2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)最新文献_第6页

Information-Driven Distributed Sensing for Efficient Bayesian Inference in Internet of Things Systems 物联网系统中高效贝叶斯推理的信息驱动分布式感知

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397111

Chongyu Zhou, Qiang Li, C. Tham

{"title":"Information-Driven Distributed Sensing for Efficient Bayesian Inference in Internet of Things Systems","authors":"Chongyu Zhou, Qiang Li, C. Tham","doi":"10.1109/SAHCN.2018.8397111","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397111","url":null,"abstract":"Distributed Bayesian inference or estimation in Internet of Things (IoT) has recently received much attention due to its broad application in the areas of object classification, target tracking and medical diagnosis etc. In many distributed IoT systems with limited resources, e.g. sensor networks and crowdsourcing systems, it is likely that only a few agents will have valuable information at any given time. Therefore, the paradigm of information-driven distributed sensing (IDDS) is essential to achieve efficient inference, where the resources are spent only on sensing and communicating valuable information. In this paper, we consider the problem of IDDS for efficient Bayesian inference with exponential family distributions. We first propose a centralized algorithm (C-IDDS) where a centralized controller exists to make sensing decisions for the sensing agents. As the centralized algorithm does not scale well in large systems, we continue to design a distributed algorithm (D-IDDS) where each individual sensing agents can make their own sensing decisions independently. Both C-IDDS and D-IDDS are online algorithms which can adapt to stochastic system conditions without any future information. Through rigorous theoretical analysis, we prove that the proposed algorithms can achieve an asymptotically optimal system-wide utility. A real testbed has been built to evaluate the performance of the proposed algorithms in real-world environments. Using the data from the real-world testbed and comparing with some baseline methods, we demonstrate the effectiveness of the proposed C-IDDS and D-IDDS algorithms.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125100458","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

FPGA Implementation of a Power-Efficient and Low-Memory Capacity Turbo Decoding Architecture 一种低功耗、低存储容量Turbo解码架构的FPGA实现

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397159

Jie Zeng, Ming Zhan, Yaqin Shi

引用次数: 0

Single-Sensor Motion and Orientation Tracking in a Moving Vehicle 移动车辆中的单传感器运动和方向跟踪

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397116

Çagdas Karatas, Luyang Liu, M. Gruteser, R. Howard

{"title":"Single-Sensor Motion and Orientation Tracking in a Moving Vehicle","authors":"Çagdas Karatas, Luyang Liu, M. Gruteser, R. Howard","doi":"10.1109/SAHCN.2018.8397116","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397116","url":null,"abstract":"Given the increasing popularity of mobile and wearable devices, this paper explores the potential use of inertial sensors that are widely available on mobile and wearable devices for vehicle and driver tracking. Such a capability would enable novel classes of mobile safety and assisted driving applications without relying on information or sensors in the vehicle. Although inertial sensors have been widely used in motion tracking, existing approaches cannot distinguish the motion of the vehicle and the device's motion in the vehicle. Additionally, the noise exerted from the electronic components in the vehicle and the ferromagnetic frame of the vehicle distorts the inertial sensor readings. This paper introduces a method to separately estimate the orientation of both the vehicle and the sensor by tracking the earth's magnetic field and the electromagnetic distortion from the vehicle, as measured by a magnetometer in addition to a gyroscope and an accelerometer. Specifically, the vehicle noise is used to estimate the orientation of the sensor within the vehicle while the earth's magnetic field combined with vehicle noise is used to estimate the vehicle's heading. Our on-road experiments show that the technique is able to estimate the sensor orientation with a mean error of $5.61^o$ for the yaw angle and $3.73^o$ for the pitch angle, as well as able to estimate the vehicle heading with a mean error of $4.12^o$.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115652111","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 5

Scalability of Wireless Fingerprinting Based Indoor Localization Systems 基于无线指纹识别的室内定位系统的可扩展性

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397112

Yingling Mao, Ke Liu, Hao Li, Xiaohua Tian, Xinbing Wang

{"title":"Scalability of Wireless Fingerprinting Based Indoor Localization Systems","authors":"Yingling Mao, Ke Liu, Hao Li, Xiaohua Tian, Xinbing Wang","doi":"10.1109/SAHCN.2018.8397112","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397112","url":null,"abstract":"Fingerprinting indoor localization systems have been studied from different perspectives in the past decades; however, a vitally important piece in the puzzle is still missing: how does the system scale with the number of users? In this paper, we study the issue from a theoretical perspective, where the upper and lower bound of the system's localization reliability with respect to the number of users are derived. Our theoretical results can be verified by experiments thus can provide meaningful guidance for practical system design, which is in contrast to the traditional scaling-law work utilizing asymptotical analysis that is valid only under unverifiable extreme conditions. The theoretical and experimental results of our work reveal two interesting observations that shed light on the insight into the scalability of the fingerprinting localization system: First, the localization reliability drops dramatically before the number of users increases to a critical point and then decreases smoothly, where the critical point tends to appear when the number of users equals the number of access points (APs) deployed in the service region; second, even if the number of users approaches to infinity, the fingerprinting localization system still retains certain level of reliability.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"134 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116513943","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

Stochastic Scheduling Towards Cost Efficient Network Function Virtualization in Edge Cloud 边缘云中面向成本高效网络功能虚拟化的随机调度

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397140

Deze Zeng, Jie Zhang, Lin Gu, Song Guo

引用次数: 9

RFID Localization Based on Multiple Feature Fusion 基于多特征融合的RFID定位

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397148

Shigeng Zhang, Yan Fu, Danming Jiang, Xuan Liu

引用次数: 4

Towards Scalable Indoor Localization with Particle Filter and Wi-Fi Fingerprint 基于粒子滤波和Wi-Fi指纹的可扩展室内定位

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397155

Feiyu Jin, Kai Liu, H. Zhang, Liang Feng, Chao Chen, Weiwei Wu

引用次数: 2

Simulating Large-Scale Social Sensing Based Edge Computing Systems with Heterogeneous Network Configurations 模拟基于异构网络配置的大规模社会感知边缘计算系统

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397156

Nathan Vance, Ryan Mackey, D. Zhang, Dong Wang

引用次数: 3

Abstract: An Indoor Localization Simulation Platform for Localization Accuracy Evaluation 摘要:一种面向定位精度评估的室内定位仿真平台

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397158

Guangsheng Feng, Sen Liang, Huiqiang Wang, Junyu Lin, Jingyao Liu, Hongwu Lv, Shuai Zhao

引用次数: 4

UABeam: UAV-Based Beamforming System Analysis with In-Field Air-to-Ground Channels ubeam:基于无人机的现场空对地信道波束形成系统分析

2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON) Pub Date : 2018-06-01 DOI: 10.1109/SAHCN.2018.8397110

Yan Shi, R. Enami, John Wensowitch, J. Camp

{"title":"UABeam: UAV-Based Beamforming System Analysis with In-Field Air-to-Ground Channels","authors":"Yan Shi, R. Enami, John Wensowitch, J. Camp","doi":"10.1109/SAHCN.2018.8397110","DOIUrl":"https://doi.org/10.1109/SAHCN.2018.8397110","url":null,"abstract":"Precise air-to-ground propagation modeling is imperative for many unmanned aerial vehicle (UAV) applications such as search and rescue, reconnaissance, and disaster recovery. Furthermore, directionalization via MIMO-based beamforming can boost the transmission range by utilizing Channel State Information (CSI). However, the high mobility and flight conditions of drones can threaten the ability to receive accurate CSI in time to achieve such gains. In this work, we design a UAV-based software defined radio (SDR) platform and perform a measurement study to characterize the air-to-ground channel between the aerial platforms and a terrestrial user in practical scenarios such as hovering, encircling, and linear topologies. Our experiments cover multiple carrier frequencies, including cellular (900~MHz and 1800~MHz) and WiFi (5~GHz) bands. Furthermore, we address three baseline issues for deploying drone-based beamforming systems: channel reciprocity, feedback overhead, and update rate for channel estimation. Numerical results show that explicit CSI feedback can increase throughput by 123.9% over implicit feedback and the optimal update rate are similar across frequencies, underscoring the importance of drone-based beamfoming design. We additionally analyze the reciprocity error and find that the amplitude error remained steady while the phase error depends on mobility. Since our study spans many critical frequency bands, these results serve as a fundamental step towards understanding drone- based beamforming systems.","PeriodicalId":139623,"journal":{"name":"2018 15th Annual IEEE International Conference on Sensing, Communication, and Networking (SECON)","volume":"146 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2018-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122858197","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 17