QualityDeepSense:Quality-Aware Deep LearningFramework for Internet of Things Applications with

Sensor-Temporal AttentionShuochao Yao

University of Illinois Urbana ChampaignYiran Zhao

University of Illinois Urbana Champaign

Shaohan HuIBM

Tarek AbdelzaherUniversity of Illinois Urbana Champaign

ABSTRACTDeep neural networks are becoming increasingly popular inmobile sensing and computing applications. Their capabil-ity of fusing multiple sensor inputs and extracting temporalrelationships can enhance intelligence in a wide range of ap-plications. One key problem however is the noisy on-devicesensors, whose characters are heterogeneous and varyingover time. The existing mobile deep learning frameworksusually treat every sensor input equally over time, lackingthe ability of identifying and exploiting the heterogeneity ofsensor noise. In this work, we propose QualityDeepSense, adeep learning framework that can automatically balance thecontribution of sensor inputs over time by their sensing qual-ities. We propose a sensor-temporal attention mechanism tolearn the dependencies among sensor inputs over time. Thesecorrelations are used to infer the qualities and reassign thecontribution of sensor inputs. QualityDeepSense can thusfocus on more informative sensor inputs for prediction. Wedemonstrate the effectiveness of QualityDeepSense usingthe noise-augmented heterogeneous human activity recogni-tion task. QualityDeepSense outperforms the state-of-the-artmethods by a clear margin. In addition, we show Quality-DeepSense only impose limited resource-consumption bur-den on embedded devices.

Permission to make digital or hard copies of all or part of this work forpersonal or classroom use is granted without fee provided that copies are notmade or distributed for profit or commercial advantage and that copies bearthis notice and the full citation on the first page. Copyrights for componentsof this work owned by others than ACMmust be honored. Abstracting withcredit is permitted. To copy otherwise, or republish, to post on servers or toredistribute to lists, requires prior specific permission and/or a fee. Requestpermissions from permissions@acm.org.EMDL’18, June 15, 2018, Munich, Germany© 2018 Association for Computing Machinery.ACM ISBN 978-1-4503-5844-6/18/06. . . $15.00https://doi.org/10.1145/3212725.3212729

CCS CONCEPTS• Human-centered computing → Ubiquitous and mo-bile computing; • Computing methodologies → Ma-chine learning; • Computer systems organization →Embedded and cyber-physical systems;

KEYWORDSDeep Learning, Sensing Quality, Mobile Computing, Internetof Things

ACM Reference Format:Shuochao Yao, Yiran Zhao, Shaohan Hu, and Tarek Abdelzaher.2018. QualityDeepSense: Quality-Aware Deep Learning Frameworkfor Internet of Things Applications with Sensor-Temporal Attention.In EMDL’18: 2nd International Workshop on Embedded and MobileDeep Learning , June 15, 2018, Munich, Germany. ACM, New York,NY, USA, 6 pages. https://doi.org/10.1145/3212725.3212729

1 INTRODUCTIONThe proliferation of embedded and mobile devices able toperform complex sensing and recognition tasks unveils thefuture of intelligent Internet of things. Nowadays Internetof Things (IoT) applications cover a broad range of areasincluding health and well-being [8], context sensing [11, 12],crowd sensing and localization [10, 15].At the same time, deep neural networks have advanced

greatly in processing human-centric data, such as images,speech, and audio. The use of deep neural network has alsogained increasing popularity in mobile sensing and comput-ing research [18]. Great efforts have been made on designingunified structures for fusing multiple sensing inputs and ex-tracting temporal relationships [9, 16], compressing neuralnetwork structures for reducing resource consumptions onlow-end devices [4, 19], and providing well-calibrated uncer-tainty estimations for neural network predictions [6, 17].

To further advance such development for IoT applications,we need to address the key challenge brought by the het-erogeneity of input sensor quality. On one hand, in order to

EMDL’18, June 15, 2018, Munich, Germany Shuochao Yao et al.

control the overall cost, IoT devices are equipped with low-cost sensors. Compared to dedicated sensors, they have in-sufficient calibration, accuracy, and granularity. The sensingquality of the heterogeneous on-device sensors can thereforebe quite different. On the other hand, the unpredictable sys-tem workload, such as heavy multitasking and I/O load, canlead to unstable sensor sampling rates, because the OS mayoften fail to attach an accurate timestamp for each sensormeasurement. The sensing quality can therefore be hetero-geneous over time as well.

In order to tackle with the heterogeneity of sensing qual-ities over sensors and time, we propose QualityDeepSensethat designs a deep learning framework with a sensor-temporal self attention mechanism. The proposed self at-tention mechanism improves a neural network’s focus ondifferent sensor inputs by inferring their sensing qualities.The key idea of QualityDeepSense is to identify the qualitiesof sensing inputs by calculating the dependencies of theirinternal representations in the deep neural network.We assume that each sensor input is the composition of

sensing quantity and noise. A sensing input with higherquality should contain a larger proportion of sensing quan-tity and a smaller proportion of noise. However, measuringthe quality of sensing input directly is a challenging task.QualityDeepSense solves the problem by exploiting the de-pendencies among all input sensing quantities. For a partic-ular IoT application, the correlated sensing quantities formcomplex dependencies that determine the final predictionor estimation, while the noises do not. Therefore, the extentof dependency and correlation among sensing inputs can beused to estimate the sensing quality. For example, a sensinginput showing strong dependencies on other inputs is morelikely a high-quality measurement.

QualityDeepSense estimates the dependencies of sensinginputs by proposing a sensor-temporal self-attention mecha-nism. It calculates the dependencies among different sensorsand over time in a hierarchical way to reduce computation.The self-attention mechanism is a component that is insertedwhen the neural network merge the information from differ-ent sensors or merge over time. The self-attention compo-nent can be viewed as a weighted sum of inputs, where theweight is controlled by the degree of dependency calculatedby internal representations.

We evaluate QualityDeepSense with noise-augmented het-erogeneous human activity recognition task (N-HHAR). Theoriginal heterogeneous human activity recognition task per-forms human activity recognition with accelerometer andgyroscope measurements [12]. We add white Gaussian noiseon the time domain or the frequency domain to generatenoise-augmented datasets. We compare QualityDeepSense tothe state-of-the-art DeepSense framework [16] to illustrate

the efficacy of our sensor-temporal self-attention mecha-nism on exploiting heterogeneous sensing quality. We alsotest QualityDeepSense on Nexus 5 phones to show the lowoverhead of QualityDeepSense.

The rest of this paper is organized as follows. Section 2 in-troduces relatedwork on dealingwith heterogeneous sensingquality and attention mechanism. We describe the technicaldetails of QualityDeepSense in Section 3. The evaluation ispresented in Section 4. Finally, we discuss the results andconclude in Section 5.

2 RELATEDWORKA key problem in mobile sensing research is to handle theheterogenous sensing quality. Stisen et al. systematicallyinvestigate sensor-specific, device-specific and workload-specific heterogeneities using 36 smartphones and smart-watches, consisting of 13 different device models from fourmanufacturers [12]. These extensive experiments witnessperformance degradation due to the heterogenous sensingquality.Recently, deep neural networks have achieved great im-

provement on processing human-centric data. Lane et al.proposed to use deep neural networks to solve commonaudio sensing tasks [9]. Yao et al. designed a unified deepneural network framework called DeepSense for mobile sens-ing and computing tasks. DeepSense can effectively fuseinformation from multiple sensing inputs and extract tem-poral relationships. However, none of these works has takenthe heterogenous sensing quality into consideration. To thebest of our knowledge, QualityDeepSense is the first deeplearning framework that exploits sensing quality for IoTapplications.At the same time, attention mechanism has made great

advances in traditional machine learning tasks. Bahdanauet al. propose the first attention mechanism for machinetranslation [3], which improves word alignment. Xu et al.design the attention mechanism for image caption with bothhard and soft attentions [14]. Recently, Vaswani et al. exploitthe attention mechanism by designing a neural networkwith only self-attention components [13]. To the best of ourknowledge, we are the first to use self-attention mechanismfor estimating and exploiting heterogenous sensing quality.

3 SYSTEM FRAMEWORKIn this section we introduce the QualityDeepSense frame-work that automatically balances the contribution of sensorinputs over time according to their sensing qualities. Weseparate our description into two parts. We first describe theoverall structure of QualityDeepSense. Then we describe thesensor-temporal self-attention module in detail.For the rest of this paper, all vectors are denoted by bold

lower-case letters (e.g., x and y), while matrices and tensors

Quality-Aware Deep Learning with Sensor-Temporal Attention EMDL’18, June 15, 2018, Munich, Germany

GRU

GRU

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Figure 1: Main architecture of the QualityDeepSense framework.

are represented by bold upper-case letters (e.g.,X and Y). Fora vector x, the jth element is denoted by x[j]. For a tensorX, the t th matrix along the third axis is denoted by X· ·t ,and other slicing denotations are defined similarly. We usecalligraphic letters to denote sets (e.g., X andY). For any setX, |X| denotes the cardinality of X.

3.1 QualityDeepSense Structure

For a particular application, we assume that there areK dif-ferent types of input sensors S = {Sk }, k ∈ {1, · · · ,K }. Takea sensor Sk as an example. It generates a series of measure-ments over time. The measurements can be represented by ad (k ) × n(k ) measured value matrix V and a n(k )-dimensionaltimestamp vector u, where d (k ) is the dimension for eachmeasurement (e.g., raw measurements along x, y, and z axesfor motion sensors have dimension 3) and n(k ) is the numberof measurements. We split the input measurements V and ualong time (i.e., columns for V) to generate a series of non-overlapping time intervals with width τ ,W = {(V(k )

t , u(k )t )},

where |W| = T . Note that, τ can be different for differentintervals, but here we assume a fixed time interval widthfor succinctness. We then apply Fourier transform to each

element inW , because the frequency domain contains bet-ter local frequency patterns that are independent of howtime-series data is organized in the time domain. We stackthese outputs into a d (k ) × 2f × T tensor X(k ) , where f isthe dimension of frequency domain containing f magnitudeand phase pairs [16]. The set of resulting tensors for eachsensor, X = {X(k ) }, is the input of QualityDeepSense.As shown in Figure 1, QualityDeepSense inserts

sensor-temporal attention modules hierarchically intoDeepSense [16], which empowers the framework to esti-mate and utilize the input sensing qualities and boost theoverall prediction performance.

The overall structure can be separated into three subnets.For each time interval t , the matrix X(k )

..t will be fed into anindividual convolutional subnet for extracting the relation-ships within the frequency domain and across the sensormeasurement dimension. The individual convolutional sub-net learns high-level relationships X(k,1)

..t , X(k,2)..t , and X(k,3)

..thierarchically for each sensing input individually.

Then we flatten the matrix X(k,3)..t into x(k,3)..t and concat all

K vectors {x(k,3)..t } into aK-rowmatrixX(3)..t , which is the input

of our sensor attention module. The sensor attention moduleestimate the sensing quality of K inputs by calculating theirinternal dependencies. Then the module generates a K-dim

EMDL’18, June 15, 2018, Munich, Germany Shuochao Yao et al.

kf

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Figure 2: The structure of sensor/temporal self-attention module.attention vector at , which is used to calculate the row-wisesumX(4)

..t fromX(3)..t . The detailed structure of sensor attention

module will be described in Section 3.2.Next, the merged convolutional subnet hierarchically

learns the relationships X(5)..t , X

(6)..t , and X

(7)..t among K sensing

inputs. The output of merged convolutional subnet is flatteninto vector x(c )..t as the input of recurrent layers.

The recurrent layers is a two-layer Gated Recurrent Unit(GRU). The input {x(c )..t } for t = 1, · · · ,T are fed into stackedGRU which generates outputs {x(r )..t } for t = 1, · · · ,T . Weconcatenate all T recurrent-layer output {x(r )..t } into a T -rowmatrixX(r ) . Then we apply the temporal attention module tolearn the sensing quality over time. The structure of temporalattention module is similar as the sensor attention module,which will be described in Section 3.2. The resulting vectorgoes through a sofmax layer for classification.

3.2 Sensor-Temporal Self-AttentionModule

In this subsection, we describe the structure of sen-sor/temporal self-attentionmodule.We assume that the inputof self-attention module is a matrix Z ∈ Rk×f , where k is thedimension for attention and f is the feature dimension. Thestructure of our self-attention module is shown in Figure 2.Notice that there is no additional parameters involves in theself-attention module.The self-attention module can be summarized into two

steps. First, we calculate the attention vector a based on inputmatrix Z.

a = Softmax(1 · (Z · Z⊺ )

)(1)

where 1 = [1]k is k-dim vector with all elements equal to 1.Second, we calculate the weighted sum over the rows of

Z with the attention vector a.

y = a · Z (2)

Here, we provide some explanations about our design.The purpose of the self-attention module is to estimate thedependencies among k vectors {Zk ·}. Since Zk · are inter-nal representation in the neural network, we calculate thepair-wise dot product among Zk · to estimate their pair-wisedependency. Next, we sum the dependency matrix D overrows to estimate the dependency of each input vector on allothers, including itself. Then, we use the Softmax functionto generate the attention vector a, which sums to 1. Finally,we calculate the weighted sum over the rows of Z with theattention vector a.

4 EVALUATIONIn this section, we evaluate QualityDeepSense using the taskof human activity recognition with motion sensors. We firstintroduce our experimental settings, including the hardware,dataset, and baseline algorithm. We then evaluate our designin terms of accuracy, time, and energy consumption.

4.1 HardwareIn this evaluation, we run all experiments on the Nexus 5phone. The Nexus 5 phone is equipped with quad-core 2.3GHz CPU and 2 GB memory. We manually set 1.1GHz forthe quad-core CPU for stable resource consumptions amongdifferent trials.

4.2 SoftwareIn all experiments, we use TensorFlow-for-Mobile to runneural networks on Android phones [1]. For other tradi-tional machine learning algorithms, we run with Weka forAndorid [2]. All experiments on Nexus 5 run solely withCPU. No additional runtime optimization is made.

4.3 DatasetWe use the dataset collected by Stisen et al. [12]. This datasetcontains readings from two motion sensors (accelerometerand gyroscope). Readings were recorded when users exe-cuted activities scripted in no specific order, while carryingsmartwatches and smartphones. The dataset contains 9 users,6 activities (biking, sitting, standing, walking, climbStair-up,and climbStair-down), and 6 types of mobile devices. Ac-celerometer and gyroscope measurements are model inputs.Each sample is further divided into time intervals of length τ ,as shown in Figure 1. We take τ = 0.25 s. Then we calculatethe frequency response of sensors for each time interval, andcompose results from different time intervals into tensors asinputs. In addition, we add white Gaussian noise on eithertime domain or frequency domain with different differentvariance σ to generate our noise-augmented dataset.

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Figure 3: The accuracy of algorithms on HHAR withadditive white Gaussian noise on frequency domain.

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Figure 4: The accuracy of algorithms on HHAR withadditive white Gaussian noise on time domain.4.4 BaselineWe evaluate QualityDeepSense models with the followingbaseline algorithms:(1) DeepSense: The state-of-the-art unified deep learning

framework for IoT applications.(2) RF: This is a random forest algorithm. It selects all

popular time-domain and frequency domain featuresfrom [5] and ECDF features from [7].

(3) SVM: Feature selection of this model is same as the RFmodel. But this model uses support vector machine asthe classifier.

4.5 EffectivenessWe first show the accuracy of all algorithms on the noise-augmented heterogeneous human activity recognition task,performing leave-one-user-out evaluation (i.e., leaving outone user’s entire data as testing data).

We add white Gaussian noise with different variance σ onfrequency or time domain. The results are illustrated in Fig-ure 3 and 4 respectively. For both cases, QualityDeepSensecan reduce performance degradation by more than 50% com-pared to DeepSense, thanks to our sensor-temporal self-attention module that estimate and exploit the input sensingquality. Compared to the traditional machine learning algo-rithm, deep neural network models are better at resistinginput noise. For all methods, noise on time domain is easyto deal with, because we can get rid of some high-frequencynoise with pre-processing.

Figure 5: The correlation between attention and addi-tive noise.Then, we run an experiment testing the correlation be-

tween the sensing quality and attentions learnt in Quality-DeepSense. Since we use the noise-augmented dataset, thequality of sensing input can be partly decided by the additivenoise. Larger additive noise indicates worse the sensing qual-ity. In QualityDeepSense, there are two types of attention,attention over sensor as and attention over time at . We caneasily obtain the overall attention by multiplying the corre-sponding elements from these two attentions. The result isshown in Figure 5. Since each sensing measurement doesnot contain the same amount of information, the correla-tion between attention and noise is not linear. However, wedo witness that the attention tends to be smaller when themeasurement has stronger noise.

4.6 Execution Time and EnergyConsumption

Finally we measure the execution time and energy consump-tion of all algorithms on Nexus 5 phone. We conduct 500experiments for each metric and take the mean value as thefinal measurement. The results are shown in Figure 6 and 7respectively. Compared to DeepSense, QualityDeepSenseonly shows limited overhead on execution time and en-ergy consumptions, while achieving better predictive per-formance. Compared to other traditional machine learningalgorithms, the execution time and energy consumption ofQualityDeepSense is acceptable.

5 CONCLUSIONIn this paper, we introduce a deep learning framework, calledQualityDeepSense, for solving the heterogeneous sensingquality problem in IoT applications. QualityDeepSense de-signs a novel sensor-temporal self-attention module to esti-mate input sensing quality by exploiting the complex depen-dencies among different sensing inputs over time. Experi-ments on noise-augmented human activity recognition showthat QualityDeepSense greatly mitigates the performance

EMDL’18, June 15, 2018, Munich, Germany Shuochao Yao et al.

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Figure 7: The energy consumption of algorithms onNexus 5.

degradation caused by low input sensing quality with littleadditional computational overhead. This framework is animportant step towards designing deep learning structuresfor handling heterogeneous sensing quality without externalcalibration. However, more exploration on model design andsystem implementation are needed. On one hand, applyingattention mechanism on IoT applications can be differentfrom its traditional usage in natural language processing andcomputer vision due to the nature of multiple-sensor fusingin IoT systems. On the other hand, more observations andmodeling of IoT systems deployed “in the wild" are neededto design specific deep learning structures that deals withheterogeneous sensing quality.

6 ACKNOWLEDGEMENTSResearch reported in this paper was sponsored in part byNSF under grants CNS 16-18627 and CNS 13-20209 and inpart by the Army Research Laboratory under CooperativeAgreementsW911NF-09-2-0053 andW911NF-17-2-0196. Theviews and conclusions contained in this document are thoseof the authors and should not be interpreted as represent-ing the official policies, either expressed or implied, of theArmy Research Laboratory, NSF, or the U.S. Government.The U.S. Government is authorized to reproduce and distrib-ute reprints for Government purposes notwithstanding anycopyright notation here on.

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