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2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)最新文献

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Proposal of interference power occupancy estimation method using chirp demodulation 提出了一种基于啁啾解调的干扰功率占用估计方法
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528528
G. Kobayashi, O. Takyu, K. Adachi, M. Ohta, T. Fujii
In recent years, Low Power Wide Area (LPWA) has been attracting attention as being suitable for utilizing the Internet of Things (IoT). In Japan, LPWA has been standardized as a specific low power radio that uses the 920MHz band, and is a communication standard that enables long-distance communication with low power consumption. In the specific low power radio of the 920MHz band, since multiple radio standards share the same frequency, communication interruption due to the same frequency interference Co-Channel Interference (CCI) becomes a serious problem. Long Range (LoRa), which is one of LPWA, uses chirp modulation (spread spectrum technology) to suppress CCI. This modulation method is resistant to CCI because it expands the desired signal power after demodulation by wideband gain. However, when operating LoRa, it is necessary to determine the necessity of frequency sharing suitable for Signal to Interference Ratio (SIR) in consideration of interference from other systems, and to set the required diffusion rate. The authors have proposed a method for estimating desired signal power and interference power using chirp demodulation. In this paper, we have established a method for estimating the occupancy rate, which is the access ratio of other systems, with high accuracy from the probability distribution of interference power and noise power.
近年来,低功率广域(LPWA)因适合利用物联网(IoT)而备受关注。在日本,LPWA已经被标准化为使用920MHz频段的特定低功率无线电,是一种能够以低功耗进行远距离通信的通信标准。在920MHz频段的特定低功率无线电中,由于多个无线电标准共用同一频率,因此由于同频干扰导致的通信中断成为一个严重的问题。远程(LoRa)是LPWA的一种,它采用啁啾调制(扩频技术)来抑制CCI。这种调制方法可以抵抗CCI,因为它通过宽带增益解调后扩大了所需的信号功率。但是,在操作LoRa时,需要考虑到其他系统的干扰,确定是否需要适合SIR (Signal to Interference Ratio,信号干扰比)的频率共享,并设定所需的扩散速率。提出了一种利用啁啾解调估计期望信号功率和干扰功率的方法。本文从干扰功率和噪声功率的概率分布出发,建立了一种高精度估计占用率(即其他系统的接入比)的方法。
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引用次数: 1
Feature expansion of single dimensional time series data for machine learning classification 用于机器学习分类的单维时间序列数据的特征扩展
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528690
Daeun Jung, Jungjin Lee, Hyunggon Park
In this paper, we propose a feature expansion approach for the lowest one-dimension (1-D) time series data classification problems, where the expanded features include temporal, frequency, and statistical characteristics. We show that the proposed feature expansion can improve the classification accuracy compared to conventional machine learning algorithms for data classification. This is because the expanded features enable classifiers to consider multiple dimensions which are not feasible for low dimension data. Experiment results show that the proposed feature expansion method can improve the classification performance compared to conventional machine learning algorithms for 1-D actual biosensor data.
在本文中,我们提出了一种用于最低一维(1-D)时间序列数据分类问题的特征扩展方法,其中扩展的特征包括时间特征、频率特征和统计特征。我们表明,与传统的机器学习数据分类算法相比,所提出的特征扩展可以提高分类精度。这是因为扩展的特征使分类器能够考虑多个维度,而这对于低维度数据是不可行的。实验结果表明,与传统的机器学习算法相比,所提出的特征扩展方法可以提高一维实际生物传感器数据的分类性能。
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引用次数: 1
Lightweight Collaboration of Detecting and Tracking Algorithm in Low-Power Embedded Systems for Forward Collision Warning 面向前向碰撞预警的低功耗嵌入式系统轻量化协同检测与跟踪算法
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528771
Sunghoon Hong, Daejin Park
The cause of the majority of vehicle accidents is a safety issue due to the driver's inattention, such as drowsy driving. A forward collision warning system (FCWS) can significantly reduce the number and severity of accidents by detecting the risk of collision with vehicles in front and providing an advanced warning signal to the driver. This paper describes a low power embedded system based FCWS for highway safety. The algorithm described in this paper computes time to collision (TTC) through detection, tracking, distance calculation for the vehicle ahead and current vehicle speed information with a single camera. Additionally, in order to operate in real time even in a low-performance embedded system, an optimization technique in the program with high and low levels will be introduced. The system has been tested through the driving video of the vehicle in the embedded system. As a result of using the optimization technique, the execution time was about 170 times faster than that when using the previous non-optimized process.
大部分交通事故的原因是由于疲劳驾驶等驾驶员的注意力不集中而引起的安全问题。前方碰撞预警系统(FCWS)通过检测与前方车辆的碰撞风险,并向驾驶员提供提前预警信号,可以显著减少事故的数量和严重程度。本文介绍了一种基于FCWS的低功耗嵌入式公路安全系统。本文所描述的算法通过单摄像头对前方车辆的检测、跟踪、距离计算和当前车速信息来计算碰撞时间(TTC)。此外,为了在低性能的嵌入式系统中也能实时运行,本文还将介绍一种高、低层次程序的优化技术。通过嵌入式系统中车辆的行驶视频对系统进行了测试。由于使用了优化技术,执行时间比使用先前未优化的进程快了大约170倍。
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引用次数: 3
A Faulty Node Detection Method in Wireless Sensor Network in Seedling for Hydroponics 水培苗木无线传感器网络故障节点检测方法
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528646
Dong-Hee Noh, Tae-Hwan Ko, Ahhyeon Hong, K. Kim, Seok-Bong Noh
This paper proposes a faulty node detection scheme in the hydroponics system. A wireless sensor system of seedling hydroponic has to considered distance-dependent bandwidth, poor quality of the links, and propagation delay. In the proposed scheme, the main control sensor node can diagnosis which sensor node are error frequently occurs. Simulation results show that the proposed method can improve the performance of the faulty detection reliability and miss-detection rate for real-time hydroponics wireless sensor systems.
提出了一种水培系统故障节点检测方案。苗木水培无线传感器系统必须考虑距离依赖带宽、链路质量差和传播延迟等问题。在该方案中,主控传感器节点可以诊断出哪些传感器节点经常出现错误。仿真结果表明,该方法可以提高实时水培无线传感器系统的故障检测可靠性和漏检率。
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引用次数: 0
A Cluster-based Mechanism for Vehicular Networks in the Scale-Free ICN Core Network 无标度ICN核心网中基于集群的车联网机制
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528770
K. Hasan, Seong-Ho Jeong
The repetition of content requests happens frequently in the vehicular networks, and it is increasing depending on the vehicular density in a certain area. On the other hand, information-centric networking (ICN) is being used in the vehicular networks to fulfill the faster content communication requirements, reduce latency, and enhance the network capacity. Although, the ICN-based vehicular networks have many benefits, it has several drawbacks, e.g., interest packet flooding, inefficient content caching, and so on. Moreover, the network scalability is related to the drawbacks of the existing ICN-based vehicular networks. It is also important that the current Internet architecture is considered as a scale-free network. Therefore, the solution of the existing drawbacks can be solved using the concept of the scale-free ICN network. In this paper, we propose a cluster-based mechanism for vehicular networks in the scale-free ICN core networks. We also simulate the various scenarios of a scale-free network and show the comparative analysis of different scenarios in terms of the total number of clusters vs the number of nodes in a cluster. Our simulation result ensures the solution to the interest flooding problem and the efficiency of the content caching mechanism.
在车载网络中,内容请求的重复频繁发生,并且随着一定区域内车辆密度的增加而增加。另一方面,信息中心网络(ICN)正被用于车载网络,以满足更快的内容通信需求,减少延迟,增强网络容量。尽管基于icn的车载网络具有许多优点,但它也存在一些缺点,如兴趣包泛滥、低效的内容缓存等。此外,网络的可扩展性与现有基于icn的车载网络的缺陷有关。同样重要的是,当前的互联网架构被认为是一个无标度的网络。因此,利用无标度ICN网络的概念可以解决现有缺点。在本文中,我们提出了一种基于集群的无标度ICN核心网车辆网络机制。我们还模拟了无标度网络的各种场景,并根据集群总数与集群中的节点数量对不同场景进行了比较分析。仿真结果保证了兴趣泛滥问题的解决和内容缓存机制的效率。
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引用次数: 0
LoRa-DuCy: Duty Cycling for LoRa-Enabled Internet of Things Devices LoRa-DuCy:支持lora的物联网设备的占空比
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528805
Thenuka Karunathilake, A. Udugama, Anna Förster
The LoRa (Long Range) communication technology has gained a lot of interest recently. Typically, Internet of Things applications would use the complete LoRaWAN stack for their purposes. However, LoRaWAN supports only three types of communication, called classes A, B, and C. Each of these classes addresses a different application scenario and resource usage goals, but all of them target star topologies, where devices communicate only to the gateway. On the other hand, wireless sensor networks (WSNs) have been successfully developing and using the concept of self-organised networks and duty cycling. In this paper, we combine these fields and implement a WSN duty-cycling medium access protocol, known as Contiki-MAC, on LoRa-enabled LoPy4 devices. We experimentally evaluate our implementation and we compare its power consumption with that of LoRaWAN-enabled devices. We show that our implementation is more power-efficient while achieving higher traffic rates and offering the freedom of self-organised networking for various applications.
LoRa(远程)通信技术近年来引起了人们的广泛关注。通常,物联网应用程序将使用完整的LoRaWAN堆栈来实现其目的。然而,LoRaWAN只支持三种类型的通信,称为类A、B和c。这些类中的每一个都针对不同的应用场景和资源使用目标,但它们都针对星型拓扑,其中设备仅与网关通信。另一方面,无线传感器网络(WSNs)已经成功地开发和使用了自组织网络和占空比的概念。在本文中,我们将这些领域结合起来,并在支持lora的LoPy4设备上实现了WSN占空介质访问协议,称为Contiki-MAC。我们通过实验评估了我们的实现,并将其功耗与支持lorawan的设备的功耗进行了比较。我们证明了我们的实现更节能,同时实现更高的流量速率,并为各种应用程序提供自组织网络的自由。
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引用次数: 4
Distance Estimation Algorithm Based on Multi-Antenna Signal Attenuation Model 基于多天线信号衰减模型的距离估计算法
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528555
Jingjing Wang, Jishen Peng, Xianqing Wang, J. Hwang, J. Park
Received Signal Strength Indicators (RSSI)-based indoor positioning technology is widely used in the field of Wi-Fi indoor positioning. However, the propagation of RSSI is still affected by indoor multipath, and we cannot obtain signals in some corner areas. This paper analyzes the distance relationship between the RSSI on each antenna of the receiver and the distance between transmitter and proposes a novel ranging algorithm based on multi-antenna RSSI measurements. This novel algorithm uses a Least Squares Method (LSM) on the basis of a signal attenuation model to optimize, eliminate the noise and redundancy of the original data and reduce the positioning error. Experimental results show that the indoor multi-antenna RSSI ranging based on the single Gaussian model has high fitting accuracy and applicability. The proposed approach achieves significant localization accuracy improvement over using the single antenna RSSI-based ranging method. Meanwhile, the algorithm improves the influence of multiple paths in a complex indoor environment on location, and the method can obtain more accurate ranging results.
基于接收信号强度指标(RSSI)的室内定位技术广泛应用于Wi-Fi室内定位领域。但是,RSSI的传播仍然受到室内多径的影响,在一些角落区域无法获得信号。分析了接收机各天线RSSI与发射机之间的距离关系,提出了一种基于多天线RSSI测量的测距算法。该算法采用基于信号衰减模型的最小二乘法(LSM)对原始数据进行优化,消除了原始数据的噪声和冗余,减小了定位误差。实验结果表明,基于单高斯模型的室内多天线RSSI测距具有较高的拟合精度和适用性。与基于单天线rssi的测距方法相比,该方法显著提高了定位精度。同时,该算法改善了复杂室内环境中多路径对定位的影响,可以获得更精确的测距结果。
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引用次数: 0
Avoiding Content Storm Problem in Named Data Networking 命名数据网络中避免内容风暴问题
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528754
Sungwon Lee, J. Ha, Junho Seo, Dongkyun Kim
Recently, methods are studied to overcome various problems for Named Data Networking(NDN). Among them, a new method which can overcome content storm problem is required to reduce network congestion and deliver content packet to consumer reliably. According to the various studies, the content storm problems could be overcame by scoped interest flooding. However, because these methods do not considers not only network congestion ratio but also the number another different paths, the correspond content packets could be transmitted unnecessary and network congestion could be worse. Therefore, in this paper, we propose a new content forwarding method for NDN to overcome the content storm problem. In the proposed method, if the network is locally congested and another paths are generated, an intermediate node could postpone or withdraw the content packet transmission to reduce congestion.
近年来,人们对命名数据网络(NDN)中存在的各种问题进行了研究。其中,需要一种能够克服内容风暴问题的新方法来减少网络拥塞,并将内容包可靠地传递给用户。根据各种研究,内容风暴问题可以通过范围利益泛滥来克服。但是,由于这些方法不仅没有考虑网络拥塞率,而且没有考虑不同路径的数量,因此可能会传输不必要的相应内容数据包,从而加剧网络拥塞。因此,在本文中,我们提出了一种新的NDN内容转发方法来克服内容风暴问题。在该方法中,如果网络局部拥塞,并且产生了其他路径,则中间节点可以延迟或撤回内容包的传输,以减少拥塞。
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引用次数: 0
Optimal Decision-Making Strategies for Self-Driving Car Inspired by Game Theory 基于博弈论的自动驾驶汽车最优决策策略
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528803
Kyoungtae Ji, Kyoungseok Han
This paper presents an optimal decision-making strategy for a self-driving car using a game-theoretic approach. To ensure the safety of the decision, Stackelberg game's maximin reward strategy, which considers concurrency, is applied. The receding horizon is included to increase the accuracy of the decision, but the computational burden is high. We assume that the follower takes only one prediction time, not the receding horizon, to relieve the computational burden. For an accurate prediction of interacting vehicles, the intention estimation model is suggested. We demonstrate the efficiency of our approach in a simulation environment and various traffic conditions.
本文利用博弈论的方法提出了一种自动驾驶汽车的最优决策策略。为了保证决策的安全性,采用了考虑并发性的Stackelberg博弈的最大奖励策略。为了提高决策精度,引入了视界后退,但计算量大。我们假设追随者只需要一个预测时间,而不是后退的视界,以减轻计算负担。为了准确预测相互作用的车辆,提出了意图估计模型。我们在模拟环境和各种交通条件下证明了我们的方法的有效性。
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引用次数: 0
Performance improvement for windowed OFDM using pre-coding and sub-carriers interleaving 利用预编码和子载波交织技术改进带窗OFDM的性能
Pub Date : 2021-08-17 DOI: 10.1109/ICUFN49451.2021.9528667
K. Ohno
This paper discusses the out-of-band emission power reduction technique by window functions and performance improvement scheme for OFDM (Orthogonal Frequency Multiple Access) systems. The windowed OFDM is effective to suppress the out-of-emission band, however, the communication performance is degraded, comparing with the conventional OFDM system. To improve the performance in frequency selective fading channel, the pre-coding using the unitary transform is adopted. Moreover, the performance improvement scheme that data sequence between the unitary transform and IFFT/FFT is interleaved for the pre-coded windowed OFDM system is proposed. The BER (Bit Error Rate) is evaluated to show the effectiveness of the proposed scheme, comparing to the conventional pre-coded windowed schemes and the out-of-band emission power is also reduced.
讨论了正交频多址(OFDM)系统的窗函数带外发射功率降低技术和性能改进方案。加窗OFDM能有效抑制发射带外,但与传统OFDM系统相比,通信性能下降。为了提高在频率选择性衰落信道中的性能,采用了幺正变换进行预编码。此外,针对预编码的加窗OFDM系统,提出了一种将酉变换与IFFT/FFT之间的数据序列交错处理的性能改进方案。与传统的预编码加窗方案相比,该方案的误码率(BER)得到了验证,带外发射功率也得到了降低。
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引用次数: 0
期刊
2021 Twelfth International Conference on Ubiquitous and Future Networks (ICUFN)
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