Adaptive Data Rate Algorithm Research Articles

Mobility Classification of LoRaWAN Nodes Using Machine Learning at Network Level.

LoRaWAN networks rely heavily on the adaptive data rate algorithm to achieve good link reliability and to support the required density of end devices. However, to be effective the adaptive data rate algorithm needs to be tuned according to the level of mobility of each end device. For that purpose, different adaptive data rate algorithms have been developed for the different levels of mobility of end devices, e.g., for static or mobile end devices. In this paper, we describe and evaluate a new and effective method for determining the level of mobility of end devices based on machine learning techniques and specifically on the support vector machine supervised learning method. The proposed method does not rely on the location capability of LoRaWAN networks; instead, it relies only on data always available at the LoRaWAN network server. Moreover, the performance of this method in a real LoRaWAN network is assessed; the results give clear evidence of the effectiveness and reliability of the proposed machine learning approach.

A Novel Approach to Improve the Adaptive-Data-Rate Scheme for IoT LoRaWAN

The long-range wide-area network (LoRaWAN) uses the adaptive-data-rate (ADR) algorithm to control the data rate and transmission power. The LoRaWAN ADR algorithm adjusts the spreading factor (SF) to allocate the appropriate transmission rate and transmission power to reduce power consumption.The updating SF and transmission power of the standard ADR algorithm are based on the channel state, but it does not guarantee efficient energy consumption among all the nodes in complex environments with high-varying channel conditions. Therefore, this article proposes a new enhancement approach to the ADR+ algorithm at the network server, which only depends on the average signal-to-noise ratio (SNR). The enhancement ADR algorithm ADR++ introduces an energy-efficiency controller α related to the total energy consumption of all nodes, to use it for adjusting the average SNR of the last records. We implement our new enhanced ADR at the network server (NS) using the FLoRa module in OMNET++. The simulation results demonstrate that our proposed ADR++ algorithm leads to a significant improvement in terms of the network delivery ratio and energy efficiency that reduces the network energy consumption up to 17.5% and improves the packet success rate up to 31.55% over the existing ADR+ algorithm.

A Collision Reduction Adaptive Data Rate Algorithm Based on the FSVM for a Low-Cost LoRa Gateway

LoRa (Long Range), a wireless communication technology for low power wide area networks (LPWANs), enables a wide range of IoT applications and inter-device communication, due to its openness and flexible network deployment. In the actual deployment and operation of LoRa networks, the static link transmission scheme does not make full use of the channel resources in the time-varying channel environment, resulting in a poor network performance. In this paper, we propose a more effective adaptive data rate (ADR) algorithm for low-cost gateways, we firstly analyze the impact of the different hardware parameters (RSSI, SNR) on the link quality and classify the link quality using the fuzzy support vector machine (FSVM). Secondly, we establish an end device (ED) throughput model and energy consumption model and design different adaptive rate algorithms, according to the different link quality considering both the link-level performance and the MAC layer performance. The proposed algorithm uses machine learning to classify the link quality, which can accurately classify the link quality using a small amount of data, compared to other adaptive rate algorithms, and the link parameter adaptation algorithm can maximize the throughput while ensuring the link stability, by considering the link-level performance and the MAC layer performance, compared to other algorithms. The results show that it outperforms the standard LoRaWAN ADR algorithm in both the single ED and the multi ED scenarios, in terms of the packets reception rate (PRR) and the network throughput. Compared to the LoRaWAN ADR in 32 multi-ED scenarios, the proposed algorithm improves the throughput by 34.12% and packets the reception rate by 26%, significantly improving the network throughput and the packets reception rate.

A Fuzzy-Logic Based Adaptive Data Rate Scheme for Energy-Efficient LoRaWAN Communication

Long Range Wide Area Network (LoRaWAN) technology is rapidly expanding as a technology with long distance connectivity, low power consumption, low data rates and a large number of end devices (EDs) that connect to the Internet of Things (IoT) network. Due to the heterogeneity of several applications with varying Quality of Service (QoS) requirements, energy is expended as the EDs communicate with applications. The LoRaWAN Adaptive Data Rate (ADR) manages the resource allocation to optimize energy efficiency. The performance of the ADR algorithm gradually deteriorates in dense networks and efforts have been made in various studies to improve the algorithm’s performance. In this paper, we propose a fuzzy-logic based adaptive data rate (FL-ADR) scheme for energy efficient LoRaWAN communication. The scheme is implemented on the network server (NS), which receives sensor data from the EDs via the gateway (GW) node and computes network parameters (such as the spreading factor and transmission power) to optimize the energy consumption of the EDs in the network. The performance of the algorithm is evaluated in ns-3 using a multi-gateway LoRa network with EDs sending data packets at various intervals. Our simulation results are analyzed and compared to the traditional ADR and the ns-3 ADR. The proposed FL-ADR outperforms the traditional ADR algorithm and the ns-3 ADR minimizing the interference rate and energy consumption.

Mobile LoRa Device 의 네트워크 연결성 개선을 위한 FCR-ADR 알고리즘

Long Range Wide Area Network (LoRaWAN), a communication technology for the Internet of Things (IoT), consumes low energy and enables long-range communication. LoRaWAN supports Adaptive Data Rate (ADR), which can conserve energy by optimizing the data rate and Time on Air (ToA). ADR is executed on an end device and on the network server side. While ADR on the end device side is an algorithm to recover the connection when it is lost, ADR on the network server side aims to set appropriate transmission parameters with the recently uplinked signal-to-noise ratio (SNR) value and to deliver the parameter change command to the end device. However, the existing ADR on the end device side requires a considerable amount of time to restore the connection when the network condition deteriorates, leading to the loss of many packets. This study presents Fast Connection Recovery ADR (FCR-ADR), a novel, end device side ADR algorithm that reduces the number of packets lost while an end device moves. The evaluation results of simulation study using the OMNeT++ simulator and FLoRa (Framework for LoRa) show that the proposed FCR-ADR algorithm surpasses the existing ADR in terms of packet delivery ratio (PDR) with no significant difference in energy consumption.

LoRa Device 의 이동성을 고려한 GRU 기반 적응형 데이터 전송 속도 제어 알고리즘

Long Range Wide Area Network (LoRaWAN), a representative technology of Low Power Wide Area Network (LPWAN), is a networking technology for the long-range Internet of Things. It features low power, a long distance, and low speed, and each device in LoRaWAN supports adaptive data rate (ADR) technology to save transmission power. For implementing ADR in LoRaWAN, a network server uses algorithms to reduce power consumption by minimizing the time LoRa Radio packets stay in the air and sends a medium access control (MAC) command frame to increase the data rate or adjust transmission power by referring to signal-to-noise ratio (SNR) values. However, because the existing ADR algorithm for the network server sets the appropriate data rate and transmission power based on the maximum SNR value of the recent 20 packets, it does not support mobile devices because the SNR values of the mobile devices vary over time depending on their speeds. This paper introduces an improved ADR, GRU-ADR, that infers the future SNR values using the deep-learning gated recurrent unit (GRU) model to set appropriate data rates and transmission power using the ADR function even in mobile devices. The simulation study based on the OMNeT++ simulator and the Framework for LoRa (FLoRa) shows that GRU-ADR outperforms the existing ADR in packet delivery ratio (PDR) and energy consumption.

A New-Dynamic Adaptive Data Rate Algorithm of LoRaWAN in Harsh Environment

The adaptive data rate (ADR) algorithm is used in LoRaWAN, allocating an appropriate transmission rate for terminal equipment to improve channel utilization and reduce power consumption. However, the standard ADR algorithm is only suitable for static terminal equipment. In addition, due to the complexity of the external environment, the selected data rate will not match the new environment and communication packets will be lost. Therefore, this article proposed a novel and more effective ADR algorithm called new-dynamic ADR (ND-ADR). The algorithm mainly solves two problems, i.e., the standard ADR algorithm cannot be applied to mobile terminal devices and the poor communication quality and high packet loss rate in harsh environments. In this study, we also developed a frame for LoRaWAN (FLoRaWAN), a simulation framework for the star network topology LoRaWAN in OPNET. Furthermore, we built an OKUMURA–HATA model and additionally introduced a noise factor <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\beta$ </tex-math></inline-formula> to simulate the loss of wireless communication in harsh environments. Finally, extensive simulation results showed that the number of data packets required by different end nodes for rate allocation was different. Compared with the standard ADR algorithm, even for mobile nodes in harsh environments, the ND-ADR algorithm reduced network energy consumption by about 13%, reduced network delay by about 18%, and increased effective throughput by about 15%. Therefore, the improved ND-ADR algorithm is more suitable for wireless communication of removable nodes in harsh environments. Its advantages are better awareness of link environment, faster data rate regulation, improved channel utilization, and further reduction of network energy consumption.

A Spreading Factor Congestion Status-Aware Adaptive Data Rate Algorithm

LoRaWAN has established itself as one of the leading MAC layer protocols in the field of LPWAN. Although the technology itself is quite mature, its resource allocation mechanism, the Adaptive Data Rate (ADR) algorithm is still quite new, unspecified and its functionalities still limited. Various studies have shown that the performance of the ADR algorithm gradually suffers in dense networks. Recent studies and proposals have been made as attempts to improve the algorithm. In this paper, the authors proposed a spreading factor congestion status aware ADR version and compared its performance against that of four other related algorithms to study the performance improvements the algorithm brings to LoRaWAN in terms of DER and EC. LoRaSim was used to evaluate the algorithms’ performances in a simple sensing application that involved end devices transmitting data to the gateway every hour. The performances were measured based on how they affected DER as the network size increases. The results obtained show that the proposed algorithm outperforms the currently existing implementations of the ADR in terms of both DER and EC. However, the proposed algorithm is slightly outperformed by the native ADR in terms of EC. This was expected as the algorithm was mainly built to improve DER. The proposed algorithm builds on the existing algorithms and the ADR and significantly improves them in terms of DER and EC (excluding the native ADR), which is a significant step towards an ideal implementation of LoRaWAN’s ADR.

LoRa Channel Characterization for Flexible and High Reliability Adaptive Data Rate in Multiple Gateways Networks

We characterize the LoRa channel in terms of multi-path fading, loss burstiness, and assess the benefits of Forward Error Correction as well as the influence of frame length. We make these observations by synthesizing extensive experimental measurements realized with The Things Network in a medium size city. We then propose to optimize the LoRaWAN Adaptive Data Rate algorithm based on this refined LoRa channel characterization and taking into account the LoRaWAN inherent macro-diversity from multi-gateway reception. Firstly, we propose ADRopt, which adjusts Spreading Factor and frame repetition number to maintain the communication below a target Packet Error Rate ceiling with optimized Time-On-Air. Secondly, we propose ADRIFECC, an extension of ADRopt in case an Inter-Frame Erasure Correction Code is available. The resulting protocol provides very high reliability even over low quality channels, with comparable Time on Air and similar downlink usage as the currently deployed mechanism. Simulations corroborate the analysis, both over a synthetic random wireless link and over replayed real-world packet transmission traces.

LoRaWAN Adaptive Data Rate With Flexible Link Margin

LoRaWAN is a remarkable network protocol to support the deployment of low-power wide-area network applications for the Internet of Things. Its modulation scheme, LoRa, uses different transmission parameters to allow long-range bidirectional communication by trading-off range for Time on Air (ToA). The LoRaWAN specification suggests using an adaptive data rate (ADR) algorithm to assign transmission parameters to nodes dynamically. The ADR algorithm used by The Things Network, a global open network, selects transmission parameters for communication without disconnection while lowering ToA and energy consumption. However, its often optimistic link quality estimate drives the assignment of inadequate parameters, especially in lossy channels. More precise channel quality estimates would improve resource allocation at the cost of increased complexity at the network server (NS). Through simulations, we observe that we can use the ADR link margin parameter to compensate for inaccurate link quality estimates, providing better service to nodes and maintaining low levels of energy consumption. In this work, we propose a modification to ADR that allows the selection of the link margin at runtime, increasing the network performance without the need of any prior knowledge and with no increase in the NS computational complexity.

A Novel Collision-Aware Adaptive Data Rate Algorithm for LoRaWAN Networks

Low-power wide-area network technologies are used to interconnect a number of devices in a simple and efficient way. One of these technologies, LoRaWAN, is deemed as one of the most promising due to its capability to allow long-range communications with very small energy consumption. LoRaWAN networks are managed by a network server implementing an adaptive data rate (ADR) algorithm to allocate proper data rates to end devices (EDs). However, the standard ADR solution focuses only on the link-level performance and assigns transmission parameters to EDs one-by-one in an independent way. In this article, we propose a novel and more efficient ADR algorithm, denoted as collision-aware ADR (CA-ADR), which tries to minimize the collision probability when assigning data rates by considering the entire set of EDs in the network and keeping the link-level performance under control. The performance of CA-ADR is characterized and benchmarked against the standard solution as well as another proposal presented in the literature. An integrated simulation–experimental approach is used to assess results for large-scale networks and to compare two architectures based on cloud and fog computing. Results show that CA-ADR outperforms standard solutions when connectivity is good, whereas it behaves similarly in large areas. It is also shown that the improvement with respect to the benchmark solutions does not depend on the channel model considered (no shadowing, uncorrelated, and correlated shadowing). Finally, a fog-based architecture is proved to be feasible, with the advantage of reducing the end-to-end latency.

A Study of LoRa Performance in Monitoring of Patient’s SPO2 and Heart Rate based IoT

In this research, a sensor that will be equipped with blood oxygen saturation function (SPO2) blood and Heart Rate is MH-ET Live max30102 Sensor with Library Max30105. The advantage of this sensor is compatible with ATmega 328P, which is the Arduino board, the first experiment using Arduino Uno. Therefore, MH-ET Sensor data is integrated with Wireless Sensor Network (WSN) devices, e.g, LoRa (Long Range) 915 MHz and calculate WSN path loss when sending sensor data in mountainous areas, the model used to represent signal analysis and measurements in this study is the Ground Reflection (2-ray) model. therefore, the conditions that can be explained are patients who will send their data over hilly areas and hospitals or medical treatments called receiving nodes or coordinator nodes in much lower areas, in the same situation adding routers is expected to be a comparison of whether the data sent faster or even no impact. Furthermore, in this study, it is expected to provide clear results on the function of the router as the sender of pulse sensor data. The point is patients who are in a higher area with the level of impossibility in bringing the patient due to the condition of the patient so that the SPO2 data transmission and heart rate of the patient are expected to be known quickly by the medical authorities through the sensor node device attached to the patient's body. The use of the Adaptive Data Rate (ADR) Algorithm is used to optimize data rate, time on air (ToA) or airtime and energy consumption in the network. Therefore, the End Device (ED) in the ADR algorithm must be static (non-mobile). In the process of measuring the ADR algorithm in the position of sending data (uplink) n-bits to n-gateway. Next, the application server used is ThingsSpeak or The Things Network (TTN).

An adaptive data rate algorithm for improving energy efficiency for multi-gateway LoRaWANs

The need of the low power, long range, and low cost connectivity for satisfying the requirement of internet of things (IoT) applications for smart city is leading the emergence of low power wide area (LPWA) networking technologies. The resource efficiency plays an important role in realistic application, LoRaWAN allows the static end devices to individually adapt and optimise the data rates and the transmission power, which is referred to as adaptive data rate (ADR) problem. In this paper, we develop a new ADR algorithm for multi-gateway LoRaWAN environment to quickly choose the appropriate LoRa transmission parameter for independent LoRa end device based on LoRaWAN specification. To successfully execute the ADR algorithm, we especially rewrite the firmware of Semtech's SX1276 transceivers to implement our proposed ADR algorithm over multi-gateway LoRaWAN system, including end device, multi-gateways and network server. Our ADR algorithm utilises the radio link quality, including RSSI, SNR, and packet reception ratio (PRR), from multi-gateways, and the network server determines the appropriate LoRa transmission parameter. Finally, the experimental results illustrate that the proposed algorithm improves the energy efficiency, the effective bit rate, and the battery life of the end device.

An adaptive data rate algorithm for improving energy efficiency for multi-gateway LoRaWANs

An adaptive data rate algorithm for improving energy efficiency for multi-gateway LoRaWANs