Efﬁcient Lightweight Cryptography Algorithm in IoT Devices with

Real-time Criteria

Paul D. Rosero-Montalvo

1 a

and Vanessa E. Alvear-Puertas

2,3 b

Computer Science Department, IT University of Copenhagen, Copenhagen, Denmark

Computer Science Department, Universidad de Salamanca, Salamanca, Spain

Instituto Superior Tecnol

ogico 17 de Julio, Ibarra, Ecuador

Keywords:

Computer Network Security, Cryptography, Embedded Systems, Internet of Things.

Abstract:

Cryptographic algorithms are used to ensure the communication channel between the transmitter and receiver.

However, these algorithms are focused on processing data blocks that consume a lot of computational re-

sources. Therefore, they have some constraints to be used in IoT devices. This work presents a lightweight

AES cryptographic algorithm designed for IoT devices with real-time operative system criteria to improve the

time response and with threads that can be suspended to leverage RAM resources for another task. As a result,

we design an AES algorithm with a cipher key updating process that uses 11k bytes of Flash, 820 bytes of

RAM, and a time response of around 14.5 us in real scenarios.

1 INTRODUCTION

Internet of Things (IoT) is a network of embedded

devices with microcontrollers, microprocessors, spe-

ciﬁc software, and sensors connected through the In-

ternet to collect and exchange data (Gehrmann and

Gunnarsson, 2019). Therefore, IoT provides shared

distributed resources and services to different organi-

zations, sites, or homes with the possibility to send

data to anyone, any device, and anytime (Latif et al.,

2020). IoT uses these embedded devices like tiny

computers with deﬁned functionalities and integrates

a large variety of sensors with their principal aim to

convert a physical phenomenon or human behavior

into an electric signal (Rosero-Montalvo et al., 2021).

Thus, IoT brings us the possibility to acquire a large

amount of data, that through machine learning algo-

rithms, people can get the intrinsic knowledge from

the data to make our life more comfortable and con-

structive (Islam et al., 2019).

However, IoT requires robust protection against

possible attacks or vulnerabilities. The security of

IoT devices becomes important when linked to human

life or industrial procedures. Also, standard secu-

rity techniques such as cryptography algorithms with

user authentication are built to run in traditional com-

https://orcid.org/0000-0003-1995-400X

https://orcid.org/0000-0003-2332-6073

puters (end-to-end manner) (Khan et al., 2021). Be-

sides, cryptography algorithms are developed to send

a considerable amount of information called ”blocks”,

and compiling them requires many computational re-

sources which IoT devices can not run due to com-

plexity. Also, the data acquired for the IoT device

does not ﬁt in these blocks leaving empty spaces and

wasting memory. Therefore, these mentioned algo-

rithms can provide overhead the computational re-

sources of the IoT devices. Since they can only com-

pile lightweight software with a smaller number of

bits at a time with computational restrictions such as

RAM and Flash sizes, power consumption, process-

ing speed, and time response (Prakash et al., 2019).

Besides, IoT devices have different wireless pro-

tocols to establish communication between them with

a low data transfer rate to the cloud, which brings la-

tency issues in real-time applications if the system has

a long time response due to run cryptography algo-

rithms (Shah and Venkatesan, 2018).

Currently, there is a new tendency to implement

Trusted Execution Environments (TEE) to isolate

users on the cloud computing side. At the same

time, microprocessors are developed with a trusted

platform module (TPM) to protect speciﬁc software

from the rest of the system operative. Otherwise,

in an IoT environment, we can consider that the en-

tire microcontroller is trusted because the device runs

Rosero-Montalvo, P. and Alvear-Puertas, V.

Efﬁcient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria.

DOI: 10.5220/0010922800003194

In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 103-109

ISBN: 978-989-758-564-7; ISSN: 2184-4976

103

only one application without multi-user security con-

cerns (Szymanski, 2017). With this assumption, the

leading security ﬂaws happen outside the IoT device.

Therefore, the communication channel can be a secu-

rity concern since most IoT applications are designed

in standard wireless protocols such as 802.11 with

many vulnerabilities (Naru et al., 2017).

Thereby, new IoT devices are designed to avoid

security ﬂaws concerning authentication, such as

Arduino IoT products incorporating a crypto chip

and speciﬁc libraries for the cloud services connec-

tion (Sung et al., 2018). The crypto chip is focused

on connecting the device to the cloud. However, in

wireless sensor networks, IoT devices constantly send

information between them, and just one (considered a

central node) is in charge of sending the information

to the cloud (Fotovvat et al., 2021). As a result, the

information sent to the cloud is protected. Neverthe-

less, this process does not ensure if a malicious user

has tampered with the IoT network.

The cryptography application in IoT devices also

brings constraints related to system time response. In

this scenario, it is essential to improve the code with

techniques to run in parallel instructions to use the

entire processor at once. A real-time operative sys-

tem carries on this process, inserting ﬂaws to priori-

tize procedures and time slots to run each instruction.

Thereby, the processor is running the crypto library,

at the same time, is checking the battery, calibrating

sensors, and sending information to other devices.

With the above mentioned, this article presents a

hybrid cryptography algorithm with a real-time oper-

ative system and memory optimization criteria. This

process is carried on to send encrypted information

on a wireless sensor network without exceeding the

computational resources of a traditional IoT device

and leaving memory space to compile other function-

alities. To accomplish this goal, we assumed that the

IoT device and sensors are trusted, and only the wire-

less channel (802.11 n) is not. Also, we deﬁne at

ﬁrst the average of RAM and Flash requirement for

the algorithm AES 128 from the lightweight crypto

library (8 bits) supported by Arduino to measure the

RAM increasing to deploy this stage. Later, we de-

sign the same application with software optimization

techniques and a real-time schedule to improve the

system time-respond. Also, we developed a secure

way to update the key without external messages from

the central node. The top results are the RTOS im-

plementation in the AES encryption/decryption algo-

rithm with only 820 bytes of RAM and 11K bytes of

Flash and the response time around 8 uS.

The rest of the manuscript is structured as fol-

lows: Section II shows representative works related

to cryptographic algorithms implemented in IoT de-

vices and WSN. Section III presents the experimental

setup with the assumptions to determine the RAM and

Flash requirements. Results are illustrated in section

IV with the real-time schedule and optimization cri-

teria. Finally, section VI shows relevant conclusions

and future works.

2 LITERATURE REVIEW

Lightweight cryptographic algorithms for IoT are an

emergent research ﬁeld due to their relation to sen-

sitive information collected from human behavior.

However, the beneﬁts of IoT come with many se-

curity challenges, and traditional cryptographic algo-

rithms need to adapt in hostile environments with con-

strained computational resources. Therefore, (Khan

et al., 2021; Fotovvat et al., 2021), present an ex-

tensive comparison of lightweight cryptographic al-

gorithms implemented in several scenarios where the

limitations of the IoT device restrict the possible secu-

rity implementations. Furthermore, (Guo et al., 2019)

designed a complexity reduction of the block encryp-

tion and their beneﬁts in a complex task such as im-

age encryption. (Jalaly Bidgoly and Jalaly Bidgoly,

2019) presents a novel chaining encryption algorithm

for the LPWAN IoT network and the statistical propri-

eties of its method. Also, (Dang et al., 2021) encrypts

the information in LORA communication in a WSN.

Works such as (Naru et al., 2017; Prakash et al., 2019;

Ramesh and Govindarasu, 2020; Prakash et al., 2019;

Hijawi et al., 2021) present novel implementations on

lightweight cryptographic modifying the process of

traditional algorithms. Speciﬁcally, in real-time ap-

plications, works like (Gope and Hwang, 2016; Islam

et al., 2019) use robust hardware (FPGA or dual ARM

processors) than the traditional IoT devices. Finally,

(Tsai et al., 2018) presents to AES 128 secure channel

in LORAWAN communication for IoT environments.

In conclusion, all these works have presented so-

lutions for data privacy in IoT environments. How-

ever, this is not seen integrally with the primary objec-

tive of sending information to the Cloud, considering

the IoT device an extension of the trusted Cloud. For

this reason, the same security trends must be adopted

within the IoT environment so that later the integra-

tion of these IoT devices is transparent to more ex-

tensive networks. As a result, there are open issues

like designing a trusted environment in IoT environ-

ments with speciﬁc assumptions, optimizing the time

response, and implementing lightweight cryptogra-

phy algorithms on particular scenarios such as WSN

and IoT.

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

104

3 EXPERIMENTAL SETUP

This section presents the description and conﬁgura-

tion of Advanced Encryption Standard. Then, we

present the assumptions and speciﬁc scenarios to de-

termine which is trusted and what is not in the IoT

device.

3.1 Advanced Encryption Standard

Advanced Encryption Standard (AES) is a symmetric

block cipher for encryption/decryption where a block

of plaintext is treated as a single block and is used

to obtain a block of ciphertext of the same size. AES

supports 128, 192, or 256 encryption keys. Also, AES

has ﬁve modes, Electronic Code Block (ECB), Cipher

Block Chaining mode (CBC mode), Cipher Feedback

mode (CFB mode), Output FeedBack mode (OFB

mode), and Counter mode (CTR mode) (Serra et al.,

2021). However, CBC mode is the most used because

CBC encrypts the result to the ciphertext block. In the

next block, the encryption results combine with xor

gate with plaintext block until the last block. There-

fore, even if this mode encrypts the same plaintext

block, it will get a different ciphertext block (Borges

et al., 2021).

3.2 Existing Libraries

Many libraries implement AES in computers and

servers in common programming languages to en-

crypt/decrypt big datasets. However, they are not

compressed into 8 or 16-bit architecture. In this sce-

nario, a few contributions exist to demonstrate the

functionality of AES into IoT devices uncommonly

send 16 bytes of data, such as in environmental con-

ditions or water quality analysis (Rosero-Montalvo

et al., 2021). In this article, we use two popular li-

braries as Lib one (Van Heesch, 2018) and Lib two

(Landman, ). However, the real-time operative sys-

tem has not been implemented in encryption libraries

yet.

3.3 Assumptions and Speciﬁc Scenario

The assumptions to design the AES encryp-

tion/decryption algorithm are: (i) the IoT device trusts

that data acquired from sensors have not been tam-

pered with in any form, (ii) all the software run-

ning into the IoT device is trusted, (iii) the RAM

must bein optimized to run complex tasks regarding

their computational constraints, (iv) the communica-

tion scheme is in star; slaves nodes send the infor-

mation to a single central node, (v) the central node

already has the encryption key before the system is

implemented, and (vi) the IoT device does not use 16

bytes in sensors data acquisition.

AES Description: is a synchronic cipher algorithm

that the transmitter and the receiver need to know the

cipher key. Therefore, updating the cipher key is a

complex task (asynchronous cipher). However, the

encryption key can be updated if two random numbers

are generated, the ﬁrst to determine how many times

the encryption key is to be used. When the data send

number reaches this number, an XOR gate is executed

between the encryption key and the second random

number. Later, this second number is inserted in a

speciﬁc position in the ciphertext that only the central

node knows to update its cipher key in the following

shipments.

Allocating the key in the Flash memory can be

risky. Therefore, we use the EEPROM to hide the

key how as possible. To leverage RAM, the Rijndael

S-box is allocated into the ﬂash. Finally, RTOS is im-

plemented to improve the time-respond performance

emulating symmetric multiprocessing into the IoT de-

vice.

Speciﬁc Scenario: The IoT system developed is an

autonomous robot working in greenhouses acquiring

data from environmental conditions. Therefore, the

sensors used are SCD30 for temperature, humidity,

and temperature. In addition, the VEML6075 is used

to measure levels of UV rays. Also, it is necessary

to acquire data from motors to get information about

their velocity and gyration. Fig. 1 shows the system

working on the greenhouse.

Figure 1: A robot working in the greenhouse.

RTOS Procedures: The autonomous robot has the

following steps: Motor control, receiving motor pa-

rameters, checking the battery, acquiring data, en-

crypting data, sending information. Hence, this re-

Efﬁcient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria

105

search only presents the encryption data. The en-

cryption data has the stages: Plain text: 16 hex-

adecimal bytes, Data rotation: byte substitution

(x-box), and shift rows. Round key: circular byte

left shift, byte substitution (x-box), adding round con-

stant. Cipher-text: xor gate between Data rotation

and Round key.

4 RESULTS AND TESTS

This section shows the memory consumption of each

library used in this work and the way of the ROS.

Then, the summary of RAM, ﬂash, and response time

are presented.

4.1 Memory Consumption

To determine the initial resources to compile the AES

algorithm, we deﬁne the memory and time-respond

of lib one and lib two. Then, we implemented the

algorithm with the majority of the variables in the

RAM without any optimization (Lib AES v1). Later,

we deﬁne the right size of each variable and put

some of them (considered as constants) in the FLASH

(Lib AES v2). Next, we deﬁne code blocks to make

threads, put them in RTOS architecture, and mea-

sure the time-respond and memory. Finally, regard-

ing the mentioned assumptions, we allocated the en-

cryption key in the ROM, hidden of the principal code

with aleatory updates explained in the next section

(AES-RTOS v3).

4.2 RTOS Implementation

AES algorithm has four threads deﬁned, Plain

text: 16 hexadecimal bytes, Data rotation: byte

substitution (x-box), and shift rows. Round key: cir-

cular byte left shift, byte substitution (x-box), adding

round constant. Cipher-text: xor gate between

Data rotation and Round key. Therefore, we updated

the current design to ﬁt into IoT devices as follows:

Plaintext: data acquisition from sensors, ﬁlling

empty spaces in hexadecimal code (the system uses

8 bytes). Also, the key-updated works as follows:

the encryption key is stored before the system runs

the ﬁrst time (the central node also has the encryption

key for each IoT node), and one ﬂag is high. When

the IoT device runs the ﬁrst time and looks at the ﬂag

in high, the IoT device runs the ﬁrst random number

to determine how many times the ciphertext will send

with the same cipher key. When this number comes

to an end, the cipher key is updated, running an XOR

gate with a second 8-bit number. Then, this second

number is allocated in the Plaintext to be encrypted

and sent to the central node. Finally, the ﬁrst random

number is generated again to repeat this process. The

system generates the ﬁrst random number without is-

sues if a hard reset is generated, even if the ﬂag is

low. The difference between the standard AES pro-

cess with the improved AES-RTOS is shown in Fig.

Table V shows the summary of RAM, Flash, and

response time for each library. For example, lib one

uses much RAM, and lib two takes too long to encrypt

the text. Otherwise, Lib AES V1 and V2 need less

computational resources with better response time

than the last libraries. Otherwise, Lib AES-RTOS V3

consumes more Flash than the others; each thread has

an average of 60 bytes. However, RTOS has better

management of resources; priorities threads make it

feasible to suspend and reactivate them, freeing up

RAM. The library is available in: AES-RTOS Lib.

The pseudocode is presented in 1

Table 1: Summary of the Computational resources con-

sumption for each AES library.

Libraries FLASH RAM Time to respond

Lib one 6888 1065 3.05 us

Lib two 7468 800 14.36 us

Lib AES V1 5056 607 10.36 us

Lib AES V2 4626 353 5,10 us

Lib AES-RTOS V3 11228 818 8.36 us

4.3 Real Scenario

The proposed algorithm has been validated in a real

scenario by sending data from environmental condi-

tions inside the greenhouses. The data is acquired by

a network of sensors installed in autonomous robots

(1). Therefore, it is assumed that its design and func-

tion have been checked. The designed data frame

is sent through the wireless LoRa protocol with a 4-

bytes preamble, a 4-bytes header, and a 2-bytes end

of frame and error control. The lightweight library is

designed to compile data with 8bits size to compile

in 8 bits architecture such as many microcontrollers

used to developt IoT devices. Therefore, the designed

payload (16-bytes) includes the robot’s identiﬁcation,

the sensor network’s data, motor movement parame-

ters, and, if necessary, the update of the key as fol-

lows: CO2: 2 Bytes, Temperature: 2 bytes, Humid-

ity:2 bytes, UV rays: 2 bytes, Battery state: 2 bytes,

motor A velocity: 1 byte, motor B velocity: 1 byte,

motor A gyration:1 byte, motor B gyration: 1 byte,

node identiﬁcation: 1 byte, and key updated: 1 byte.

We have to add an extra function to divide one param-

eter into two bytes, requiring less memory to use 16

bits.

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

106

(a) Traditional AES. (b) AES-RTOS implementation.

Figure 2: AES execution time comparison, execution time: (→).

Consequently, the AES algorithm was imple-

mented into the IoT device to get performance mea-

surements and determine if the server has issues de-

crying the information when the data frame traveled

for wireless communication. We tested the system

with the RTOS implementation and without them. As

a result, the system with RTOS has a time response

of around 14.5 us, and just encrypting the data and

sending it to the server takes 21.4 us; this is because,

with RTOS, we can run in parallel stages to improve

the performance. Also, we can stop the machine ef-

ﬁciently by changing the priority of each task or sus-

pending them. For this reason, the battery consump-

tion decreased around 15

5 CONCLUSIONS AND FUTURE

WORKS

Cryptographic algorithms allow us to ensure that the

data sent reaches its receiver safely. However, this

computational cost is essential in implementing a

more signiﬁcant amount of code to strengthen the ap-

plication, such as machine learning algorithms. It has

been seen that the previous libraries have an adequate

consumption for microcontrollers with 8-bit architec-

tures. However, they are not intended for their gen-

uine use in real applications. For this reason, the

implementation of RTOS allows to determine pro-

cesses and know important information such as their

response time and memory size required to compile

them.

In addition, it helps code optimization by freeing

up RAM when suspending a thread. We were able to

show that despite optimally implementing AES, we

left much of the Flash and RAM free for other pro-

cesses by the microcontroller. For example, our li-

brary, compiled on an Arduino Uno, occupies 32%

of Flash and 39% of RAM with response times be-

tween 5 and 8 us. Also, if we use an ESP8266 board,

AES-RTOS uses 25% of the Flash and 33% of the

RAM with response times between 7 and 12 us. In

addition, the receiver with the highest computational

capacity can efﬁciently manage the exchange of keys,

and there was no loss of packets due to the update of

the encryption key. As a result, we leave space avail-

able for future work to implement machine learning

algorithms for on-site decision-making.

ACKNOWLEDGEMENT

Novo Nordisk Fonden supports this research with the

project: Privacy through Co-Design for Real-World

Data Analytics in the Cloud.

REFERENCES

Borges, M., Paiva, S., Santos, A., Gaspar, B., and Cabral,

J. (2021). Azure rtos threadx design for low-end nb-

iot device. In 2020 2nd International Conference on

Societal Automation (SA), pages 1–8.

Dang, T.-P., Tran, T.-K., Bui, T.-T., and Huynh, H.-T.

(2021). Lora gateway based on soc fpga platforms.

In 2021 International Symposium on Electrical and

Electronics Engineering (ISEE), pages 48–52.

Fotovvat, A., Rahman, G. M. E., Vedaei, S. S., and Wahid,

K. A. (2021). Comparative performance analysis of

lightweight cryptography algorithms for iot sensor

nodes. IEEE Internet of Things Journal, 8(10):8279–

8290.

Gehrmann, C. and Gunnarsson, M. (2019). An Identity

Privacy Preserving IoT Data Protection Scheme for

Cloud Based Analytics. Proceedings - 2019 IEEE In-

ternational Conference on Big Data, Big Data 2019,

pages 5744–5753.

Efﬁcient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria

107

Algorithm 1: AES Implementation,

Require: Queue, s-box[], input [4][4], output[16],

cipher[4][4], key[16], random

procedure PLAIN TEXT:(priority: 3, stack size:

80)

for j ← 0, 16 do

if j < sensors then

input[:] ← data

else

input[:] ← 0x00

end if

if r == random then

input[:sensors] ← ﬂag

random function

end if

end for

end procedure

procedure DATA ROTATION:(priority: 2, stack

size: 80)

for i ← 0, 10 do

input ← S-box(input[:])

for k ← 1, 4 do

input[:i] ← input[:i] >> k

Round constant input[:]

end for

Queue ← i

end for

end procedure

procedure ROUND KEY:(priority: 2, stack size:

80)

cipher ← key

for l ← 0, 10 do

g(cipher[3])

S-box(cipher[3])

cipher[:] ← cipher[:] ⊕ S-box(cipher[:3])

for n ← 1, 4 do

cipher[:n]← cipher[:n-1] ⊕ cipher[:n]

end for

end procedure

procedure CIPHER TEXT:(priority: 1, stack size:

80)

if Queue 6= 0 ∧ Queue 6= 10 then

out [:] ← input[:] ⊕ cipher[:]

else if Queue == 10 then

Send → out[:]

end if

end procedure

Gope, P. and Hwang, T. (2016). A Realistic Lightweight

Anonymous Authentication Protocol for Securing

Real-Time Application Data Access in Wireless Sen-

sor Networks. IEEE Transactions on Industrial Elec-

tronics, 63(11):7124–7132.

Guo, X., Hua, J., Zhang, Y., and Wang, D. (2019). A

complexity-reduced block encryption algorithm suit-

able for internet of things. IEEE Access, 7:54760–

54769.

Hijawi, U., Unal, D., Hamila, R., Gastli, A., and Ellabban,

O. (2021). Lightweight kpabe architecture enabled

in mesh networked resource-constrained iot devices.

IEEE Access, 9:5640–5650.

Islam, M. S., Verma, H., Khan, L., and Kantarcioglu, M.

(2019). Secure real-time heterogeneous IoT data man-

agement system. Proceedings - 1st IEEE Interna-

tional Conference on Trust, Privacy and Security in

Intelligent Systems and Applications, TPS-ISA 2019,

pages 228–235.

Jalaly Bidgoly, A. and Jalaly Bidgoly, H. (2019). A novel

chaining encryption algorithm for lpwan iot network.

IEEE Sensors Journal, 19(16):7027–7034.

Khan, M. N., Rao, A., and Camtepe, S. (2021). Lightweight

Cryptographic Protocols for IoT-Constrained De-

vices: A Survey. IEEE Internet of Things Journal,

8(6):4132–4156.

Landman, D. Arduino library for aes encryption.

Latif, M. A., Ahmad, M. B., and Khan, M. K. (2020). A Re-

view on Key Management and Lightweight Cryptog-

raphy for IoT. 2020 Global Conference on Wireless

and Optical Technologies, GCWOT 2020.

Naru, E. R., Saini, H., and Sharma, M. (2017). A recent

review on lightweight cryptography in iot. In 2017

International Conference on I-SMAC (IoT in Social,

Mobile, Analytics and Cloud) (I-SMAC), pages 887–

890.

Prakash, V., Singh, A. V., and Kumar Khatri, S. (2019). A

New Model of Light Weight Hybrid Cryptography for

Internet of Things. Proceedings of the 3rd Interna-

tional Conference on Electronics and Communication

and Aerospace Technology, ICECA 2019, pages 282–

285.

Ramesh, S. and Govindarasu, M. (2020). An efﬁcient

framework for privacy-preserving computations on

encrypted iot data. IEEE Internet of Things Journal,

7(9):8700–8708.

Rosero-Montalvo, P. D., L

opez-Batista, V. F., Arciniega-

Rocha, R., and Peluffo-Ord

nez, D. H. (2021). Air

Pollution Monitoring Using WSN Nodes with Ma-

chine Learning Techniques: A Case Study. Logic

Journal of the IGPL. jzab005.

Serra, L. F. D., Gonc¸alves, P. G. B., Lopes Fraz

ao, L. A.,

and Antunes, M. J. G. (2021). Performance analysis

of aes encryption operation modes for iot devices. In

2021 16th Iberian Conference on Information Systems

and Technologies (CISTI), pages 1–6.

Shah, T. and Venkatesan, S. (2018). Authentication of IoT

Device and IoT Server Using Secure Vaults. Proceed-

ings - 17th IEEE International Conference on Trust,

Security and Privacy in Computing and Communica-

tions and 12th IEEE International Conference on Big

Data Science and Engineering, Trustcom/BigDataSE

2018, pages 819–824.

Sung, B.-Y., Kim, K.-B., and Shin, K.-W. (2018). An aes-

gcm authenticated encryption crypto-core for iot secu-

IoTBDS 2022 - 7th International Conference on Internet of Things, Big Data and Security

108

rity. In 2018 International Conference on Electronics,

Information, and Communication (ICEIC), pages 1–3.

Szymanski, T. H. (2017). Security and Privacy for a Green

Internet of Things. IT Professional, 19(5):34–41.

Tsai, K.-L., Huang, Y.-L., Leu, F.-Y., You, I., Huang, Y.-

L., and Tsai, C.-H. (2018). Aes-128 based secure low

power communication for lorawan iot environments.

IEEE Access, 6:45325–45334.

Van Heesch, D. (2018). Arduino cryptography library.

Efﬁcient Lightweight Cryptography Algorithm in IoT Devices with Real-time Criteria

109