Enhanced ADR for LoRaWAN networks with mobility

International audienceLoRa is becoming an attractive low cost and low power WAN solution for many real-world IoT applications. LoRa has been designed for static end-devices to individually use the optimal configuration through an adaptive data rate mechanism (ADR), thanks to the possibility to choose a set of LoRa physical layer transmission parameters. However a large class of IoT applications (e.g. connected farm) also includes mobile nodes with specific mobility patterns. For those applications, the current ADR control algorithm may not be efficient when the radio channel attenuation rapidly changes because of the node mobility. This paper contributes to enhance the ADR mechanism by taking into account the position of the mobile devices and their trajectories in order to have a dynamic allocation. The Enhanced-ADR (E-ADR) minimizes the transmission time and energy consumption as well as packet loss for mobile devices. The testbed-based experiments show that E-ADR improves the quality of service (QoS) of the overall networks

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

A fair channel hopping scheme for LoRa Networks with multiple single-channel gateways

LoRa is one of the most prominent LPWAN technologies due to its suitable characteristics for supporting large-scale IoT networks, as it offers long-range communications at low power consumption. The latter is granted mainly because end-nodes transmit directly to the gateways and no energy is spent in multi-hop transmissions. LoRaWAN gateways can successfully receive simultaneous transmissions on multiple channels. However, such gateways can be costly when compared to simpler single-channel LoRa transceivers, and at the same time they are configured to operate with pure-ALOHA, the well-known and fragile channel access scheme used in LoRaWAN. This work presents a fair, control-based channel hopping-based medium access scheme for LoRa networks with multiple single-channel gateways. Compared with the pure-ALOHA used in LoRaWAN, the protocol proposed here achieves higher goodput and fairness levels because each device can choose its most appropriate channel to transmit at a higher rate and spending less energy. Several simulation results considering different network densities and different numbers of single-channel LoRa gateways show that our proposal is able to achieve a packet delivery ratio (PDR) of around 18% for a network size of 2000 end-nodes and one gateway, and a PDR of almost 50% when four LoRa gateways are considered, compared to 2% and 6%, respectively, achieved by the pure-ALOHA approachinfo:eu-repo/semantics/publishedVersio

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

Practical Experiences of a Smart Livestock Location Monitoring System leveraging GNSS, LoRaWAN and Cloud Services.

Livestock farming is, in most cases in Europe, unsupervised, thus making it difficult to ensure adequate control of the position of the animals for the improvement of animal welfare. In addition, the geographical areas involved in livestock grazing usually have difficult access with harsh orography and lack of communications infrastructure, thus the need to provide a low-power livestock localization and monitoring system is of paramount importance, which is crucial not for a sustainable agriculture, but also for the protection of native breeds and meats thanks to their controlled supervision. In this context, this work presents an Internet of things (IoT)-based system integrating low-power wide area (LPWA) technology, cloud and virtualization services to provide real-time livestock location monitoring. Taking into account the constraints coming from the environment in terms of energy supply and network connectivity, our proposed system is based on a wearable device equipped with inertial sensors, Global Positioning System (GPS) receiver and LoRaWAN transceiver, which can provide a satisfactory compromise between performance, cost and energy consumption. At first, this article provides the state-of-the-art localization techniques and technologies applied to smart livestock. Then, we proceed to provide the hardware and firmware co-design to achieve very low energy consumption, thus providing a significant positive impact to the battery life. The proposed platform has been evaluated in a pilot test in the Northern part of Italy, evaluating different configurations in terms of sampling period, experimental duration and number of devices. The results are analyzed and discussed for packe delivery ratio, energy consumption, localization accuracy, battery discharge measurement and delay

Space-Terrestrial Integrated Internet of Things: Challenges and Opportunities

Large geographical regions of our planet remain uncovered by terrestrial network connections. Sparse and dense constellations of near-Earth orbit satellites can bridge this gap by providing Internet of Things (IoT) connectivity on a world-wide scale in a flexible and cost-effective manner. This paper presents a novel space-terrestrial integrated IoT network architecture spanning direct- and indirect-to-satellite access from IoT assets on the surface. Framed on the identified requirements, we analyze NB-IoT and LoRa/LoRaWAN features to put these technologies forward as appealing candidates for future satellite IoT deployments. Finally, we list and discuss the key open research challenges to be addressed in order to achieve a successful space-terrestrial IoT integration

Development Education of Blind Adaptive Data Rate LoRaWAN Network on Mobile Node

Adaptive Data Rate is one of the essential mechanisms that continue to be developed for LoRaWAN. Adaptive Data Rate (ADR) is one of the mechanisms for saving energy and bit-rate efficiency of LoRaWAN. Using ADR is expected to increase throughput (bps) and minimize packet loss (bps), packets can be lost during data collisions, LoRaWAN networks are formed from several end-nodes that are connected to each other, can reach 100 nodes, and this causes collisions data if no settings are made with ADR. ADR talks about scheduling and set other parameters. The ADR method was developed on mobile nodes, ADR can handle the node’s data rate condition in its movement, but this is also influenced by the distance (km). The farther the end-node is moving from its gateway, causing packet loss data can occur even in percentage (%) different. ADR development in the Blind ADR for mobile node includes Node-ADR and Net-ADR. from the experimental results of the estimation of battery usage (mAH), the average battery used in position 1 is 409 mAH, position 2 is 482 mAH, and position 3 is 992 mAH, this shows that the larger the SF, the greater the energy required

An efficient adaptive data-link-layer architecture for LoRa networks

LoRa is one of the most popular low-power wireless network technologies for implementation of the Internet of Things, with the advantage of providing long-range communication, but lower data rates, when compared with technologies such as Zigbee or Bluetooth. LoRa is a single-channel physical layer technology on top of which LoRaWAN implements a more complex multi-channel network with enhanced functionalities, such as adaptive data rate. However, LoRaWAN relies on expensive hardware to support these functionalities. This paper proposes a LoRa data-link-layer architecture based on a multi-layer star network topology that adapts relevant LoRa parameters for each end node dynamically taking into account its link distance and quality in order to balance communication range and energy consumption. The developed solution is comprised of multiple components, including a LoRa parameter calculator to help the user to configure the network parameters, a contention-free MAC protocol to avoid collisions, and an adaptive spreading factor and transmission power mechanism. These components work together to ensure a more efficient use of the chosen ISM band and end node resources, but with low-cost implementation and operation requirements.This research was funded by FEDER through COMPETE2020—Programa Operacional Competitividade e Internacionalização (POCI) grant number POCI-01-0145-FEDER-028247 and by FCT—Fundação para a Ciência e Tecnologia within the R&D Units Project Scope: UIDB/00319/202

Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios

[EN] Low-power wide-area network (LPWAN) technologies are becoming a widespread solution for wireless deployments in many applications, such as smart cities or Industry 4.0. However, there are still challenges to be addressed, such as energy consumption and robustness. To characterize and optimize these types of networks, the authors have developed an optimized use of the adaptative data rate (ADR) mechanism for uplink, proposed its use also for downlink based on the simulator ns-3, and then defined an industrial scenario to test and validate the proposed solution in terms of packet loss and energy.The research leading to these results received funding from the Horizon 2020 Programme of the European Commission under Grant Agreement No. 825631 "Zero Defect Manufacturing Platform (ZDMP)". It was also partially supported by the MCyU (Spanish Ministry of Science and Universities) under the project ATLAS (PGC2018-094151-B-I00).Todoli Ferrandis, D.; Silvestre-Blanes, J.; Sempere Paya, VM.; Planes, A. (2020). Analysis of Bidirectional ADR-Enabled Class B LoRaWAN Networks in Industrial Scenarios. Applied Sciences. 10(22):1-17. https://doi.org/10.3390/app10227964S1171022Ayoub, W., Samhat, A. E., Nouvel, F., Mroue, M., & Prevotet, J.-C. (2019). Internet of Mobile Things: Overview of LoRaWAN, DASH7, and NB-IoT in LPWANs Standards and Supported Mobility. IEEE Communications Surveys & Tutorials, 21(2), 1561-1581. doi:10.1109/comst.2018.2877382Zero Defect Manufacturing Platformhttps://www.zdmp.eu/Finnegan, J., Brown, S., & Farrell, R. (2018). Evaluating the Scalability of LoRaWAN Gateways for Class B Communication in ns-3. 2018 IEEE Conference on Standards for Communications and Networking (CSCN). doi:10.1109/cscn.2018.8581759Luvisotto, M., Tramarin, F., Vangelista, L., & Vitturi, S. (2018). On the Use of LoRaWAN for Indoor Industrial IoT Applications. Wireless Communications and Mobile Computing, 2018, 1-11. doi:10.1155/2018/3982646Kim, S., & Yoo, Y. (2018). Contention-Aware Adaptive Data Rate for Throughput Optimization in LoRaWAN. Sensors, 18(6), 1716. doi:10.3390/s18061716Ta, D.-T., Khawam, K., Lahoud, S., Adjih, C., & Martin, S. (2019). LoRa-MAB: A Flexible Simulator for Decentralized Learning Resource Allocation in IoT Networks. 2019 12th IFIP Wireless and Mobile Networking Conference (WMNC). doi:10.23919/wmnc.2019.8881393Abdelfadeel, K. Q., Cionca, V., & Pesch, D. (2018). Fair Adaptive Data Rate Allocation and Power Control in LoRaWAN. 2018 IEEE 19th International Symposium on «A World of Wireless, Mobile and Multimedia Networks» (WoWMoM). doi:10.1109/wowmom.2018.8449737ns-3 Model Library, Online Resourcehttps://www.nsnam.org/doxygen/classns-3_1_1_hybrid_buildings_propagation_loss_model.html#detailsCode Repositoryhttps://github.com/dtodoli/ns3_lorawan_wor