AutoEncoder based Active Signal Map Crowdsourcing
Chengyong Liu, Yanchao Zhao, Kun Zhu

College of Computer Science and Technology, Nanjing University of Aeronautics and AstronauticsSheng Zhang
State Key laboratory for Novel Software Technology, Nanjing University
Jie WuCenter for Networked Computing, Department of Computer and Information Sciences, Temple University

Abstract
Signal maps, which consist of signal strengths at different locations, play animportant role in site spectrum monitoring[1], LBS[2] and network
construction[3]. But constructing the signal maps through on-site surveys, is timeconsuming and laborious. And the signal may change with time and surroundings,
which leads to a dilemma (Figure 1) that the acquisition and updating of the signal
map could hardly meet the requirements of the applications, no matter in quality
or timing.
Crowdsourcing schemes are proposed to address this issue, but collecting signals
through crowdsourcing suffers from random and insufficient participants, whichleads to incomplete or low-quality measurements.
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).
Figure 1: Application scenario and the dilemma

Results

Methodology
In order to solve these problems in a comprehensive way, we propose anovel signal map reconstruction algorithm based on auto-encoder based
active matrix completion.
In this work, we adopt the auto-encoder to learn the signal correlation
existing in the historical signal and effectively reconstruct the signal map.
The algorithm is divided into the offline training phase and the online
update phase (as shown in Fig. 2).

(a) RMSE at various number of samples(b) RMSE at different batch sizes(c) CDF of signal map reconstruction error(dB)(d) RE at various number of samples(e) RE at different batch sizes(f) CP at different sampling rates

Figure 3 :Experiment Result (The upper part is the result of no noise
and the lower part is the result of noise.)

Offline training phase

Figure 2 :The workflow of the AER.

Faced with a large amount of incomplete historical data, we extend
the training of auto-encoder with parallel training method.
• We divide the massive crowdsourced historical signals and thenuse each compute node to train part of the fragmentation data.
• We combine the gradient parameters generated by multiple
compute nodes and update them through the parameter server totrain the auto-encoder.

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 andreconstructs the signal map using the previously learned features.
• A fluctuation inference method infers the fluctuation range of thereconstructed signal map.
• An active crowdsourcing method finds the signal with the highest
informativeness.

References[1] Yang Bo, Suining He, and S. H. Gary Chan. Updating wireless signal map with bayesian compressive sensing. In Proc. ofAcm MSWiN, 2016.
[2] Simon Yiu, Marzieh Dashti, Holger Claussen, and Fernando PerezCruz. Wireless rssi fingerprinting localization. Signal
Processing,131:235–244, 2017.
[3] Zhijing Li, Ana Nika, Xinyi Zhang, Yanzi Zhu, Yuanshun Yao, Ben Y. Zhao, and Haitao Zheng. Identifying value incrowdsourced 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. IEEEJournal of Selected Topics in Signal Processing, 11(3):492–503, 2017.
[5] Zaiwen Wen. Solving a low-rank factorization model for matrix completion by a 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 bayesian compressive sensing. In Proc. of IEEE
INFOCOM, pages 1016–1024, April 2018.

Relative Error
From Fig.3(a) and Fig.3(d), we can clearly see that our algorithm is far superior to theother two algorithms. And we find that the Relative Error of our algorithm is 1% lower oreven 2% lower than the error of BCS, and AER is much better than MC. Then we compare
the cumulative distribution of errors in Fig.3(c). We can see that the performance of our
proposed 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 RSSI error isabout 90% less than 5.5dB.
Active Crowdsouring Scheme
For the active crowdsourcing problem, we found that the signal obtained by the active
method is much better than the random sampling method, and under the same errors, theactive AER only needs less than half of the number of random sampling in Fig.3(b) andFig.3(e).
Fluctuation Estimation
We use the fluctuation estimation algorithm to estimate the signal fluctuation range,
making the reconstructed signal map more practical. From Fig.3(f), we can see that evenat the lowest sampling rate, we can still guarantee that more than 90% of the signal range
contains the ground-truth of the signal.

Conclusion
In this paper, we propose a comprehensive solution for signal map acquisition, where autoencoder is used to learn the nonlinear features of and compose an algorithm called autoencoder(AER) firstly. The AER can effectively utilize historical incomplete signal maps
collected and learn the nonlinear temporal features therein and effectively reconstruct thesignal map; Secondly, we propose an active crowdsourcing scheme for better performanceof AER. This method can reveal the more valuable measurement sites for reconstruction
algorithm and effectively reduce the reconstruction error with lower crowdsourcing budget.
Finally, we also propose a more realistic signal map model with the description of thesignal 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 the effectiveness of our solution.