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Performance Analysis of Internet of Things Protocols Based Fog/Cloud over High Traffic

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The Internet of Things (IoT) becomes the future of a global data field in which the embedded devices communicate with each other, exchange data and making decisions through the Internet. IoT could improves the qualityoflife in smart cities, but a massive amount of data from different smart devices could slow down or crash database systems. In addition, IoT data transfer to Cloud for monitoring information and generating feedback thus will lead to highdelay in infrastructure level. Fog Computing can help by offering services closer to edge devices. In this paper, we propose an efficient system architecture to mitigate the problem of delay. We provide performance analysis like responsetime, throughput and packet loss for MQTT (Message Queue Telemetry Transport) and HTTP (Hyper Text Transfer Protocol) protocols based on Cloud or Fog serverswith large volume of data form emulated traffic generator working alongsidewith one real sensor. We implement both protocols in the same architecture, with low cost embedded devices to local and Cloud servers with different platforms. The results show that HTTP response time is 12.1 and 4.76 times higher than MQTT Fog and cloud based located in the same geographical area of the sensors respectively. The worst case in performance is observed when the Cloud is public and outside the country region. The results obtained for throughput shows that MQTT has the capability to carry the data with available bandwidth and lowest percentage of packet loss. We also prove that the proposed Fog architecture is an efficient way to reduce latency and enhance performance in Cloud based IoT.
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doi: http://dx.doi.org/10.4314/jfas.v10i6s.113
J Fundam Appl Sci. 2018, 10(6S), 176-181 176
Performance Analysis of Internet of Things
Protocols Based Fog/Cloud over High Traffic
Istabraq M. Al-Joboury1, Emad H. Al-Hemiary2
Department of Networks Engineering, College of Information Engineering
Baghdad, Iraq
{1estabriq_94, 2emad}@coie-nahrain.edu.iq
Published online: 22 March 2018
Abstract—The Internet of Things (IoT) becomes the future of
a global data field in which the embedded devices communicate
with each other, exchange data and making decisions through the
Internet. IoT could improves the qualityoflife in smart cities, but
a massive amount of data from different smart devices could slow
down or crash database systems. In addition, IoT data transfer to
Cloud for monitoring information and generating feedback thus
will lead to highdelay in infrastructure level. Fog Computing can
help by offering services closer to edge devices. In this paper, we
propose an efficient system architecture to mitigate the problem
of delay. We provide performance analysis like responsetime,
throughput and packet loss for MQTT (Message Queue
Telemetry Transport) and HTTP (Hyper Text Transfer Protocol)
protocols based on Cloud or Fog serverswith large volume of
data form emulated traffic generator working alongsidewith one
real sensor. We implement both protocols in the same
architecture, with low cost embedded devices to local and Cloud
servers with different platforms. The results show that HTTP
response time is 12.1 and 4.76 times higher than MQTT Fog and
cloud based located in the same geographical area of the sensors
respectively. The worst case in performance is observed when the
Cloud is public and outside the country region. The results
obtained for throughput shows that MQTT has the capability to
carry the data with available bandwidth and lowest percentage of
packet loss. We also prove that the proposed Fog architecture is
an efficient way to reduce latency and enhance performance in
Cloud based IoT.
Keywords—Internet of Things; Web of Things; Fog
Computing; Cloud Computing; Edge Computing; MQTT; HTTP;
Tsung.
I. INTRODUCTION
IoT is a new concept and paradigm; in which the real
worlds of things linked to the virtual world, subsequently
enabling anything to anyone [1]. It is becoming a revolution of
devices as well as representing the future of the Internet.
Therefore, it has attracted wide attention from researchers.
The IoT technology consists of two terms "Internet" and
"Things". The firstterm gives the meaning of protocols,
services and networks, whereas the secondterm refers to
sensors, smart devices [2]. The basic idea of IoT is that objects
(such as electronics) connected together to provide an
efficient, low power, and seamless connectivity to humans [3].
Then, new technologies allow objects to be more intelligent,
which can transfer data generated from different things, as
well as, make them recognizable by using IP and RFID. This
leap leads to the integration of IoT and cloud computing.
Furthermore, IoT moves into the unlimited capabilities of IP6
addresses [4]. The elements of IoT can be a physical/digital
entity, which perform various daily tasks for individual users
and then IoT applicationprotocols and technologies used to
achieve IoT vision; for instance, wireless sensor networks
allow objects to measure in real time and data is collected by
using IoT protocols.Smart city provides services to
governments (e.g smart transportation and mobility, smart
building and infrastructure, organizations (e.g e-learning,
manufacturing, smart factories) and humans (e.g smart home,
smart hospital). The common IoT layers are threemodel
categorizing Application, Network, and Perception Layers.
new layers: Business and Middleware Layers are recently
proposed[5].
Fig. 1 Cloud, Fog and embedded devices layers.
Fog Computing is a concept made by Cisco in 2012, that
aiming the realtime applications to handle billions of IoT
devices [6]. It refers as an intermediate layer between cloud
and embedded devices enabling storage, computing and
networking services, the same as Cloud Computing. Itconsists
of servers, routers, switches and access points [7]. Fog
Computing brings all based cloudfeatures and services near to
edge devices "ground" like sensors, smartphones, wearables
Research Article
Special Issue
J Fundam Appl Sci. 2018, 10(6S), 176-181 177
and embedded devices [8] as shown in Fig. 1. Smart cities
including smart hospitals and infrastructures for IoT
environments that they handle big data and stream for realtime
application. Thereby, a real time cityis enableddue to offer
new services for governments and societies, as well as big data
analysis in realtime of infrastructure level and the person's
lifestyle. Here, data generated from IoT devices sent to the
cloud in order to be stored and processed. Cloud computing
enables services (Software as a Service - SaaS) and
(Infrastructure as a Service – IaaS) and Platform as a Service -
PaaS) and provides data processing. It is suitable for
applications that their data is stored and processed in
centralized. Some application such as health care systems
depends on distributed storage and lowlatency, at this point
cloud fails to handle these conditions [9].However, there are
some differences between the two concepts as it discusses
briefly below in Table I.
Table I Comparison between Fog and Cloud[10]
Fog Cloud
Location Local Internet
Data Thousands Hundreds
Latency and
Delay Low High
Storage Distributed Centralized
The rest of this paper is organized as follows:Section II
provides an overview of the web IoT protocol (MQTT and
HTTP) and the difference between them.SectionIII, describes
problem definition of how to manage traffic and protocol
selection. SectionIVcovers related work to the paper.
SectionVproposes IoT architecture and gives the tools and
programs used for testing performance. SectionVI, shows the
results of responsetime, throughput and packet loss of
protocols based on Cloud/Fog.Finally, section VII, concludes
this paper.
II. OVERVIEW OF WEB PROTOCOLS
The MQTT is an application layerprotocol designed for
lightweight M2M (machine to machine) communications,
simple, easy to implement and fast transportation protocol.
MQTT is suitable for resource constrained devices,
lowbandwidth, low latency and reliable networks. Stanford-
Clark and Nipper [11] release the first version of MQTT
protocol in 1999; IBM originally created it. The latest version
of MQTT is 3.1.1 [Nov, 2014] and it is becoming an open
standard protocol.
MQTT is an OASIS (Advancing Open Standards for the
Information Society) runs over TCP/IP protocol. It is
publish/subscribe model based on topics and consists of three
elements: two types of clients (publisher or subscriber) and
one server (called broker). Publishers send messages within a
specific topic, then subscriber clients receive these messages
that refers to the same topic that they subscribed via broker as
shown in Figure 2. Also, publisher do not require the address
of the subscribers[12,13].
Fig.2The operation of MQTT based on Publish/Subscribe model [14].
MQTT has a lower overhead, a synchronous and reliable
with a multiple different levels of quality of services. There
are three types of QoS for a delivery assurance that are used
between client and server[11,15]. There are:
QoS level 0:the publisher sends the message to the
subscriber through the broker andthe subscriber
receives the message at most once. In addition, the
broker never sends an acknowledgement to the
publisher.
QoS level 1: the publisher delivers the message to the
clients at least once, and the brokersend back an
acknowledgement if the message is lost.
QoS level 2:publisher uses level 2 when message lost
or duplicate and this requires fourway handshake to
deliver the message at exactly once, hence this cause
increase in the overhead for this reason level 2 and 3
are not included in this paper
The broker may require authentication(username and
password) from subscribers to allow them to connect, so that
the broker will confirm the privacy by using (Secure Sockets
Layer -SSL)/ (Transport Layer Security-TLS).
HTTP is an application layer protocol based on TCP/IP
suite of protocols. It used to transfer data from client side like
smart phone, personal computer to server side such a Web
server over the World Wide Web. HTTP v2. is last version
[May 2015] [16]. The most commands are GET and POST for
processing data on web. It is request/response model based on
Uniform Resource Locators (URLs) the user request data on
web server, then server not only response to data but all
relevant data to that request. There are some differences
between the two protocols as it summarizes below in Table II.
Table IIDifferences betweenMQTT and HTTP [17]
MQTT
HTTP
Transport
TCP
TCP
Architecture
Client
/
Broker
Client
/
Server
Model
Publish
/
Subscribe
Request
/
Response
QoS
3 Types
None
Messages
Topic
URL
Standard
OASIS
Arch. Style
Encoding
Binary
Different Types
Security
Username and
Password, SSL/TLS SSL/TLS
Pub (topic, data)
Publisher Broker Subscriber
Pub (topic, data)
Sub (topic)
J Fundam Appl Sci. 2018, 10(6S), 176-181 178
III. PROBLEM DESCRIPTION
Smart hospitals generate a large amount of data from
thousands of sensors that it can be useful for monitoring and
analyzing. However, an unprecedented volume of data can
crash storage systems and realtime applications.Cloud
Computing could provide storage “ondemand” and processing
ofsystems, but Cloud could be anywhere and away far from
systems, as well as transferring data from sensors to Cloud and
then giving a feedback to end user and this is a problem for
sensitive healthcare applications because of high delay. Fog
Computing consider to be temporally near to the sensor;
thuswilldecreasedelay [18].There are several of IoT and OSI
application protocols relies on TCP used to communicate and
deliver data. This paper, provides an answer to thesequestions
"Which protocol will be used with low responsetime and
highthroughput?", "Which is the best location for servers that
represents the lowest delay in order to rapidly send
notification to end user" and "Is Fog Computing actually has
better performance than Cloud Computing?".
IV. LITERATURE REVIEW
MQTT and HTTPprotocolsare used in communication
between people and devices especially in the medicalfield.
However, up to our knowledge few papers present the
performance of these protocols under conditions such as over
large volume of traffic and based on Fog and Cloudlayer.The
performance testing of XMPP protocol was tested andthe
evaluation methodology was developed using Tsung traces to
check the requirement need of the protocol [19].Also, the
performance of XMPP server was tested using load distributed
Tsung over high traffic and from honeypot sensors to find the
limit of number of concurrent request [20], but MQTT and
HTTP are not included in the above two papers, as well as
Cloud and Fog layer are not mention. While in [21],the
performance of Web IoT protocols (DDS, XMPP and MQTT)
was compared according to the latency of message delivery
from sensors and throughput, however these protocols arenot
implemented in Fog efficiency concept. Among the above
works, [22] including Fog and the selection of network
management protocols such SNMP, NETCONF and CoAP
were evaluated. But, theyhave mentioned only the
management protocols and implemented using OMNeT++
simulator not the real hardware.And in [23], MQTT,
WebSocket and CoAP application protocols were compared in
IoT scenario based on local via Ethernet and remote server via
internet and cellular network. However, the response time and
throughput of MQTT and HTTP over a huge volume of data
were not included.In [24], they proposed system architecture
to the problem of middleware, scalability and interoperability
between Cloud and sensors. In this system,
publisher/subscriber model was applied using MQTT protocol
and average response time and throughput was measured. In
[25] the overhead and payload size matricesof HTTP and
MQTTwere compared but without the relation to the Cloud
and Fog servers, and then a queuing theory was proposed to
evaluate the performance of MQTT.
V. METHODOLOGY
The main objective of this paper is comparing the
performance of Web IoT protocols with each other, in term of
number of sensors that it can handle with low response time
and packet loss, and then finding the best location for the
servers.
The operation of the proposed IoT architecture is as
follows:
We implement two IoT scenarios as in the figures 3and 4,
and provide the performance analysis of MQTT and HTTP
protocols in six data communication paths: sensors to Fog
(located in Al-Nahrain University, College of Information
__________________________________________________
1http://pulsesensor.com/
2http://tsung.erlang-projects.org/
3http://www.nodemcu.com/index_cn.html
4https://mosquitto.org/
5https://www.mongodb.com/
6https://test.mosquitto.org/
7http://dweet.io/
8http://freeboard.io/
Engineering), sensors to Cloud (located in Ministry of Higher
Education and Scientific Research, Department of Research
and Development) IaaS/PaaS, sensors to Cloud SaaS (located
in different country) and all these steps will be repeated for
http protocol.There is a similarity in some of the settings in the
two scenarios. Such as, we setup one real pulse
sensor1(heartbeat pulse sensor) and emulate the other sensors
using TSUNG2(also called Tsunami).With TSUNG, we solve
the problem of having hundreds or even thousands of sensors
to simulate a real environment— Tsung (also called Tsunami)
is an open source program with GPLv2 (General Public
License version 2) and developed by Erlang, which provides
multi protocols like MQTT and HTTP.For data collection
from sensors, we use NodeMCU3 (also called ESP8266-
12E)programmed using C/C++ programming language. Then,
these sensorsconnect to an IEEE802.11n Access Points. The
last similar settings consist of two type of server Fog server
and Cloud server. So as a whole, APs are connected to Fog
layer by using Ethernet, while connected to remote servers via
Internet, in both cases with constant bandwidth.There are
some different settings in each scenario: in first scenario the
MQTT v3.1.1 protocol with QoS level 0 and 1 is used in the
first scenario, MQTT broker (mosquitto4) is necessary to
mediate the transferring data between subscriber and
publisher, then, data is stored using MangoDB5 temporary
database with Robomongo GUI through Node.js by using TTL
(Time to Live) Fog based. A Path to another MangoDB Cloud
based with same configuration and this is a permanent storage
and also another path to public (mosquitto6) located in
different country as shown in Figure 3. As it shown in Figure
4, HTTP protocol v1.1 and GET command are used to request
data. The Fog layer is LAMP (Linux, Apache, MySQL, PHP)
server used to a temporally store data and Cloud layer is also
lamp server but in contrary it considers a permanent storage.
The final path is to Dweet7 Cloud located in different country
and Freeboard8 for monitoring data or to infrastructure LAMP
Cloud at the same region.
J Fundam Appl Sci. 2018, 10(6S), 176-181 179
Fig. 3IoT architecture based Fog/Cloud and MQTT protocol.
Fig. 4IoT architecture based Fog/Cloud and HTTP protocol.
VI. EXPERMENTAL SETUP AND RESULTS
This section shows experimentalresults from the
performance analysis with comments.
In this paper, one session is programmed in Tsung by
using XML v1.0 language and is executed to handle all
requests of protocols with this session to do authentication and
connection with server side. Also, Tsung is configured to
generate a large number of virtual sensors— or what is called
the average arrival rate— to publish a huge number of
messages only per one physical computer.Finally log level of
Tsung is set to type of debug, so that can handle long logging.
Also, in order to calculate the response time, throughput and
packet loss, every request and message generated by the
MQTT and HTTP protocol are recorded using Tsung. At the
end, the overall running time of test takes 170 minutes.
Thesize of packet contentsof HTTP and MQTT protocols
from sensors using open source network analyzer
Wireshark9as is shown in TableIII.
TableIII Size of Packet Contents (in Bytes)
Message PDU Response
size
MQTT 75 11 2
HTTP 75 79 67
IPerf10is used as a tool to measure network bandwidth
between sensors and Fog, and between sensor and Cloud
(located inMinistry of Higher Education and Scientific
Research). IPerf is a powerful and simple testing tool,
client/server model written in C++, it used to analyze
performance network quality, loss and bandwidth based on
TCP or User Datagram Protocol (UDP). Table IV summarizes
the correlation between location of servers and ISP (Internet
Service Provider) based on available bandwidth.
TableIVPerformance between sensor andFog/Cloud
Metric Type of
Server Bandwidth Protocol
Response
Time
Cloud 20.4 Mbits/sec HTTP
Fog 89.3 Mbits/sec HTTP
Cloud 26.8 Mbits/sec MQTT QoS 0
Cloud 26.8 Mbits/sec MQTT QoS 1
Fog 93.9 Mbits/sec MQTT QoS 0
Fog 94.0 Mbits/sec MQTT QoS 1
Throughput
Cloud 4.11 Mbits/sec HTTP
Fog 6.05 Mbits/sec HTTP
Cloud 6.53 Mbits/sec MQTT QoS 0
Cloud 16.4 Mbits/sec MQTT QoS1
Fog 5.72 Mbits/sec MQTT QoS 0
Fog 7.64 Mbits/sec MQTT QoS 1
The proposed IoT architecture consists of integrated the
simulation and practical work. Each of (mosquitto, MongoDB
and LAMP) of Fog/Cloud basedare installed and configured
on HP ProLiant 380 G7 for Fog server and on HP ProLiant
380 G8 for Cloud server, OS: Ubuntu server 14.04 LTS,
RAM: 32 GB, processor: 32 and 500 GB.Tsung was installed
on different machine with characteristics: OS: Ubuntu 14.04.5
LTS, Memory: 3.7 GB, processor:Intel(R) Core(TM) i3-380
CPU @ 2.53GHz *4, disk: 488.1 GB.
The performance analyses are:
1) Response Time:
The responsetime of protocols is the elapsed time taken by
a web to respond to a request for web services.In this test, the
number of sensors set for requesting and publishing data from
100 to 1500 sensors. In Figure 5, architecture based Fog
shows that sensors requesting a web page as using HTTP is
12.1 times higher than sensors using MQTT protocol, Cloud
J Fundam Appl Sci. 2018, 10(6S), 176-181 180
based HTTP is 4.7 times higher than MQTT where Cloud
located in the same region as the region of sensors, and Cloud
based HTTP achieves 2.5 slower than MQTT and the later
Cloud locatedin different country, and these results compare
withQoS 0 of MQTT, while MQTT QoS level 1 is used with
HTTP the results showed:7.8%, 2.7%, 1.8% as respectively, as
an example if number of sensors is 1000. The reason for that is
the MQTT has long keep a live time for connection to handle
multiple requests and low overhead whereas HTTP opens the
TCP connection for short time. Also, the MQTT has low
overhead size only 2 Bytes in handshake than HTTP. As a
result, it is not efficient that sensors depend on Cloud for
processing data and send feedback to interested persons. Also
Figure 6, shows that MQTT connection based Cloud 3.4 times
slower than MQTT connection based Fog and MQTT based
Cloud (located in different country)151 times higher than
protocol based Fog. And this results are the same with HTTP
protocol.
9https://www.wireshark.org/
10https://iperf.fr/
Fig. 5Requests for MQTT and HTTP Fog/Cloud based.
Fig. 6Connections for MQTT and HTTP Fog/Cloud based.
2) Throughput:
Throughput is the amount of data that server could handle
in period of time. The next Figure 7, shows that the throughput
of two protocols MQTT and HTTP. Throughput performance
shows that HTTP is 7.1 times higher than MQTT protocol
QoS 0 Fog based or 6.38 times higher in Cloud based (located
in the same region). The location of the server does not impact
so much on throughput performance of both protocols and
even if it impacted, factor 1 will be affected.Also, we notice
that the HTTP protocol has reached the saturation level earlier
than MQTT protocol in both cases Fog or Cloud based. The
impact performance of throughput depends on server
capabilities to handle data and load.
Fig. 7Throughput for MQTT and HTTP Fog/Cloud based.
3) Packet Loss:
The packet loss defined as number of packets of data fail
to reach the final destination when they travel through
network.In Figure 8 below, packets loss was compared in
terms of the number of messages that published and requested,
the two protocols MQTT and HTTP, and QoS levels and
location of local and remote servers. The results shows MQTT
QoS 1 packet loss is 6.2 times higher than MQTT QoS 0 Fog
based. Also, HTTP message loss is 49.7 higher than MQTT
QoS 0 Fog based and in case of Cloud based located in the
same regionit would be 41.1 higher than MQTT QoS 0, as an
example if the number of messages per sec is 50,000.And all
this happened because of the following reasons: MQTT has
lowest handshake and lowest PDU, Fog has a lower packet
loss than Cloud because the Fog is local and there is no need
for the network to have routers, these routers are unable to
hold trafficwith limited bandwith, unlikeCloud. In addition,
the path to the Cloud may contain multiple routers connecting
together by links, if one of these links is busy the packets have
to wait in the queue. Also, if the queue is at full capacity the
packets will be dropped. Furthermore, the packet loss impact
on response time of protocols because of the retransmission of
lost packets, thus leads to higher response time.
J Fundam Appl Sci. 2018, 10(6S), 176-181 181
Fig. 8Packet loss for MQTT and HTTP Fog/Cloud based
VII. CONCLUSION
In this paper, the proposed IoT architecture suggest a
middle layer named Fog consists of high speed temporary
storage to enable fast end users reporting. We perform an
experimental setup to analyze two Web IoT protocols: MQTT
and HTTP.We implement these protocols using low cost
embedded devices with private and public servers working as
Fog and Cloud (there are two Cloud: one in the same region
with Fog and other at different country). The work
concentrates on generating large traffic volume from sensor-
like terminal running Tsung tool integrated with real heart
sensor traffic to simulate the required scenarios. The obtained
results of response time and throughput for both scenarios
(Fog based and Cloud based) show that the MQTT protocol
advances the HTTP protocol since the latte one consists of an
extra handshaking and more overhead than MQTT. On the
other hand, using Fog servers as a middleware layer close to
the embedded devices (organization levels) enhances
performance and this is clearly shown in the results obtained.
Fog servers may be designed as close as possible to the end
user devices in distributed layers. While, the throughput in
both scenarios is related directly to the available bandwidth
between the gateway and Fog/Cloud servers.
REFERENCES
[1] H. Sundmaeker, P. Guillemin, P. Friess, and S. Woelfflé, Vision and
challenges for realising the Internet of things. Luxembourg: EUR-OP,
2010.
[2] D. Bandyopadhyay and J. Sen, "Internet of things: Applications and
challenges in technology and standardization," Wireless Personal
Communications, vol. 58, no. 1, pp. 49–69, May, 2011.
[3] G. M. Lee, J. Park, N. Kong, and N. Crespi, "The internet of things:
concept and problem statement: 01," 2011. [Online]. Available:
https://tools.ietf.org/id/draft-lee-iot-problem-statement-01.txt [Accessed:
1- March- 2017].
[4] O. Vermesan and P. Friess, Eds., Internet of things: Converging
technologies for smart environments and integrated ecosystems.
Aalborg: River Publishers., 2013.
[5] A. Al-Fuqaha, M. Guizani, M. Mohammadi, M. Aledhari and M.
Ayyash, "Internet of Things: A Survey on Enabling Technologies,
Protocols, and Applications," IEEE Communications Surveys &
Tutorials, vol. 17, no. 4, pp. 2347-2376, 2015.
[6] I. Stojmenovic and S. Wen, “The Fog Computing Paradigm: Scenarios
and Security Issues,” Proceedings of the 2014 Federated Conference on
Computer Science and Information Systems, 2014.
[7] R. Mahmud and R. Buyya, "Fog Computing: A Taxonomy, Survey and
Future Directions". arXiv preprint arXiv:1611.05539, 2016.
[8] E. M. Tordera, X. Masip-Bruin, J. Garcia-Alminana, A. Jukan, G. Ren,
J. Zhu and J. Farre, "What is a Fog Node A Tutorial on Current
Concepts towards a Common Definition". arXiv preprint
arXiv:1611.09193, 2016.
[9] A. V. Dastjerdi, H. Gupta, R. N. Calheiros, S. K. Ghosh and, R. Buyya,
"Fog Computing: Principles, Architectures, and Applications," Internet
of Things: Principles and Paradigms, Massachusetts, USA, 2016.
[10] Cisco, "Fog Computing and the Internet of Things: Extend the Cloud to
Where the Things Are," cisco public, white paper, 2015.
[11] "MQTT," 2014. [Online]. Available: http://mqtt.org/. [Accessed: 1-
March- 2017].
[12] S. Lee, H. Kim, D.-K. Hong, and H. Ju, “Correlation analysis of MQTT
loss and delay according to QoS level,” The International Conference on
Information Networking 2013 (ICOIN), 2013.
[13] A. W. Gordon, "MQTT-S–A Publish/Subscribe Protocol for Wireless
Sensor Networks," 2010.
[14] P. Masek, J. Hosek, K. Zeman, M. Stusek, D. Kovac, P. Cika, J. Masek,
S. Andreev, and F. Kröpfl, “Implementation of True IoT Vision: Survey
on Enabling Protocols and Hands-On Experience,” International Journal
of Distributed Sensor Networks, vol. 12, no. 4, p. 8160282, 2016.
[15] A. Banks and R. Gupta. "MQTT Version 3.1. 1." OASIS standard, 2014.
[16] M. Belshe, M. Thomson, R. Peon, and rfcmarkup version 1, "Hypertext
transfer protocol version 2 (HTTP/2)," 2015. [Online]. Available:
https://tools.ietf.org/html/rfc7540. [Accessed: 1- March- 2017].
[17] V. Lampkin, W. T. Leong, L. Olivera, S. Rawat, N. Subrahmanyam, R.
Xiang and M. Keen, Building Smarter Planet Solutions with MQTT and
IBM WebSphere MQ Telemetry. IBM, 2012.
[18] A. V. Dastjerdi and R. Buyya, “Fog Computing: Helping the Internet of
Things Realize Its Potential,” Computer, vol. 49, no. 8, pp. 112–116,
2016.
[19] X. Che and S. Maag, "Testing protocols in Internet of things by a formal
passive technique," Science China Information Sciences, vol. 57, no. 3,
pp. 1–13, Feb. 2014.
[20] K. J. Kurniawan, C. Lim and K. I Eng, "XMPP Performance Analysis
using large volume traffic from honeypot sensor," The International
Conference on Innovation, Enterpreneurship, and Technology
(ICONIET 2015), 2015.
[21] Z. B. Babovic, J. Protic, and V. Milutinovic, "Web performance
evaluation for Internet of things applications," IEEE Access, vol. 4, pp.
6974–6992, 2016.
[22] M. Slabicki and K. Grochla, "Performance evaluation of CoAP, SNMP
and NETCONF protocols in fog computing architecture," NOMS 2016 -
2016 IEEE/IFIP Network Operations and Management Symposium, p.p
1315 - 1319, 2016.
[23] S. Mijovic, E. Shehu, and C. Buratti, “Comparing application layer
protocols for the Internet of Things via experimentation,” 2016 IEEE
2nd International Forum on Research and Technologies for Society and
Industry Leveraging a better tomorrow (RTSI), 2016.
[24] M. A. Triawan, H. Hindersah, D. Yolanda, and F. Hadiatna, “Internet of
things using publish and subscribe method cloud-based application to
NFT-based hydroponic system,” 2016 6th International Conference on
System Engineering and Technology (ICSET), 2016.
[25] T. Yokotani and Y. Sasaki, “Comparison with HTTP and MQTT on
required network resources for IoT,” 2016 International Conference on
Control, Electronics, Renewable Energy and Communications
(ICCEREC), 2017.
... Esses estudos investigaram abordagens para minimizar a latência na transmissão (8,20) e processamento de dados, bem como propuseram técnicas para lidar com falhas e instabilidades nas redes de comunicações móveis (21) . Além disso, utilizaram simulações para avaliar o desempenho dos sistemas em diferentes cenários e também realizaram coletas de dados reais para validarem suas propostas (10,21) . ...
... Esses estudos investigaram abordagens para minimizar a latência na transmissão (8,20) e processamento de dados, bem como propuseram técnicas para lidar com falhas e instabilidades nas redes de comunicações móveis (21) . Além disso, utilizaram simulações para avaliar o desempenho dos sistemas em diferentes cenários e também realizaram coletas de dados reais para validarem suas propostas (10,21) . A revisão dos trabalhos relacionados abordou critérios relevantes que estão alinhados com a arquitetura proposta neste artigo, tais como: (20,21) . ...
... Além disso, utilizaram simulações para avaliar o desempenho dos sistemas em diferentes cenários e também realizaram coletas de dados reais para validarem suas propostas (10,21) . A revisão dos trabalhos relacionados abordou critérios relevantes que estão alinhados com a arquitetura proposta neste artigo, tais como: (20,21) . ...
Article
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A crescente demanda por soluções de monitoramento de saúde em ambientes externos, como praias, estádios e centros urbanos, destaca a necessidade de arquiteturas eficientes e resilientes no contexto da Internet das Coisas. Objetivos: Propor uma arquitetura em camadas para o monitoramento de saúde em ambientes externos, combinando computação em névoa e nuvem para a coleta e análise eficiente e confiável de dados de saúde. Métodos: O monitoramento é realizado através de smartbands e smartphones, utilizando a computação em névoa na borda da rede para mitigar instabilidades de conexão. Resultados: A arquitetura proposta assegura a integridade dos dados mesmo em ambientes com redes instáveis. Conclusão: A solução demonstra eficácia no monitoramento de saúde em ambientes externos, oferecendo uma solução confiável para a coleta e análise de dados em tempo real, apesar das possíveis instabilidades nas redes de comunicação.
... Alguns trabalhos realizaram implementações de computação em névoa e nuvem para o monitoramento de saúde, contudo, poucos [3] [1] exploraram testes e avaliações de integridade dos dados coletados e latência em ambientes externos com alta densidade populacional e interferências, como praias, estádios de futebol e metrôs. Esses ambientes apresentam desafios adicionais devido à alta quantidade de dispositivos conectados e à instabilidade das redes. ...
... Tendo em vista os aspectos observados, modelos de IoT utilizando apenas a camada de computação em nuvem não necessariamente oferecem a solução mais viável para aplicativos críticos [3] e enfrentamento dos desafios de IoT atrelados à área médica [22]. Nesse contexto, computação em névoa surge como um paradigma de computação distribuída que estende a capacidade da nuvem para dispositivos localizados próximos aos usuários ou no limite da rede, como sensores, câmeras e redes corporais sem fio ...
... É observado também a capacidade de ampliar a escalabilidade horizontal, permitindo a expansão do sistema para gerenciar mais aplicações e dados coletados[4], garantindo uma resposta rápida e eficaz às necessidades dos pacientes.Em relação a ferramentas de sistemas de mensageria e streaming de dados distribuídos para lidar com fluxos contínuos de dados em tempo real, é empregada a comunicação assíncrona entre os componentes do sistema, tornando a troca de informações mais flexível e escalável[14], desempenhando papel fundamental no monitoramento em ambientes externos, onde as condições de rede podem ser instáveis. Além disso, do padrão Transactional Outbox garante a consistência e a atomicidade nas operações de envio de mensagens[22][21], evitando perdas ou duplicações de dados durante a comunicação assíncrona.Por fim, para o tráfego dos dados para a camada de nuvem, estudos recentes demonstram que o protocolo de comunicação Message Queuing Telemetry Transport (MQTT) apresenta alta eficiência e tolerância a falhas de conexão[3][5], permitindo reconexão automática quando há interrupção ou alteração na conectividade, sendo possível adaptar-se a diferentes níveis de qualidade de rede, incluindo as de baixa largura de banda e conexões instáveis, como as de 2G e 3G, ou em situações de sobrecarga da rede devido a vários dispositivos móveis conectados simultaneamente, otimizando o tráfego dos dados com base nas condições da rede[22][15].Apesar dos avanços e contribuições significativas dos trabalhos analisados, algumas limitações foram identificadas. Primeiramente, muitos estudos não abordaram de forma abrangente a integração entre diferentes tecnologias de comunicação, como Wi-Fi e redes móveis de diferentes gerações (2G, 3G, 4G e 5G), resultando em possíveis lacunas na continuidade do monitoramento em ambientes ...
Conference Paper
A multilayer architecture was developed for real-time health data collection and processing, optimized for outdoor environments with high population density and significant network interferences, integrating fog and cloud computing. With the increasing adoption of the Internet of Things (IoT) and Wireless Body Area Networks (WBANs) using smartbands, continuous health monitoring generates vast amounts of data that require efficient processing and reliable transmission. Traditional cloud-based solutions, while scalable, often face high latency and data integrity challenges in unstable network conditions. By leveraging fog computing, the developed architecture performs data preprocessing at the network edge, reducing dependency on cloud connectivity and enhancing system responsiveness. Real-world tests were conducted in complex environments such as football stadiums, beaches, and metro systems, with varying network conditions (5G, 4G, 3G). The architecture consistently achieved over 96% success in packet delivery and significantly reduced latency compared to cloud-only solutions. These results highlight the architecture’s resilience and effectiveness for real-time health monitoring, ensuring data integrity and low response times in high-density, interference-prone environments.
... Al-Joboury et al. experimented with analyzing the packet loss aspect between MQTT and HTTP [18]. Data loss is defined as the number of packets of data that fail to reach the destination when the data travel through the network. ...
... Moreover, no research implements a database per service (multiple databases). On the other hand, three pieces of research compare MQTT and HTTP based on a few aspects, like transmitting time, data loss, and required network resources [9,18,19]. The three papers insisted on the superiority of MQTT over HTTP in the three evaluated aspects. ...
... The first weakness is that it uses HTTP protocol on the sensor gateway. HTTP response times are 4.7 times higher than MQTT on cloud-based IoT systems [18]. HTTP uses a single connection for a single request which causes HTTP needs to open and close connections between requests. ...
Article
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Monitoring health status requires collecting a large amount of data from the human body. The sensor can be used to collect data from the human body. The sensor transmits data for almost every second across the internet. The challenge of the health monitoring system is the massive amount of incoming data. Therefore, a system capable of sending, storing, analyzing, and visualizing vast amounts of data is required for health monitoring. A previous study proposed microservice and event-driven architecture. It also proposed a single database for all services and a relational database management system (RDBMS) for storing time series data, which might reduce the data transmission performance and reliability. This research intends to improve the monitoring system from the previous study to accommodate a greater throughput, faster database read and write operations, and a more reliable system design. The improvement consists of multiple changes in system architecture and technology. A multi-database is proposed in the system architecture to improve system reliability. Time series database and Message Queue Telemetry Protocol (MQTT) server are proposed as an upgrade on technology. As a result, the proposed system throughput is 2.43 times faster than the old system. On database performance, the new system's database write speed is 20.95 times faster and the database read speed is 1.64 times faster than the old system. The proposed system also achieves better scalability, resilience, and independence.
... When comparing different parameters for communication protocols, especially for IoT related application, latency comes as one of the priorities. In [95], authors have analyzed the behavior of two HTTP and MQTT in a fog-to-cloud IoT based architecture scenarios. The results of the experiments have shown that the measured response times for the requests were shorter for MQTT then the ones for HTTP. ...
Preprint
The fast increment in the number of IoT (Internet of Things) devices is accelerating the research on new solutions to make cloud services scalable. In this context, the novel concept of fog computing as well as the combined fog-to-cloud computing paradigm is becoming essential to decentralize the cloud, while bringing the services closer to the end-system. This paper surveys on the application layer communication protocols to fulfil the IoT communication requirements, and their potential for implementation in fog- and cloud-based IoT systems. To this end, the paper first presents a comparative analysis of the main characteristics of IoT communication protocols, including request-reply and publish-subscribe protocols. After that, the paper surveys the protocols that are widely adopted and implemented in each segment of the system (IoT, fog, cloud), and thus opens up the discussion on their interoperability and wider system integration. Finally, the paper reviews the main performance issues, including latency, energy consumption and network throughput. The survey is expected to be useful to system architects and protocol designers when choosing the communication protocols in an integrated IoT-to-fog-to-cloud system architecture.
... Data from a smart city or health care are two examples of the many sources and formats of the vast volumes of data [2]. Data sizes have become widely distributed and need effective techniques for resource management in storage, processing, and analysis [3], such as cloud computing [4,5]. However, collecting and sending raw data to the remote cloud suffers from high latency because of network congestion, and low processing throughput. ...
Article
Abstract: Massive amounts of heterogeneous data are produced by Internet of Things (IoT) devices utilized in daily life and numerous fields, and these data streams need to be stored, processed, analyzed, and transmitted to the cloud. It usually suffers from missing values and anomalies; system services also suffer from congestion due to slow processors, resulting in low throughput, a high response time, slow decision-making, and data loss, resulting in low quality of service and the deterioration of the system's performance. In this study, propose to integrate the smart controller (SC) with the Message Queuing Telemetry Transport (MQTT) broker and services in the fog node to make decisions automatically to prevent congestion in the system's services and speed up the processing. The IoT stream is inspected in the services for anomalies using one-class support vector machines (OCSVM). Then, using the integrating technique of principal component analysis (PCA) and the k-nearest neighbors (KNN) algorithm in the SC, obtain the best prediction of the efficient number of services that must be deployed in the system. The operating model proposed showed significantly stable system performance in terms of throughput, latency, response time, the amount of data loss, and preventing congestion.
... It demonstrates the potentials of providing significant enhancement in energy and spectral efficiencies, as well as increasing the capacity of mobile networks. Another major technology that is often featured in the list of 5G enablers is the small cell network, and the deployment of these small cell base stations only requires low power, it is self-organizing and cost efficient [2, 3] The main aim of using small cells is to improve the energy efficiency and throughput of cellular networks. The small cells have become attractive to mobile operators that are involved in the development of wireless transmission systems, because there is need for the next generation wireless networks to support higher volumes of data (one thousand times higher mobile data rate volume per area) with lower energy consumption, and this can only be achieved through the use of small cells that enhance the overlaying of a small geographical location of outdoor/indoor applications by the wireless transmission systems. ...
Article
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span>Due to the evaluation of mobile devices and applications in the current decade, a new direction for wireless networks has emerged. The general consensus about the future 5G network is that the following should be taken into account; the purpose of thousand-fold system capacity, hundredfold energy efficiency, lower latency, and smooth connectivity. The massive multiple-input multiple-output (MIMO), as well as the Millimeter wave (mm Wave) have been considered in the ultra-dense cellular network (UDN), because they are viewed as the emergent solution for the next generations of communication. This article focuses on evaluating and discussing the performance of mm Wave massive MIMO for ultra-dense network, which is one of the major technologies for the 5G wireless network. More so, the energy efficiencies of two kinds of architectures for wireless backhaul networks were investigated and compared in this article. The results of the simulation revealed some points that should be considered during the deployment of small cells in the two architectures UDN with backhaul network capacity and backhaul energy efficiency, that the changing the frequency bands in Distribution approach gives the same energy efficiency reached to 600 Mb/s at 15 nodes while the Conventional approach results reached less than 100 Mb/s at the same number of nodes.</span
Article
The advancement of Internet of Things based technologies in healthcare monitoring systems has evolved a new terminology, named Internet of Medical Things for medical services and devices. It is integrated with cloud-fog computing environment to facilitate load balancing solutions and enhance the quality of service using message exchange protocols. However, challenges for the quality parameters such as energy consumption, latency, resource utilization, scalability and packet loss associated with the existing architectural models are still of a great concern for researchers. This paper presents an energy efficient fuzzy data offloading scheme with a four tier cloud-fog architectural design to improve the quality parameters. The proposed Message Queuing Telemetry Transfer protocol based message exchange mechanism encapsulates the payload area of messages with client-ID and timestamp to provide authentication and ordering in packet transmission. Fuzzy logic based categorization of medical data has been used to classify it as emergency data, significant data and general data. Clustering of fog nodes has been done based on CPU speed and remaining energy. An adaptive scheduling technique has been proposed which considers the memory value for assigning the weight to different classified queues. The proposed scheme is evaluated and validated using iFogSim toolkit. The performance analysis shows maximum of 77% reduction in energy consumption, 48%, 60% and 44% reduction in end to end delay for different Quality of Services QoS 0, QoS 1, and QoS 2, respectively compared to other existing schemes.
Article
This article has been withdrawn: please see Elsevier Policy on Article Withdrawal (https://www.elsevier.com/about/our-business/policies/article-withdrawal). This article has been withdrawn as part of the withdrawal of the Proceedings of the International Conference on Emerging Trends in Materials Science, Technology and Engineering (ICMSTE2K21). Subsequent to acceptance of these Proceedings papers by the responsible Guest Editors, Dr S. Sakthivel, Dr S. Karthikeyan and Dr I. A. Palani, several serious concerns arose regarding the integrity and veracity of the conference organisation and peer-review process. After a thorough investigation, the peer-review process was confirmed to fall beneath the high standards expected by Materials Today: Proceedings. The veracity of the conference also remains subject to serious doubt and therefore the entire Proceedings has been withdrawn in order to correct the scholarly record.
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Fog computing has emerged as a promising technology that can bring the cloud applications closer to the physical IoT devices at the network edge. While it is widely known what cloud computing is, and how data centers can build the cloud infrastructure and how applications can make use of this infrastructure, there is no common picture on what fog computing and a fog node, as its main building block, really is. One of the first attempts to define a fog node was made by Cisco, qualifying a fog computing system as a mini-cloud, located at the edge of the network and implemented through a variety of edge devices, interconnected by a variety, mostly wireless, communication technologies. Thus, a fog node would be the infrastructure implementing the said mini-cloud. Other proposals have their own definition of what a fog node is, usually in relation to a specific edge device, a specific use case or an application. In this paper, we first survey the state of the art in technologies for fog computing nodes as building blocks of fog computing, paying special attention to the contributions that analyze the role edge devices play in the fog node definition. We summarize and compare the concepts, lessons learned from their implementation, and show how a conceptual framework is emerging towards a unifying fog node definition. We focus on core functionalities of a fog node as well as in the accompanying opportunities and challenges towards their practical realization in the near future.
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An area of intensive research under the umbrella of the Internet of Things (IoT) has resulted in intensive proliferation of globally deployed sensor devices that provide a basis for the development of different use-case applications working with real-time data and demanding a rich user interface. Overcoming the lack of the standard HTML platform, HTML5 specifications WebSocket and Canvas graphics strongly supported the development of rich real-time applications. Such support has been offered by browser plug-ins such as Adobe Flash and Microsoft Silverlight for years. In order to provide a deep insight into IoT Web application performance, we implemented two test applications. In the first application, we measured latencies induced by different communication protocols and message encodings, as well as graphics rendering performance, while comparing the performance of different Web platform implementations. In the second application, we compared Web performance of IoT messaging protocols such as MQTT, AMQP, XMPP, and DDS by measuring the latency of sensor data message delivery and the message throughput rate. Our tests have shown that although Adobe Flash has the best performance at the moment, HTML5 platform is also very capable of running real-time IoT Web applications, whereas Microsoft Silverlight is noticeably behind both platforms. On the other hand, MQTT is the most appropriate messaging protocol for a wide set of IoT Web applications. However, IoT application developers should be aware of certain MQTT message broker implementation shortcomings that could prevent the usage of this protocol.
Conference Paper
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Different architectures of interactions between the objects in the Internet of Things have been proposed, based on direct exchange of data between the wireless devices or based on synchronization with fixed network devices or cloud infrastructure. We evaluated how the selection of the communication architecture influences the performance of the data exchange using discrete event simulation. The distribution of time needed to pass information between smart objects is calculated for three popular network management protocols: CoAP, SNMP and NETCONF. The evaluation is executed for 3 types of communication proposed in the fog computing paradigm: direct synchronization between devices, synchronization through the local gateway or synchronization via the cloud servers. The analysis shows that the communication via the cloud infrastructure requires up to the three times longer time than the data exchange through a local gateway and up to 6 times longer than the direct communication between the network nodes. Transmission through CoAP and SNMP introduces similar delay, considerably lower than NETCONF.
Conference Paper
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In this paper, we will test the performance Extensible Messaging and Presence Protocol server, until this point of time XMPP is a well-known for near real-time message -oriented middleware and for near real time data exchange. But the performance of XMPP server under huge load itself is still unclear, number of XMPP clients that can be handled by XMPP server, numerous factor that affect it’s performance, and the throughput of message transmission. The experiment was done by load testing XMPP server by using Tsung to test XMPP server with huge number of connection, and XMPP clients from honeypot sensors. The result from XMPP server performance test is reaching it’s peak when it reach 1000 concurrent connection established and joining a MUC room. This shows that XMPP is robust but it have a limit on how many concurrent request it is able to handle.
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Internet of Things (IoT) is expected to become a driver in an emerging era of interconnected world through the advanced connectivity of smart devices, systems, and services. IoT goes beyond a broad range of Machine-to-Machine (M2M) communication technologies and covers a wide variety of networking protocols. There exist solutions like MQTT or SIP collecting data from sensors, CoAP for constrained devices and networks, or XMPP for interconnecting devices and people. Also there is a plethora of standards and frameworks (OSGi, AllJoyn) bringing closer the paradigm of IoT vision . However, the main constraint of most existing platforms is their limited mutual interoperability. To this end, we provide a comprehensive description of protocols suitable to support the IoT vision. Further, we advocate an alternative approach to already known principles and employ the SIP protocol as a container for M2M data. We provide description of data structures and practical implementation principles of the proposed structures (JSON and Protocol Buffers are discussed in detail) transmitted by SIP as a promising enabler for efficient M2M communication in the IoT world. Our reported findings are based on extensive hands-on experience collected after the development of advanced M2M smart home gateway in cooperation with the operator Telekom Austria Group.
Conference Paper
HTTP has been widely applied for data transfer. However, in networks for IoT, this protocol causes a large overhead. To solve this problem, named based transfer protocols have been discussed. This paper compares the performance of HTTP with that of MQTT, a type of named based transfer protocol. Additionally, the paper proposes enhancements to MQTT for better performance.
Conference Paper
In this paper we compare the performance of three application layer protocols, that are Constrained Application Protocol (CoAP), WebSocket and Message Queuing Telemetry Transport (MQTT), in an Internet of Things (IoT) scenario. The three protocols have been implemented on the same low cost and low complexity hardware platform, suitable for IoT applications. The performance, in terms of protocol efficiency, strictly related to the overhead, and average Round Trip Time (RTT), were experimentally evaluated. In the considered scenario an IoT device was transmitting data to a server and waiting for replies. IEEE 802.11.b/g/n air interface was used for the communication between the IoT device and an Access Point (AP), connected to the final server. Two different settings have been considered: a local area network (LAN) configuration, where the AP and the server were in the same LAN; and a more realistic IoT configuration, where the AP was connected to a remote server via the Internet. Furthermore, in the IoT configuration, two types of Internet connection are considered: a connection established through a home router and another via cellular network. Results show that CoAP achieves the highest protocol efficiency and the lowest average RTT, closely followed by WebSocket. The performance of MQTT protocol strongly depend on the Quality of Service (QoS) profile. Changing the environment, from a LAN network to a realistic IoT scenario, does not significantly impact the protocol efficiency, but has a considerable influence on the average RTT, which increases by a factor of 2 or 3, depending on the protocol. Finally, we give some insights on the impact of routing through a cellular network on the system performance.
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The Internet of Things (IoT) could enable innovations that enhance the quality of life, but it generates unprecedented amounts of data that are difficult for traditional systems, the cloud, and even edge computing to handle. Fog computing is designed to overcome these limitations.
Conference Paper
Fog Computing is a paradigm that extends Cloud computing and services to the edge of the network. Similar to Cloud, Fog provides data, compute, storage, and application services to end-users. In this article, we elaborate the motivation and advantages of Fog computing, and analyse its applications in a series of real scenarios, such as Smart Grid, smart traffic lights in vehicular networks and software defined networks. We discuss the state-of-the-art of Fog computing and similar work under the same umbrella. Security and privacy issues are further disclosed according to current Fog computing paradigm. As an example, we study a typical attack, man-in-the-middle attack, for the discussion of security in Fog computing. We investigate the stealthy features of this attack by examining its CPU and memory consumption on Fog device.