AutoEncoder based Active Signal Map Crowdsourcing
Chengyong Liu

Nanjing University of Aeronautics
and Astronautics
lhoien@nuaa.edu.cn

Yanchao Zhao

Kun ZhuNanjing University of Aeronautics
and Astronautics
yczhao@nuaa.edu.cn

Sheng Zhang

Nanjing University of Aeronautics
and Astronautics
zhukun@nuaa.edu.cn

Jie WuNanjing University
sheng@nju.edu.cn

Temple University
jiewu@temple.edu

ABSTRACT2IIOLQHWUDLQLQJ

Signal map is of great importance, especially in the dawn of 5G
network, location-based services (LBS) and cellular planning. But
due to participants and budget issues, the signal map constructedby crowdsourcing is often sparse and incomplete. In this work,
we study how to effectively reconstruct and update the signal
map in the case of partially measured signal maps and propose
an auto-encoder-based active signal map reconstruction method(AER). Our method is mainly innovative in three parts. Firstly, AER
can effectively update the signal map with only a small numberof observations online. Secondly, AER consists of an active querymechanism which further reduces the measurement cost to a large
extent. Thirdly, we give a new signal map model describing not
only the signal strength but also the signal dynamics, based on
which an advanced AER algorithm with parallel acceleration isproposed. The simulation results demonstrate the advantages andeffectiveness of our approach in both accuracy and cost.

Signal map, active matrix completion, auto-encoder, crowdsourcing

1

INTRODUCTION

Signal maps, which consist of signal strengths at different locations,play an important role in site spectrum monitoring [1], LBS [2], andnetwork construction [3]. But constructing the signal maps throughon-site surveys, is time-consuming and laborious. And the signal
may change with time and surroundings, which leads to a dilemmathat the acquisition and updating of the signal map could hardly
meet the requirements of the applications, no matter in quality ortiming. Crowdsourcing schemes are proposed to address this issue,
but collecting signals through crowdsourcing suffers from random
and insufficient participants, which leads to incomplete or lowquality measurements. To cope with this, existing works mainly try
to complete the signal map in condition of partial observations withsignal propagation model[4], matrix completion[5] or compressive
sensing[6]. These algorithms either have higher computational
Permission to make digital or hard copies of all or part of this work for personal orclassroom use is granted without fee provided that copies are not made or distributedfor profit or commercial advantage and that copies bear this notice and the full citationon the first page. Copyrights for components of this work owned by others than ACM
must be honored. Abstracting with credit is permitted. To copy otherwise, or republish,
to post on servers or to redistribute to lists, requires prior specific permission and/or afee. Request permissions from permissions@acm.org.
ICPP’ 19, August 05–08, 2019, Kyoto, Japan© 2019 Association for Computing Machinery.

0LVVLQJGDWD$YDLODEOHGDWD
5HFRYHUHGGDWD

SDUDOOHOWUDLQLQJ

2QOLQHXSGDWH

KEYWORDS

+LVWRULFDOLQFRPSOHWHPDSV/HJHQG)OXFWXDWLRQLQIHUHQFH

1HZ
6DPSOHG
6LJQDOV /RFDWLRQV$XWR(QFRGHU

5HFRQVWUXFWHGPDS$FWLYH&URZGVRXUFLQJ6FKHPH

Figure 1: The workflow of the AER
costs which are not conducive to updating the signal map in time,or lower reconstruction accuracy which does not meet the actual
requirements. In order to solve these problems in a comprehensive
way, we propose a novel signal map reconstruction algorithm basedon auto-encoder based active matrix completion.

2METHODOLOGY

In this section, we will propose an auto-encoder-based signal map
reconstruction method (AER in Fig.1). The algorithm is divided into
the offline training phase and the online update phase.
Offline training phase: faced with a large amount of incomplete historical data, we extend the training of auto-encoder withparallel training method.
• We divide the massive crowdsourced historical signals andthen use each compute node to train part of the fragmentation data.
• We combine the gradient parameters and update them throughthe parameter server.
Online update phase: we use the uploaded signal to reconstruct the signal map in time.
• The AER takes the partial signal collected at time t as input
and reconstructs the signal map using the previously learnedfeatures.
• A fluctuation inference method infers the fluctuation rangeof the reconstructed signal map.
• An active crowdsourcing method finds the signal with thehighest informativeness.

1
0.09

0.055

0.8
0.08

0.05

0.6

0.07

0.4

0.045

0.06

0.2

0.05

0.04
0.04
500

01000

1500

2000

2500

03000(a) RMSE at various number of samples.

100200300

400

500600(b) RMSE at different batch sizes.

0246810(c) CDF of signal map reconstruction error (dB)

0.07

100

0.065

98

0.09
0.08

0.06
0.07

96
0.055

0.06

94
0.05

0.05

0.04

0.045

0.03
500

92

90

0.04
1000

1500

2000

2500

3000(d) RE at various number of samples

0100

200

300

400500600(e) RE at different batch sizes

510152025(f) CP at different sampling rates

Figure 2: Experiment Result(The upper part is the result of no noise and the lower part is the result of noise.)

3

RESULTS

4

In this section, we will verify the effectiveness of our algorithm.
The experimental data is the simulated WiFi indoor positioning
data set. And we compare the performance of our algorithm to twostate-of-the-art missing value inference algorithms, compressivesensing[6] and matrix-completion[5].
Relative Error: from Fig.2(a) and Fig.2(d), we can clearly seethat our algorithm is far superior to the other two algorithms. And
we find that the Relative Error of our algorithm is 1% lower or even
2% lower than the error of BCS, and AER is much better than MC.
Then we compare the cumulative distribution of errors in Fig.2(c)and we can see that the performance of our algorithm far exceeds
the other two algorithms. AER’s RSSI error is about 90% less than
3.5dB, MC’s RSSI error is about 90% less than 5dB, and BCS’s RSSIerror is about 90% less than 5.5dB.
Active Crowdsouring Scheme: for the active crowdsourcing
problem, we use the active AER algorithm to compare with therandom sampling AER algorithm. And we found that the signal
obtained by the active method is much better than the random sampling method, and under the same errors, the active AER only needs
less than half of the number of random sampling in Fig.2(b) andFig.2(e). So active AER algorithm can greatly reduce the collection
cost required to reconstruct signals.
Fluctuation Estimation: we use the fluctuation estimation algorithm to estimate the signal fluctuation range, making the reconstructed signal map more practical. From Fig.2(f), we can seethat even at the lowest sampling rate, we can still guarantee that
more than 90% of the signal range contains the ground-truth of thesignal.

CONCLUSIONSIn this work, we propose a comprehensive solution for signal map
acquisition, where auto-encoder is used to learn the nonlinear features of and compose an algorithm called auto-encoder(AER) firstly.
The AER can effectively utilize historical incomplete signal maps
collected and learn the nonlinear temporal features therein andeffectively reconstruct the signal map; Secondly, we propose an
active crowdsourcing scheme for better performance of AER. Thismethod can reveal the more valuable measurement sites for reconstruction algorithm and effectively reduce the reconstruction errorwith lower crowdsourcing budget. Finally, we also propose a more
realistic signal map model with the description of the signal dynamics in the same location over time, and correspondingly, an extended
AER algorithm is proposed to solve the reconstruction problem on
this model. The simulation experiments results demonstrate theeffectiveness of our solution.

REFERENCES[1] Yang Bo, Suining He, and S. H. Gary Chan. Updating wireless signal map withbayesian compressive sensing. In Proc. of Acm MSWiN, 2016.
[2] Simon Yiu, Marzieh Dashti, Holger Claussen, and Fernando Perez-Cruz. Wirelessrssi fingerprinting localization. Signal Processing, 131:235–244, 2017.
[3] Zhijing Li, Ana Nika, Xinyi Zhang, Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao, andHaitao Zheng. Identifying value in crowdsourced wireless signal measurements.
In Proc. of Springer WWW, 2017.
[4] Feng Yin and Fredrik Gunnarsson. Distributed recursive gaussian processes for rss
map applied to target tracking. IEEE Journal of Selected Topics in Signal Processing,11(3):492–503, 2017.
[5] Zaiwen Wen. Solving a low-rank factorization model for matrix completion bya nonlinear successive over-relaxation algorithm. Mathematical Programming
Computation, 4(4):333–361, 2012.
[6] S. He and K. G. Shin. Steering crowdsourced signal map construction via bayesiancompressive sensing. In Proc. of IEEE INFOCOM, pages 1016–1024, April 2018.
2