duty cycle constraints imposed on LoRa devices by the current regulations, the maximum number of messages that can be sent per hour is also reduced. However, industrial environments typically cover very large areas and require bit rate values in the order of a few thousands of bits per second for LoRa-based networks. To cope with both coverage and bit rate requirements of IIoT, a multi-hop protocol able to provide bounded delays to the real-time flows typical of industrial applications is a better solution than a single-hop approach. For this reason, this paper proposes MRT-LoRa, a multi-hop communication protocol running on top of LoRa that provides support for real-time flows. The MRT-LoRa protocol enables long-range communications while maintaining shorter time on air at each hop, thus lowering the impact on the duty cycle of each node. The paper describes the MRT-LoRa design and configuration, and discusses the performance obtained through OMNeT++ simulations in realistic industrial IoT scenarios.

FlyingLoRa: Towards energy efficient data collection in UAV-assisted LoRa networks

Energy efficiency is an increasingly crucial consideration due to the battery-powered end devices in LoRa networks. With the adoption of chirp spread spectrum modulation, end devices far apart from the gateway have to use a high spreading factor and transmit power to send uplink packets, which causes longer transmission time and higher energy consumption compared to the end devices near the gateway, and further causes unfairness issues on energy efficiency. To tackle this problem, we investigate a novel energy-efficient data collection scheme named FlyingLoRa, where an unmanned aerial vehicle carrying one gateway is dispatched to harvest packets from end devices. The goal of FlyingLoRa is to minimize the energy consumption of end devices for packet transmission by jointly optimizing the 3D UAV trajectory, scheduling strategies, and transmission parameters of end devices. We formulate our design as a mixed-integer non-convex optimization problem and propose an efficient iterative algorithm to find a sub-optimal solution. The proposed approach is numerically simulated and the results show that FlyingLoRa improves energy efficiency by 16.13× on average compared with the existing fixed gateway schemes. In addition, we realize FlyingLoRa in real scenarios and evaluate its performance by presenting the actual packet reception rate (PRR) and transmission energy consumption, which shows its effectiveness.