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Inferring Energy Consumption Patterns in Public Buildings 推断公共建筑的能源消耗模式
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-11-16 DOI: 10.1145/3416011.3424753
R. Redondo, Ana Fernández Vilas, Antonio Abadía Rodríguez
The advent of smart meters have radically changed the mechanisms traditionally used for energy consumption monitoring. The possibility of having (i) highly frequent readings (even every minute); (ii) accurate energy data gathering; and (iii) real time data exchange capabilities to share the energy consumption data with other elements in the Smart Grid, open the door to further deeper analysis within this context. In this paper, we focus on obtaining energy consumption patterns. Our approach combines clustering and predictive techniques in order to infer these patterns using data gathered from public buildings in a university campus. Our analysis allows us to infer that clustering is not an appropriate mechanism, since the use of buildings is mixed (administrative, labs, classrooms, etc.). However, predictive approaches give promising results, specially LSTM and XGBoost.
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引用次数: 2
Artificial Neural Network Performance Evaluation for a Hybrid Power Domain Orthogonal / Non-Orthogonal Multiple Access (OMA / NOMA) System 混合功率域正交/非正交多址(OMA / NOMA)系统的人工神经网络性能评价
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-11-16 DOI: 10.1145/3416011.3424760
Juan Diego Belesaca, Pablo Avila-Campos, Andrés Vázquez Rodas
Next-generation wireless technologies face considerable challenges in terms of providing the required latency and connectivity for new heterogeneous mobile networks. Driven by these problems, this study focuses on increasing user connectivity together with system throughput. For doing so, we propose and evaluate a hybrid machine learning-driven orthogonal/non-orthogonal multiple access (OMA/NOMA) system. In this work, we use an artificial neural network (ANN) to assign an OMA or NOMA access method to each user equipment (UE). As part of this research we also evaluate the accuracy and training time of the three most relevant learning algorithms of ANN (L-M, BFGS, and OSS). The main objective is to increase the sum-rate of the mobile network in the introduced beamforming and mmWave channel environment. Simulation results show up to a $20%$ sum-rate average performance increase of the system using the ANN management in contrast to a random non-ANN managed system. The Leveberg-Marquard (L-M) training algorithm is the best overall algorithm for this proposed application as presents the highest accuracy of around $77%$ despite 37 minutes of training and lower accuracy of $73%$ with approximately 28 seconds of training time.
下一代无线技术在为新的异构移动网络提供所需的延迟和连接方面面临着相当大的挑战。在这些问题的驱动下,本研究的重点是提高用户连接性和系统吞吐量。为此,我们提出并评估了一种混合机器学习驱动的正交/非正交多址(OMA/NOMA)系统。在这项工作中,我们使用人工神经网络(ANN)为每个用户设备(UE)分配OMA或NOMA访问方法。作为本研究的一部分,我们还评估了三种最相关的人工神经网络学习算法(L-M, BFGS和OSS)的准确性和训练时间。主要目标是在引入的波束形成和毫米波信道环境中提高移动网络的和速率。仿真结果表明,与随机的非人工神经网络管理系统相比,使用人工神经网络管理的系统的平均性能提高了20%。Leveberg-Marquard (L-M)训练算法是该应用程序的最佳整体算法,尽管训练时间为37分钟,但准确率最高,约为77%;训练时间约为28秒,准确率较低,为73%。
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引用次数: 2
Improving Link Reliability of IEEE 802.15.4g SUN with Re-Transmission Shaping 通过重传整形提高IEEE 802.15.4g SUN的链路可靠性
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-10-22 DOI: 10.1145/3416011.3424750
D. Solimini, Pere Tuset, Guillem Boquet, Xavier Vilajosana, F. V. Gallego
Packet re-transmissions are a common technique to improve link reliability in low-power wireless networks. However, since packet re-transmissions increase the end-device energy consumption and the network load, a maximum number of re-transmissions per packet is typically set, also considering the duty-cycle limitations imposed by radio-frequency regulations. Moreover, the number of re-transmissions per packet is typically set to a constant value, meaning that all packet re-transmissions are treated the same regardless of actual channel conditions (i.e., multi-path propagation or internal/external interference effects). Taking that into account, in this paper we propose and evaluate the concept of re-transmission shaping, a mechanism that manages packet re-transmissions to maximize link reliability, while minimizing energy consumption and meeting radio-frequency regulation constraints. The proposed re-transmission shaping mechanism operates by keeping track of unused packet re-transmissions and allocating additional re-transmission when the instantaneous link quality decreases due to channel impairments. To evaluate the re-transmission shaping mechanism we use trace-based simulations using a IEEE~802.15.4g SUN data-set and two widely used metrics, the PDR (Packet Delivery Ratio) and the RNP (Required Number of Packets). The obtained results show that re-transmission shaping is a useful mechanism to improve link reliability of low-power wireless communications, as it can increase PDR from 77.9% to 99.2% while sustaining a RNP of 2.35 re-transmissions per packet, when compared to using a single re-transmission per packet.
分组重传是一种在低功耗无线网络中提高链路可靠性的常用技术。但是,由于分组重传增加了终端设备的能耗和网络负载,因此通常设置每个分组的最大重传次数,同时还考虑到射频法规施加的占空比限制。此外,每个数据包的重传次数通常设置为一个恒定值,这意味着无论实际信道条件(即多路径传播或内部/外部干扰效应)如何,所有数据包的重传都被视为相同。考虑到这一点,在本文中,我们提出并评估了重传成形的概念,这是一种管理分组重传以最大化链路可靠性的机制,同时最小化能量消耗并满足射频监管约束。所提出的重传整形机制通过跟踪未使用的数据包重传并在瞬时链路质量由于信道损伤而下降时分配额外的重传来运行。为了评估重传塑形机制,我们使用基于跟踪的模拟,使用IEEE~802.15.4g SUN数据集和两个广泛使用的指标,PDR(包投递率)和RNP(所需包数)。所获得的结果表明,重传整形是提高低功耗无线通信链路可靠性的有用机制,因为它可以将PDR从77.9%提高到99.2%,同时保持每个数据包2.35次重传的RNP,而不是使用每个数据包一次重传。
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引用次数: 1
Literature Survey 文献调查
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-07-26 DOI: 10.1201/9780429281051-2
Damodar Reddy Edla, Mahesh Chowdary Kongara, Amruta Lipare, Venkatanareshbabu Kuppili, K. Kannadasan
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引用次数: 0
Conclusion and Future Research 结论与未来研究
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-07-25 DOI: 10.1201/9780429281051-9
D. Edla, Mahesh Chowdary Kongara, Amruta Lipare, Venkatanareshbabu Kuppili, K. Kannadasan
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引用次数: 0
PE-WASUN '20, Proceedings of the 17th ACM Symposium on Performance Evaluation of Wireless Ad Hoc, Sensor, & Ubiquitous Networks, Alicante, Spain, November 16-20, 2020 PE-WASUN '20,第17届ACM无线自组织、传感器和无处不在网络性能评估研讨会论文集,阿利坎特,西班牙,2020年11月16-20日
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-01-01 DOI: 10.1145/3416011
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引用次数: 0
Wireless Sensor Networks: 14th China Conference, CWSN 2020, Dunhuang, China, September 18–21, 2020, Revised Selected Papers 无线传感器网络:第十四届中国会议,CWSN 2020,中国敦煌,2020年9月18-21日,论文选集
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2020-01-01 DOI: 10.1007/978-981-33-4214-9
J. Filipe, Ashish Ghosh, R. Prates, Zhanjun Hao, Xiao-chao Dang, Honghong Chen, Fenfang Li
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引用次数: 0
Optimization of Key Devices Positions in Large-Scale RF Mesh Networks 大规模射频Mesh网络中关键器件位置的优化
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2019-11-25 DOI: 10.1145/3345860.3361515
A. M. Mezher, N. Rajendran, Pedro Enrique Iturria Rivera, Carlos Lester Dueñas Santos, J. Meng, Eduardo Castillo Guerra
At the present time, a great interest has been shown by the research and the industrial community concerning smart grid communications where important technical advances have arisen as a consequence. Concretely, one of the most important goals of RF mesh network is the design and development of an efficient routing protocol to connect different devices of the network between each other. Nevertheless, the overall performance of the RF Mesh network communications depends in high measure on the positions of key devices in Radio Frequency (RF) Mesh network such as routers and collectors. With this in mind, we focus our work in this paper to design a strategy to optimize the positions of the key devices of RF Mesh network such as routers and collectors to achieve the highest possible connectivity between different components of RF Mesh network. To do so, we have used a well-known clustering algorithm called Lloyd's algorithm, to design our strategy, with the clear objective of allocating routers and collectors in their optimal positions in a smart grid scenario. Extensive simulations have been carried out with the proposed strategy, and good results have been obtained.
目前,智能电网通信已经引起了研究和工业界的极大兴趣,并取得了重要的技术进步。具体来说,射频网状网络的一个重要目标是设计和开发一种有效的路由协议,使网络中的不同设备之间相互连接。然而,射频Mesh网络通信的整体性能在很大程度上取决于射频Mesh网络中关键设备(如路由器和采集器)的位置。考虑到这一点,我们在本文中重点设计了一种策略来优化RF Mesh网络的关键设备(如路由器和收集器)的位置,以实现RF Mesh网络的不同组件之间尽可能高的连接。为此,我们使用了一种著名的聚类算法Lloyd's算法来设计我们的策略,其明确的目标是将路由器和收集器分配到智能电网场景中的最佳位置。利用所提出的策略进行了大量的仿真,取得了良好的效果。
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引用次数: 4
Session details: Mesh and Ubiquitous Networks 会议细节:网格和无处不在的网络
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2019-11-25 DOI: 10.1145/3373261
L. Urquiza-Aguiar
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引用次数: 0
Minkowski Distance Order Effect on the Optimization of Key Devices Positions in Large-Scale RF Mesh Networks 闵可夫斯基距离序对大规模射频Mesh网络中关键器件位置优化的影响
IF 0.9 4区 计算机科学 Q3 Computer Science Pub Date : 2019-11-25 DOI: 10.1145/3345860.3361516
A. M. Mezher, Pedro Enrique Iturria Rivera, Carlos Lester Dueñas Santos, J. Cardenas-Barrera, J. Meng, Eduardo Castillo Guerra
Recently, smart grid communications have attracted much attention from the research community. An excellent performance of a RF Mesh network in terms of quality of service parameters depends not only on having an efficient routing protocol to route packets but also on the positions of routers and collectors located in the same network. This makes the positioning of routers and collectors in RF Mesh networks a challenging task. Moreover, an efficient strategy to optimize jointly routers and collectors positions have been proposed recently by us. However, in this work, we have studied the impact of using different Minkowski distance order on the proposed strategy in a RF Mesh context. Extensive simulations in OMNET++ have been carried out with the proposed strategy for different Minkowski distance order. Results indicate that the Euclidean distance is the best Minkowski distance order choice to obtain the best results in terms of average percentage of packet delivery rate and average end-to-end delay.
近年来,智能电网通信受到了研究界的广泛关注。就服务质量参数而言,RF Mesh网络的优异性能不仅取决于具有有效的路由协议来路由数据包,还取决于位于同一网络中的路由器和收集器的位置。这使得射频Mesh网络中的路由器和收集器的定位成为一项具有挑战性的任务。此外,我们最近还提出了一种有效的路由器和收集器位置联合优化策略。然而,在这项工作中,我们研究了在RF Mesh环境中使用不同闵可夫斯基距离顺序对所提出策略的影响。在omnet++中对不同闵可夫斯基距离阶的策略进行了大量的仿真。结果表明,在平均分组发送率百分比和平均端到端延迟方面,欧几里得距离是获得最佳结果的最佳闵可夫斯基距离顺序选择。
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Ad Hoc & Sensor Wireless Networks
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