325 research outputs found

    Cross-layer framework and optimization for efficient use of the energy budget of IoT Nodes

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    Both physical and MAC-layer need to be jointly optimized to maximize the autonomy of IoT devices. Therefore, a cross-layer design is imperative to effectively realize Low Power Wide Area networks (LPWANs). In the present paper, a cross-layer assessment framework including power modeling is proposed. Through this simulation framework, the energy consumption of IoT devices, currently deployed in LoRaWAN networks, is evaluated. We demonstrate that a cross-layer approach significantly improves energy efficiency and overall throughput. Two major contributions are made. First, an open-source LPWAN assessment framework has been conceived. It allows testing and evaluating hypotheses and schemes. Secondly, as a representative case, the LoRaWAN protocol is assessed. The findings indicate how a cross-layer approach can optimize LPWANs in terms of energy efficiency and throughput. For instance, it is shown that the use of larger payloads can reduce up to three times the energy consumption on quasi-static channels yet may bring an energy penalty under adverse dynamic conditions

    Survey on wireless technology trade-offs for the industrial internet of things

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    Aside from vast deployment cost reduction, Industrial Wireless Sensor and Actuator Networks (IWSAN) introduce a new level of industrial connectivity. Wireless connection of sensors and actuators in industrial environments not only enables wireless monitoring and actuation, it also enables coordination of production stages, connecting mobile robots and autonomous transport vehicles, as well as localization and tracking of assets. All these opportunities already inspired the development of many wireless technologies in an effort to fully enable Industry 4.0. However, different technologies significantly differ in performance and capabilities, none being capable of supporting all industrial use cases. When designing a network solution, one must be aware of the capabilities and the trade-offs that prospective technologies have. This paper evaluates the technologies potentially suitable for IWSAN solutions covering an entire industrial site with limited infrastructure cost and discusses their trade-offs in an effort to provide information for choosing the most suitable technology for the use case of interest. The comparative discussion presented in this paper aims to enable engineers to choose the most suitable wireless technology for their specific IWSAN deployment

    Internet of things and LoRaWAN enabled future smart farming

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    It is estimated that to keep pace with the predicted population growth over the next decades, agricultural processes involving food production will have to increase their output up to 70 percent by 2050. "Precision" or "smart" agriculture is one way to make sure that these goals for future food supply, stability, and sustainability can be met. Applications such as smart irrigation systems can utilize water more efficiently, optimizing electricity consumption and costs of labor; sensors on plants and soil can optimize the delivery of nutrients and increase yields. To make all this smart farming technology viable, it is important for it to be low-cost and farmer-friendly. Fundamental to this IoT revolution is thus the adoption of low-cost, long-range communication technologies that can easily deal with a large number of connected sensing devices without consuming excessive power. In this article, a review and analysis of currently available long-range wide area network (LoRaWAN)-enabled IoT application for smart agriculture is presented. LoRaWAN limitations and bottlenecks are discussed with particular focus on their effects on agri-tech applications. A brief description of a testbed in development is also given, alongside a review of the future research challenges that this will help to tackle

    LoRa ๋„คํŠธ์›Œํฌ์—์„œ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ์œ„ํ•œ ๋…ธ๋“œ ๊ธฐ๋ฐ˜ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ๊น€์ข…๊ถŒ.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.์ตœ๊ทผ IoT ์‹œ์Šคํ…œ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ๋ฌด์„  ์„ผ์„œ ๋„คํŠธ์›Œํฌ๊ฐ€ ๋„“์–ด์ง€๋ฉด์„œ ์ด์™€ ๊ด€๋ จ๋œ ์—ฐ๊ตฌ๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๊ทธ ์ค‘์—์„œ๋„ ์žฅ๊ฑฐ๋ฆฌ ํ†ต์‹  ๊ธฐ์ˆ ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ์ด์™€ ๊ด€๋ จํ•˜์—ฌ LoRa, Weightless, Sigfox์™€ ๊ฐ™์€ LPWA๋„คํŠธ์›Œํฌ ๊ธฐ์ˆ ๋“ค์ด ๋“ฑ์žฅํ•˜๊ณ  ์žˆ๋‹ค. ๋˜ํ•œ, ISM ๋ฐด๋“œ์—์„œ ์‚ฌ์šฉ ๊ฐ€๋Šฅํ•œ LoRa์™€ LoRaWAN ๊ด€๋ จ ๋‹ค์–‘ํ•œ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜๊ณ  ์žˆ๋‹ค. LoRa ๋„คํŠธ์›Œํฌ์—์„œ ๋…ธ๋“œ๋“ค์€ ์Šคํƒ€ ํ† ํด๋กœ์ง€๋ฅผ ๊ตฌ์„ฑํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๊ฒŒ์ดํŠธ์›จ์ด์™€ 1ํ™‰์œผ๋กœ ์—ฐ๊ฒฐ๋˜์–ด ์žˆ๋‹ค. LoRa ๋…ธ๋“œ๋“ค์€ ํ”„๋ ˆ์ž„์„ ์ „์†กํ•˜๊ธฐ ์œ„ํ•˜์—ฌ SF, TP, BW, CR, CF์™€ ๊ฐ™์€ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์‚ฌ์šฉํ•œ๋‹ค. ์ด ๊ณผ์ •์—์„œ ์ฑ„๋„ ์ƒํƒœ์™€ ์ „์†ก ํƒ€์ด๋ฐ์œผ๋กœ ์ธํ•œ ํ”„๋ ˆ์ž„ ์†์‹ค๊ณผ ํ”„๋ ˆ์ž„ ๊ฐ„ ์ถฉ๋Œ์ด ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ๋ฅผ ์™„ํ™”ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ LoRaWAN์—์„œ๋Š” ๋…ธ๋“œ ์ธก์—์„œ ๋„คํŠธ์›Œํฌ ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜์—ฌ ์ ์ ˆํ•œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ ํƒํ•˜๊ธฐ ์œ„ํ•œ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜์„ ์‚ฌ์šฉํ•œ๋‹ค. ๊ฒŒ๋‹ค๊ฐ€ ์„œ๋ฒ„ ์ธก์—์„œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ํ• ๋‹นํ•˜๋Š” ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜์ด ์กด์žฌํ•œ๋‹ค. ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜๋“ค์€ ์„œ๋ฒ„์™€ ๋…ธ๋“œ์˜ ์—ฐ๊ฒฐ์„ ์œ ์ง€ํ•˜๊ณ  ์ ์ ˆํ•œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ค์ •ํ•œ๋‹ค. ํ•˜์ง€๋งŒ ๊ธฐ์กด์˜ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜๋“ค์€ ์ผ๋ถ€ ํ•œ๊ณ„์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ, ์„œ๋ฒ„ ์ธก ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ์ „์†กํ•˜๋Š” ํ”„๋ ˆ์ž„์— ๋น„๋ก€ํ•˜์—ฌ ์„œ๋ฒ„์˜ ๋ถ€ํ•˜๋ฅผ ์ฆ๊ฐ€์‹œํ‚จ๋‹ค. ๋‘ ๋ฒˆ์งธ, ๋™์ ์ธ ์ฑ„๋„์—์„œ ๋น ๋ฅด๊ณ  ํšจ์œจ์ ์œผ๋กœ ๋Œ€์ฒ˜ํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ์„ธ ๋ฒˆ์งธ, ์ด๋Ÿฌํ•œ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜๋“ค์—์„œ ์„ ํƒ๋œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋“ค์ด ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ์œ„ํ•œ ์ตœ์ ์˜ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๊ฐ€ ์•„๋‹ ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋ฌธ์ œ์ ๋“ค์€ ๋ฐฐํ„ฐ๋ฆฌ๋กœ ๋™์ž‘ํ•˜๋Š” ๋…ธ๋“œ๋“ค์˜ ํฐ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋ฅผ ์•ผ๊ธฐํ•˜๊ณ  LoRa ๋„คํŠธ์›Œํฌ์˜ ์ฑ„๋„์ด ๋™์ ์œผ๋กœ ๋ณ€๊ฒฝ๋˜๋Š” ํ™˜๊ฒฝ์—์„œ ์„ฑ๋Šฅ์„ ๊ฐ์†Œ์‹œํ‚จ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ ์šฐ๋ฆฌ๋Š” ์„œ๋ฒ„์˜ ๋ถ€ํ•˜๋ฅผ ์ตœ์†Œํ™”ํ•˜๋ฉฐ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ์„ ์ตœ๋Œ€ํ™”ํ•˜๋Š” ๋…ธ๋“œ ๊ธฐ๋ฐ˜์˜ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜์ธ NbADR์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆํ•˜๋Š” ๋ฉ”์ปค๋‹ˆ์ฆ˜์€ ๋…ธ๋“œ ์ธก์—์„œ ์ „์†ก ๋ฐ›์€ ๋‹ค์šด๋งํฌ ํŒจํ„ด์„ ๊ธฐ๋ฐ˜์œผ๋กœ ์ฑ„๋„ ์ƒํ™ฉ์— ๋น ๋ฅด๊ฒŒ ๋Œ€์‘ํ•˜๊ณ , Efficiency of Energy (EoE) ๋ฉ”ํŠธ๋ฆญ์„ ํ™œ์šฉํ•˜์—ฌ ํšจ์œจ์ ์ธ ์—๋„ˆ์ง€ ์†Œ๋ชจ๋ฅผ ์œ„ํ•œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ๋ฅผ ์„ ํƒํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” EoE ๊ธฐ๋ฐ˜์œผ๋กœ ์„ ํƒํ•œ ์ „์†ก ํŒŒ๋ผ๋ฏธํ„ฐ์˜ ํšจ์œจ์„ฑ์„ ๋ถ„์„ํ•˜๊ณ , ๊ด‘๋ฒ”์œ„ํ•œ ์‹คํ—˜์„ ์ง„ํ–‰ํ•œ๋‹ค. ๊ฒฐ๋ก ์ ์œผ๋กœ, NbADR์€ ๊ธฐ์กด์˜ ADR ๋ฉ”์ปค๋‹ˆ์ฆ˜๋“ค๊ณผ ๋น„๊ตํ•˜์—ฌ ์—๋„ˆ์ง€ ํšจ์œจ์„ฑ ์ธก๋ฉด์—์„œ ํšจ๊ณผ์ ์ด๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, NbADR์€ ๊ธ‰๊ฒฉํ•˜๊ฒŒ ๋ณ€ํ™”ํ•˜๋Š” ์ฑ„๋„ ํ™˜๊ฒฝ์—์„œLoRa ๋„คํŠธ์›Œํฌ์˜ ์ฒ˜๋ฆฌ๋Ÿ‰์„ ๋ณด์žฅํ•˜๊ณ  ๋…ธ๋“œ ๊ฐ„ ๊ณตํ‰์„ฑ์„ ํ–ฅ์ƒ์‹œํ‚จ๋‹ค.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

    Low overhead scheduling of LoRa transmissions for improved scalability

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    Recently, LoRaWAN has attracted much attention for the realization of many Internet of Things applications because it offers low-power, long-distance, and low-cost wireless communication. Recent works have shown that the LoRaWAN specification for class A devices comes with scalability limitations due to the ALOHA-like nature of the MAC layer. In this paper, we propose a synchronization and scheduling mechanism for LoRaWAN networks consisting of class A devices. The mechanism runs on top of the LoRaWAN MAC layer. A central network synchronization and scheduling entity will schedule uplink and downlink transmissions. In order to reduce the synchronization packet length, all time slots that are being assigned to an end node are encoded in a probabilistic space-efficient data structure. An end node will check if a time slot is part of the received data structure in order to determine when to transmit. Time slots are assigned based on the traffic needs of the end nodes. We show that in case of a nonsaturated multichannel LoRaWAN network with synchronization being done in a separate channel, the packet delivery ratio (PDR) is easily 7% (for SF7) to 30% (for SF12) higher than in an unsynchronized LoRaWAN network. For saturated networks, the differences in PDR become more profound as nodes are only scheduled as long as they can be accommodated given the remaining capacity of the network. The synchronization process will use less than 3-mAh extra battery capacity per end node during a one year period, for synchronization periods longer than three days. This is less than the battery capacity used to transmit packets that are going to be lost in an unsynchronized network due to collisions

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

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    [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 ยซA World of Wireless, Mobile and Multimedia Networksยป (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

    6LoRa: Full Stack IPv6 Networking with DSME-LoRa on Low Power IoT Nodes

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    Long range wireless transmission techniques such as LoRa are preferential candidates for a substantial class of IoT applications, as they avoid the complexity of multi-hop wireless forwarding. The existing network solutions for LoRa, however, are not suitable for peer-to-peer communication, which is a key requirement for many IoT applications. In this work, we propose a networking system - 6LoRa, that enables IPv6 communication over LoRa. We present a full stack system implementation on RIOT OS and evaluate the system on a real testbed using realistic application scenarios with CoAP. Our findings confirm that our approach outperforms existing solutions in terms of transmission delay and packet reception ratio at comparable energy consumption

    Sub-GHz LPWAN network coexistence, management and virtualization : an overview and open research challenges

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    The IoT domain is characterized by many applications that require low-bandwidth communications over a long range, at a low cost and at low power. Low power wide area networks (LPWANs) fulfill these requirements by using sub-GHz radio frequencies (typically 433 or 868 MHz) with typical transmission ranges in the order of 1 up to 50 km. As a result, a single base station can cover large areas and can support high numbers of connected devices (> 1000 per base station). Notorious initiatives in this domain are LoRa, Sigfox and the upcoming IEEE 802.11ah (or "HaLow") standard. Although these new technologies have the potential to significantly impact many IoT deployments, the current market is very fragmented and many challenges exists related to deployment, scalability, management and coexistence aspects, making adoption of these technologies difficult for many companies. To remedy this, this paper proposes a conceptual framework to improve the performance of LPWAN networks through in-network optimization, cross-technology coexistence and cooperation and virtualization of management functions. In addition, the paper gives an overview of state of the art solutions and identifies open challenges for each of these aspects

    Low-Power Wide-Area Networks: A Broad Overview of its Different Aspects

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    Low-power wide-area networks (LPWANs) are gaining popularity in the research community due to their low power consumption, low cost, and wide geographical coverage. LPWAN technologies complement and outperform short-range and traditional cellular wireless technologies in a variety of applications, including smart city development, machine-to-machine (M2M) communications, healthcare, intelligent transportation, industrial applications, climate-smart agriculture, and asset tracking. This review paper discusses the design objectives and the methodologies used by LPWAN to provide extensive coverage for low-power devices. We also explore how the presented LPWAN architecture employs various topologies such as star and mesh. We examine many current and emerging LPWAN technologies, as well as their system architectures and standards, and evaluate their ability to meet each design objective. In addition, the possible coexistence of LPWAN with other technologies, combining the best attributes to provide an optimum solution is also explored and reported in the current overview. Following that, a comparison of various LPWAN technologies is performed and their market opportunities are also investigated. Furthermore, an analysis of various LPWAN use cases is performed, highlighting their benefits and drawbacks. This aids in the selection of the best LPWAN technology for various applications. Before concluding the work, the open research issues, and challenges in designing LPWAN are presented.publishedVersio
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