LoRa 네트워크에서 에너지 효율성을 위한 노드 기반 ADR 메커니즘

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 김종권.Recently, as Internet of Things (IoT) systems have increased and Wireless Sensor Network (WSN) has been expanding, studies related to them are increasing. Among them, the interest in long range communication technologies has increased. In this regard, Low Power Wide Area (LPWA) network technologies such as Long Range (LoRa), Weightless, and Sigfox have emerged. Also, various studies related to LoRa and LoRaWAN, which are available in Industrial Scientific and Medical (ISM) bands, are being conducted. In LoRa networks, the nodes are connected to the gateway by one hop to form a start topology. LoRa nodes use the transmission parameters such as Spreading Factor (SF), Transmission Power (TP), Bandwidth (BW), Coding Rate (CR), and Carrier Frequency (CF) to transmit frames. In this process, the frame losses and the collisions between frames may occur because of the channel condition and transmission timing. To alleviate this problem, LoRaWAN utilizes the ADR mechanism to select appropriate transmission parameters considering the channel condition on the node side. In addition, there is the ADR mechanism for allocating the transmission parameters on the server side. The ADR mechanisms maintain the connection between the server and the nodes, and set appropriate transmission parameters. However, these existing ADR mechanisms have some limitations. First, the server side ADR mechanism increases the overhead of the server in proportion to the transmitted frames. Second, it is difficult to quickly and efficiently respond to dynamic channel. Third, the transmission parameters selected by these ADR mechanisms may not be the optimal transmission parameters for energy efficiency. These problems cause large energy consumption of the battery-powered nodes and decrease performance when the channel condition changes dynamically. In this paper, we propose a Node-based ADR Mechanism (NbADR), which is the ADR mechanism for Class A nodes in confirmed mode to minimize the server load and maximize energy efficiency. The proposed mechanism responds quickly to the channel condition based on the downlink pattern and selects the transmission parameters for efficient energy consumption by utilizing Efficiency of Energy (EoE) metric. We analyze the efficiency of the transmission parameters selected through EoE, and conduct extensive experiments. In conclusion, NbADR is more effective in terms of energy efficiency than the existing ADR mechanisms. Additionally, NbADR guarantees throughput of LoRa networks even in dynamically changing channel environments and improves fairness between the nodes.최근 IoT 시스템이 증가하고 무선 센서 네트워크가 넓어지면서 이와 관련된 연구가 증가하고 있다. 그 중에서도 장거리 통신 기술에 대한 관심이 증가하고 있다. 이와 관련하여 LoRa, Weightless, Sigfox와 같은 LPWA네트워크 기술들이 등장하고 있다. 또한, ISM 밴드에서 사용 가능한 LoRa와 LoRaWAN 관련 다양한 연구가 진행되고 있다. LoRa 네트워크에서 노드들은 스타 토폴로지를 구성하기 위하여 게이트웨이와 1홉으로 연결되어 있다. LoRa 노드들은 프레임을 전송하기 위하여 SF, TP, BW, CR, CF와 같은 전송 파라미터를 사용한다. 이 과정에서 채널 상태와 전송 타이밍으로 인한 프레임 손실과 프레임 간 충돌이 발생할 수 있다. 이러한 문제를 완화하기 위하여 LoRaWAN에서는 노드 측에서 네트워크 상황을 고려하여 적절한 전송 파라미터를 선택하기 위한 ADR 메커니즘을 사용한다. 게다가 서버 측에서 전송 파라미터를 할당하는 ADR 메커니즘이 존재한다. ADR 메커니즘들은 서버와 노드의 연결을 유지하고 적절한 전송 파라미터를 설정한다. 하지만 기존의 ADR 메커니즘들은 일부 한계점을 가지고 있다. 첫 번째, 서버 측 ADR 메커니즘은 전송하는 프레임에 비례하여 서버의 부하를 증가시킨다. 두 번째, 동적인 채널에서 빠르고 효율적으로 대처하기 어렵다. 세 번째, 이러한 ADR 메커니즘들에서 선택된 전송 파라미터들이 에너지 효율성을 위한 최적의 전송 파라미터가 아닐 수 있다. 이러한 문제점들은 배터리로 동작하는 노드들의 큰 에너지 소모를 야기하고 LoRa 네트워크의 채널이 동적으로 변경되는 환경에서 성능을 감소시킨다. 본 논문에서 우리는 서버의 부하를 최소화하며 에너지 효율성을 최대화하는 노드 기반의 ADR 메커니즘인 NbADR을 제안한다. 제안하는 메커니즘은 노드 측에서 전송 받은 다운링크 패턴을 기반으로 채널 상황에 빠르게 대응하고, Efficiency of Energy (EoE) 메트릭을 활용하여 효율적인 에너지 소모를 위한 전송 파라미터를 선택한다. 우리는 EoE 기반으로 선택한 전송 파라미터의 효율성을 분석하고, 광범위한 실험을 진행한다. 결론적으로, NbADR은 기존의 ADR 메커니즘들과 비교하여 에너지 효율성 측면에서 효과적이다. 추가적으로, NbADR은 급격하게 변화하는 채널 환경에서LoRa 네트워크의 처리량을 보장하고 노드 간 공평성을 향상시킨다.Chapter 1 Introduction 1 Chapter 2 Related Work 4 Chapter 3 Preliminaries 7 3.1 LoRa/LoRaWAN 7 3.2 Transmission Parameters 8 3.3 ADR Mechanism 9 Chapter 4 Channel Modeling 10 4.1 Loss 10 4.2 Collision 12 Chapter 5 Node-based ADR Mechanism 14 5.1 Approach for Energy Efficiency 15 5.2 Node-based ADR Mechanism (NbADR) 17 Chapter 6 Evaluation 21 6.1 Simulation Settings 22 6.2 Simulation Results 23 Chapter 7 Conclusion 33 Bibliography 35Maste

Optimisation de la capacité des réseaux LoRa

International audienc

Extending the Lora modulation to add further parallel channels and improve the LoRaWAN network performance

In this paper we present a new modulation, called DLoRa, similar in principle to the conventional LoRa modulation and compatible with it in terms of bandwidth and numerology. DLoRa departs from the conventional LoRa modulation as it is using a decreasing instantaneous frequency in the chirps instead of an increasing one as for the conventional LoRa modulation. Furthermore we describe a software environment to accurately evaluate the "isolation" of the different virtual channels created both by LoRa and DLoRa when using different Spreading Factors. Our results are in agreement with the ones present in literature for the conventional LoRa modulation and show that it is possible to double the number of channels by using simultaneously LoRa and DLora. The higher (double) number of subchannels available is the key to improve the network level performance of LoRa based networks.Comment: This work has been submitted on Feb.1 2020 to European Wireless 2020 conference for possible presentation and subsequent publication by the IEE

A Survey on Long-Range Wide-Area Network Technology Optimizations

Long-Range Wide-Area Network (LoRaWAN) enables flexible long-range service communications with low power consumption which is suitable for many IoT applications. The densification of LoRaWAN, which is needed to meet a wide range of IoT networking requirements, poses further challenges. For instance, the deployment of gateways and IoT devices are widely deployed in urban areas, which leads to interference caused by concurrent transmissions on the same channel. In this context, it is crucial to understand aspects such as the coexistence of IoT devices and applications, resource allocation, Media Access Control (MAC) layer, network planning, and mobility support, that directly affect LoRaWAN’s performance.We present a systematic review of state-of-the-art works for LoRaWAN optimization solutions for IoT networking operations. We focus on five aspects that directly affect the performance of LoRaWAN. These specific aspects are directly associated with the challenges of densification of LoRaWAN. Based on the literature analysis, we present a taxonomy covering five aspects related to LoRaWAN optimizations for efficient IoT networks. Finally, we identify key research challenges and open issues in LoRaWAN optimizations for IoT networking operations that must be further studied in the future

Fair allocation for transmission parameters to achieve scalability in LoRaWAN

LoRa and LoRaWAN are promising solutions for the upcoming challenges that the Internet of Things (IoT) presents. But even with the recent developments in LoRaWAN, there are still problems to overcome due to the scale of applications required for IoTs. LoRa and LoRaWAN provide many of the desired characteristics for IoT such as long-range transmissions, low power use, and low device cost, but also have an issue with node capacity. Scaling LoRaWAN networks for the capacity that the IoTs desires is the current challenge, so therefore, I aim to improve current solutions to meet this challenge. Since LoRaWAN is a new technology, research in the area is in early development, with some contributions existing but no complete solution that can be applied to a diverse range of LoRaWAN applications. My research extended upon previous research focused on the fairness of collision probabilities across nodes in a network, and thus, I created a solution that improved the scalability of LoRaWAN. Overall, I contributed a parameter allocation algorithm, a transmission power assignment scheme, and a path loss rule for node placement for LoRaWAN networks that, in simulation, achieved better performance than current solutions. This was possible due to lack of consideration regarding the importance of LoRa spreading factor parameters by current solutions and the realistic implications of path loss. By factoring for these two aspects, a more adaptable solution can be created. This solution can effectively maximise LoRaWAN's most important capability and also consider the physical limitations of the real-world like path loss. The proposed solution, Fair Allocation for Transmission Parameters (FATP) for LoRaWAN, adapts the idea of fair device performance in a network to the realistic limitations a real-world application would introduce, such as node distribution and path loss. Firstly, the parameter allocation algorithm is responsible for understanding the path loss of every device and the ideal method of leveraging the spreading factor parameter to achieve a fair network. A fair network, in this instance, is one where every device has the same probability for collision when transmitting. This algorithm then adapts the ideal method of assigning the spreading factor parameter to devices based on their path loss so that the network remains as near to optimal as possible, while respecting the limitations of the real world. Secondly, the transmission power scheme is responsible for minimising the power usage of devices by controlling how much power is used to transmit with. This power scheme functions by minimising every device's transmission power to the minimum amount that will still guarantee a viable transmission. Finally, the path loss rule is a guideline to follow when placing devices in a network. This guideline describes the minimum path loss a device should be allowed to have before it becomes impossible to mitigate its interference with other devices. In the simulation, FATP achieved better performance in terms of data extraction rate (DER) and reducing the variance of performance across nodes in a network for a range of node distributions when compared with current solutions. FATP provided up to a 9% improvement for network DER with an average of a 4% network DER improvement when compared to the nearest best performing current solution, an RSSI-based solution. Most notably, FATP, on average, reduced the variance of individual node performance by a factor of approximately 59. FATP achieved this while consuming approximately the same amount of energy as the RSSI-based solution. Overall, FATP achieved its goals of improving network DER to increase scalability while also ensuring that nodes operate more fairly with more consistent DER performance

Multiagent Reinforcement Learning with an Attention Mechanism for Improving Energy Efficiency in LoRa Networks

Long Range (LoRa) wireless technology, characterized by low power consumption and a long communication range, is regarded as one of the enabling technologies for the Industrial Internet of Things (IIoT). However, as the network scale increases, the energy efficiency (EE) of LoRa networks decreases sharply due to severe packet collisions. To address this issue, it is essential to appropriately assign transmission parameters such as the spreading factor and transmission power for each end device (ED). However, due to the sporadic traffic and low duty cycle of LoRa networks, evaluating the system EE performance under different parameter settings is time-consuming. Therefore, we first formulate an analytical model to calculate the system EE. On this basis, we propose a transmission parameter allocation algorithm based on multiagent reinforcement learning (MALoRa) with the aim of maximizing the system EE of LoRa networks. Notably, MALoRa employs an attention mechanism to guide each ED to better learn how much ''attention'' should be given to the parameter assignments for relevant EDs when seeking to improve the system EE. Simulation results demonstrate that MALoRa significantly improves the system EE compared with baseline algorithms with an acceptable degradation in packet delivery rate (PDR).Comment: 6 pages, 3 figures, This paper has been accepted for publication in IEEE Global Communications Conference (GLOBECOM) 202

Spatiotemporal Modelling of Multi-Gateway LoRa Networks with Imperfect SF Orthogonality

Meticulous modelling and performance analysis of Low-Power Wide-Area (LPWA) networks are essential for large scale dense Internet-of-Things (IoT) deployments. As Long Range (LoRa) is currently one of the most prominent LPWA technologies, we propose in this paper a stochastic-geometry-based framework to analyse the uplink transmission performance of a multi-gateway LoRa network modelled by a Matern Cluster Process (MCP). The proposed model is first to consider all together the multi-cell topology, imperfect spreading factor (SF) orthogonality, random start times, and geometric data arrival rates. Accounting for all of these factors, we initially develop the SF-dependent collision overlap time function for any start time distribution. Then, we analyse the Laplace transforms of intra-cluster and inter-cluster interference, and formulate the uplink transmission success probability. Through simulation results, we highlight the vulnerability of each SF to interference, illustrate the impact of parameters such as the network density, and the power allocation scheme on the network performance. Uniquely, our results shed light on when it is better to activate adaptive power mechanisms, as we show that an SF-based power allocation that approximates LoRa ADR, negatively impacts nodes near the cluster head. Moreover, we show that the interfering SFs degrading the performance the most depend on the decoding threshold range and the power allocation scheme.Comment: IEEE Global Communications Conferenc