Adaptive Data Rate Mechanism Research Articles

Multi-objective Stochastic Gradient Based ADR Mechanism for Throughput and Latency Optimization in LoRaWAN

Background: In Long Range Wide Area Networks (LoRaWAN), the goal of Adaptive Data Rate (ADR) is to allocate resources to End Devices (ED) like Transmission Power (TP) and Spreading Factor (SF). The EDs are designed in a way that they can choose optimal configuration resource parameters from a set of LoRa physical layer parameters. The SF parameter has to be chosen correctly, as an incorrect one may cause collisions and interference if multiple nodes have the same SF. This paper focuses on throughput and latency optimization using an effective ADR mechanism for LoRaWAN-based IoT networks. Objective: The objective of this study is to maximize the total throughput. SF should be used by multiple nodes as it will have less Time on Air (ToA), but it may cause collision, contention, and co-spreading factor interference problems. The idea is to find an optimal SF allocation to end devices and the optimal number of total devices using the same SF to avoid collision and interference. Methods: This paper proposes a multi-objective stochastic gradient descent method to solve the constrained optimization problem for optimizing throughput and latency. Results: This work compares throughput and latency results for the static, quasi-static, and dynamic environments. Trade-offs between latency and throughput for the simulated scenarios are also presented. Conclusion: The simulation results show that the throughput obtained using this technique is higher than the naive ADR approach and the existing gradient descent methods.

Dynamic transmission policy for enhancing LoRa network performance: A deep reinforcement learning approach

Long Range (LoRa) communications, operating through the LoRaWAN protocol, have received increasing attention from the low-power and wide-area network communities. Efficient energy consumption and reliable communication performance are critical aspects of LoRa-based applications. However, current scientific literature tends to focus on minimizing energy consumption while disregarding channel changes affecting communication performance. Other works attain appropriate communication performance without adequately considering energy expenditure. To fill this gap, we propose a novel solution to maximize the energy efficiency of devices while considering the desired network performance. This is done using a maximum allowed Bit Error Rate (BER) that can be specified by users and applications. We characterize this problem as a Markov Decision Process and solve it using Deep Reinforcement Learning to dynamically and quickly select the transmission parameters that jointly satisfy energy and performance requirements over time. Moreover, we support different payload sizes, ensuring suitability for applications with varying packet lengths. The proposed selection of parameters is evaluated in three different scenarios by comparing it with the traditional Adaptive Data Rate (ADR) mechanism of LoRaWAN. The first scenario involves static nodes with varying BER requirements. The second one realistically simulates urban environments with mobile nodes and fluctuating channel conditions. Finally, the third scenario studies the proposed solution under dynamic frame payload length variations. These scenarios cover a wide range of operational conditions to ensure a comprehensive evaluation. The results of our experiments demonstrate that our proposal achieves a 60% improvement in performance metrics over the default ADR mechanism.

A Survey on Scalable LoRaWAN for Massive IoT: Recent Advances, Potentials, and Challenges

Long-range (LoRa) technology is most widely used for enabling low-power wide area networks (WANs) on unlicensed frequency bands. Despite its modest data rates, it provides extensive coverage for low-power devices, making it an ideal communication system for many internet of things (IoT) applications. In general, LoRa is considered as the physical layer, whereas LoRaWAN is the medium access control (MAC) layer of the LoRa stack that adopts a star topology to enable communication between multiple end devices (EDs) and the network gateway. The chirp spread spectrum modulation deals with LoRa signal interference and ensures long-range communication. At the same time, the adaptive data rate mechanism allows EDs to dynamically alter some LoRa features, such as the spreading factor (SF), code rate, and carrier frequency to address the time variance of communication conditions in dense networks. Despite the high LoRa connectivity demand, LoRa signals interference and concurrent transmission collisions are major limitations. Therefore, to enhance LoRaWAN capacity, the LoRa Alliance released many LoRaWAN versions, and the research community has provided numerous solutions to develop scalable LoRaWAN technology. Hence, we thoroughly examine LoRaWAN scalability challenges and state-of-the-art solutions in both the physical and MAC layers. These solutions primarily rely on SF, logical, and frequency channel assignment, whereas others propose new network topologies or implement signal processing schemes to cancel the interference and allow LoRaWAN to connect more EDs efficiently. A summary of the existing solutions in the literature is provided at the end of the paper, describing the advantages and disadvantages of each solution and suggesting possible enhancements as future research directions.

Extending ADR mechanism for LoRa enabled mobile end-devices

A considerable percentage of Internet of Things end-devices are characterised by mobility, a feature that adds extra complexity to protocols used in Wireless Sensor Networks. LoRa is one of the newly introduced wireless sensor protocols, capable of delivering messages in long distances and consuming low energy, features that make it proper for low cost devices. Although LoRa was introduced as a technology for stationary devices, it can also be used for mobile devices of low speed. In this paper, we introduce an enhancement to Adaptive Data Rate (ADR) mechanism to enable mobile LoRa, by improving the connection reliability of mobile end-devices, while keeping energy consumption at low levels. Firstly, we propose the Linear Regression-ADR (LR-ADR) mechanism for the Network Server side to smooth the Signal to Noise Ratio (SNR) estimates per gateway and predict the SNR of the next transmission. Secondly, we propose the Linear Regression + ADR (LR+ADR) mechanism, an adaptive method for the end-device side to regain the connectivity faster with the Network Server. We conducted simulation modelling to evaluate the performance of our implementation while we compared our results with four alternative solutions ADR, ADR+, EMA-ADR, G-ADR. The results prove that our first approach (LR-ADR) performs better than the best competitor, and our second approach (LR+ADR) brings an additional improvement in terms of Packet Delivery Ratio (PDR), while they retain the Energy Consumption per Packet Delivered (ECPD) at low levels. In particular, in a scenario that mimics real world conditions, LR+ADR presents an increase of up to 520% for PDR compared to the original ADR and an improvement of up to 38% compared to the best competitor (G-ADR). Moreover, it reduces ECPD up to 74% compared to the original ADR, while keeping it at the same level with the best competitor (G-ADR).

Soil moisture sensor based on Internet of Things LoRa

This study discusses the Performance of Moisture soil sensor, which is useful as a sensor to detect soil moisture level used as plant nutrition. The monitoring process is carried out in real-time using internet of things technology based on LoRa or Long-Range Radio Frequency (IoT-LoRa). The application server used is Thingspeak. With this research, agricultural processes and monitoring can be carried out dynamically and efficiently. Therefore, when the soil conditions are dry or the soil moisture level is <300, it will immediately affect automatic watering by opening the valve or rotating the Servo motor. Furthermore, the watering process can be done automatically by looking at the soil conditions on potted plants or agricultural land. The position of the sensor on the ground level is not immediately removed and moved to another place, but it is always in the same condition and location, so the Adaptive Data Rate mechanism is used for the management of Power Consumption on IoT-LoRa

Integrating Cost-231 Multiwall Propagation and Adaptive Data Rate Method for Access Point Placement Recommendation

A new approach has been developed to provide an overview about signal behavior in indoor environments using Cost-231 Multiwall Model (Cost-231 MWM) and Adaptive Data Rate (ADR) method. This approach used as a reference for access point (AP) placement for campus building. The Cost-231 MWM plays a role in estimating the measured power received by user (usually called as Received Signal Strength Indicator/RSSI) by considering the existence of obstacles around the transmitter (AP). We used Institut Asia Malang environments as the case study and gave some recommendations for AP placement: ten optimal placements for the first, third and fourth floor, also seven optimal placements for the second floor. These recommendations were based on the RSSI for good and excellent level signal (-50 dBm until -10dBm). This research also uses the Adaptive Data Rate (ADR) mechanism approach to reduce the amount of packet loss (kbps) resulting from obstacles that cause attenuation (-dB). With the Adaptive Data Rate mechanism, it means increasing the number of access points, the signal attenuation (-dB) occurs from the obstacles (Walls) that are penetrated by the Radio Frequency device and causes attenuation (-dB), the more Access points on the Multi-Wall, will allow communication and data transmitting stability.

EARN: Enhanced ADR With Coding Rate Adaptation in LoRaWAN

As low-power wide-area (LPWA) networks emerge as a cost-effective choice of technologies for city-wide Internet-of-Things (IoT) applications, LoRaWAN, one of the most promising unlicensed band techniques, has received much attention from academia. LoRaWAN presents a set of tunable transmission parameters, along with an adaptive data rate (ADR) mechanism, to promote the best performance under the variable link state. But the performance of ADR, whose design neglects the complex correlation between such parameters, is yet to be practical in terms of both efficiency and scalability. In this article, we derive theoretical performance models of class A unconfirmed-mode LoRaWAN, focusing on the impact of coding rate (CR), a parameter that has not been explored in prior researches. Then, we present EARN , an enhanced greedy ADR mechanism with CR adaptation, to optimize the tradeoff between delivery ratio and energy consumption. In EARN design, we leverage the capture effect to increase the survival rate of colliding signals and introduce a concept of adaptive SNR margin to endure noisy link states. We validate our models and the feasibility of the CR adaptation with an empirical study, and large-scale simulations reveal that our method outperforms the conventional schemes.