Enabling Distributed Intelligence in the Internet of Things using the
IOTA Tangle Architecture
Tariq Alsboui, Yongrui Qin and Richard Hill
School of Computing and Engineering, University of Huddersfield, U.K.
Internet of Things, Distributed Intelligence, IOTA, Mobile Agent.
It is estimated that there will be approximately 26 to 30 billion Internet of Things (IoT) devices connected
to the Internet by 2020. This presents research challenges in areas such as data processing, infrastructure
scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence to
overcome these challenges. This article reviews existing state-of-the-art distributed intelligence approaches in
IoT and focuses on the motivations and challenges for distributed intelligence in IoT. We propose a potential
solution based on IOTA (Tangle), a platform that enables highly scalable transaction-based data exchange
amongst large quantities of smart things in a peer-to-peer manner, together with mobile agents to support
distributed intelligence. Challenges and future research directions are also discussed.
The Internet of Things (IoT) is a mature field of re-
search that was brought to attention by Auto-ID cen-
tre, when they used Electronic Product Code (EBC)
along with Radio Frequency Identification (RFID) to
automatically identify the and track the itinerary of
items in supply chain (Ashton, 2009). IoT is consid-
ered as a novel paradigm that connects physical ob-
jects to the Internet. The basic idea behind it is to
connect physical objects to the virtual world and al-
low them to sense and modify the environment by us-
ing sensors and actuators (Atzori et al., 2010). Con-
necting the physical world to the Internet plays a cru-
cial role in enhancing our lives 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). According to sev-
eral research reports, it is estimated that there will be
approximately 26 to 30 billion devices connected to
the Internet in 2020 (Gartner, 2013; Research, 2013).
Consequently, this in turn brings many challenges in
a number of areas, such as data processing, saving re-
sources, and scalability (Esposito et al., 2017).
One of the key technologies being explored for
overcoming many of the challenges associated with
the growing number of connected IoT devices is dis-
tributed intelligence (Byers and Wetterwald, 2015).
Distributed intelligence is defined as a system of en-
tities e.g., smart sensors, working together to reason,
plan, and solve problems (Lynne, 2007). The main
aim of such technology is to enable entities (smart
objects) in an IoT system to cooperate at optimal effi-
ciency to achieve desired goals.
In the context of IoT, according to (Van den
Abeele et al., 2015), distributed intelligence is de-
fined as Cooperation between devices, intermediate
communication infrastructures (local networks, ac-
cess networks, global networks) and or cloud sys-
tems in order to optimally support IoT communication
and IoT applications. As stated in (Van den Abeele
et al., 2015) in order to enable distributed intelligence,
communication and computation capability should be
placed at the right place.
Based on the above definition, this paper presents
a new scalable, and energy efficient distributed in-
telligence approach for IoT. We propose the utiliza-
tion of IOTA Tangle architecture (Serguei, 2017) and
Mobile Agent (Lepp
anen et al., 2014) to enable dis-
tributed intelligence. IOTA is an emerging platform
that is particularly designed for the Internet of Things
to overcome the problems of scalability, transaction
fees, and mining of the blockchain technology. The
main component of IOTA is the Tangle, which is
based on the concept of a Directed Acyclic Graph
(DAG)(Serguei, 2017). The IOTA platform provides
a potential and highly scalable solution to enable
distributed intelligence. The mobile agent technol-
ogy can provide cooperation and information shar-
ing among different types of nodes (Lepp
anen et al.,
Alsboui, T., Qin, Y. and Hill, R.
Enabling Distributed Intelligence in the Internet of Things using the IOTA Tangle Architecture.
DOI: 10.5220/0007751403920398
In Proceedings of the 4th International Conference on Internet of Things, Big Data and Security (IoTBDS 2019), pages 392-398
ISBN: 978-989-758-369-8
2019 by SCITEPRESS Science and Technology Publications, Lda. All rights reserved
2014). A description about the architecture is pre-
sented in Section 4.
The remainder of this paper is structured as fol-
lows: Section 2 identifies the motivation and chal-
lenges behind the need for distributed intelligence in
IoT. Section 3 presents an overview of the recent dis-
tributed intelligence approaches in IoT. In Section 4
a summary on future research direction is discussed.
Finally, Section 5 concludes the paper.
In this section, we present the need for distributed in-
telligence in the Internet of things (IoT) by enlisting
some of the key factors that dictate the challenges in
IoT related research. We then briefly describe how
IOTA platform can be utilized to realize distributed
2.1 Saving Resources
It is generally expected that IoT would produce a mas-
sive amount of data, which when sent to a central lo-
cation results in larger consumption of network re-
sources. IoT consists of nodes that has limited re-
sources such as power consumption (battery limits),
computational capability and maximum memory stor-
age, which makes distributed intelligence a challeng-
ing task. IOTA Qubic protocol (Foundation, 2016b)
saves resources by outsourcing intensive computa-
tions to an external more powerful nodes. This is
can be achieved through Qubic-enabled IOTA nodes
(Q. Nodes). Qubics are inserted as messages in IOTA
transactions. It consists of instructions, called meta-
data responsible for deciding how and when to pro-
cess data.
2.2 Scalability
Scalability refers to the ability of the network to deal
with the growing amount of work needed when the
network grows. It can be divided into two parts: Hor-
izontal scaling and Vertical scaling. In Horizontal
scaling, the network is expected to grow by adding
more nodes to it. On the other hand, Vertical Scal-
ing is to equip the existing devices in the network
by adding more (CPU, RAM, power) (Bondi, 2000).
IoT is constantly changing and developing to fit in
environmentally in order to deal constantly with en-
larging demands and in accordance with the predic-
tions provided in (Gartner, 2013; Research, 2013).
Hence, potential solutions should be highly scalable
to deal with billions of smart objects, which will be
soon connected to the Internet.IOTA Tangle (Serguei,
2017) can provide a valuable solution to accommo-
date the fast growth of interconnected things. IOTA
Tangle scales well when the number of Tangle nodes
2.3 Privacy
Privacy refers to the capability of a system to keep
information/data private, e.g, to make sure that if any-
one has accessed the data will be unable to make sense
of it. Moreover. Information leakage is generally the
ultimate user concern, especially relating to sensitive
data, such as location, and movement trajectory in-
formation. Potential solutions should identify in what
form the data should be, and who can get access to it.
Consequently, the IOTA Masked Authenticated Mes-
saging (MAM) protocol (Foundation, 2016a) can be
utilized for achieving privacy. An example where pri-
vacy is of concern for users is in health care applica-
tions where information about patients is sensitive.
2.4 Offline Capability
Offline Capability is also known as resiliency and is
often defined as the capability of the system, to work
in emergency cases, such as Internet connection not
reachable. This indicates that if the internet connec-
tion goes down, the system will not function. There-
fore, there is no need for a network to be connected to
the Internet all the time. IOTA tasks can be done on
an offline network. IOTA Tangle offers this capabil-
ity, but the transactions have to be re-attached to the
main tangle if further processing is needed. In such a
manner, distributed intelligence and processing is de-
sirable and well supported.
Over the last few years, distributed intelligence has
started to gain attention from many researchers in
the field of IoT (Van den Abeele et al., 2015; Byers
and Wetterwald, 2015; Sahni et al., 2017; Rahman
and Rahmani, 2018). Most of these research efforts
are to deal with problems relating to data process-
ing, data management, scalability, and privacy. Re-
cently, the authors in (Van den Abeele et al., 2015)
introduced the concept of Sensor Function Virtualiza-
tion (SFV) as a potential technique to support dis-
Enabling Distributed Intelligence in the Internet of Things using the IOTA Tangle Architecture
Table 1: Comparison Among Distributed Intelligence Approaches in IoT.
Distributed Intelligence Approaches Saving Resources Scalability Privacy Offline Capability
(Van den Abeele et al., 2015) High High Low High
(Byers and Wetterwald, 2015) High High Low Low
(Sahni et al., 2017) High High Medium Medium
(Rahman and Rahmani, 2018) High Low Low Low
tributed intelligence in IoT. The basic idea is to en-
able distributed processing of certain functionalities
by offloading them from constrained devices to un-
constrained infrastructure such as a virtualized gate-
way, the cloud and other in-network infrastructure.
SFV focuses on the three main points including, scal-
ability, heterogeneity of the IoT, and transparency. To
achieve scalability, the approach relies on cloud in-
frastructure by allowing part of SFV functionalities
to run on the cloud benefiting from the elasticity pro-
vided by the cloud, and tired design. this handles the
increased load, when devices are joining the network.
The second point is related to heterogeneity of IoT
in terms of resources constraints devices, i.e., lim-
ited power, limited processing, infrastructure and it
should shift the user from the low level details of the
devices. The final point is related to transparency in
which any virtual functions that are added to the de-
vices must build on top of existing communication in-
terfaces and that changes to protocols running on end
devices must be minimal and preferably non-existent.
The advantages of their approach are reduction in en-
ergy consumption, scalability, flexibility, and trans-
parency. However, security and privacy issues are
briefly acknowledged in their approach. Moreover,
implementation and evaluation of the proposed ap-
proach is not provided in which they outline as part
of their future work.
The work by (Byers and Wetterwald, 2015)
presents fog computing architecture as a solution to
enable distributed intelligence in IoT. The proposed
approach described fog nodes in terms of hardware
architecture as well as software architecture. From
a hardware point of view, fog nodes can be imple-
mented as ancillary functions on traditional network
elements such as gateways, edge devices, and appli-
ances or as stand-alone fog boxes. From a software
point of view, fog nodes are highly virtualized ma-
chines with multiple VMs running under a highly ca-
pable hypervisor. The benefits of using fog nodes are
to enhance reliability, bandwidth, and security. How-
ever, fog computing still has issues regarding security,
privacy (Esposito et al., 2017; Yi et al., 2015; Gillam
et al., 2018). In addition to that, implementation and
evaluation of the proposed approach is not provided.
More recently, a new computing paradigm, called
Edge Mesh that aims to enable distributed intelligence
in IoT is proposed in (Sahni et al., 2017). The pro-
posed paradigm distributes the decision-making tasks
among edge devices within the network rather than
transferring all the data to a centralized server for
further processing. In Edge Mesh, all the computa-
tion tasks and data are shared using a mesh network
of edge devices and routers. Edge Mesh architec-
ture consists of four main types of devices. First,
end devices are mainly used for sensing and actuat-
ing. Second, edge devices are used for processing
and connecting with end devices. Third, routers are
used for transferring data among edge devices. Fi-
nally, cloud is used to perform big data analytics on
historical data. The advantages of edge mesh are, dis-
tributed processing, low latency, fault tolerance, bet-
ter scalability, better security, and privacy. However,
they have component for achieving security and pri-
vacy, but how privacy can be achieved is not consid-
ered. Furthermore, implementation and evaluation is
not provided.
Different from the above, the work presented
in (Rahman and Rahmani, 2018) proposed an
AI based distributed intelligence assisted approach
named as Future Internet of Things Controller
(FITC). The proposed approach uses both edge and
cloud based to distribute intelligence. In particular,
edge controller is used to provide low-level intelli-
gence and cloud based controller to provide high-level
intelligence, which they refer to as distributed intelli-
gence. The benefits of their work are to reduce re-
sponse time and loosen the requirements for rules.
However, the approach lack of a mechanisms that en-
ables privacy, and offline capability.
Table 1 provides a comparison of the reviewed
distributed intelligence approaches according to the
challenges provided in Section 2. Overall there are
many pieces of solutions to enable distributed intelli-
gence in IoT. We compare the approaches and ranked
as High, Medium, and Low based on potentiality of
tackling the identified technical challenges. A field
in the table is given a rank of High if the approach
satisfies the challenge corresponding to that column.
The approach is ranked with Medium if it supports the
challenge, but not providing a way of how to achieve
it. Low is given to the approach if it does not address
IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security
the challenge at all.
3.1 Limitations of Prior Work
From the above we can see that most of the existing
approaches to enabling distributed intelligence in IoT
suffer from inherent problems. Firstly, they rely on
centralized architecture for processing data (Gillam
et al., 2018), which introduces a high cost and de-
lay that is not acceptable for distributed applications.
Such examples include health monitoring, emergency
response, autonomous driving, and so on. In addi-
tion to that, it would consume much network band-
width (Perera et al., 2017). Besides, solutions based
on fog computing still have issues regarding security
and privacy (Esposito et al., 2017). Moreover, there is
a need for a standardized way for describing the data
generated by IoT, such as the one promised by IOTA
Identity of Things (IDoT) (Foundation, 2016a), which
will also help secure the network. Another problem is
the lack of a mechanism to describe in what form the
data should be, and who can get access to it (multi-
party authentication scenarios), all of which are re-
lated to privacy. Finally, only a few of the approaches
facilitate an implementation and evaluation of their
proposed approach.
The problems and limitations presented above lead
to future research opportunities. Possible solutions
for enabling distributed intelligence in IoT can be
achieved through the use of the IOTA platform (Ser-
guei, 2017) and mobile agent technology (Lepp
et al., 2014).
4.1 Fundamental Tools and Techniques
IOTA (a.k.a. distributed ledger (Serguei, 2017)) is an
emerging platform that is particularly designed for the
internet of things to overcome the problems of scala-
bility, transaction fees, and mining, which is consid-
ered as a resource extensive task that other crypto-
currency lacks by utilizing the blockchain technol-
ogy (Nakamoto et al., 2008). The main component
of IOTA is the Tangle, which is based on the concept
of a Directed Acyclic Graph (DAG) (Serguei, 2017).
Tangle is the protocol, or the data structure used in
IOTA in order to store the transactions, which has col-
Table 2: Node Types in IOTA Network.
Node Type Storage Validation
Full Node Full Tangle last Snapshot Yes
Light Node None No
PermaNode Full Tangle Permanently Yes
lection of nodes (also called as Sites or Vertices) and
arrows (also called as Edges). All the vertices or sites
which hold data (transactions) are connected to one
another using edges, and these edges are used to val-
idate the transaction and to check whether it is valid
transaction, in order to achieve approval or confirma-
tion of the transaction eventually. Edges can range
from a minimum of two to a maximum of many and
are called as Parent. If there is a site with less than two
edges, it represents that the actions are unconfirmed,
and these are called as Tips of the tangle. Genesis is
the unique site or the very first site which do not have
any previous site or parents (Serguei, 2017). The Tan-
gle architecture through several nodes QubicNodes,
Full Nodes, Light Node, and Masked Authentication
Messaging. As discussed in Section 2 will be utilized
to achieve distributed intelligence in IoT.
Table 2 Describes the features of the participant’s
nodes of IOTA network. the following paragraphs de-
scribes each participants node in the network
The concept of IOTA Full Node (Foundation,
2016a), which can be defined as node within the tan-
gle architecture that is capable of finding neighbours
and communicate with them, attaching data to the tan-
gle, bundling and signing, tip selection, validation,
PoW, and attaching data to the tangle. IOTA full
nodes have high computational capacity and are re-
sponsible for doing the PoW on behalf of the end
nodes taking into consideration that end nodes have
limited resources.
The concept of Light Node (Foundation, 2016a)
that participates in the network and can be defined as
a node within the tangle architecture that relies on the
full node to interact with the Tangle; it distinguishes
itself from other nodes in the sense that it does not
store a copy of the tangle, and does not either validate
transactions or communicate with neighbors. It has
been specifically designed as a lightweight node for
resource constrained nodes.
The concept of Qubic protocol (Foundation,
2016b), which is under development and is defined as
a protocol that describes IOTAs solution for quorum-
based computations. Qubic focuses on three types of
computations including: oracle machines, outsource
computations, and smart contracts. We are mainly
concerned with the outsourcing part to save resources
of IoT devices. Qubic is optimized for IoT and
makes it possible to offload computations function-
Enabling Distributed Intelligence in the Internet of Things using the IOTA Tangle Architecture
alities from nodes with battery-limits to an external
more powerful nodes, which in turns reduces energy
consumption. Qubic are inserted as messages in IOTA
transactions. It consists of instructions, called meta-
data responsible for deciding how and when to pro-
cess them.
The concept of Permanodes (Foundation, 2016a),
which is under development and is defined as node
within the IOTA tangle architecture that has features
including: finding a neighbouring nodes and commu-
nicate with them, attaching data to the tangle, tip se-
lection, and do the Proof-of-Work (PoW). The PoW is
a short computational operation compared to mining
in blockchain. Furthermore, these nodes are distin-
guishable from other nodes by having the capability
of storing the whole tangle data permanently. This is
beneficial for some of the IoT applications in which
access to the full data history is required.
In order to ensure privacy, IOTA developed a
protocol called Masked Authenticating Messaging
(MAM) (Handy, 2017). In the tangle, each transac-
tion carries a message, which allows these messages
to be exchanged between the nodes. This indicates
that anyone can view the messages in the network.
MAM utilizes the Merkle tree based signature scheme
and enables privacy by encrypting data. Encryption
occurs through three techniques including: private
mode, public mode, restricted mode, thereby enabling
The mobile agent (MA) technology can provide
cooperation and information sharing among different
types of nodes (Lepp
anen et al., 2014). Mobile agent
is defined as a piece of software that performs data
processing autonomously while moving from node
to node in the network (Alsboui et al., 2017). The
agent can collect local data and perform any neces-
sary data aggregation. Mobile agents can make de-
cision autonomously without user input. They pro-
vide flexibility in terms of decision making, and reli-
ability in terms of node failure. (Lange and Oshima,
1999) listed seven good reasons for using MA such as,
reducing network load, they adapt dynamically, and
They execute asynchronously and autonomously.
4.2 A New Enabling Approach
Based on the above tools and techniques, we pro-
pose an approach that enables distributed intelligence
in IoT while taken into consideration the challenges
identified in Section 2.
As the number of the Internet of Things nodes are
expected to reach 30 billion devices in 2020, any pro-
posed solution should be highly scalable, and energy
efficient to deal with billions of smart objects that will
be connected to the internet. Aiming at this, two im-
portant points are to be noted. Firstly, by offloading
functionality using IOTA Qubic from resource con-
straints nodes i.e., battery limit to a more powerful un-
constrained infrastructure, we expect our solution to
take advantage of the mechanisms provided by these
environments to save resources i.e., power consump-
tion, and storage. Secondly, towards a scalable ap-
proach, IOTA scales well when the number of nodes
are added. This requires only the verification of two
previous transactions by signing nodes with a private
keys, apply tip selection using Random Walk Monte-
carlo Algorithm (RWMC), and Proof-of-Work (PoW)
to solve the cryptographic puzzle. If the PoW is heavy
on nodes with limited processing, the PoW can be di-
rected to the IOTA full node.
Another final impotent point is to ensure pri-
vacy by employing Masked Authenticated Messaging
(MAM). MAM is offered by IOTA as an extension
module, which acts as a second layer data communi-
cation protocol to encrypt or mask data. This means
that IoT edge nodes running the MAM client are able
of transmitting encrypted sensor data by using this
communication protocol. This fulfills a crucial need
in IoT applications, i.e., health care in which access
and privacy meet.
Figure 1: A New Distributed Intelligence Approach for IoT.
Having in mind the requirements of the previous para-
graphs, our proposed approach is presented in Figure
1. Our approach consists of three layers. In the first
layer, which comprises end nodes running an IOTA
light client and will act as an end points to the IOTA
network. Since there will be no interactions among
nodes in the network running IOTA light clients, we
employ MA to facilitate cooperation among nodes in
IoTBDS 2019 - 4th International Conference on Internet of Things, Big Data and Security
the network and by cooperation we mean data shar-
ing. To do so, MA will carry transactions that con-
tains the data and aggregate the data. Furthermore,
by dispatching MA, we reduce the amount of sensory
data by eliminating redundancy. For example, nodes
that are placed in proximity of each other are likely to
generate redundant data. Ultimately, data aggregation
is required to reduce the data traffic in the network.
Second Layer, comprise of a less unconstrained
devices running the IOTA full Node. Finally, the ap-
plication layer utilize from the proposed approach in
terms of energy efficiency, scalability, and privacy. To
this end, a number of questions should be tackled in
the future: How is data offloaded? What kind of prim-
itive mode will be used? How is the PoW outsourced
to IOTA full Node? How MA will be routed between
nodes in an energy efficient manner?
IOTA Tangle architecture has the potential of enabling
distributed intelligence in IoT, which will be benefi-
cial for a wide-range of applications, such as smart
cities, and healthcare. IOTA Tangle allows for dis-
tributed computation, making it suitable for enabling
distributed intelligence in IoT. In this article, we have
discussed the need for distributed intelligence in IoT
as well as how the IOTA platform can be utilized to
enable it. We have presented a review of the recent
state of the art distributed intelligence approaches in
IoT. Also, we have discovered that there is a need for
a a lightweight solution for enabling distributed intel-
ligence based on the identified limitations of existing
approaches. We have discussed the challenges as well
as future research directions in developing a new dis-
tributed intelligence approach and we believe that the
integration of IOTA and mobile agent would solve the
problems. Finally, we outline pathways to solutions
to the problems identified and envision that an IOTA
Tangle architecture will facilitate distributed intelli-
gence in IoT.
In the authors opinion, MA migrates between
nodes in the network and facilitates cooperation be-
tween them. This leads to several advantages, such as
reduction in the network load by moving MA carrying
data instead of sending data to a central location for
further processing, and overcomes network latency.
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