OwLL: Accurate LoRa Localization using the TV Whitespaces

被引:21
作者
Bansal, Atul [1 ]
Gadre, Akshay [1 ]
Singh, Vaibhav [1 ]
Rowe, Anthony [1 ]
Iannucci, Bob [1 ]
Kumar, Swarun [1 ]
机构
[1] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA
来源
IPSN'21: PROCEEDINGS OF THE 20TH ACM/IEEE CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS | 2021年
基金
美国国家科学基金会;
关键词
LPWAN; Localization; Sensor Networks; TV whitespaces;
D O I
10.1145/3412382.3458263
中图分类号
TP [自动化技术、计算机技术];
学科分类号
0812 ;
摘要
LoRa is a popular Low-Power Wide-Area Networking (LPWAN) technology that allows devices powered by a ten year AA battery to connect to radio infrastructure miles away. One of the most promising features of LoRa is the ability to track the location of radios from a distance, enabling applications ranging from inventory tracking, smart infrastructure monitoring and structural health sensing. Yet, state-of-the-art LoRa localization systems experience errors of several tens or even hundreds of meters in location tracking, owing to the narrow bandwidth and limited battery life of LoRa devices. This paper presents OwLL, a LoRa localization system that limits location error to few meters with commodity LoRa clients in a wide-area network. Our key innovation is the development of a distributed base station network made of individually low-cost components that together span a wide bandwidth that encompasses the TV whitespaces and offers high aperture, crucial to localization accuracy. We demonstrate how this network can aggregate signal measurements made across multiple different narrowband channels of a LoRa client to triangulate it at fine accuracy. We implement and evaluate OwLL on a testbed spanning a large university campus centered in a major U.S. city and demonstrate a 9 m (across line-of-sight and non-line-of-sight) median error in localization.
引用
收藏
页码:148 / 162
页数:15
相关论文
共 49 条
  • [1] TDAoA: A combination of TDoA and AoA localization with LoRaWAN
    Aernouts, Michiel
    BniLam, Noori
    Berkvens, Rafael
    Weyn, Maarten
    [J]. INTERNET OF THINGS, 2020, 11
  • [2] Sigfox and LoRaWAN Datasets for Fingerprint Localization in Large Urban and Rural Areas
    Aernouts, Michiel
    Berkvens, Rafael
    Van Vlaenderen, Koen
    Weyn, Maarten
    [J]. DATA, 2018, 3 (02)
  • [3] ALIPPI C, 2006, 4 ANN IEEE INT C PER
  • [4] Andrade T., 2011, 2011 International Conference on Localization and GNSS (ICL-GNSS), P77, DOI 10.1109/ICL-GNSS.2011.5955273
  • [5] Ayyalasomayajula Roshan, 2020, ACM MOBICOM
  • [6] Bargh M., 2008, ACM WORKSHOP MOBILE, DOI 10.1145/1410012.1410024
  • [7] HIGH-RESOLUTION FREQUENCY-WAVENUMBER SPECTRUM ANALYSIS
    CAPON, J
    [J]. PROCEEDINGS OF THE IEEE, 1969, 57 (08) : 1408 - &
  • [8] Low-Power LoRa Signal-Based Outdoor Positioning Using Fingerprint Algorithm
    Choi, Wongeun
    Chang, Yoon-Seop
    Jung, Yeonuk
    Song, Junkeun
    [J]. ISPRS INTERNATIONAL JOURNAL OF GEO-INFORMATION, 2018, 7 (11):
  • [9] Charm: Exploiting Geographical Diversity Through Coherent Combining in Low-Power Wide-Area Networks
    Dongare, Adwait
    Narayanan, Revathy
    Gadre, Akshay
    Luong, Anh
    Balanuta, Artur
    Kumar, Swarun
    Iannucci, Bob
    Rowe, Anthony
    [J]. 2018 17TH ACM/IEEE INTERNATIONAL CONFERENCE ON INFORMATION PROCESSING IN SENSOR NETWORKS (IPSN), 2018, : 60 - 71
  • [10] A hybrid outdoor localization scheme with high-position accuracy and low-power consumption
    Du, Hongwei
    Zhang, Chen
    Ye, Qiang
    Xu, Wen
    Kibenge, Patricia Lilian
    Yao, Kang
    [J]. EURASIP JOURNAL ON WIRELESS COMMUNICATIONS AND NETWORKING, 2018,