Real-time Approach for Decision Making in IoT-based Applications

Hassan Harb

1 a

, Diana Abi Nader

, Kassem Sabeh

and Abdallah Makhoul

2 b

Faculty of Sciences, Lebanese University, Hadat, Lebanon

FEMTO-ST Institute, Univ. Bourgogne Franche-Comt

e, CNRS, 1 cours Leprince-Ringuet, 25200, Montb

eliard, France

Keywords:

IoT, Wireless Sensor Networks, Real-time Applications, Energy Saving, Data Reduction Techniques, Score

System, K-means Algorithm.

Abstract:

Nowadays, the IoT applications beneﬁt widely many sectors including healthcare, environment, military,

surveillance, etc. While the potential beneﬁts of IoT are real and signiﬁcant, two major challenges remain

in front of fully realizing this potential: limited sensor energy and decision making in real-time applications.

To overcome these problems, data reduction techniques over data routed to the sink should be used in such a

way that they do not discard useful information. In this paper, we propose a new energy efﬁcient and real-time

based algorithm to improve the decision making in IoT. At ﬁrst data reduction is applied at the sensor nodes

to reduce their raw data based on a predeﬁned scoring system. Then, a second data reduction phase is applied

at intermediate nodes, called grid leaders. It uses K-means as a clustering algorithm in order to eliminate data

redundancy collected by the neighbor nodes. Finally, decision is taken at the sink level based on a scoring risk

system and a risk-decision table. The evaluation of our technique is made based on a simulation from data

collected on sensors at Intel Berkeley research lab. The obtained results show the relevance of our technique,

in terms of data reduction and energy consumption.

1 INTRODUCTION

Wireless sensor network (WSN) is an indispensi-

ble part of Internet of Things (IoT). It allows mon-

itoring, data collection, processing and transmission

in several IoT based applications (Kadiravan et al.,

2021) (Mostafa et al., 2018), such as smart cities and

smart farms, health care, agriculture, business inno-

vation, environmental monitoring, and so on (Atzori

et al., 2017). In a typical scenario, data collected by

sensor nodes are transmitted to a middleware for pro-

cessing before being forwarded to a speciﬁc access

point (sink) for further analyses and decision making

(Kaur and Sood, 2015).

Following the increasing number of IoT users, the

quantity of data generated and collected is continu-

ously increasing. For that, differentiating between

what data to keep and what data to dismiss is crucial

for data accuracy and decision making optimization

especially in real-time cases. However, data trans-

mission is the most expensive in terms of sensor en-

ergy consumption. Additionally, IoT devices, espe-

cially sensor nodes, are limited in their lifetime en-

https://orcid.org/0000-0003-3066-9189

https://orcid.org/0000-0003-0485-097X

ergy due to the battery power limitation which af-

fects the device’s longevity (Ruan et al., 2019; Harb

and Makhoul, 2018). Thus, many data reduction

techniques have been introduced in order to solve

this problem. It aims at removing similar routed

data in such a way of minimizing the amount of

data transmitted and thus, saving energy (Harb et al.,

2018) (Harb et al., 2020).

In this paper, we propose a novel approach for

energy saving and decision making in real-time IoT-

based applications. Its purpose resides in reducing

the quantity of transmitted data to the sink without

loosing information and guaranteeing data integrity.

By this mean, data transmission rate will be reduced,

leading to the optimization of the network resources.

Our technique is composed of two data reduction lev-

els and one decision level. The ﬁrst data reduction

technique is applied at the sensor level where raw data

are reduced by mean aggregation based on a prede-

ﬁned score system. The second data reduction tech-

nique is applied at intermediate nodes, called grid

leaders. Each grid leader works on removing data re-

dundancy collected by neighboring nodes based on K-

means clustering algorithm. The ﬁnal decision phase

is achieved at the sink level. The dataset obtained will

Harb, H., Nader, D., Sabeh, K. and Makhoul, A.

Real-time Approach for Decision Making in IoT-based Applications.

DOI: 10.5220/0010985800003118

In Proceedings of the 11th International Conference on Sensor Networks (SENSORNETS 2022), pages 223-230

ISBN: 978-989-758-551-7; ISSN: 2184-4380

223

be used to calculate the risk ratio based on the score

system, then it will determine the right decision ac-

cording to a predeﬁned risk-decision table. To evalu-

ate our approach several simulations have been con-

ducted while using real dataset collected at the Intel

Berkeley research lab. The obtained results show the

relevance of our technique in terms of data reduction

and energy consumption.

The rest of the paper is organized as follows. Sec-

tion 2 overviews various existing data reduction tech-

niques for IoT. Section 3 describes the real-time grid

architecture used in our scenario. In Section 4, we

present the ﬁrst layer of our data reduction technique

applied at the sensor nodes level. Section 5 describes

the second layer of data reduction applied at the grid

leaders level. In Section 6 we detail the decision mak-

ing algorithm at the sin level. Simulation results are

presented in Section 7. Finally, Section 8 concludes

our paper and gives some perspectives.

2 RELATED WORK

Many approaches have been suggested for data reduc-

tion in WSNs. The importance of reducing data col-

lected in IoT based sensors relies in avoiding packets

loss in network or delay in packets delivery which will

affect decision making.

Many energy efﬁciency techniques based on ma-

chine learning techniques have been proposed for ef-

fective routing decisions in WSN and IoT. For in-

stance, in (Thangaramya et al., 2019), neuro-fuzzy

based cluster formation protocol (FBCFP) was pro-

posed. The network is trained with convolutional neu-

ral network with fuzzy rules for weight adjustment,

learning four components: current energy level of the

CH (Cluster Head), distance of the CH from the sink

node, change in area between the nodes present in

the cluster and the CH due to mobility and the de-

gree of the CH. Then using Mamdani Inference Sys-

tem for adjusting new CHs depending on energy. In

(Preeth et al., 2018), another fuzzy energy efﬁcient

scheme is proposed called adaptive fuzzy multi cri-

teria decision making (AF-MCDM) approach, where

fuzzy Analytic Hierarchy Process (FAHP) and TOP-

SIS methods are combined together for selection of

cluster head.

In (Santiago et al., 2018), an energy aware load

balancing algorithm is proposed for IoT network by

selecting parent nodes using event rate for cluster for-

mation and neural network predictor for ELT (Ex-

pected Life Time) prediction. In (Elappila et al.,

2018), a congestion and interference aware energy ef-

ﬁcient routing technique in WSN for IoT is presented.

It consists of selecting the signal to interference and

noise ratio of the link, the survivability factor of the

path from the next hop node to the destination, and

the congestion level at the next hop node. In (Khan

et al., 2018), a new routing protocol named Modiﬁed-

Percentage LEACH Protocol is introduced based on

existing protocol namely Percentage LEACH. Energy

wastage is reduced by reducing communication be-

tween Cluster Heads and sink through threshold cal-

culation on each CH for cluster selection and taking

into consideration the distance of nodes from sink.

Authors in (Kaur and Sood, 2015) proposed to

switch to sleep the IoT sensors based on three con-

ditions: if it is not necessary to sense the target envi-

ronment in a given period of time; if the coverage area

can be compromised for battery life; and if the battery

level is critically low. In (Hong et al., 2018), TCEB,

a topology control algorithm with energy balance, ad-

dresses the problem of how to ﬁnd a reasonably re-

duced topology and the packets forwarding route for

underwater wireless sensor networks for energy con-

servation. Non-cooperative game theory is introduced

to the the cluster-head selection in order to optimize

the set of the cluster-heads selection and to ensure the

energy consumption of the whole network balance.

Then, the intra-cluster and the inter-cluster topology

construction are used in order to ﬁnd the optimum re-

liable nodes to perform the forwarding task.

On bio-smart levels, (Roy et al., 2019) a bio-

inspired distributed event sensing and data collection

framework was proposed based on the gene regula-

tory networks (GRNs) in living organisms. This is

achieved by customizing a heuristic for the Maxi-

mum Weighted Independent Set problem encompass-

ing both quality and quantity of sensed data, where

the ﬁrst depends on the device energy levels while

the second on the number of events sensed. A sub-

optimal device will be proposed depending on the

residual energy.

In (Muhammad et al., 2019), an energy-efﬁcient

Data Prioritization framework is proposed by intel-

ligent integration of the Internet of Things, artiﬁcial

intelligence, and big data analytics for green smart

cities. In (Ejaz et al., 2017; Tomasoni et al., 2018),

a unifying framework for energy-efﬁcient optimiza-

tion and scheduling of IoT-based smart cities. Fi-

nally, in (Wang et al., 2017),an Integer Linear Pro-

gramming (ILP) formulation as well as two effec-

tive polynomial-time heuristic algorithms are pro-

posed for energy-efﬁcient task scheduling problem on

smartphones in mobile crowd sensing systems.

Despite that most of the proposed techniques al-

low efﬁcient energy saving, they fail to reach all as-

pects of IoT applications. Indeed, they are very com-

SENSORNETS 2022 - 11th International Conference on Sensor Networks

224

plex and require huge processing. In this paper, we

present an energy efﬁcient data reduction method that

it is less complex and suitable for limited resources

sensor nodes.

3 THE NETWORK

ARCHITECTURE

In this section, we introduce the network architecture

used in our technique. Two main concepts have been

adopted in our scenario: grid-based architecture and

periodic data acquisition. In the following, we de-

scribe each of them in more details.

3.1 Grid-based Network

We consider a grid-based distribution, where each

square can be deﬁned based on the dimensions of the

whole area of interest and the density of the sensor

nodes. In each square, a grid leader is elected or se-

lected using an appropriate method. This grid leader

is considered as intermediate node. It can be an ordi-

nary node or a node with more capabilities. We con-

sider that the sensor nodes in the same square will

send their data directly to their grid leader, which in

his turn summarizes and aggregates the received data

coming from neighboring nodes. The idea is to re-

move useless redundant data, While following spe-

ciﬁc rules and before sending them to the sink. Figure

1 shows our sensor network architecture, where data

transmission between sensor nodes and their appro-

priate grid leader is based on single-hop communica-

tion.

Sink

Grid Leader

Sensor node

Figure 1: Two-level data transmission architecture.

3.2 Periodic Data Acquisition Model

Data collection from Iot applications can be made by

data streaming where, data ﬂows primarily from the

sensor node to the grid leader and ﬁnally to the sink.

We distinguish two kinds of data streaming the peri-

odic sampling and the event driven. In our approach

we are interested in ”periodic sampling” data model,

where the acquisition of sensor data is forwarded to

the grid leader and the to the sink on a periodic basis.

We consider that each period p is partitioned into

time slots. At each slot t, each sensor node N

cap-

tures a new reading r

. At the end of the period p, N

collects a vector of τ readings, e.g. R



, r

, . . . ,r



then it sends it to the sink. In our system, each sensor

node sends periodically (period p) its data to the ap-

propriate grid leader, which in turn sends it to the sink

(Fig. 2) (Harb et al., 2018).

Sensor nodes

Grid Leaders

Sink

Decision level

Figure 2: Periodic data reduction scheme.

4 FIRST PHASE DATA

REDUCTION

In this section we describe how the sensor node it-

self reduces the amount of its collected data before

sending it to the grid leader. Indeed, data collected

in vector R contains redundant measurements espe-

cially when the slot interval between two collected

measures is short. The idea is to reduce the size of

the the vector R

by selecting similar and consecutive

measures from it and computing their mean to send to

the grid leader instead of sending the whole readings.

Our proposed model is based on a measurement score

aggregation system used to identify similar measures.

4.1 Score System

The score system is a guide used to determine simi-

lar measures within a vector R

by assigning scores

to each measure. Based on a score system, all mea-

sures belonging to the same interval will be assigned

the same score and aggregated as similar measures.

Table 1 gives an example of such a score system.

4.2 Sensory Data Reduction Algorithm

In this section we introduce the algorithm proposed to

reduce the number of readings collected periodically

by each sensor. The idea is to reduce the number of

readings in the vector R

to send to the grid leader.

The algorithm works as follows: ﬁrst we ﬁnd the

Real-time Approach for Decision Making in IoT-based Applications

225

Table 1: Score System.

Measure Score

≤16 0

]16,17] 1

]17,18] 2

]18,19] 3

]19,20] 4

]20,21] 5

]21,22] 6

]22,23] 7

]23,24] 8

>24 9

score of each reading present in R

using the function

score, which takes the reading as an input and

returns the score of the reading according to a well

deﬁned score system. Then, if consecutive readings

in R

have the same score, we calculate the mean

value of those readings. The basic idea is that we

consider readings having the same score as similar

and redundant and instead of sending all of them we

send only their mean. Therefore, the process starts

by ﬁnding the score for the ﬁrst reading in R

, then it

checks the score of the next values until it reaches a

reading whose score is different from the current one.

Then, we calculate the mean of all previous readings,

and the process iterates again to ﬁnd readings with

same score as current value. Hence, the ﬁnal vector

contains the mean value of the readings having

same score, as well as the weight of the mean value

(lines 11-13). The weight of the mean value indicates

the number of readings represented by the mean

value. Note that only consecutive values having

same score are aggregated by their mean, and not all

values within R

having same score. The algorithm

is represented in algorithm 1.

Algorithm 1: Sensor Mean Aggregation Algorithm.

Require: Reading vector: R

= [r

, r

, . . . ,r

Ensure: Vector of representative readings of R

1: V

←

2: checkbe f ore = Score(r

) // score of ﬁrst reading

3: f requency = 0

4: for each set reading r

∈ R

5: check = Score(r

) // score of current reading

6: if checkbe f ore = check then

7: f requency = f requency + 1

8: else

9: ﬁnd the mean value, r

, of readings in R

10: wgt(r

) = f requency

11: V

← V

∪ {r

, wgt(r

)}

12: f requency = 1

13: end if

14: checkbe f ore = check

15: end for

16: return V

After applying Algorithm 1, each sensor

will send a vector of representative readings



, f

), (r

, f

), .. . ,(r

, f

)



to its proper grid

leader, where k ≤ τ and f

represents the weight or

frequency of r

5 SECOND PHASE DATA

REDUCTION

The grid leader receives a vector of reduced data from

all neighboring sensors at the end of each period. At

this stage, we propose an algorithm to reduce the

number of data collected at grid leader level by elim-

inating similar or redundant vectors. Our objective

is to group the readings observed by the grid leader

in clusters using the K-means algorithm, then elimi-

nate redundant data within each cluster by applying

a mean based technique similar to the one applied at

sensor level. In the next sections we explain in more

details how we combined the K-means algorithm with

a mean aggregation technique to clean data at grid

leader level.

5.1 K-means Clustering Algorithm

K-means clustering is a simple unsupervised learning

algorithm that is used to solve clustering problems.

k-means tries to divide a set of samples in k disjoint

groups or clusters using the mean value of the mem-

bers as the main indicator. The clusters are partitioned

as points and all observations or data points are asso-

ciated with the nearest cluster, computed, adjusted,

and then the process starts over using the new adjust-

ments until a desired result is reached. Although it

can be proved that the procedure will always termi-

nate, the algorithm is also signiﬁcantly sensitive to the

initial randomly selected cluster centers.

The performance of a clustering algorithm may be

affected by the chosen value of K. Therefore, instead

of using a single predeﬁned K, a set of values might be

adopted. It is important for the number of values con-

sidered to be reasonably large, to reﬂect the speciﬁc

characteristics of the data sets. At the same time, the

selected values have to be signiﬁcantly smaller than

SENSORNETS 2022 - 11th International Conference on Sensor Networks

226

the number of objects in the data sets, which is the

main motivation for performing data clustering. The

optimal value of K for many studied applications var-

ied in the interval [3, 5].

5.2 Absolute Value Distance

One of the fundamental steps when applying the K-

means algorithm is computing the distance between

a reading and a mean value of a cluster. In this pa-

per, we considered the absolute value distance which

is a simple yet effective method to ﬁnd the distance

between two values. In mathematics, the distance be-

tween x and y is deﬁned by a subtracting relationship:

| x − y |. Let us consider a data value in R

and mean

value of a cluster µ , then the absolute value distance

) between them can be calculated as follows:

, µ) =| µ − r

|, (1)

where r

∈ R

and µ is the mean value of a cluster.

5.3 K-means and Absolute Value

Distance

Algorithm 2 describes the procedure of K-Means

algorithm to divide the n vectors R

received by the

grid leader from the n neighboring sensors into a set

of k clusters. First, the algorithm assigns k random

values from the readings as centroids and allocates

a cluster for each centroid. Then, we calculate the

absolute value distance between each value r

and

the k centroids of all clusters and assign the readings

to the clusters according to the minimum distance.

In the next step, we calculate the mean value for

each cluster and use it as the new centroid for that

cluster. Then, we calculate the distance between

the new centroid and the old one. If the distance

is zero for all clusters, the algorithm stops and we

return the clusters with the corresponding centroid

of each. Else, we iterate and recalculate the distance

between the readings r

and all centroids to allocate

them to the one with the minimum distance. The

algorithm stops when the distance between the mean

and current centroid is zero in all clusters.

Algorithm 2: K-Means Adopted to Absolute Value Dis-

tance Algorithm.

Require: List of datasets R

= {R

, R

, . . . ,R

}, K,

where R



, f

), (r

, f

), . . . ,(r

, f

)



Ensure: List of k clusters C

= {C

, . . . ,C

}

where C



, f

), value(r

, f

), .. . ,(r

, f

)



1: for j ← 1 to k do

2: C

← φ

3: and randomly choose centroid x

belongs to C

4: end for

5: repeat

6: for each value r

∈ R

7: Assign r

to the cluster C

with nearest x

8: end for

9: for each cluster C

, where j ∈ 1, . . . , k do

10: Update the centroid x

to be the centroid of

all values currently in C

so that x

∑

for i ∈ C

11: end for

12: until

13: clusters’ centroids no longer changes

14: return C

5.4 Cluster Reduction at the Grid

Leader

After applying the K-Means algorithm to the data re-

ceived by the grid leader we will have K-clusters.

Next, we will reduce the data present in each cluster

by utilizing the score system we deﬁned earlier (cf.

Section 4.1).

For the K clusters found in (5.3) we will aggregate

the values having the same score using their mean

value. This approach is similar to the one we ini-

tially performed in (4.2) but this time we use the fre-

quency to calculate the mean value. So for each clus-

ter C



, f

), (r

, f

), .. . ,(r

, f

)



, if n consecutive

values r

, r

i+1

, . . . ,r

i+n

have the same score according

to our score system, we aggregate them by calculating

the mean value:

∗ f

+ r

i+1

∗ f

i+1

, +···+ r

i+n

∗ f

i+n

(2)

At the end of this phase we will have a

set of K clusters C

= {C

, . . . ,C

} where



, f

), (m

, f

), . . . ,(m

, f

)



, where f repre-

sents the frequency value used to compute the mean

and i is ≤ 10 (10 being the number of scores avail-

able). This data will be sent to the sink for a decision

to take place as illustrated in the next section.

6 DECISION MAKING METHOD

AT THE SINK

The sink receives the data from the grid leader as clus-

ters and stores this data after aggregating it, in a sin-

gle cluster form D=



, f

), (m

, f

), .. . ,(m

, f

)



Then it makes a decision based on this data and the

evaluated risk as shown in Table 2.

Real-time Approach for Decision Making in IoT-based Applications

227

Table 2: Risk Decision.

Risk Decision

≤0.2 d

]0.2, 0.4] d

]0.4, 0.6] d

]0.6, 0.8] d

> 0.8 d

To calculate the risk, ﬁrst we have to ﬁnd

the frequency associated with each score. We

then transform the aggregated data set D into

the following form according to our score system:



(0, f req

), (1, f req

), (2, f req

), . . . ,(9, f req

)



Where the numbers 0, 1, 2, . . . ,9 represent scores

from our score system in Table 1 while f req

repre-

sents the frequency of values having a score i.

Furthermore, the weight parameter is calculated

according to the following formula:

weight

f req

∗ i

(3)

where |D

| is the total frequency of the set D

Finally, the risk is obtained according to the for-

mula:

0 ∗ weight

+ 1 ∗ weight

+ 2 ∗ weight

+ ·· ·+9 ∗weight

(4)

Based on the risk level, the decision will be taken

according to Table 2.

7 SIMULATION RESULTS

In this section, we show the relevance of our proposed

technique after performing a simulation on real sensor

data nodes deployed in Intel Berkeley research lab. 47

sensors were deployed where each of them collected

temperature, humidity, light and voltage data from 28

February to 5 April 2004. A total of 2.3 millions read-

ings were collected (Harb et al., 2015). In these series

of simulation, we tested our algorithm while consid-

ering the temperature values. The sensors send their

collected data to a grid leader where the K-means al-

gorithm will be applied. The value of k is set ﬁrst to 3,

then to 4 and ﬁnally to 5. The period size in its turn is

set ﬁrst to 50 readings, then 100 readings and ﬁnally

to 150 readings. Figure 3 shows the distribution of

sensors inside the laboratory.

In these simulations Table 1 is adopted for the

score system.

Figure 3: Distribution of sensors in Intel Berkeley lab.

7.1 Data Aggregation Ratio at Sensor

Nodes

As mentioned before, the mean aggregation according

to the score system, allows each sensor node to min-

imize the size of its sensed data by removing similar

readings. Figure 4 shows the average number of tem-

perature readings implemented on different size peri-

ods, using our technique. The obtained results show

that our data ﬁltering model allows sensors to sig-

niﬁcantly reduce its data transmission in a redundant

zone, up to 92% the temperature readings for τ = 50.

data aggregation ratio (%)

50 100 150

Figure 4: Aggregation ratio at each sensor during different

periods.

7.2 Data Aggregation Ratio at Grid

Leader

In Figure 5, we show the average number of remain-

ing sets after applying K-means algorithm at the grid

leader, when varying K values to 3, 4 and 5 respec-

tively. The obtained results show that K-means can

signiﬁcantly eliminate redundant data sets generated

by neighboring sensors. Subsequently, we observe

that K-means can reduce up to 36% of the whole re-

ceived sets at the grid leader. These results conﬁrm

that the clustering is a very efﬁcient approach in terms

of eliminating redundant data and providing useful in-

SENSORNETS 2022 - 11th International Conference on Sensor Networks

228

formation to the end user, comparing to other existing

approaches.

100

% of sets sent to the sink

3 4 5

Figure 5: Filtering set ratio after applying K-means at the

grid leader.

7.3 Energy Consumption at Sensors

Energy consumption is the main metric being studied

in this research. Figure 6 shows the average consump-

tion of energy at sensor level by studying the decrease

in the average energy remaining in sensors as a func-

tion of rounds. We suppose that the initial energy for

each sensor is 50mJ and run the simulation until the

energy becomes zero. A round consists of 15 periods.

We can observe that the sensors survive for approxi-

mately 160 rounds when τ = 50, and 100 rounds when

τ = 150.

Figure 6: Average energy consumption at sensors level.

7.4 Energy Consumption at Grid

Leader

Figure 7 shows the results of energy consumption of

K-means, when varying K. The obtained results are

highly dependent on the number of remaining sets af-

ter applying K-means (see results of Figure 5); less

the number of remaining sets thus less of energy is

lost. Indeed, we observe that our technique gives

important results regarding reducing the energy con-

sumption of the sent data.

0.002

0.004

0.006

0.008

0.01

0.012

0.014

energy consumption (mJ)

3 4 5

Figure 7: Percentage energy consumption.

8 CONCLUSION AND FUTURE

WORK

Internet of Things (IoT) will play an important role

in the future by collecting surrounding conditions and

environment information. Thus, designing new en-

ergy efﬁcient techniques while preserving data in-

tegrity for decision making appears to be crucial in

order to eliminate redundant raw data and make such

networks operate as long as possible. This paper pro-

posed a real-time framework for energy-efﬁcient and

decision making in IoT-based applications. The ﬁrst

phase uses a score aggregation system and aims to re-

duce raw data collected by the sensors. The second

phase allows grid leader nodes to eliminate redundant

data collected by neighboring nodes using K-means

clustering algorithm. The third phase will be dedi-

cated to decision making based on a risk-decision ta-

ble. Our technique has been evaluated based on a sim-

ulation on real sensors data. The results obtained with

our technique showed signiﬁcant energy savings.

Many future directions for our work can be traced.

We seek to try another data clustering methods at the

grid leader level, like decision trees and neural net-

works.

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