A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents

System and Blockchain

Chaima Gharbi

, Lobna Hsairi

and Ezzeddine Zagrouba

University of Tunis El Manar, Laboratory of Informatics, Modeling and Information and Knowledge Processing (LIMTIC),

Higher Institute of Computer Science, Ariana, Tunisia

University of Jeddah, Saudi Arabia Kingdom

Keywords:

Cloud-IoT, Fog Computing, Multi-Agents System, Blockchain.

Abstract:

Nowadays, the integration of Cloud Computing and the Internet of Things (Cloud-IoT) has drawn attention

as new technologies in the Future Internet. Cloud-IoT accommodates good solutions to address real-world

problems by offering new services in real-life scenarios. Nonetheless, the traditional Cloud-IoT will be prob-

ably not going to give suitable service to the user as it handles enormous amounts of data at a single server.

Furthermore, the Cloud-IoT shows huge security and privacy problems that must be solved. To address these

issues, we propose an integrated Fog Cloud-IoT architecture based on Multi-Agents System and Blockchain

technology. Multi-Agents System has proven itself in decision-making aspects, distributed execution, and its

effectiveness in acting in the event of an intrusion without user intervention. On the other side, we propose

Blockchain technology as a distributed, public, authentic ledger to record the transactions. The Blockchain

represents a great advantage to the next generation computing to ensures data integrity and to allows low la-

tency access to large amounts of data securely. We evaluated the performance of our proposed architecture

and compared it with the existing models. The result of our evaluation shows that performance is improved by

reducing the response time.

1 INTRODUCTION

The IoT is the internetworking of physical devices,

embedded with electronics, software, sensors, actua-

tors, and network connectivity, that enable these de-

vices to collect and exchange data (Malik and Om,

2018). Recently, IoT has reached so much devel-

opment and importance that several reports foresee

it as one of the technologies of higher impact until

2025 (J. Molano and R. Crespo, 2017). It permits bil-

lions of connected objects to communicate with each

other to share data that improves the quality of our

everyday lives. So, this will produce a high, unstruc-

tured, and varied volume of data that must be col-

lecting, analyzing, managing, and storing to be in-

terpreted proﬁciently and simply. However, IoT de-

vices are limited in terms of processing and storage

capacity. So, to solve the shortcomings of IoT, cloud

computing comes into the picture. Cloud Computing

can be deﬁned as a model that allows accessing a set

of shared and conﬁgurable computing resources (e.g

networks, servers, storage, and applications) offered

as services (E. Cavalcante, 2016). The Cloud-IoT

offers the possibility of managing IoT resources and

provides a more cost-effective and efﬁcient means to

produce services. However, the transfer of enormous

amounts of data generated by distributed IoT systems

to and from Cloud Computing presents a challenge,

since it is expensive to consume an enormous amount

of bandwidth, time, and energy. Besides, the cen-

tralized clouds will be unlikely to deliver satisfac-

tory services to customers, since cloud servers suffer

from a high processing delay that can affect the over-

all efﬁciency of real-time applications. Also, it man-

ages huge amounts of data in a single server point,

which can generate a bottleneck in cloud servers. To

solve these problems, the concept of Fog Comput-

ing has been introduced in Cloud-IoT architecture.

Fog Computing is an extension of Cloud Computing

in which the data generated by terminals are not di-

rectly downloaded to the Cloud but is pre-processed

beforehand in a decentralized mini-center (Prakash P,

2017). If the data does not require higher comput-

ing power then their processing is done in the Fog

Nodes which represent a distributed fog computing

entities that allow the deployment of fog services. If

1184

Gharbi, C., Hsairi, L. and Zagrouba, E.

A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents System and Blockchain.

DOI: 10.5220/0010345111841191

In Proceedings of the 13th International Conference on Agents and Artiﬁcial Intelligence (ICAART 2021) - Volume 2, pages 1184-1191

ISBN: 978-989-758-484-8

the data requires high computing power, the process-

ing is done partially after which the data is transferred

to cloud computing for the remaining computations.

This greatly reduces the delay as the central server

is not overloaded. The integration of Fog Comput-

ing and Cloud-IoT with all its beneﬁts is hindered by

severe privacy and security problems. More speciﬁ-

cally, the major problems in the IoT environment re-

lated to security concern authentication and data in-

tegrity. So, it is necessary to propose a new solution

that provides a satisfactory level of security. Besides,

the development of new IoT application introduces

new challenges such as the ability to monitor, man-

age, and control IoT devices remotely, and to make

new bits of knowledge from huge streams of real-time

data (A. Giordano and A. Vinci, 2016). Hence, to sup-

port these new applications, it is necessary to adopt

new paradigms. To overcome the above problems, we

propose the synergic integration of two paradigms: (i)

Multi-Agents System, which completely bolsters the

development of decentralized, dynamic, cooperative

systems, and (ii) the Blockchain technology, which is

aim at create a distributed ledger to record the transac-

tions and to ensures data integrity. The main compo-

nents of our contribution are summarized as bellow:

• Fog Computing: we have chosen Fog Computing

as the appropriate technology for the Internet of

Things since it offers the lowest-possible latency,

communicates directly with mobile devices, and

allows real-time delivery of data, especially for

delay-sensitive services.

• Multi-Agents System: was adopted since it has

proven itself in decision-making aspects, execu-

tion distribution, communicating objects, and act-

ing in the event of an intrusion without user inter-

vention. (N. Harbi, 2018).

• Blockchain Technology: was integrated into our

proposed architecture as a secure database for

storing data and ensuring data integrity.

The rest of the paper is structured as follows: In Sec-

tion 2, various related works are discussed. Section

3 provides a detailed description of our proposed ap-

proach. Experiment results and analyses are reported

in Section 4. Finally, the conclusion and ongoing

works are presented in Section 5.

2 RELATED WORK

The integration of Cloud Computing and Internet of

Things represents an enormous jump ahead in the Fu-

ture Internet, and many approaches have been pro-

posed in this ﬁeld. We will discuss some of them

to obtain a comprehensive understanding on the inte-

gration of Coud-IoT and to recognize what has been

investigated in this context.

Many Cloud-IoT architectures have as their main

objective security. In (T. Wang and Q. Jin, 2018) it is

proposed Edge-based Cloud-IoT architecture with a

Trust Evaluation Mechanism. The contribution of this

study is to select trusted devices via an Edge Comput-

ing environment to generate or transfer data. How-

ever, the edge cannot perform processing or decision

without returning to the cloud; hence, the architec-

ture is still centralized in the cloud. Also (P. Sharma,

2017) it is presented new distributed blockchain cloud

architecture with Software-Deﬁned Networking to

efﬁciently manage the raw data streams produced

by large IoT devices in the distributed cloud and

at the edge of the network. The proposed archi-

tecture is based on three technologies; Fog Nodes,

Blockchain technology, and SDN controller. Al-

though, the experiment result shows that when the

number of requests for services increases the delay

increases which means there is a problem of scalabil-

ity. Besides, this architecture shows its efﬁciency in

an application with a limited number of IoT devices,

but it must be tested in another big data application

to conﬁrm its effectiveness. In (T. Alam, 2018) the

authors suggested a new integrated model with fog,

IoT, and blockchain technologies to solve the issue

of communication security. The proposed framework

is not dedicated to all IoT applications, it’s specially

dedicated to applications in which data is periodi-

cally transmitted. The main drawback of the proposed

model is that by increasing the number of IoT devices

the transmission delay increases attentively.

On the other hand, some of the research works

were oriented to put in place systems with energy ef-

ﬁciency. In (T. Ogino, 2018) it is proposed a multi-

agent-based ﬂexible IoT edge computing architecture

to balance global optimization by a cloud and lo-

cal optimization by edges. An application is divided

into multiple subtasks that are assigned to a cloud or

edges according to their characteristics as agents. The

main drawback of the proposed architecture is that it

lacks a security mechanism to protect the data. Also

(T. Baker and Buyya, 2017) it is proposed a high-end

energy-efﬁcient service composition algorithm to ad-

dress the overall amount of energy required by the ap-

propriate composite services. The authors proposed

a novel multi-cloud IoT service composition algo-

rithm named E2C2 to emphasize energy awareness

when searching for optimum composition plans to

meet speciﬁed user requirements.

Further, many studies have been proposed to

provide optimal Quality of Service (QoS). In (L.

A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents System and Blockchain

1185

Carnevale, 2019) the authors proposed Osmotic Com-

puting architecture, based on a Multi-Agents System,

according to a new software abstraction called Mi-

croELement (MEL) that encapsulates resources, ser-

vices, and data necessary to run IoT applications. In

the case of Overloading, the microservices can mi-

grate from an agent to another one. However, addi-

tional details and experimental results about the pro-

posed model are required to evaluate its performance,

especially in complex applications. Also (A. Munir

and S. Khan, 2017) it is presented a novel reconﬁg-

urable fog cloud IoT (IFCIoT) architectural paradigm.

The new model is applied to intelligent transporta-

tion systems as consumer applications use cases. This

study seeks to reconﬁgure the architectural resource

to better meet the peak workload requirements of an

application at a given time. However, this architec-

ture is not efﬁcient for high-end-batch processing jobs

which are very frequent in the business and scientiﬁc

world. The authors in (Lu Hou and W. Xiang, 2016)

proposed an IoT cloud architecture based on both

the Hypertext Transfer Protocol (HTTP) and Mes-

sage Queuing Telemetry Transport (MQTT) protocols

to guarantee high performance. The HTTP servers

can provide services for end-users and devices, while

the MQTT servers ensure a large number of device

connections and real-time communication among de-

vices. The simulation results show that the proposed

model has a signiﬁcant impact on the perceived qual-

ity of the services of the IoT cloud. However, the

proposed solution cannot support the big data, and

security should be taken into account in designing

the IoT Cloud. Also in (A. Abdelaziz and A. Mah-

moud, 2018), the authors proposed a hybrid intel-

ligent model for predicting chronic kidney diseases

(CKD) based on Cloud-IoT by using two intelligent

techniques, which are linear regression (LR) and neu-

ral network (NN). The contribution of this study is

to predict patients of CKD anywhere and anytime in

smart cities. The simulation results show that the pro-

posed model greatly improves the accuracy of predic-

tion. However, the proposed solution has not been

tested in a big data case which is a very important cri-

terion in the healthcare ﬁeld. Moreover, it needs to

be tested in different application domains to evaluate

the performance of the proposed method. In (Ju Ren,

2017) presented an Edge-based IoT Cloud architec-

ture that exploits transparent computing to build scal-

able IoT platforms. The transparent offer a scalable

IoT platform that can provide desired services on time

for lightweight IoT devices on-demand to address the

changing needs of users. The main drawback of the

proposed architecture is that it lacks a security mech-

anism to protect the system from different attacks.

Other research works have drawn attention to the ﬁeld

of Big Data. In (M. Elhoseny, 2018) it is introduced

a new model for Cloud-IoT-based health service ap-

plications in an integrated industry 4.0 environment.

The main contribution of this study is to optimize vir-

tual machine selection VMs in Cloud-IoT health ser-

vice applications to efﬁciently manage a big amount

of data in integrated industry 4.0.

From the literature reviewed, we can ﬁnd that a

lot of research works have used either Fog Comput-

ing or Edge Computing to their solutions. Both Fog

Computing and Edge Computing provide reliable and

improved quality of service to IoT applications when

compared with Cloud Computing. However, they

are still different from each other. The key differ-

ence is the data in Fog Computing can be stored for

days while Edge Computing provides temporary stor-

age. On the other hand, Fog Computing has multi-

ple wireless access technologies including WIFI, 4G,

and LTE whereas Edge Computing is accessible via

home/Enterprise networks and wiﬁ hotspot (G. Prem-

sankar and T. Taleb, 2018). Based on that, we choose

to use Fog Computing as the appropriate paradigm

for our architecture. There is still a lot of possibility

for the improvement of Cloud-IoT architectures un-

til now. Therefore, our proposed architecture can be

separated from the above state-of-the-art architectures

by the integration of Fog Computing, Multi-Agents

System, and Blockchain technology to help the real-

ization of secure and efﬁcient IoT applications. We

consider Smart Home as an example of an IoT ap-

plication, to provide a more realistic scenario that is

reader-friendly. It is important to note here, that, the

proposed architecture is not restricted to Smart Home

only, but can also be applied to any other IoT applica-

tions.

3 PROPOSED ARCHITECTURE

3.1 Integrated Fog Cloud-IoT

Architecture based on Multi-Agents

System and Blockchain

Figure 1 presents an overview of our distributed

Cloud-IoT architecture, which is categorized into

three layers and each layer has speciﬁc roles and re-

sponsibilities within the architecture.

1. Devices Layer: This layer contains all the de-

vices connected to the internet. It aims to collect

data from these devices and transmit it to the next

layer.

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1186

2. Fog Layer: It is composed of several fog nodes

and each fog node is responsible for the small as-

sociated community. The main objective of this

layer is to execute the most time-sensitive requests

and the geographically closest.

3. Cloud Layer: it is the layer that contains more ef-

ﬁcient storage and processing resources than the

fog layer. The Cloud Layer stores a lot of histor-

ical data to be used for deeper data mining and

analysis.

Figure 1: Overview of the proposed architecture.

In this paper, inspired by the approach presented

in (M. Ghazouani and L. ErRajy, 2019), we proposed

a distributed Cloud-IoT architecture that relies on the

combination of three paradigms that are Fog Com-

puting, Multi-Agents System, and Blockchain tech-

nology (Figure 2). Given that, in (M. Ghazouani and

L. ErRajy, 2019) the authors propose the synergic in-

tegration of Multi-Agents System and Blockchain to

solve the problem of managing data deduplication in

Cloud Computing. We integrate into our proposed ar-

chitecture a Multi-Agents System where seven intelli-

gent agents are working in cooperation to manage and

execute the user’s request (Table 1).

When a client sends a request (for service or data),

the Interface Agent is responsible to monitor, ﬁlter,

and send the ﬁltered data to the Fog Layer. At the Fog

layer, we ﬁrst check user authentication, and then the

different agents cooperate with each other to execute

the user’s request. Once the request is executed, the

result is stored in the Local Storage which represents

the local database of the Fog Layer. Local Storage

aims to stock the data locally. Then, a new block is

created in Local BC which represents a secure and

private Blockchain that keeps track of transactions. A

local and private BC is used to provide secure access

control to the IoT devices and their data. If the fog

layer cannot perform this request, then it can ofﬂoad

their computing workloads to the distributed cloud

when they do not have sufﬁcient computing resources

Table 1: Roles of each agent.

Agents Roles

Interface Agent Interact with users for

receiving requests,

ﬁltring data, and

transmitting them to

mediator agent.

Mediator Agent Manage the

communication

between agents.

Analysis-F

Agent/Analysis-C

Agent

Analysis-F Agent is

devoted to Fog Layer

and Analysis-C

Agent is devoted to

Cloud Layer.It aim to

check the necessary

resources and has

access to all the

devices in order to

resolve the request.

Control Agent Checks the

authentication and

access control of

users and devices.

Data-Fog Agent /

Data-Cloud Agent

Create a new block

for each transaction

in the Blockchain and

store the data to the

Storage Server or

Local Storage.

to process their local data streams with the sacriﬁce

of increased latency in communications and resource

consumption.

The following algorithm presents the pseudo-code of

the proposed approach.

INPUT: SRequest, FogN, Cloud

OUTPUT:SRequest_Result

Begin

1.Filter the data.

2.Send the filtred data to Mediator Agent.

3.Check the access control.

4.If user or device has the right to access

FogN Then

5. Allow the request to be processing at FogN.

6. If (SRequest.Resource <= FogN.Resource)

Then

7. FogN can perfect final result processing.

8. Store the final result in Local Storage.

9. Store the final result in Local BC.

10. Return the result to user or device.

11. Else

12. Select the cloud as the platform

for final result processing.

13. Sotre the final result in Storage Server.

14. Store the final result in BLockchain.

A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents System and Blockchain

1187

15. Return the final result to user or device.

16. EndIf

17.EndIf

18.End

Figure 2: The Distributed Cloud-IoT Architecture based on

Multi-Agents System and Blockchain.

To exemplify our ideas, we use an illustrative ex-

ample of a smart home in the next section. However,

our proposed architecture is well suited for diverse

IoT applications.

3.2 Case Study: Smart Home

A smart home creates a future home network, where

embedded sensors and intelligent devices are self-

conﬁgured and can be controlled remotely through

the Internet to provide a comfortable environment for

humans (A. Dorri and P. Gauravaramz, 2017). The

smart home is very important especially to the elderly

and people with disabilities who will ﬁnd the house

capable of taking charge of activities that today may

require excessive effort or manual assistance. Based

on the case study presented in (A. Dorri and P. Gau-

ravaramz, 2017), we consider a typical smart home

setting where a user has equipped his home with a

number of IoT devices including a smart thermostat,

an IP camera and several other sensors. The proposed

architecture for the Smart Home case is presented by

Figure 3.

The smart home architecture is comprised of the

following components:

• User: the ﬁnal users of the system can be the per-

son that lives in the house, his family, or the tech-

nicians.

Figure 3: Smart Home Architecture.

• Devices: all the smart devices located in the

home.

• Fog Node: is a device that processes incoming

and outgoing transactions to and from the smart

home and is responsible for data analysis and ser-

vice delivery in a timely manner.

• Local Storage: is a storing device that is used by

devices to store data locally.

• Local BC: is a secure and private BC speciﬁed to

one smart home. Each block in the local BC con-

tains two headers that are block header and policy

header. The block header has the hash of the pre-

vious block to keep the BC immutable. The policy

header is used for authorizing devices and enforc-

ing the owner’s control policy over his home.

The communication between different devices and

the user are known as transaction. The different trans-

actions we can found in this smart home are:

• Access to data from different devices in smart

home.

• Modify the status of a device.

• Store data.

In the following, we will detail the different transac-

tions in our case study.

3.2.1 Transaction: Access to Data

The home owner or the user can access/check certain

information from their smart home devices in real-

time. For example, he can check the current temper-

ature of his smart thermostat. The execution of the

monitor transaction is illustrated in the sequence dia-

gram (Figure 4).

1. The user sends a request for the current status of

the thermostat.

2. The Interface Agent receives this request and trans-

mits it to the Mediator Agent.

3. The Mediator Agent sends the request of the user

to the Control Agent.

4. The Control Agent checks the policy in the Lo-

cal BC to verify if the user has permission to access

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data, which should have been granted previously by

the home owner.

5. If so, the Control Agent sends the request to the

Analysis-Fog Agent.

6. The Analysis-Fog Agent requests the current status

from the Thermostat.

Afterward, the Analysis-Fog Agent demands to Data-

Fog Agent to store the data in the Local Storage.

7. Data-Fog Agent stores the data in the Local Stor-

age.

8. Then, Data-Fog Agent creates a new block in the

Local BC.

9. Data-Fog Agent sends to the Mediator Agent a

pointer to the block corresponding to that data.

10.The Mediator Agent sends to the Interface Agent

the pointer and data required.

11. The Interface Agent re-transmits the pointer and

the current status of the thermostat to the concerned

user.

Figure 4: Sequence diagram of Access to data transaction.

3.2.2 Transaction: Modify Data

Some users have the right to modify the status of a

device. For example, one of the children in the house

forgot his keys. One of the parents can from a Smart-

phone open the door of the house from his ofﬁce. The

execution of the monitor transaction is illustrated in

the sequence diagram (Figure 5).

1. The user sends a request to open the door.

2. The Interface Agent receives this request and trans-

mits it to the Mediator Agent.

3. The Mediator Agent sends the demand of the user

to the Control Agent.

4. The Control Agent checks the policy in the Lo-

cal BC to verify if the user has permission to modify

data, which should have been granted previously by

the homeowner.

5. If so, the Control Agent sends the request to the

Analysis-Fog Agent.

6. The Analysis-Fog Agent modiﬁes the current sta-

tus of the door lock from “close” to “open”.

7. Afterward, the Analysis-Fog Agent demands to

Data-Fog Agent to store the data in the Local Stor-

age.

8. Data-Fog Agent stores the Modiﬁed data in the Lo-

cal Storage.

9. Then, Data-Fog Agent creates a new block in the

Local BC.

10. Data-Fog Agent sends to the Mediator Agent a

pointer to the block corresponding to that data.

11. The Mediator Agent sends to the Interface Agent

the pointer and data required.

12. The Interface Agent re-transmits the pointer and

the new status of the door to the parent.

Figure 5: Sequence diagram of Modify Data Transaction.

3.2.3 Transaction: Store Data

Each device can store data in local, or in cloud stor-

age. For example, the surveillance camera can store

the recordings locally for a week, and afterward, it

transmits it to the cloud Storage.The execution of the

monitor transaction is illustrated in the sequence dia-

gram (Figure 6).

1. The device sends a request to store the data.

2. The Interface Agent receives this request and trans-

mits it to the Mediator Agent.

3. The Mediator Agent sends the demand of the de-

vice to the Control Agent.

4. The Control Agent checks the policy in the Lo-

cal BC to verify if the device has permission to store

data, which should have been granted previously by

the homeowner.

5. If so, the Control Agent sends the request to the

Data-Cloud Agent.

6. Data- Cloud Agent stores the data in Cloud Stor-

age.

7. Then, the Data-Cloud Agent creates a new block in

the BC.

8. Data-Cloud Agent sends to the Mediator Agent a

pointer to the block corresponding to that data.

9. The Mediator Agent sends to the Interface Agent

the pointer.

A Secure Integrated Fog Cloud-IoT Architecture based on Multi-Agents System and Blockchain

1189

Figure 6: Sequence diagram of Store Data Transaction.

4 EVALUATION

The simulation platform is Eclipse. We carry out sim-

ulation experiments on an Intel Core i5 2.4 GHz CPU

and 4 GB RAM personal computer. We evaluate the

efﬁciency of the proposed model by measuring the

speed with which it can host the request to the cor-

responding resources (Fog Node or Cloud). The ex-

perimental setting consists of two Fog Nodes and one

cloud, where every Fog Node has different types of

IoT devices. The parameters are shown in Table 2.

Table 2: Experimental parameters.

Parameter Value

Number of Cloud 1

Number of Fog Nodes 2

Latency from Fog

Node to Cloud (ms)

100

Latency from IoT

device to Fog Node

(ms)

In this experiment, we will focus on the strategy

used by our distributed architecture to host requests.

When a device sends a service request to Fog Node

through Interface Agent; the ﬁrst step is to decide

where the request should be executed; either in the

Cloud or the Fog Node. So for each request, we ﬁrst

try to place it on a Fog Node which provides the mini-

mum delay. However, if there is no sufﬁcient resource

in Fog Node, then it can send the request to the cloud.

We use the Response Time metric in our simulation

experiment. This measurement reveals to us what

amount of time needed to receive a response from

the system. In this experiment, we evaluate the per-

formance of the proposed architecture by varying the

number of requests from 5 to 20. Figure 7 shows the

efﬁciency of our distributed architecture against com-

pared centralized Cloud-IoT architecture. The exe-

cution time is smaller in the proposed model than in

Figure 7: Average Execution time with different number of

requests.

the case using the centralized Cloud-IoT architecture,

which demonstrates the efﬁciency of our proposed ar-

chitecture. The results of the simulation demonstrate

how placement strategy can impact the execution time

of requests.

5 CONCLUSION AND ONGOING

WORKS

In this paper, we presented a new distributed Cloud-

IoT architecture to support real-time data delivery, se-

curity, and low latency. It is based on three emerging

technologies; Fog Computing, Multi-Agents System,

and Blockchain. Fog Computing can greatly reduces

the delay since it is located near to IoT devices. Multi-

Agents System provides distributed execution and has

very efﬁcient proactive and reactive features which

are very useful in IoT applications. We also integrated

Blockchain technology into our architecture as it is a

great advantage to the next generation computing to

ensures data integrity and to allows low latency access

to large amounts of data securely. A simpliﬁed case

study is presented to illustrate that our approach can

be used in any other IoT applications. The results of

our performance evaluation can greatly improve the

response time compared to the traditional cloud-IoT

computing infrastructure. However, there is still a lot

of work to be ﬁnished. Future work will seek to im-

prove the architecture. Additional experiments will

be implemented to evaluate the proposed architecture

performance in different environments.

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