LoRa Network Research Articles

Performance Evaluation of Collision Avoidance for Multi-node LoRa Networks based on TDMA and CSMA Algorithm

LoRa can be used as the communication technology for the intelligent monitoring system. However, LoRa is usually used for outdoor communication. The usage of LoRa as indoor communication has many challenges. One of the challenges is that collision happens when using standard LoRa devices with only one channel. The algorithms based on TDMA (Time-division Multiple Access) and CSMA (Carrier-sense Multiple Access) protocols can be used to address this challenge. These two algorithms can be modified by applying the device that is the center of the network (gateway) as a central control and the data transmitter (sensor node) as a passive device. The test was conducted on the Intelligent Laboratory Monitoring System to evaluate this design on a multi-node LoRa network. RSSI testing proves that the distance and building interference affect the signal strength or RSSI of sensor nodes, so the average RSSI value is -73.75 with an RSSI threshold of value -106. The gateway successfully collected each sensor node data with an average success of about 64.953%. The experiment results show the success rate of the CSMA-based algorithm is 10% versus 100% in TDMA-based algorithm; the delay is 4125 ms for CSMA-based and 428.3 ms for TDMA-based. This result means that the CSMA-based algorithm is more complex, takes more time to process the data than the TDMA-based algorithm, has a low success rate, and is more prone to collisions.

Energy-Aware Relaying in Dense LoRa Networks With Heterogeneous Batteries

Energy-Aware Relaying in Dense LoRa Networks With Heterogeneous Batteries

LoRa Network Performance Analysis on Landslide Monitor for Landslide Disaster Mitigation in the Greater Malang Area

LoRa Network Performance Analysis on Landslide Monitor for Landslide Disaster Mitigation in the Greater Malang Area

FLoRa+: Energy-efficient, Reliable, Beamforming-assisted, and Secure Over-the-air Firmware Update in LoRa Networks

The widespread deployment of unattended LoRa networks poses a growing need to perform Firmware Updates Over-The-Air (FUOTA). However, the FUOTA specifications dedicated by LoRa Alliance fall short of several deficiencies with respect to energy efficiency, transmission reliability, multicast fairness, and security. This article proposes FLoRa+ , energy-efficient, reliable, beamforming-assisted, and secure FUOTA for LoRa networks, which is featured with several techniques, including delta scripting, channel coding, beamforming, and securing mechanisms. Specifically, we first propose a joint differencing and compression algorithm to generate the delta script for processing gain, which unlocks the potential of incremental FUOTA in LoRa networks. Then, we design a concatenated channel coding scheme with outer rateless code and inner error detection to enable reliable transmission for coding gain. Afterward, we develop a beamforming strategy to avoid biased multicast and compromised throughput for power gain. Finally, we present a securing mechanism incorporating progressive hash chain and packet arrival time pattern verification to countermeasure firmware integrity and availability attacks for security gain. Experimental results on a 20-node testbed demonstrate that FLoRa+ improves transmission reliability and energy efficiency by up to 1.51× and 2.65× compared with LoRaWAN. Additionally, FLoRa+ can defend against 100% and 85.4% of spoofing and Denial-of-Service (DoS) attacks.

LoRaSync: Energy efficient synchronization for scalable LoRaWAN

AbstractLow‐power wide‐area networks connect a large number of battery‐powered wireless devices over long distances. Among them, long range wide area networks (LoRaWAN) implement a pure ALOHA medium access scheme to save device energy by minimizing the radio usage. However, frame collisions restrain the network scalability when the traffic load increases. In this context, synchronization can be used to exploit the available bandwidth more efficiently by controlling the timing of frame transmissions and reducing the collision probability. Such strategy allows to increase the network throughput at the cost of an extra energy demand due to the inherent overhead. In that, the entailed network scenario is still supposed to address low power applications. Therefore this article timely presents LoRaSync, an energy‐efficient synchronization scheme designed for LoRa networks of any size. An accurate clock drift model was established based on measurements made on real cheap devices, and leveraged to support the design of LoRaSync. Our mechanism has been used to evaluate the same ALOHA‐based random access but on a time‐slotted basis, thus increasing the maximum achievable throughput compared to the legacy access. Throughput and energy efficiency models are established to evaluate the performances of a LoRaSync‐operated network. These models are validated with a simulation environment mimicking large‐scale deployments, and then used to determine the most energy efficient slot size for any traffic load. As a final proof of concept, LoRaSync has been implemented and tested on a LoRa testbed to demonstrate the feasibility of our solution on real hardware.

Advancing Performance in LoRaWAN Networks: The Circular Region Grouped Bit-Slot LoRa MAC Protocol

LoRaWAN is an emerging Low-Power Wide-Area Network (LPWAN) technology, widely adopted in various Internet of Things (IoT) applications due to its long transmission range, low power consumption, and robust anti-interference capabilities. However, using the ALOHA medium access control (MAC) protocol in LoRaWAN significantly reduces the packet delivery rate, particularly in high-density networks where end devices (EDs) access the network randomly. It seriously affects the overall network performance. This paper introduces the Circular Region Grouped Bit-Slot CGBS-LoRa MAC protocol to address this challenge. The protocol takes a proactive approach by allocating transmission parameters for end devices, executing regional segmentation based on the distance between EDs and the gateway using different spreading factors. Concurrently, improving the ALOHA access method ensures the efficient communication of EDs in the region. Experimental results demonstrate that our proposed protocol markedly improves the scalability of LoRa networks and minimizes device collisions compared to three other MAC protocols. Even as the LoRaWAN network expands, the proposed protocol maintains a high packet delivery rate and low latency.

Ensuring Reliability for LoRa Networks Using a Reinforcement-Learning-Assisted Time Division Duplex Protocol

Ensuring Reliability for LoRa Networks Using a Reinforcement-Learning-Assisted Time Division Duplex Protocol

Explainable Machine Learning for LoRaWAN Link Budget Analysis and Modeling

This article explores the convergence of artificial intelligence and its challenges for precise planning of LoRa networks. It examines machine learning algorithms in conjunction with empirically collected data to develop an effective propagation model for LoRaWAN. We propose decoupling feature extraction and regression analysis, which facilitates training data requirements. In our comparative analysis, decision-tree-based gradient boosting achieved the lowest root-mean-squared error of 5.53 dBm. Another advantage of this model is its interpretability, which is exploited to qualitatively observe the governing propagation mechanisms. This approach provides a unique opportunity to practically understand the dependence of signal strength on other variables. The analysis revealed a 1.5 dBm sensitivity improvement as the LoR’s spreading factor changed from 7 to 12. The impact of clutter was revealed to be highly non-linear, with high attenuations as clutter increased until a certain point, after which it became ineffective. The outcome of this work leads to a more accurate estimation and a better understanding of the LoRa’s propagation. Consequently, mitigating the challenges associated with large-scale and dense LoRaWAN deployments, enabling improved link budget analysis, interference management, quality of service, scalability, and energy efficiency of Internet of Things networks.

Multi-Hop and Mesh for LoRa Networks: Recent Advancements, Issues, and Recommended Applications

A comprehensive review is presented on the latest approaches to solutions focusing on multi-hop and mesh LoRa networks through the evaluation of simulations and real-world experiments, based on papers published between 2015 and 2023. The approaches are systematically classified into four (4) aspects: energy awareness, concurrent access and duty cycle regulations, routing protocols, and security. The first aspect encompasses parameter selection, distance, and clustering. The second aspect focuses on network segregation and time synchronisation. The third aspect covers proactive, reactive, and hybrid routing protocols. The aspect of security includes privacy and Denial-of-Service (DoS). Findings show a consensus that multi-hop networks and adherence to radio duty cycle are able to lower average power consumption. Concurrent access method can improve network performance, but more research is needed due to different conclusions. Reactive protocols have a slight advantage over proactive protocols in terms of node deployment scalability and lower power consumption due to less frequent routing table updates. Hybrid protocols are only beneficial in specific topology deployments. Security is still a major concern for LoRa networks, particularly on attacks such as Man-In-The-Middle (MITM), spoofing, wormholes, flooding, packet replay, and information disclosure. The upcoming trend of implementing machine learning algorithms to multi-hop and mesh LoRa networks opens up a wide range of possibilities, ranging from airspace efficiency, efficient route selection, and improving data throughput. A detailed discussion of the aspects’ issues and recommended industry applications that will benefit from multi-hop and mesh LoRa networks are also provided.

Solar Irradiance and Temperature Effects on Signal Transmission Performance of LoRa Network for a Monitoring System on Island

With the increasing interest in wireless technologies, Long Range (LoRa) communication is one of the most promising alternatives for providing long-range and low-power-consumption wireless networks. Low-cost, long-distance communication, and long battery life shelf, are the best description of LoRa technology which is very useful for data transmission from an island environment. This study presents the effect of solar irradiance, air temperature, and onboard temperature on the LoRa transmission signal from the sensor node (Bidong Island). Solar irradiance was obtained from online weather satellite data (Solcast) and a solar irradiance meter, while air and onboard temperature were determined using a temperature sensor and thermal imager, respectively. The findings show that higher solar irradiance significantly decreases signal transmission strength, Received Signal Strength Indicator (RSSI). Likewise, with the decrease in onboard temperature, the RSSI signals perform better.

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach: Multistory Round Building Scenario over LoRa Network

SNR and RSSI Based an Optimized Machine Learning Based Indoor Localization Approach: Multistory Round Building Scenario over LoRa Network

QoS Based IoT Applications in LoRa Networks Using Multi-Dimensional Allocation Strategy

QoS Based IoT Applications in LoRa Networks Using Multi-Dimensional Allocation Strategy

Automatic spread factor and position definition for UAV gateway through computational intelligence approach to maximize signal-to-noise ratio in wooded environments.

The emergence of long-range (LoRa) technology, together with the expansion of uncrewed aerial vehicles (UAVs) use in civil applications have brought significant advances to the Internet of Things (IoT) field. In this way, these technologies are used together in different scenarios, especially when it is necessary to have connectivity in remote and difficult-to-access locations, providing coverage and monitoring of greater areas. In this sense, this article seeks to determine the best positioning for the LoRa gateway coupled to the drone and the optimal spreading factor (SF) for signal transmission in a LoRa network, aiming to improve the connected devices signal-to-noise ratio (SNR), considering a suburban and densely wooded environment. Then, multi-layer perceptron (MLP) networks and generalized regression neural networks (GRNN) were trained to predict the signal behavior and determine the best network to represent this behavior. The MLP network presented the lowest RMSE, 2.41 dB, and was selected for use jointly with the bioinspired Grey-Wolf optimizer (GWO). The optimizer proved its effectiviness being able to adjust the number of UAVs used to obtain 100% coverage and determine the best SF used by the endnodes, guaranteeing a higher transmission rate and lower energy consumption.

Design and Performance Analysis of MEC-Aided LoRa Networks with Power Control

Design and Performance Analysis of MEC-Aided LoRa Networks with Power Control

A pipelined image transmission protocol for WSNs utilizing LoRa networks

A pipelined image transmission protocol for WSNs utilizing LoRa networks

SF-Adaptive Duty-Cycled LoRa Networks: Scalability, Reliability, and Latency Tradeoffs

SF-Adaptive Duty-Cycled LoRa Networks: Scalability, Reliability, and Latency Tradeoffs

PROGRESS AND PERSPECTIVES: LPWA AND LORA NETWORKS IN THE ERA OF THE IOT REVOLUTION

<p class="MsoNormal" style="text-align: justify; line-height: normal;"><i><span lang="BS-LATN-BA" style="font-family: 'Times New Roman',serif;">LoRa (Long Range) and LPWA ( Low Power Wide Area) networks represent wireless communication technologies, specially designed for the needs of the Internet of Things (IoT). LoRa is a specific technology within the LPWA category. These networks make it possible to connect a large number of devices over long distances with minimal energy consumption. Key features include long reach, low latency, energy efficiency and the ability to connect devices from different manufacturers. Applications include smart cities, agriculture, industry, healthcare and energy. The success of these technologies in Bosnia and Herzegovina depends on various factors, including regulatory framework support, user understanding, investment in infrastructure, and cooperation between the public and private sectors. Further development is expected with a focus on increasing capacity, interoperability, security and application in different sectors, which will contribute to the growing adoption of IoT technologies. The implementation of LoRA and LPWA networks requires a multidisciplinary approach that includes aspects of communication technology, hardware, security and software. It is important to carefully plan, test and maintain the network to ensure its effectiveness and stability over time.</span></i></p>

Energy Efficiency Optimization in LoRa Networks—A Deep Learning Approach

The optimal transmit power that maximizes energy efficiency (EE) in Longe Range (LoRa) networks is investigated by using the deep learning (DL) approach. Particularly, the proposed artificial neural network (ANN) is trained two times; in the first phase, the ANN is trained by the model-based data which are generated from the simplified system model while in the second phase, the pre-trained ANN is re-trained by the practical data. Numerical results show that the proposed approach outperforms the conventional one which directly trains with the practical data. Moreover, the performance of the proposed ANN under both partial and full optimum architecture are studied. The results depict that the gap between these architectures is negligible. Finally, our findings also illustrate that instead of fully re-trained the ANN in the second training phase, freezing some layers is also feasible since it does not significantly decrease the performance of the ANN.

Long Range (LoRa) and Alert Network System for Forest Fire Prediction

Forest fires are a regular occurrence throughout the year with an increasing intensity in the summer and fall periods. Forest fires pose a significant threat to ecosystems, property and human lives. Early detection and rapid response are critical to mitigate the devastating effects of these fires. This article presents a Long Range (LoRa) Alert Network System designed for the early prediction and timely notification of forest fires. The system leverages LoRa technology to create a robust and cost-effective wireless communication network in remote forested areas. These fires primarily stem from various natural and environmental phenomena and natural disasters. The timely dissemination of forest fire alerts was hampered, resulting in delays in fire management. In order to address this issue, efforts were made to enhance the capability for prompt forest fire detection. The challenges predominantly revolve around forested regions where data communication infrastructure is deficient. In the event of a forest fire, there exists a network barrier that impedes information transmission. Consequently, forest fire detection systems leveraging Mesh LoRa networks and image processing networks have been devised. An integrated module within the LoRa/GPS HAT has been explored as a potential solution to the fire predicament. The Flame Sensor Module, functioning as a fire detection sensor component, and the LoRa/GPS HAT, serving as a hardware medium for radio frequency data transmission communication, have been employed. The interconnection of these devices within a network facilitates the development of a prototype fire detection system. Combining a Flame Sensor Module with a LoRa/GPS HAT is indeed a viable approach to creating a prototype fire detection and alert system. By combining the Flame Sensor Module with the LoRa/GPS HAT, a scalable and cost-effective fire detection and alert system appropriate for distant and wooded locations with limited traditional communication infrastructure is created. This system can play a crucial role in early fire detection, potentially reducing the severity of forest fires and protecting both natural resources and human lives.

A multi-agent federated reinforcement learning-based optimization of quality of service in various LoRa network slices

The innovations heralded through the implementation of next-generation 5G (Fifth-Generation) networks provide an opportunity for the efficient coexistence of heterogeneous services distributed by a single physical virtualized infrastructure. Indeed, thanks to the possibility of virtualization of network functions implemented in 5G networks, the management of physical resources will be able to become more flexible and users will be able to benefit from a service customization to satisfy their demand in terms of energy efficiency, throughput and communication reliability. However, these advances are not without constraints. The management of physical and virtual resources will become more complex given the number of connected objects which will increase, generating a large volume of data to manage. This therefore requires the implementation of much more intelligent systems in the network controllers in order to guarantee the QoS (Quality of Service) of the communications. Thus, an important axis of research is now oriented towards artificial intelligence techniques, more precisely reinforcement learning to overcome this problem. Driven by this context, we are directing our research towards improving the QoS offered to users of connected objects by proposing an optimization model based on network slicing and federated reinforcement learning in order to minimize energy consumption, maximize user throughputs and reduce latency during communications between LoRa (Long Range) devices. The results obtained by the simulations carried out in a realistic framework clearly demonstrate that our proposal optimizes the traffic in each network slice and also for the individual user.