An Approach to Privacy-Preserving Distributed Intelligence for the

Internet of Things

Tariq Alsboui

, Hussain Al-Aqrabi

, Richard Hill

and Shamaila Iram

School of Computing and Engineering, University of Huddersﬁeld, U.K.

Keywords:

Internet of Things (IoT), Distributed Ledger Technology (DLT), IOTA Tangle, Masked Authenticated

Messaging (MAM), Privacy.

Abstract:

In the Internet of things (IoT), security and privacy issues are a fundamental challenge determining the suc-

cessful implementation of many IoT applications. Distributed ledger technology (e.g., Blockchain) offers a

great promise to solve these issues. Blockchain-based solutions support security and privacy, yet they involve

signiﬁcant energy due to mining, low throughput, and computational overhead that is not acceptable for IoT

resource-constrained devices. In this paper, we propose a scalable Privacy-Preserving Distributed Intelligence

approach (PPDI) by leveraging the IOTA technology. IOTA is an emerging distributed ledger technology that

allows for zero fees transactions for the IoT. The proposed PPDI aims to address the privacy issues in the IoT

by using the IOTA Masked Authenticated Messaging (MAM) protocol. MAM ensures privacy by encrypting

and granting permission to authorized users to access data. This paper presents a healthcare scenario that

demonstrate how IOTA MAM can be used to address the privacy issue in the IoT. The experimental results

clearly show that the IOTA MAM is a feasible solution that can be used to solve privacy related issues in the

IoT domain.

1 INTRODUCTION

The Internet of Things (IoT) is considered to be an

enabling technology for several applications. It con-

nects physical objects together with the main aim of

exchanging data with other systems over the Internet

to enable communications between these objects (At-

zori et al., 2010), also referred to as Cyber-Physical

Systems (CPS) (Cares et al., 2019).

The elements of IoT applications include: (1)

sensing to perceive the environment; (2) communica-

tion for efﬁcient data transfer between objects, and (3)

computation, which is performed to generate useful

information from the raw data.

IoT systems have already enhanced the quality of

life by turning cities into smart cities (Perera et al.,

2017), homes into smart homes (Doan et al., 2018),

and campuses into smart campuses (Angelis et al.,

2015). The research reports estimate the rapid growth

of IoT, i.e., in the order of 125 billion devices con-

nected to the Internet in 2030 (Cisco, 2016; Gartner,

https://orcid.org/0000-0001-6004-3756

https://orcid.org/0000-0003-1920-7418

https://orcid.org/0000-0003-0105-7730

https://orcid.org/0000-0003-0217-500X

2013; Research, 2013). Therefore, this will present

many challenges with regard to data volume, velocity,

timely processing, privacy and scalability (Al-Aqrabi

et al., 2019; Alsboui et al., 2020a).

Distributed Ledger Technology (DLT) is an

emerging development that shares data among dif-

ferent participants deployed over various locations all

over the world. This technology provides several ben-

eﬁts to various IoT applications. DLT is being in-

vestigated by many researchers across the world as

a promising solution to the challenges of IoT, such

as scalability, energy-efﬁciency, security, and pri-

vacy (Alsboui et al., 2020a; Fan et al., 2019; Alsboui

et al., 2019).

In this paper, we propose a privacy-preserving

distributed intelligence approach using the IOTA

Masked Authenticated Messaging (MAM) (See Sec-

tion 2 for Further details) as a suitable solution to

tackle the privacy, and scalability issues for IoT ap-

plications. MAM is a second layer data communica-

tion protocol used to authenticate, and encrypt data

streams through the use of a mixture of modes, such

as public, private, and restricted.

Contributions: In this paper, we propose a sys-

tem architecture for IoT, called Privacy-Preserving

174

Alsboui, T., Al-Aqrabi, H., Hill, R. and Iram, S.

An Approach to Privacy-Preserving Distributed Intelligence for the Internet of Things.

DOI: 10.5220/0011056400003194

In Proceedings of the 7th International Conference on Internet of Things, Big Data and Security (IoTBDS 2022), pages 174-182

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

Distributed Intelligence Approach (PPDI). This ap-

proach addresses privacy realted issues in IoT, whilst

supporting the popular proof-of-work (PoW) mecha-

nism in an energy-efﬁcient way. The key contribu-

tions can be summarised as follows:

• A Privacy-Preserving Distributed Intelligence ar-

chitecture that ensures privacy by using IOTA

MAM protocol with a mixture of modes.

• Evaluation of an existing Proof of Work (PoW)

ofﬂoading mechanism for efﬁcacy with regard to

energy efﬁciency and transaction throughput;

• Preliminary experimental results to verify the ef-

fectiveness and scalability of the proposed ap-

proach.

The rest of this paper is organized as follows: Sec-

tion 2 presents an overview of the IOTA technology

and a detailed description of the masked authenti-

cated messaging. Section 3 presents the recent re-

search efforts in distributed intelligence in the IoT do-

main. In Section 4, we describe our proposed privacy-

preserving distributed intelligence for the IoT. Sec-

tion 5 considers a healthcare application scenario in

the context of the proposed privacy preserving ap-

proach. Section 6 presents an implementation of the

proposed approach and analysis of the results. Fi-

nally, in Section 7, we conclude the paper and discuss

future work.

2 IOTA PLATFORM: AN

OVERVIEW

IOTA’s tangle architecture is an evolving DLT plat-

form aimed at addressing transaction costs, mining

and scalability issues (in the context of Blockchain

technology) (Zhang and Jacobsen, 2018), that are re-

lated to IoT. The architecture of a Tangle (Serguei,

2017), which is central to IOTA, a DAG that offers a

potentially scalable IoT-enabled applications. IOTA

technology offers the necessary privacy for IoT ap-

plications. In the context of transactions, IOTA may

promote IoT interactions. This approach radically

changes the overall design, development, implemen-

tation and management process of IoT systems.

2.1 The Tangle

The IOTA Tangle was developed to cope with the re-

quirements of IoT applications such as privacy, and

security. Tangle is built upon a Directed Acyclic

Graph (DAG), which is considered to be the ledger

that stores transactions. The Tangle is the data

structure that consists of a collection of sites and

edges (Serguei, 2017). In order to issue a transaction

by a node, the node should work to approve two pre-

vious transactions. Choosing the two previous trans-

actions is done by using the tip selection technique

whereby default is the Markov Chain Monte Carlo

(MCMC) technique (Serguei, 2017). The main aim

of the tangle network is to make all the transactions to

be conﬁrmed and to make all the unconﬁrmed trans-

actions to conﬁrmed transactions, the MCMC tech-

nique is executed n number of times. Genesis is the

ﬁrst transaction of the network, which is approved di-

rectly or indirectly by the other transactions.

It is possible to securely store information within

the Tangle, or even spread larger amounts of informa-

tion across multiple bundled or linked transactions.

This particular type of structure also enables high

scalability of transactions. According to the IOTA

foundation (Serguei, 2017), the more activity in the

Tangle, the faster transactions can be conﬁrmed.

2.2 Masked Authentication Messaging

IOTA offers a second layer of data communication

called Masked Authenticating Messaging (MAM).

MAM is responsible for masking, authenticating, and

encrypting data streams. Consequently, data streams

are broadcasted and retrieved through the Tangle as

zero fee transactions. Given these properties, MAM

fulﬁlls an important need in which integrity and pri-

vacy are required.

Every MAM data transaction is linked with an ad-

dress in which a user can refer to the transaction. Data

transactions would be transmitted using the MAM

protocol at any point in time, but a small amount of

Proof of Work (PoW) is needed in order to broad-

cast data streams to the IOTA network. Transaction

data broadcasted using MAM are linked together in

chronological order. Furthermore, a signature of the

user is attached to all MAM data streams. This en-

sures that subscribers are required to verify the au-

thenticity of the user. By adopting MAM, users will

certainly ensure safety when exchanging data to the

Tangle.

MAM transactions can be broadcasted and fetched

from the IOTA Tangle, by communicating with a fully

functional node. This indicates that an IoT device will

be able to transmit encrypted data streams using the

IOTA MAM protocol.

2.2.1 MAM Privacy and Encryption Modes

MAM enables encryption to occur through several

modes including: public, private, and restricted. In

An Approach to Privacy-Preserving Distributed Intelligence for the Internet of Things

175

public mode, the user uses the tree’s root as the ad-

dress of the transaction that the message is published

to. A user will be able to decode it by using the ad-

dress of the message. Public mode enables any user to

read the content of the data, but it adds immutability

and data integrity.

In the case of private mode, there is an added level

of security that controls the access in order to be able

to read the content of the transaction data. It enables

access to users who have only the hash of the channel

key. The users would request the tangle for the hash

of the channel key. Then, it would enable them to

decode the transaction data by using the channel key.

Figure 1: The Process of Publishing Transaction Using Re-

stricted Mode.

In the case of restricted mode, it adds an autho-

rization key to the private mode. The address used

to attach to the network is the hash of the authoriza-

tion key and the Merkle root. It also offers granular

access to users who have the secret key. Therefore,

access would be revoked from users if needed. If the

secret key change, the new authorized key is required

and should be distributed to the users that needs to

gain access to the data. Fig. 1 describes the process

of sending transactions using the restricted mode. In

our implementation, we focus on the public mode, to

ensure authenticity, and data integrity, as well as the

restricted mode to enable granular access to data.

3 RELATED WORK

There has been sustained research into distributed

intelligence approaches for IoT over the last few

years. In a recent publication (Alsboui et al., 2021),

the authors classiﬁed distributed intelligence tech-

niques into ﬁve categories depending on the factors

that support distributed functionality and data acquisi-

tion: cloud-computing, mist-computing, distributed-

ledger-technology, service-oriented-computing and

hybrid. For a comprehensive and recent literature re-

view, we refer the interested readers to (Alsboui et al.,

2021) and the references therein. Such research ef-

forts focused on developing distributed intelligence

approaches for IoT. However, there has been little at-

tention on addressing the privacy issue.

Recently, a computing paradigm called Edge

Mesh is being suggested in (Sahni et al., 2017) to al-

low distributed intelligence in IoT. Decision-making

task is distributed through the network among de-

vices, instead of data being transmitted directly to a

central location for processing. Combining the use of

both computation and data, tasks are exchanged with

Edge Mesh through a network of routers and edge de-

vices. The architecture of Edge Mesh comprises of

several devices. First of all, the end devices are con-

cerned with actuation and sensing purposes. Second,

edge devices can be used to process and connect end

devices. Third, routers are being utilized to transmit

data. Finally, the cloud is increasingly being used to

perform advanced analysis of data. The incorpora-

tion of Edge Mesh could bring various beneﬁts such

as increased scalability, improved security. However,

some will have a concern over privacy and security,

but how privacy can be accomplished is not taken into

account. Also, the architecture lacks support for inter-

operability.

In (Klonoff, 2017), the authors applied fog com-

puting as a means to support distributed intelligence

by setting up an architecture that is made up of three

layers. The sensing layer is concerned with the trans-

mission of data to the upper layer. A fog layer plays

the role of data processing transferred from the sen-

sor nodes. The cloud computing layer is used for the

heavy processing of data. The system is suitable for

timely response applications and is energy efﬁcient

since processing is performed near the data source.

It also provides support for interoperability. How-

ever, the approach lacks support for other IoT tech-

nical challenges such as scalability, and privacy.

Most recently, the authors in (Alsboui et al.,

2020a) proposed a distributed intelligence approach

called Mobile Agent Distributed Intelligence Tangle-

based approach (MADIT) that adopts the IOTA tan-

gle. The approach supports distributed intelligence

at two levels including high-level and low-level. At

the high-level, a Tangle based architecture is used to

deal with transaction data, while the low-level, em-

ploys a mobile agent to cater for node level commu-

nications. The proposed approach is scalable, energy-

efﬁcient, and eliminates redundant data. However, the

approach lacks support for privacy, which is outlined

as future work.

Another recent approach is introduced by the au-

thors in (Alsboui et al., 2020b) in support of dis-

tributed intelligence. The approach mainly solves

some of the IoT technical challenges such as scala-

bility, energy-consumption, and decentralization. A

PoW enabled server is used to deal with heavy com-

putation tasks on behalf of constrained IoT devices.

The proposed approach is scalable, energy-efﬁcient,

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

176

and decentralized. However, security and elimination

of redundant data are not considered. Also, they out-

line to develop a schema to deal with privacy issues

as part of their future work.

In comparison to the above, the research in (Rah-

man and Rahmani, 2018) suggested an AI-based dis-

tributed intelligence solution. The solution incorpo-

rates the use of both cloud based and edge controller

to enable distribute intelligence. To be speciﬁc, it has

been shown that the cloud-based controller is capa-

ble of providing intelligence at a high level. The edge

controller is designed to support intelligence at a low

level. The advantages of their research are reduc-

ing response time and loosening rules requirements.

However, the approach lacks a mechanism which al-

lows ofﬂine capability and privacy preserving.

The authors in (V

ogler et al., 2015) introduced

The LEONORE system to support distributed intel-

ligence. LEONORE is built up using a service-

oriented architecture and supports several applica-

tion components in large-scale IoT deployments.

The LEONORE framework works according to two

phases push-based and pull-based. The pull-based

is responsible to independently propose a run time

method, while provisioning of push-based, responsi-

ble for providing control for the application by pro-

viding software updates and maintains security. The

proposed framework is energy-efﬁcient, and scalable.

However, ofﬂine-capability, security, and privacy are

not well supported (Al-Aqrabi et al., 2020).

A distributed intelligence approach that adopts the

IOTA protocol is proposed in (Fan et al., 2019). It

establishes an infrastructure network for smart home

paying a particular attention to ensure scalability. All

of the home IoT nodes in the system are linked with

neighbouring nodes to exchange information and en-

sure synchronization with the ledger. The approach is

only suitable for small scale applications, and would

lead to higher energy to be consumed in all nodes

since PoW computation is performed on local IoT

nodes. The approach does not support a schema to

deal with privacy.

Table 1 compares the selected most recent dis-

tributed intelligence approaches and presents compar-

isons in regards to energy-efﬁciency, scalability, secu-

rity, and privacy of each approach.

From the table we can see that privacy is a crit-

ical challenge in distributed intelligence approaches.

Meanwhile, Most of the distributed intelligence ap-

proaches focuses on solving the energy-consumption,

and scalability. Our work adopts the IOTA Masked

Authenticated Messaging (MAM) protocol as a solu-

tion to solve the privacy issue.

4 PRIVACY-PRESERVING

DISTRIBUTED INTELLIGENCE

APPROACH

Fig. 2 presents an abstract view of the system ar-

chitecture of the proposed Privacy-Preserving Dis-

tributed Intelligence approach (PPDI). It describes all

relevant components including IoT devices, transac-

tion data ﬂow, Node JS with MAM, Gateway, and

PoW computation server. The IoT devices are respon-

sible for sending transaction data using MAM client

and transmits transaction data to a receiver, which is

the gateway. The gateway is connected to the Inter-

net and transmits transactions data to a server, which

runs the Node JS Masked Authenticated Messaging

(MAM) application. The Node JS MAM is responsi-

ble for sending transaction data to the IOTA Tangle.

For example, the transaction data ﬂow from IoT de-

vices (e.g., dash lines) represents the way how trans-

action data is transmitted to the IOTA Tangle using

MAM.

Figure 2: The Proposed Privacy-Preserving Distributed In-

telligence Approach (PPDI).

The PoW enabled server is responsible for per-

forming heavy computation tasks on behalf of con-

strained IoT devices.

The ﬂowchart of the proposed PPDI approach is

shown in Fig. 3. First, the MAM state is initialized

using the main Tangle provider. After MAM state is

initialized, its patched with the Proof of Work (PoW)

for performing the PoW, which patches MAM sate

with the PoW Provider. Then, the channel mode is

set to restricted on the MAM state. Once this step is

completed, the payload is created and prepared to be

transferred to the main Tangle. Then, the PoW is per-

formed on the PoW provider. Once the PoW is com-

pleted, the payload will be attached to the main Tan-

gle. In order for users to be able to access healthcare

records, the root ID and the correct secret key should

An Approach to Privacy-Preserving Distributed Intelligence for the Internet of Things

177

Table 1: Comparisons Among Distributed Intelligence (DI) Approaches.

DI Approaches Energy-Efﬁciency Scalability Security Privacy

(Sahni et al., 2017) X X X X

(Klonoff, 2017) X X X X

(Alsboui et al., 2020a) X X X X

(Alsboui et al., 2020b) X X X X

(Rahman and Rahmani, 2018) X X X X

ogler et al., 2015) X X X X

(Fan et al., 2019) X X X X

be provided. Consequently, If the correct secret key

is provided, grant access to that user otherwise deny

access.

Figure 3: Flowchart of the basic PPDI approach.

4.1 PoW Ofﬂoading

There are two types of ofﬂoading including: data

ofﬂoading and computation ofﬂoading. The former

refers to the use of novel network techniques to trans-

mit mobile data originally planned for transferring

via cellular networks. The latter refers to ofﬂoading

heavy computation tasks to reserve resources (Zheng

et al., 2020). Fig. 4 illustrates the PoW computation

ofﬂoading mechanism used in the PPDI approach. It

shows how constrained IoT devices in terms of power

are able to ofﬂoad the PoW computation to a node

with higher resources to save energy consumption.

The selected node is an IOTA full node, which is re-

sponsible for performing the PoW as described in Fig.

Figure 4: PoW Computation Ofﬂoading in PPDI Approach.

The aim of ofﬂoading is to save total energy con-

sumption or overall task execution time, or both of

them. A proof of work (PoW) is a piece of data that

is calculated by using trial and error to meet certain

requirements. The key to PoW is that it is difﬁcult to

perform but easy to verify. Since MAM transaction

data requires a small amount of a PoW to be com-

puted, we ofﬂoad the PoW computation to a node with

higher resources in order to save energy consumption

of IoT devices.

In particular, we address the issue of privacy and

scalability by adapting the IOTA MAM protocol. We

have presented the proposed approach in view of the

system architecture, and the role of the PoW compu-

tation ofﬂoading technique employed.

5 PPDI APPROACH: AN

APPLICATION SCENARIO

This section of the paper describes the proposed PPDI

approach in the context of IoT healthcare application

scenario. In healthcare, IoT devices are responsible

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

178

for collecting health data such as heart rate, blood

pressure, and temperature from patients (Zhang et al.,

2018). Distributed Ledger Technology plays an im-

portant role in healthcare scenarios as it offers sig-

niﬁcant features, such as immutability, decentraliza-

tion, security, privacy, and transparency, which even-

tually will overcome pressing issues in healthcare sys-

tems (McGhin et al., 2019). The efﬁciency and effec-

tiveness of healthcare systems depend on the interop-

erability, and data access management. Interoperabil-

ity enables software applications and several technol-

ogy platforms to establish a secure communication,

exchange data, and utilize the exchanged data across

healthcare organizations.

Data access management is required in health-

care applications and therefore, when a transaction is

recorded in the IOTA network, MAM allows trans-

action to be encrypted and enables authorized users

to access the data. Through the storage of the data

on the IOTA Tangle, doctors, nurses, patients or any

other authorized user or device can control access to

the data. When a user requests access to the personal

information of a particular patient’s, the application

checks their credentials and then either grants or de-

nies access to the data accordingly. It is important

to note that accessing healthcare records is based on

the secret key, which in turns does not disclose any

private information to unauthorized users of the sys-

tem. In conclusion, the use of IOTA technology and

in particular MAM to manage access to data ensures

that authorized users will be able to access data when

needed, while guaranteeing that private information

cannot be accessed by unauthorized users of the sys-

tem.

IOTA MAM offers the opportunity to encrypt,

mask, and enable access to healthcare records with

a mixture of modes, while the IOTA Tangle is used to

handle transactions data in an efﬁcient way.

6 IMPLEMENTATION, RESULTS

AND ANALYSIS

In this section, we present our preliminary experimen-

tal results and an evaluation of the proposed PPDI ap-

proach in terms of privacy, and scalability. In addi-

tion, we provide analysis and discussion of the results.

6.1 Environment Setup

The implementation of our proposed approach

is based on Node JS. The functionality related

to IOTA addresses, transactions, broadcasting,

and multi-signatures has been implemented using

iota.lib.js (Foundation, 2018), the ofﬁcial JavaScript

library of the IOTA Distributed Ledger Technology

that enables issuing and fetching transactions. We

have used the IOTA Devnet as clients, which in par-

ticular communicates with IOTA full node

to issue

and fetch MAM transactions data. Another IOTA full

node was deployed on a local server dedicated for per-

forming the Proof of Work (PoW) operations.

Our implementation focuses on the public and re-

stricted modes of MAM. Public mode ensures authen-

ticity, and data integrity, while restricted mode pro-

vides users with the ability to gain access to the data

using the secret key. This is particularly useful for

healthcare applications where permissions to access

data is needed.

6.2 Results and Analysis

Fig. 5 shows the result of sending transaction data to

the Tangle using the public mode. It also shows the

output of fetching masked transaction data in which

the user would only require the root, which is the en-

cryption and decryption key. The transaction data sent

using public mode ensures authenticity and data in-

tegrity i.e., conﬁrming that the data is coming from

a particular IoT device. The Public mode uses the

root as the address of the transaction that contains the

MAM message (channel ID). Consequently, any user

can ﬁnd and decrypt the message in a public mode.

Figure 5: Publishing Transaction Data from IoT devices

Using Public Mode.

Sending Transactions with Restricted Mode:

Fig. 6 shows the result of publishing transactions data

using the restricted mode.

Access Right Authorization: Restricted mode

enables granular access control to transactions and

provides inherent privacy to the transaction data

stored on the Tangle. It only allow users who have

the secret key to decode the transaction data. Fig.

7 demonstrates the granular access control over the

transaction data stored on the Tangle. It also shows

https://nodes.devnet.iota.org

An Approach to Privacy-Preserving Distributed Intelligence for the Internet of Things

179

Figure 6: Publishing Transactions Data from IoT de-

vices Using Restricted Mode.

that when a user fetches transaction data with the cor-

rect secret key, access will be authorized, and a user

is allowed to decrypt the transaction data. Therefore,

access will be authorized.

Figure 7: Accessing Transaction Data with Right Secret

Key (Authorized Access).

Unauthorized Access: Fig. 8 demonstrates the

use of restricted mode when providing the wrong se-

cret key. A user who has the wrong secret key will

be unable to access the transaction data stored on the

Tangle. It also shows that when a user is attempt-

ing to fetch the transaction data with the wrong secret

key, access will be unauthorized. This is particularly

useful in healthcare applications for data access man-

agement.

Figure 8: Accessing Transaction Data with Wrong Se-

cret Key (Unauthorized Access).

PoW Execution Time: Fig 9 shows the result of

the execution time when ofﬂoading the PoW com-

pared to the baseline. As it can be seen that when

the number of sent transactions are 100, the execution

time of the PoW reaches 1177.5 second and the base-

line reaches 1440.1 second. This is because the PoW

is being ofﬂoaded to a dedicated node with higher re-

sources, while in the baseline the PoW is computed

on the same node.

Scalability: Fig 10 shows the result of the PPDI

approach in terms of scalability with different Min-

imum Weight Magnitude (MWM) settings, and dif-

ferent number of nodes. it is clear that as the num-

Figure 9: PoW Execution Time Compared to the Base-

line.

ber of nodes increases, the Transaction Per Second

(TPS) transaction speed increases linearly. Conse-

quently, the transaction speed has a good linear scala-

bility when the number of nodes increases. As it can

be seen that when 100 nodes are sending transactions,

the average TPS reaches 1.543 tx/s when the MWM

is set to 14, while the average TPS reaches 1.764 tx/s

when the MWM is set to 9.

Figure 10: Scalability in Tangle with 250 Nodes.

7 CONCLUSION AND FUTURE

WORK

In this paper, we have addressed the issue of privacy

in IoT by using the IOTA distributed ledger tech-

nology. In particular, we have employed the MAM

protocol using a mix of modes, such as public and

restricted, to grant granular access to data. In or-

der to achieve privacy and ensure scalability, an ap-

proach to privacy preserving distributed intelligence,

called PPDI, has been proposed. Restricted and pub-

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

180

lic modes are implemented to ensure data authentic-

ity, integrity as well as in what form the data should

be and who can gain access to it all of which privacy

related issues (Alsboui et al., 2021).

This paper is not the result of a completed project,

but the exposition of the start of one. We feel that this

area of research is pertinent to internet of things, and

in this paper we have taken initial steps towards inte-

grating IOTA MAM to enable distributed intelligence

in the internet of things.

There are a number of interesting directions for fu-

ture work. Firstly, we plan to thoroughly investigate

the saving in energy consumption with the PoW com-

putation ofﬂoading mechanism. Secondly, we plan

to develop an interactive model and access control

mechanisms that enables the users to access health-

care records (Atlam et al., 2018; Florea, 2018).

Thirdly, we plan to investigate the possibilities of

integrating the IOTA MAM with cloud computing

infrastructure to build a model that supports multi-

party authentication (Al-Aqrabi and Hill, 2018). Fi-

nally, we plan to design and develop a complete hy-

brid distributed intelligence framework that tackles

all of the IoT technical challenges including, scala-

bility, energy-efﬁciency, security, and privacy by in-

tegrating components from various technologies and

demonstrate it is applicability, and efﬁciency to sev-

eral real-world IoT application scenarios including

smart transportation system.

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