A Real-time Temperature Anomaly Detection Method for IoT Data

Wei Liu

1,2 a

, Hongyi Jiang

1 b

, Dandan Che

1,2

, Lifei Chen

1,3

and Qingshan Jiang

Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, P.R. China

Shenzhen School of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, P.R. China

Digital Fujian IoT Laboratory of Environmental Monitoring, Fujian Normal University, Fuzhou, P.R. China

Keywords:

IoT, Real-time Data, Anomaly Detection, Smoothed Z-Score Algorithm, Dynamic Threshold.

Abstract:

Temperature control plays a vital part in medical supply management, of which effective monitoring and

anomaly detection ensure that the medication storage is maintained properly to meet health and safety

requirements. In this paper, an unsupervised temperature anomaly detection method, called DTAD (Dynamic

Threshold Anomaly Detection), is proposed to detect anomalies in real-time temperature time series. The

DTAD sets dynamic thresholds based on the Smoothed Z-Score Algorithm, rather than set ﬁxed thresholds

of a temperature range by experience. The comparative evaluation is performed on the DTAD and four other

commonly employed methods, the results of which shows that the DTAD reaches a higher accuracy and a

better time efﬁciency. The DTAD is fully automated and can be used in developing a real-time IoT temperature

anomaly detection system for medical equipment.

1 INTRODUCTION

With the rapid development of Internet of Things

(IoT) technology, various IoT applications emerge

in different industries including agriculture, logistics,

manufacturing, healthcare, ﬁnance, education, etc

(Li and Chen, 2014). Among them, healthcare is

closely related to the physical and mental well-being

of people, and thus is an issue of utmost concern to the

society. Under the stress of the aging populations and

the greater prevalence of chronic diseases, healthcare

stakeholders have been continuously striving to ﬁnd

a solution for the increasing healthcare demand gap

(Due

nas et al., 2016). Fortunately, technological

advances in areas including biotechnology, pharma-

ceuticals, information technology, the development

of medical equipment, and more have all made

signiﬁcant contributions to the construction of the

smart healthcare system, improving the health of

people all around the world.

Information technologies, including IoT, mobile

Internet, cloud computing, big data, 5G, micro-

electronics, and artiﬁcial intelligence, together with

modern biotechnology constitute the cornerstone of

smart healthcare (Clauson et al., 2018). The scenarios

https://orcid.org/0000-0002-5165-8051

https://orcid.org/0000-0003-0655-0532

of smart healthcare include medical nursing, medical

equipment management, medical supplies manage-

ment, telemedicine and medical incident management

(Liang, 2012a).

Medical supplies are core assets for healthcare

facilities (B

elanger et al., 2018). It is of vital im-

portance that healthcare organizations manage their

assets to keep their expenditures under control as well

as ensure the quality of healthcare delivery. Storage

temperature management, as an indispensable part

of the medical supply management, contributes to

the maintenance of the normal storage conditions

of medication, as medication storage requires strict

temperature control requirements in order to maintain

product potency (Makui et al., 2019). Tempera-

ture management of medical supplies calls for an

integrated and systematic process to monitor, alert

and remedy, and any failure of which may result

in economic losses or even medical malpractices

(Ukil et al., 2016). Under that demand, a real-time

IoT temperature monitoring and anomaly detection

system with a high accuracy and a good time

efﬁciency can be a solution.

To explore an anomaly detection method suitable

for medication storage temperature data, we acquired

real temperature data sets of 100 refrigerators

sensors placed in different medical refrigerators

in biomedicine laboratories from November, 2018

112

Liu, W., Jiang, H., Che, D., Chen, L. and Jiang, Q.

A Real-time Temperature Anomaly Detection Method for IoT Data.

DOI: 10.5220/0009410001120118

In Proceedings of the 5th International Conference on Internet of Things, Big Data and Security (IoTBDS 2020), pages 112-118

ISBN: 978-989-758-426-8

to December, 2019 (more than 6,000,000 records

in total). We evaluate the performances of the

commonly emoployed anomaly detection methods on

the data sets but they all show comparatively low

accuracy and poor time efﬁciency (see Comparative

Evaluation in section 4.2 for details).

Hence, to improve the quality and efﬁciency of

the temperature management of medication storage,

a temperature anomaly detection method based on

adaptive dynamic threshold and Smoothed Z-Score

Algorithm is proposed to detect anomalies IoT

refrigerator time series data. This method takes the

stationarity and periodicity of the temperature time

series into consideration, and thus develops a more

accurate detection approach. Moreover, this paper

compares the effectiveness indicator of this method

and several commonly employed anomaly detection

methods to prove its validity.

2 RELATED WORK

Due to the large variety of scenarios and demands,

there are numerous models developed to detect

anomalies, each has its own characteristics and

applications. Traditionally for temperature anomaly

detection, the most commonly adopted method

is Delphin method of ﬁxed threshold: once the

temperature goes beyond the ﬁxed thresholds, it will

be identiﬁed as an anomaly (Munir et al., 2019).

However, as the thresholds are set at ﬁxed levels, this

methold can not detect anomalies at their beginning

periods, which leads to poor timeliness.

Statistical models are also commonly employed

to detect anomalies. Boxplot is a simple statistical

method which deﬁnes its outlier as a data point

that is located outside the whiskers of the boxplot

(Shevlyakov et al., 2013). Another popular statistical

method, Local Outlier Factor (LOF), is an unsuper-

vised density-based method which detects the outliers

by measuring the local deviation of a given data

point with respect to its k-nearest-neighbors (k-NN)

(Lei et al., 2018). However, the basic assumption

of LOF is that the data is distributed in a spherical

way around the instance (Goldstein, 2014), which is

not the case for IoT temperature data. Classiﬁcation

method such as k-NN is also used to detect anomaly

points by classifying data based on similarities in

distance metrics. However, k-NN is a supervised

learning algorithm and the effectiveness of k-NN

highly depends on the choice of k (Liu et al., 2017).

Anomaly detection models also exist based on

machine learning. Isolation Forest is an unsupervised

model-based algorithm which identiﬁes the point

anomaly by separating/isolating it from the rest of

the instances. The way of isolation is recursively

generating partitions on the sample by randomly

selecting a feature and then randomly selecting a split

value between the maximum and minimum values of

that selected feature (Puggini and Mcloone, 2018).

However, Isolation Forest is only sensitive to global

outliers, and is weak in dealing with local outliers,

which may decrease its effectiveness in anomaly

detection for temperature time series.

The main restriction of the above mentioned

methods on refrigerator temperature time series data

is that they neglect the gradual variation process

of the temperature anomaly points, which results in

lower accuracy and worse timeliness. To address this

problem, we try to include the temperature variation

tendencies revealed by the history data in the anomaly

detection process, and the method we propose will be

presented in the next section.

3 A DYNAMIC ANOMALY

DETECTION METHOD

There are mainly two phases of our Dynamic

Threshold Anomaly Detection (DTAD) Method: a)

conduct Augmented Dickey-Fuller Test to verify the

stationary of the IoT temperature data acquired; b)

conduct anomaly detection using Smoothed Z-Score

Algorithm. The ﬂow chart of the Dynamic Threshold

Anomaly Detection Method is shown in Figure 1.

Figure 1: Flow Chart of Dynamic Threshold Anomoaly

Detection Method.

3.1 Data Pre-processing

There are three parts of data pre-processing and the

detailed explanations are stated below.

3.1.1 Stationary Test

Temperature sensor data is time series. It’s generally

assumed that the characteristics of the time series

reﬂected in the historical data still exist in the

future, which describes the stationary quality of

the time series: the invariance under translation in

time. Considering this requirement of time series,

A Real-time Temperature Anomaly Detection Method for IoT Data

113

the stationary test was conducted before anomaly

detection.

Stationary time series satisﬁes the following

conditions: a) the mean is a constant, independent

of time; b) the variance is a constant, independent of

time; c) the covariance between the values at any two

time points, depends only on the difference between

the two times, and not on the location of the points

along the time axis.

Here, Augmented Dickey-Fuller Test (ADF Test)

is adopted to test the stationary of our time

series(Mushtaq, 2011). The purpose of the ADF

Test is to test whether a time series variable is non-

stationary and possesses a unit root (a stochastic

trend): if yes, then the time series is not stationary;

otherwise, it is stationary.

3.1.2 Data Type Identiﬁcation

Medical refridgerators can be catagorized into two

calsses, inverter or conventional refrigerator, each

generating different type of data. Inverter refrigerator

can adjust its inside temperature according to the

temperature detected and keep it at a constant

level, but conventional refrigerator does not have the

function. As a result, the temperature data of inverter

refrigerator is periodic, while that of conventional

refrigerator is not. Hence, the ﬁrst step in anomaly

detection is to identify the models of the refrigerators

and categorize them into inverter refrigerator or

conventional refrigerator.

Considering the periodicity of the inverter refrig-

erator data, it is necessary to decycle the time series

before the next step. For the purpose, the moving

average model is used (Liang, 2012b):

t+1

∑

i=t−n+1

(1)

where F

t+1

denotes the temperature value at time t +1

after smoothing, x

represents the temperature value at

time i, and n is the number of values to be averaged.

3.1.3 Time Series Initialization

Time Series initialization aims to allow anomalies to

be accurately detected even when they appear in the

beginning periods of the time series. The DTAD

is predicting future temperature values based on a

certain period (which is deﬁned as the lag and will

be further explained in the next section) of historical

data, and it assumes that there is no anomaly in this

period so the predicted values are normal temperature

values. Hence, we create a short series manually

where there is no anomaly in the series and insert it

ahead of the original time series.

The time series initialization is based on the

Algorithm 1. The time series initialization will

transform the original time series (T S) into the new

initialized time series (IT S). There are two phases

of the time series initialization: a) examine the

temperature data values, select the m numbers which

appears most in yesterday’s series, and repeat each

number for n times (so there will be totally m × n

numbers); b) randomly assort the m × n numbers into

a series, position the series right in front of the actual

temperature data series, splice the two series together

into a new initialized series.

Algorithm 1: Time Series Initialization.

Input:

Original Time Series (T S), m, n

Output:

Initialized Time Series (IT S)

1: CountTS ⇐ count the occurances of each

temperature value

2: SortedTS ⇐ sort CountTS in descending order

3: for i = 1 to m do

4: for j = 1 to n do

5: IT S[m × n] = SortedTS[m];

6: end for

7: end for

8: IT S ⇐ ramdomly shufﬂe IT S

9: return IT S;

Note that m × n equals to the lag of the mean

and variance to ensure that they reﬂect a lag period

without anomaly. Through series initialization, it is

assured that there is no anomaly in the lag period,

the mean and variance are not be affected, and the

following detection will not be inﬂuenced.

3.2 Smoothed Z-Score Algorithm

The core concepts of Smoothed Z-Score Algorithm

are as follows(Moore et al., 2011) : a) use the

historical data in the lag to predict the next value, and

if the actual value exceeds a certain threshold range

of the predicted value, it will be considered as an

outlier, or a point anomaly; b) smooth the outlier in

order to eliminate the effects to the following anomaly

detections.

The Smoothed Z-Score Algorithm is outlined in

Figure 2. The ﬁrst step is to set a lag and calculate

the mean of the historical data in the lag to predict

the next value (Lima et al., 2019). Then adjust

the data range that would be considered acceptable

through the soft-threshold setting. The soft-threshold

is the number of standard deviations from the moving

mean above which the algorithm will classify a new

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

114

Figure 2: Outline of the Smoothed Z-score Algorithm.

datapoint as being an anomaly. The soft-threshold

is problem-dependent, and is therefore value that

must be tuned according to the characteristics of

different data sets and the algorithm sensitivity that

will be considered proper (Perkins and Heber, 2018).

Afterwards, calculate the difference of the actual

value and the predicted value. If the difference

exceeds the range, the point would be considered as

an outlier, or a point anomaly; otherwise, it is normal.

The detailed process description is stated below:

Step1: Predict the value of point

i+1

, the next point of

point

, according to the set lag:

i+1

∑

l=i−N+1

(2)

where p

i+1

is the predicted value of the point

i+1

, p

is the actual value of point

, i = 1, 2, ..., N, N is the

number of data in the lag.

Step2: Calculate the data range that would be

considered acceptable (range), according to the

variance of the historical data in the lag (σ

lag

) and

the soft-threshold (S threshold), which is the number

of standard deviations from the moving mean above

which the algorithm will classify a new datapoint as

being an anomaly (Dons et al., 2019).

range = σ

lag

× S threshold (3)

Step3: Calculate the difference of the actual value

and the predicted value (di f f

i+1

) of point

i+1

di f f

i+1

= p

i+1

− p

i+1

(4)

where p

i+1

is the actual value of the p

i+1

Step4: According to the range in step2, calculate the

anomaly detection result (signal

i+1

) for point

i+1

signal

i+1







−1 if di f f

i+1

< 0

0 if 0≤ di f f

i+1

≤ range

1 if di f f

i+1

> range

(5)

If signal

i+1

equals to 0, which means that the

difference is less than the range, then point

i+1

is an

outlier, or a point anomaly. When point

i+1

has been

identiﬁed as an outlier, compare the difference with

0. If the difference is greater than 0, which means the

actual value is greater than the predicted value, then it

is an outlier of higher temperature; if the difference is

smaller than 0, which means the actual value is lower

than the predicted value, then it is an outlier of lower

temperature.

Step5: If signal

i+1

does not equals to 0 in step4,

smooth p

i+1

by setting it equal to the value of the

previous point point

i+1

= p

(6)

where p

i+1

means that point

i+1

is an outlier, p

is the

actual value of point

Based on the above mentioned steps, the

Smoothed Z-score Algorithm is outlined into Algo-

rithm 2.

Algorithm 2: Smoothed Z-score Algorithm.

Input:

Initialized Time Series (IT S), lag, soft-threshold

(S threshold)

Output:

Anomaly Detection Result (signal)

1: for i = lag to t do

2: PIT S

i+1

⇐ mean(IT S

i+1−lag

,...,IT S

)

3: sigmalag

⇐ std(IT S

i+1−lag

,...,IT S

)

4: if absolute(IT S

i+1

−PIT S

i+1

) > S threshold ∗

sigmalag

then

5: if IT S

i+1

> PIT S

i+1

then

6: signal

i+1

⇐ +1

7: else

8: signal

i+1

⇐ −1

9: end if

10: IT S

i+1

⇐ IT S

/∗smooth ∗/

11: else

12: signal

i+1

⇐ 0

13: end if

14: end for

15: return signal;

4 DATA ANALYSIS

We collected real temperature data of 100 refrigera-

tors sensors placed in biomedicine laboratories over

a time range of 1 year and 2 months, and among

which, we picked 3-week data (September 26, 2019

to October 16, 2019) of 18 different refrigerators

sensors (10 invertor refrigerators and 8 conventional

A Real-time Temperature Anomaly Detection Method for IoT Data

115

refrigerators) to evaluate the performance of DTAD.

The temperature data was collected every 10 minutes,

each time series contains 3,024 instances, so the

dataset contains 54,432 records in total. We labeled

the data and compared the anomaly detection results

of the DTAD method with the labeled data. Detailed

description of the evaluation process is provided in

this section.

4.1 Method Evaluation

Here, considering the periodicity reﬂected in invertor

refrigerator data, we list the results of invertor

refrigerator data and conventional refrigerator data

separately. The tables display the statistics for

actual anomalies and anomalies detected, as well as

the accuracy for DTAD. The results are shown in

Table 1 and Table 2, where NR is the number of

real anomaly points, AR is abnormal rate, ND is

the number of anomaly points detected, ACC is the

accuracy. Accuracy (ACC) is adopted here to evaluate

the effectiveness of this method. Accuracy is the ratio

of points detected correctly (including anomaly points

which were detected as abnormal, and normal points

which were not detected as abnormal) to the total

number of points.

AR =

(7)

where NR is the number of real anomaly points, NP

is the total number of points.

ACC =

NDC

(8)

where NDC is the number of points detected

correctly, NP is the total number of points.

Table 1: Results of Periodic Invertor Refrigerator Data.

Sensor ID NR AR ND ACC

Sensor1 448 0.1481 413 0.9812

Sensor2 153 0.0506 148 0.9692

Sensor3 46 0.0152 48 0.9940

Sensor4 24 0.0079 26 0.9884

Sensor5 39 0.0129 52 0.9884

Sensor6 0 0 0 1

Sensor7 51 0.0169 40 0.9950

Sensor8 133 0.0440 101 0.9709

Sensor9 344 0.1138 291 0.9722

Sensor10 0 0 0 1

Table 1 and 2 show that the accuracies of DTAD

for different sensors are all above 97%, and do

not show any tendency of increase or decrease

as the number of anomalies goes up. It proves

Table 2: Results of Non-periodic Conventional Refrigera-

tors Data.

Sensor ID NR AR ND ACC

Sensor11 0 0 0 1

Sensor12 0 0 0 1

Sensor13 0 0 0 1

Sensor14 25 0.0083 20 0.9884

Sensor15 6 0.0020 13 0.9924

Sensor16 0 0 0 1

Sensor17 0 0 0 1

Sensor18 2 0.0007 3 0.9983

that DTAD has high accuracy, or in other words,

excellent performance in detecting anomaly points for

temperature time series data.

4.2 Comparative Evaluation

Different models are employed here to verify

their effectiveness in detecting anomaly points for

temperature times series. The comparative methods

are Fixed Threshold method, 3sigma method, Boxplot

method (Shevlyakov et al., 2013) and Isolated Forest

method (Liu et al., 2009).

Precision (P), Recall (R) and F1-score (F1) are

adopted here to evaluate the effectiveness of the ﬁve

methods (Bishop, 2006). Precision is deﬁned as the

number of anomaly points detected correctly divided

by the number of anomaly points detected. Recall

is deﬁned as the number of anomaly points detected

correctly divided the number of actual anomaly

points. Precision and recall are a pair of contradictory

indicators. For most cases, if the precision is high,

the recall is low; if the precision is high, the recall

is low. In anomaly detection, if you want a higher

Precision, the data range that would be considered

abnormal needs to be narrowed down, so there will

be fewer anomaly points detected and usually fewer

anomaly points detected correctly, which results in

a lower Recall. However, in cases where we want

to ﬁnd an optimal blend of precision and recall we

can combine the two metrics using what is called F1-

score.

P =

NDC

(9)

where NDC is the number of anomaly points detected

correctly, ND is the number of anomaly points

detected.

R =

NDC

(10)

where NDC is the number of anomaly points detected

correctly, NR is the number of real anomaly points.

IoTBDS 2020 - 5th International Conference on Internet of Things, Big Data and Security

116

F1 =

2 × P × R

P + R

(11)

where P is the precision, R is the recall.

Table 3: Comparative Evaluation Results for Different

Anomaly Detection Methods.

ACC P R F1

Fixed

Threshold

0.9728 0.9208 0.9297 0.9252

3sigma 0.9173 1 0.2828 0.4409

Boxplot 0.9788 0.9869 0.8788 0.9297

Isolation

Forest

0.9292 0.8274 0.6133 0.7044

DTAD 0.9910 0.9432 0.9390 0.9411

Table 3 shows that among the ﬁve methods,

DTAD has the highest accuracy (0.9910) and

F1(0.9411), followed by Boxplot (Shevlyakov et al.,

2013) (P:0.9788 and F1:0.9297) and Fixed Threshold

(P:0.9728 and F1:0.9252). Isolation Forest (Puggini

and Mcloone, 2018) and 3sigma (Goldstein, 2014) do

not perform well with temperature time series, with

accuracies of 0.9292 and 0.9173 respectively and F1

of 0.7044 and 0.4409 respectively.

Besides accuracy, the time efﬁciency of different

methods should also be taken into consideration when

evaluating these methods. Here, we take the average

time cost for the ﬁve methods to run a sensor’s data to

measure their time efﬁciency.

Table 4: Time Cost for Different Anomaly Detection

Methods.

Models Time(second)

Fixed Threshold 9.7713

3sigma 9.5046

Boxplot 9.0531

Isolation Forest 10.1892

DTAD 8.9544

In terms of time efﬁciency, the DTAD takes the

shortest time and is the fastest of the ﬁve methods.

Isolation forest takes the longest time, probably

because it is a method of integrating multiple weak

models with high time complexity.

4.3 Detection Results of Single Sensor

The above sections verify the effectiveness of the

DTAD by calculating evaluation indexes for it as

well as other commonly-employed anomaly detection

methods. To further proof its validity, we compare

the anomaly detection results (signals) with the

observable anomalies in the actual temperature data

visually. We select one-week data (September 30,

2019 to October 6, 2019) of Sensor1, plot the actual

temperature data and the signals calculated using

the DTAD in the same coordinate, and compare the

peaks on the same time axis. In Figure 3, the blue

lines represent the actual temperature data, the cyan

lines represent the predicted temperature, the green

lines represent the acceptable temperature range limit

(upper and lower), and the red lines represent the

signal. Figure 3 shows that the peaks in the actual

temperature are in good agreement with the signals

detected using the DTAD both for their occurrences

and durations. It is also shown in this ﬁgure that the

DTAD is acute as it can detect peaks that are visually

smaller.

Figure 3: The DTAD Detection Results of Sensor1 From

September 30 to October 6, 2019.

5 CONCLUSION

In this paper, we propose an unsupervised anomaly

detection method based on dynamic threshold for

temperature time series data. The commonly

employed methods for anomaly detection neglect

the gradual variation process of the anomaly points,

which leads to comparatively lower accuracy and

timeliness. To address the problem, this paper, for the

ﬁrst time, introduces Smoothed Z-score Algorithm

in the ﬁeld of temperature anomaly detection. The

proposed DTAD method adjusts the acceptable

temperature range through the data-driven adaptive

thresholds which take the temperature variation

pattern of the historical data into consideration. The

proposed method also includes series initialization to

eliminate the inﬂuences of the anomalies for future

detection. We evaluate this method on real world

temperature datasets of 3-week IoT data and provide

the comparative evaluation of 4 other commonly

employed maly detection methods. Experiments

show that DTAD outperforms the other methods in

both accuracy and time efﬁciency.

DTAD can be used in developing a fully

A Real-time Temperature Anomaly Detection Method for IoT Data

117

automated real-time monitoring anomaly detection

system for IoT temperature data. One avenue of

further study is to extend the detection to non-

stationary time series data. Another effort will be

oriented to the improvement of DTAD’s applications

on a wider range of datasets in the real-world context.

ACKNOWLEDGEMENTS

This work is supported by the Joint Engineering

Research Center for Health Big Data Intelligent Anal-

ysis Technology and the SIAT-Zongheng Intelligence

Techniques Lab. The work of L. Chen is supported

by the National Natural Science Foundation of China

under Grant No. 61672157.

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