537 research outputs found

    NOMA based resource allocation and mobility enhancement framework for IoT in next generation cellular networks

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    With the unprecedented technological advances witnessed in the last two decades, more devices are connected to the internet, forming what is called internet of things (IoT). IoT devices with heterogeneous characteristics and quality of experience (QoE) requirements may engage in dynamic spectrum market due to scarcity of radio resources. We propose a framework to efficiently quantify and supply radio resources to the IoT devices by developing intelligent systems. The primary goal of the paper is to study the characteristics of the next generation of cellular networks with non-orthogonal multiple access (NOMA) to enable connectivity to clustered IoT devices. First, we demonstrate how the distribution and QoE requirements of IoT devices impact the required number of radio resources in real time. Second, we prove that using an extended auction algorithm by implementing a series of complementary functions, enhance the radio resource utilization efficiency. The results show substantial reduction in the number of sub-carriers required when compared to conventional orthogonal multiple access (OMA) and the intelligent clustering is scalable and adaptable to the cellular environment. Ability to move spectrum usages from one cluster to other clusters after borrowing when a cluster has less user or move out of the boundary is another soft feature that contributes to the reported radio resource utilization efficiency. Moreover, the proposed framework provides IoT service providers cost estimation to control their spectrum acquisition to achieve required quality of service (QoS) with guaranteed bit rate (GBR) and non-guaranteed bit rate (Non-GBR)

    Energy Efficient Uplink Transmissions in LoRa Networks

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    LoRa has been recognized as one of the most promising low-power wide-area (LPWA) techniques. Since LoRa devices are usually powered by batteries, energy efficiency (EE) is an essential consideration. In this paper, we investigate the energy efficient resource allocation in LoRa networks to maximize the system EE (SEE) and the minimal EE (MEE) of LoRa users, respectively. Specifically, our objective is to maximize the corresponding EE by jointly exploiting user scheduling, spreading factor (SF) assignment, and transmit power allocations. To solve them efficiently, we first propose a suboptimal algorithm, including the low-complexity user scheduling scheme based on matching theory and the heuristic SF assignment approach for LoRa users scheduled on the same channel. Then, to deal with the power allocation, an optimal algorithm is proposed to maximize the SEE. To maximize the MEE of LoRa users assigned to the same channel, an iterative power allocation algorithm based on the generalized fractional programming and sequential convex programming is proposed. Numerical results show that the proposed user scheduling algorithm achieves near-optimal EE performance, and the proposed power allocation algorithms outperform the benchmarks. ยฉ 2020 IEEE

    Prediction-Based Energy Saving Mechanism in 3GPP NB-IoT Networks

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    The current expansion of the Internet of things (IoT) demands improved communication platforms that support a wide area with low energy consumption. The 3rd Generation Partnership Project introduced narrowband IoT (NB-IoT) as IoT communication solutions. NB-IoT devices should be available for over 10 years without requiring a battery replacement. Thus, a low energy consumption is essential for the successful deployment of this technology. Given that a high amount of energy is consumed for radio transmission by the power amplifier, reducing the uplink transmission time is key to ensure a long lifespan of an IoT device. In this paper, we propose a prediction-based energy saving mechanism (PBESM) that is focused on enhanced uplink transmission. The mechanism consists of two parts: first, the network architecture that predicts the uplink packet occurrence through a deep packet inspection; second, an algorithm that predicts the processing delay and pre-assigns radio resources to enhance the scheduling request procedure. In this way, our mechanism reduces the number of random accesses and the energy consumed by radio transmission. Simulation results showed that the energy consumption using the proposed PBESM is reduced by up to 34% in comparison with that in the conventional NB-IoT method

    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

    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

    Goodbye, ALOHA!

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    ยฉ2016 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.The vision of the Internet of Things (IoT) to interconnect and Internet-connect everyday people, objects, and machines poses new challenges in the design of wireless communication networks. The design of medium access control (MAC) protocols has been traditionally an intense area of research due to their high impact on the overall performance of wireless communications. The majority of research activities in this field deal with different variations of protocols somehow based on ALOHA, either with or without listen before talk, i.e., carrier sensing multiple access. These protocols operate well under low traffic loads and low number of simultaneous devices. However, they suffer from congestion as the traffic load and the number of devices increase. For this reason, unless revisited, the MAC layer can become a bottleneck for the success of the IoT. In this paper, we provide an overview of the existing MAC solutions for the IoT, describing current limitations and envisioned challenges for the near future. Motivated by those, we identify a family of simple algorithms based on distributed queueing (DQ), which can operate for an infinite number of devices generating any traffic load and pattern. A description of the DQ mechanism is provided and most relevant existing studies of DQ applied in different scenarios are described in this paper. In addition, we provide a novel performance evaluation of DQ when applied for the IoT. Finally, a description of the very first demo of DQ for its use in the IoT is also included in this paper.Peer ReviewedPostprint (author's final draft
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