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Enabling Distributed Intelligence in the Internet of Things with IOTA and Mobile Agents


Abstract and Figures

It is estimated that there will be approximately 125 billion Internet of Things (IoT) devices connected to the Internet by 2030, which are expected to generate large amounts of data. This will challenge data processing capability, infrastructure scalability, and privacy. Several studies have demonstrated the benefits of using distributed intelligence (DI) to overcome these challenges. We propose a Mobile-Agent Distributed Intelligence Tangle-Based approach (MADIT) as a potential solution based on IOTA (Tangle), where Tangle is a distributed ledger platform that enables scalable, transaction-based data exchange in large P2P networks. MADIT enables distributed intelligence at two levels. First, multiple mobile agents are employed to cater for node level communications and collect transactions data at a low level. Second, high level intelligence uses a Tangle based architecture to handle transactions. The Proof-of-Work offloading computation mechanism improves efficiency and speed of processing, while reducing energy consumption. Extensive experiments show that transaction processing speed is improved by using mobile agents, thereby providing better scalability.
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Enabling distributed intelligence for the Internet of Things
with IOTA and mobile agents
Tariq Alsboui1·Yongrui Qin1·Richard Hill1·Hussain Al-Aqrabi1
Received: 30 July 2019 / Accepted: 18 March 2020
© Springer-Verlag GmbH Austria, part of Springer Nature 2020
It is estimated that there will be approximately 125 billion Internet of Things (IoT)
devices connected to the Internet by 2030, which are expected to generate large
amounts of data. This will challenge data processing capability, infrastructure scala-
bility, and privacy. Several studies have demonstrated the benefits of using distributed
intelligence (DI) to overcome these challenges. We propose a Mobile-Agent Dis-
tributed Intelligence Tangle-Based approach (MADIT) as a potential solution based
on IOTA (Tangle), where Tangle is a distributed ledger platform that enables scalable,
transaction-based data exchange in large P2P networks. MADIT enables distributed
intelligence at two levels. First, multiple mobile agents are employed to cater for
node level communications and collect transactions data at a low level. Second, high
level intelligence uses a Tangle based architecture to handle transactions. The Proof-of-
Work offloading computation mechanism improves efficiency and speed of processing,
while reducing energy consumption. Extensive experiments show that transaction pro-
cessing speed is improved by using mobile agents, thereby providing better scalability.
Keywords Internet of Things IoT ·Distributed intelligence DI ·Distributed ledger
technology DLT ·IOTA Tangle ·Mobile agent MA
BTariq Alsboui
Yongrui Qin
Richard Hill
Hussain Al-Aqrabi
1School of Computing and Engineering, University of Huddersfield, Huddersfield, UK
T. Alsboui et al.
1 Introduction
The Internet of Things (IoT) was brought to prominence by the Auto-ID centre, where
Electronic Product Codes (EBC) and Radio Frequency Identification (RFID) technol-
ogy automatically identified physical items in supply chains [1]. IoT is considered a
novel paradigm which connects physical objects to the Internet to form ubiquitous
networks that enable the sensing and modification of environments in response to
dynamic stimuli [1], also referred to as Cyber-Physical Systems (CPS).
Such systems have already demonstrated the potential to enhance the quality of
life by turning cities into smart cities [2], homes into smart homes [3], and campuses
into smart campuses [4]. Research reports estimate the rapid growth of IoT; in the
order of 125 billion devices connected to the Internet in 2030 [57]. Consequently,
this presents many challenges with regard to data volume, velocity, timely processing,
privacy and scalability [8,9].
Distributed Intelligence (DI) has the potential to overcome many of these chal-
lenges [10] and is a sub-discipline of artificial intelligence that distributes processing
functionality, enabling collaboration between smart objects, and mediating commu-
nications to optimally support communications for IoT applications. This definition
is the basis for the research described in this article.
The augmentation of capabilities to plan, reason, and solve goal-directed problems,
onto CPS [11], facilitates the coordination and subsequent optimisation of complex IoT
systems [12]. These systems require computational power that is local to the problem
to be solved, and can also become an integral part of a much larger computational
DI relies on efficient communication between interacting entities. Distributed
Ledger Technologies (DLT) are emerging as platforms with considerable potential for
CPSs such as IoT, by assisting the recording and verification of transactions between
participating nodes without requiring a central database or authority. IOTA is an emerg-
ing DLT platform that is designed to overcome the problems of scalability, transaction
fees, and mining (in the case of the blockchain technology) and is thus applicable
to IoT. Central to IOTA is the Tangle, a Directed Acyclic Graph (DAG)[13], which
provides a potentially scalable solution to enable DI with IoT.
Contribution This paper presents a Mobile Agent Distributed Intelligence Tangle-
based approach (MADIT) for IoT that is capable of providing local interactions among
IoT devices while offloading computation to rich resource devices to reduce energy
In summary, our key contributions are as follows:
We propose a multi-mobile-agent Tangle-based architecture that manages
resources and enables the deployment of IoT applications that are scalable and
energy efficient.
We propose a task off-loading mechanism for performing proof-of-work (PoW)
on IoT devices, minimising energy consumption on resource constrained devices.
We propose mobile agents as an efficient architectural approach to facilitate local
interaction, collection and aggregation of transaction data with an efficient itinerary
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We conduct a set of experiments that verify the effectiveness and benefits of the
proposed approach.
The local interactions among IoT devices will be finally attached to the IOTA
Tangle. We propose an integration of IOTA Tangle [13] and Mobile Agents [14]
techniques, in order to realise a complete DI approach by providing low-level and
high-level intelligence. Functionalities are distributed to both low-level and high-
level intelligence layers. MADIT specifically recognises resource-constrained devices,
which might not be able to perform the required computation at low-level. High-level
computation is performed by more advanced computational devices.
This article is organized as follows: Sect. 2identifies the motivation and challenges
behind the need for distributed intelligence in the IoT era. In Sect. 3, we present
the use of mobile agents to assist in enabling DI with a brief overview of the recent
developments of interest. Sect. 4presents our proposed approach, followed by a robust
assessment of the performance of the proposed implementation in Sect. 5. In Sect. 5,
we evaluate MADIT and compare it with alternative approaches. Sect. 6discusses
related work. Finally, Sect. 7concludes the paper and presents future directions.
2 Motivations and challenges
IoT systems produce a massive amount of data, which creates large demands upon
network resources. IoT networks typically consist of nodes that have limited resources
such as constrained energy (battery or solar power), computational capability and
memory storage, which makes distributed intelligence a challenging task.
2.1 Scalability
Scalability can be separated into two parts: Horizontal Scaling and Vertical Scaling.
Through Horizontal Scaling, the network is expected to increase by adding more nodes.
Vertical Scaling is designed to increase existing devices with additional resources such
as CPU, RAM, power [15]. IoT needs to react dynamically to broader demands [6,7],
and potential solutions should be scalable and can be used to deal with possibly billions
of smart objects. IOTA Tangle [13] may provide a way to handle the rapid growth of
interconnected things and scales well when the number of Tangle nodes grows.
2.2 Privacy
It is essential to build systems to keep information private, e.g., to make sure that if
any unauthorised party has accessed the data, they 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 information. IOTA
Masked Authenticated Messaging (MAM) protocol [16] offers a great option to achieve
privacy. For instance, IOTA MAM can be applied in healthcare applications where user
data and privacy are concerned, including sensitive information about patients.
T. Alsboui et al.
2.3 Offline capability
Offline Capability is also known as resiliency and is often defined as the capability of
the system, to work in mission-critical or emergency cases, such as when an Internet
connection not reachable. Therefore, there should be no need for a network to be
connected to the Internet at all times. IOTA Tangle offers the capability to function
while offline, but the transactions have to be re-attached to the main tangle if further
processing is needed. In such cases, distributed intelligence and processing is desirable
and well supported.
3 Mobile agents and distributed intelligence
Mobile agents (MAs) are software abstractions that perform data processing
autonomously while physically migrating between nodes in the network to enable
the sharing of data amongst participants’ nodes [17]. MA facilitates the flexibility
and scalability problems of centralised models [18], and are commonly deployed in
Wireless Sensor Networks (WSN) for data collection and in-network processing.
Many MA approaches dispatch agents to collect data from the network rather than
sending the data back to a gateway. The benefits of using MAs as stated in [19] include:
reduced task redundancy, lower network bandwidth, and reduced network load. We
refer the interested readers to the recent surveys in chronological order [14,20] and the
references therein for a comprehensive review of the mobile agent itinerary planning
approaches in WSNs.
The authors in [21] proposed a new itinerary planning strategy, which consists of
three phases. First, the network is partitioned into clusters according to the distance
between the sensor nodes using the k-means algorithm. Second, the number of MAs
is determined for each partition based on the volume of data from each source node
and the geographical distance. Third, an optimised itinerary plan is produced for each
partition group, identifying the source nodes to be visited according to a greedy ran-
domized adaptive search procedure (GRASP). This approach is scalable, and delay is
minimised due to the dispatch of multiple agents for each group. However, this par-
ticular algorithm is not sufficiently robust as the data volume increases. Furthermore,
the number of partitions has to be manually identified by the user, which can result in
sub-optimal partitions of the network.
Similar to the above work is the approach proposed in [22], a spawn multi-mobile
agent itinerary planning (SMIP) that uses the x means algorithm for defining the
itinerary of the MA. After partitioning the network, the sink node is responsible for
assigning a MA to each partition. They also use the concept of agent spawning, which
has the ability to create a new agent that has different capacities and capabilities that
are contrary to the original agent. The proposed approach achieved better performance
and reduction in energy. However, the approach does not support fault tolerance in the
case of node failure. This leads to an inability to decide the next hop on the fly.
In [23], a hybrid planning mechanism, mobile agent-based directed diffusion
(MADD) is presented. In MADD, if the sources in the target region detect an event
of interest, they flood exploratory packets to the sink individually. Based on these
Enabling Distributed Intelligence for the Internet of…
exploratory packets, the sink selects sources that will be visited by a mobile agent,
which autonomously decides on the source-visiting sequence as it migrates among the
nodes in the source-visiting set. As a result, the mobile agent follows a cost-efficient
path among target sensors in MADD.
An improvement of the MADD approach is the mechanism introduced in [24].
This works according to three phases, including the controlled gradients setup phase,
the exploratory data dissemination phase, and the MA action phase. In the controlled
gradients phase, a sink node floods its neighbour with interest messages and sets up
an itinerary towards the next hop according to two metrics; minimum hop count and
threshold of remaining energy. The operation of the exploratory data dissemination
phase is employed for the discovery of the source nodes as well as the setup of the
TargetSrcTable (TST, which directs MA’s migration routing among source nodes) in
each target node. Consequently, the sensory data will be stored in each source node’s
cache, wait for the MA’s operations in the next phase.
In the MA action phase, the MA will be created and dispatched to the identified
target region, while the next hop is determined dynamically. The proposed approach
is considered a hybrid approach since it uses both static and dynamic techniques.
However, due to the use of a single MA, the approach lacks scalability and would
result in a delay if the network is large.
More recent advanced techniques for a static itinerary is the algorithm presented
in [25], named Iterated Local Search (ILS). The algorithm is centralised, and MA’s
itinerary is built from the sink node and only considers nodes that are reachable by
the transmission range of the sink node. The sink node obtains location information
from sensor nodes to estimate the physical distance amongst all node pairs. Based
on this information, it finds out which nodes can communicate directly and estimates
the power level to enable communication. This information is sufficient to build a
network topology graph. Finally, the sink executes the Dijkstra shortest-path algorithm
to calculate the communication cost among all possible SN pairs. However, for a
network compromising thousands of nodes, the approach would not be scalable to
accommodate growth.
Another recent hybrid approach is proposed in [26], which is a multi-agent itinerary
planning based energy and fault aware data aggregation (MAEF) approach. It consists
of three phases. First, a cluster head selection and cluster construction is built. Second,
a cluster head-based itinerary plan that aims to select nodes in range of the sink is used
by a minimum spanning tree to plan the itineraries among cluster heads. Third, the
sink node dispatches a MA to gather data from the cluster head nodes. The proposed
algorithm is energy efficient and scalable as efficient grouping and dispatching of
multiple MAs is applied (Table 1).
Table 2shows the typical mobile agent approaches and presents comparisons in
regards to the scalability, the grouping mechanism, type of itinerary, and the delay of
each approach.
From the table we can see that scalability is a critical challenge. Our work uses
a new grouping mechanism of the DAG and dispatches several agents, which is also
considered as a novel mechanism [14].
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Table 1 Comparison among mobile agent (MA) approaches
MA approaches Scalability Grouping Type of itinerary Delay
[21] Yes Yes Static Yes
[22] Yes Yes Static No
[24] No No Hybrid Yes
[25] No No Static No
[26] Yes No Hybrid No
[23] No No Hybrid Yes
Table 2 Performance metrics for experimental work
Performance metrics
Evaluation metrics Definition
Transaction per second (TPS) Refers to the number of transactions published to the Tan-
gle network per second
Throughput Refers to the efficiency in processing transactions in a
given amount of time
4 MADIT: system architecture
The envisioned architecture, Mobile-Agent Distributed Intelligence Tangle-Based
approach (MADIT), represents the novel contribution of the work and is depicted
in Fig. 1. One of the key contributions of this work is the attempt to establish a base-
line for a reference framework for Tangle-based MADIT that can be used to support
various IoT applications.
The architecture is divided into four main parts: (1) IoT devices; (2)Tangle to
process transactions(txs); (3) PoW enabled server, and; (4) Mobile Agent to carry
a list of transactions data. Each IoT device is connected with neighbouring nodes
via TCP/IP protocols for communication, and interactions with the Tangle are in the
form of transactions. IoT devices are responsible for managing and processing the
transactions. A PoW-enabled server is an IoT device that has rich resources, and is
responsible for performing costly computations on behalf of IoT devices. Mobile
Agents are responsible for transporting a list of transactions when visiting nodes on
their routes. This is an impotent task that supports inter-node communications. The
Tangle can act as a data management layer for processing and storing data in an
efficient way.
4.1 Mobile agent transactions for local interactions
We have employed multiple MAs to avoid delays in reporting transaction data and to
support local interactions (i.e., low-level intelligence). We consider that nodes in close
proximity of each other will most likely generate similar data; therefore we apply data
aggregation techniques to eliminate redundancy using a similar method as described
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Fig. 1 The mobile agent distributed intelligence Tangle-based approach (MADIT)
in [23,27] to calculate the size of transaction data accumulated by the MA. Transaction
data results are fused with an aggregation ratio (ρ,0ρ1). Consider Li
ma to be
the amount of accumulated transactions data result after the MA finishes from source
i, where Aiis the amount of transactions data to be aggregated by p, then:
ma =Ai
ma =Ai+(1p)×A2(1)
ma =Li
ma +(1p)×A2(2)
In Eq. (3) there will be no data aggregation in the first node and the value of pdepends
upon the type of deployed application.
The packet message format of the proposed MADIT is described in Fig. 2. The pair
of Itinerary Planning and List of transactions are the payload of the agents. Dispatcher
ID is used to identify the root node that creates and dispatches MA. FirstNode, denotes
T. Alsboui et al.
Fig. 2 Message format of the proposed (MADIT) approach
the first node that the MA will visit. Static Routes, denotes the computed routes for
MAs with all of the assigned nodes to be visited. ToVisitFlag, is set to indicate that
whether the node has been visited by an agent or not.
The reason for applying mobile agents in our work is not just to support low-level
intelligence. It was stated in [28] that one of the most power hungry operations is radio
communication; therefore, we dispatch agents to collect transactions data rather than
sending it. Furthermore, to simulate a real life scenario, we assume that IoT sensor
devices in proximity of each other are most likely to generate the same transactions
data. Consequently, agents are also capable of eliminating redundant transactions data
by fusing them.
Algorithm 1: Generate a random directed acylic graph G
Input:nodeN um,edgeNum
1Initialize Gto a directed acylic graph (DAG) with nodeNum nodes but without
any edges, and nodes range from 0 to nodeNum 1
2while edge N um 0do
3nodearandint(0, nodeNum)
5while nodeb== nodeado
6nodebrandint(0, nodeNum)
7Add edge(nodea,nodeb)toG
8if G is still DAG then
9edge N um ed ge N um 1
10 else
11 Remove edge(nodea,nodeb)fromG
12 Return G
Algorithm 1presents the pseudocode of establishing a random DAG G. Algorithm 2
presents the pseudocode of computing the routes for all mobile agents. Algorithm 3
presents the pseudocode of dispatching multi-mobile agent to start collecting transac-
tions data.
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Initially, we introduce the establishment of a random Directed Acyclic Graph (DAG)
of IoT as described in (Algorithm 1), which is designed to build a graph with a random
number of Nodes and Edges. The algorithm iterates to add the required number of
nodes nodeNum. Then, it performs a check to ensure that the graph Gis a directed
acyclic graph.
Algorithm 2: Compute mobile agent routes
Input: DAG G,number of routes Nr
1Initialize Ras an empty set of mobile agent routes
2Generate Nrrandom routes each of which traverse Gand add these routes to R
3Return R
Algorithm 3: Dispatch a mobile agent MA to collect transactions
Input: Visiting route r, mobile agent MA, and data load d
Output: Transactions Tcollected by MA
1Initialize Tas an empty set of transactions collected by MA
2while M A has not completed the allocated tasks do
3Move to visit the next node naccording to the given route r
4if n has been visited by any other mobile agent then
5Repeat Step 3, until all nodes in rhave been visited
6if all nodes in r have been visited then
7MAcompletes the allocated tasks
9Dispatch MAto visit node n
10 Collect transactions T(not exceeding limitation din total) from node n
11 Add transactions in Tto T
12 Set visited flag of node nto true
13 if T contains d transactions then
14 MAcompletes the allocated tasks
15 Return T
The compute mobile agent route Algorithm, as presented in (Algorithm 2), takes
Gas input from Algorithm 1and is specifically designed to generate random routes
for all mobile agents. Each route is a sequence of nodes in order to traverse G.The
routes are considered as static itinerary, i.e., a pre-deterministic plan because paths for
agents are planned in advance.
The algorithm that dispatches mobile agents is described in Algorithm 3.Itstarts
by taking the following as input (1) a visiting route rR, generated by Algorithm 2,
(2) a mobile agent MA, and (3) data load dfor MAwhich is the maximum number
transactions the MA can carry in one trip. Then, it initializes Tas an empty set of
transactions collected by MA. It starts dispatching mobile agents with a specific route
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in Rand ensures that no two agents will follow the same route. During the trip, each
MA will visit nodes according to the given route r. It will first check whether the
current visiting node has been visited by any of the mobile agents or not. If the flag
visited of the node is true, the MA will move on to visit the next node on the route.
Otherwise, if the current node is not visited during the same mission, the MA collects
transactions data up to its data load d, and sets the flag visited of the node as true.
The MA completes the allocated tasks and returns either when all nodes on the given
route have been visited, or when the MA has collected dtransactions on the trip. The
data load dthreshold for each agent ensures that the agent buffer is not overloaded
with transactions data during one single trip.
4.2 Computation offloading
Offloading can be divided into two categories:data offloading and computation offload-
ing. The former refers to the use of novel network techniques to transmit mobile data
Fig. 3 Computation offloading
in MADIT approach
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originally planned for transferring via cellular networks. The latter refers to offloading
heavy computation tasks to reserve resources [29]. The main goal of offloading is to
reduce total energy consumption 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
Figure 3illustrates the computation offloading mechanism used in the MADIT
approach: the IOTA PoWbox (Proof of Work box). This is a service provided by the
IOTA Foundation that allows the offloading of the PoW to nodes with rich resources,
thus reducing energy consumption of constrained IoT devices and speeding up the
development workflow [30]. Such approach was suggested by the authors in [31]in
order to reserve the energy of IoT devices.
In particular, we address the problem of scalability, energy efficiency, and decen-
tralization without loss of efficiency by adapting and integrating the IOTA Tangle and
Mobile Agents. We have presented the proposed approach in view of the architecture, a
consensus mechanism, and the role of MA and the computation offloading techniques
5 Experiments, evaluation and analysis
In this section, we present our experimental results and an evaluation of the proposed
solution in terms of scalability, energy efficiency and decentralization. Additionally,
we provide analysis and discussion of the results, to establish important insights that
illustrate the usefulness of IOTA Tangle integrated with Mobile Agents for the IoT
5.1 Environment setup
We have deployed the latest release of the IOTA Reference Implementation (IRI
1.8.2),1which is the official Java build embodying the IOTA network specifications,
on the DigitalOcean cloud platform,2and another IOTA Reference Implementation
(IRI 1.8.2) on a local server dedicated for performing Proof of Work (PoW) operations.
The functionality related to IOTA addresses, transactions, broadcasting, routing,
and multi-signatures has been implemented using [32], the official Python
library of the IOTA Distributed Ledger. Different numbers of IOTA participant nodes
were used to create the network in order to simulate real life scenarios. In order to
measure transaction speed and scalability, we configured each data node to generate
transactions based on a time-driven technique as described in [33]. We also used a set
of different Minimum Weight Magnitudes (MWM) (9, 11, 14) [34]. The reason for
choosing different MWMs is due to the effect they have on the Transaction Per Second
(TPS) measure. Consequently, higher a MWM will require more time in attaching
transactions and hence the transactions are less likely to be selected as tips by others.
1 releases/tag/v1.8.2- RELEASE.
T. Alsboui et al.
Fig. 4 Scalability in Tangle with/without mobile agents
These transactions are broadcasted and shared amongst all participant nodes. Note
that, We have tested TPS for different numbers of nodes (e.g., 50, 100, 150, 250) with
different MWM configurations as presented above.
5.2 Results and analysis
The following two performance metrics are used in our experiments: TPS, and
Scalability The obtained results can be seen in Fig. 4. As shown in Fig. 4, it is clear
that as the number of nodes increases, the TPS transaction speed increases linearly.
For example, when the MWM is 9 and 50 nodes are engaged, with one mobile agent
dispatched, as shown by the green line, the TPS of MADIT (WA denotes with mobile
agents dispatched) reaches 3.749 tx/s (i.e., transactions per second) compared to the
baseline (NA denotes no mobile agents dispatched) TPS, which is 2.942 tx/s. Hence,
MADIT is 1.27 times faster than the baseline method. Still when the MWM is 9, and
the number of nodes is 150, in this case, the average TPS with MA reaches 5.422 tx/s
whereas in the baseline, TPS reaches 3.997 tx/s. This time, MADIT is 1.36 times faster
than the baseline method. This demonstrates that our proposed MADIT approach is
more scalable than the baseline method.
Throughput As shown in Fig. 4, it is clear that our proposed MADIT approach
brings an improvement over the baseline approach in terms of efficiency in processing
transactions. For example, in the situation in which 150 nodes are engaged, and the
MWM is set to 14, the average TPS of baseline reaches 4.176 tx/s (shown by the red
line), whereas when employing MAs, the average TPS reaches 2.776 tx/s, as shown by
the green line. This is due to two factors: (1) the computation offloading mechanism,
and (2) the inclusion of mobile agents in the MADIT approach.
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Fig. 5 Performance of baseline-TPS and agent-based under different MWM
Energy-efficiency All nodes involved in performing PoW have an impact upon total
energy consumption. Therefore, computation offloading not only conserves energy
but also reduces the time to process transactions. MADIT reduces energy because of
the use of the offloading mechanism and an associated reduction in the number of
Figure 5demonstrates the effect of MWM on the TPS. In this experiment, we set
the MWM to 9,11,14 to measure the effect on the TPS. In Fig. 5, it is clear that the
TPS is affected by the use of different MWM configurations as when it is set to 11, it
reaches 6.455 tx/s, and when it is set to 14, it reaches 7.141 tx/s.
Decentralization Our proposed MADIT approach is fully decentralized as the use of
the consensus mechanism is adopted.
6 Existing distributed intelligence approaches in IoT
For the last several years, distributed intelligence has begun to attract the attention of
a number of researchers from the field of IoT [10,12,35,36]. Many of these research
projects address issues related to data management and processing, scalability and
privacy. In earlier studies, distributed intelligence is achieved by integrating the wire-
less sensor network architecture with IoT to enable distributed intelligence across
different layers [37,38]. These approaches aim to present a flexible architecture for
connecting wireless sensor networks to the Internet and distribute intelligence and
decision-making processes across different layers [39]. Such approaches are energy
efficient due to the distribution of data processing, flexible, and application-agnostic.
Nonetheless, there is a lack of scalability, security and offline processing capabilities
that are perceived to be crucial obstacles for the IoT domain.
T. Alsboui et al.
In order to overcome many of the inherent problems in earlier studies, the authors
in [12] have introduced the concept of Sensor Function Virtualization (SFV) as a
possible future technique to assist distributed intelligence in IoT. This enables dis-
tributed processing of certain functionalities by offloading them from constrained
devices to unconstrained infrastructure such as a virtualized gateways, clouds and
other in-network infrastructure. SFV focuses on scalability, IoT heterogeneity, and
transparency. To achieve scalability, the approach relies on cloud infrastructure by
allowing part of SFV functionalities to run on the cloud benefiting from the elastic-
ity, and a tiered design. This handles the increased load when devices are joining the
network. The second point refers to the heterogeneity of IoT in terms of constraining
resources, and the user should be taken from the low-level information of the devices.
The final point addresses simplicity in which any virtual functions that are applied to
the devices must be built on top of current communication interfaces, and modifications
in protocols operating on edge applications must be limited and ideally non-existent.
Nevertheless, the issues of security and privacy are only narrowly considered in their
The research work by [10] incorporates fog computing architecture as a method
for the delivery of distributed intelligence in IoT. The suggested solution defined
fog nodes in terms of both hardware architecture and software architecture. From a
hardware perspective, fog nodes can be used as ancillary functions on standard network
components such as gateways, edge devices and routers, or as stand-alone fog boxes.
From a software perspective, fog nodes are highly virtualised machines with several
VMs operating under a highly capable hypervisor. Nevertheless, fog computing still
has security and privacy concerns [9,40,41].
Most recently, a current computing paradigm called Edge Mesh aims at allowing
distributed intelligence in IoT and is being introduced in [35]. This paradigm dis-
tributes decision-making tasks between edge devices on a network instead of sending
all data to a centralised server for further processing and analysis. Through Edge Mesh,
all these massive computation tasks and data are generally exchanged using a mesh
network of edge devices and routers. The Edge Mesh architecture consists of four
major device types. First, end devices are primarily used for sensing and actuating.
Second, edge devices can be used for pre-processing and connecting to end devices.
Second, routers were used to transfer data among edge devices. Finally, the cloud is
used to conduct big data analytics on historical data. Further advantages of the edge
mesh approach include distributed processing, low latency, fault tolerance, as well as
improved performance and scalability, better security, and privacy protection. Never-
theless, they have components to ensure security and privacy, but no consideration is
given to how privacy can be achieved. Therefore, implementation and evaluation are
not given.
In contrast, the work presented in [36] proposes an AI-based distributed intelligence
assisted approach named as the Future Internet of Things Controller (FITC). The
proposed approach uses both edge and clouds to distribute intelligence. In particular,
edge controllers are used to provide low-level intelligence, and cloud based controllers
to provide high-level intelligence, which they refer to as distributed intelligence. The
benefits of their work are to reduce response time and loosen the requirements for rules.
However, the approach lacks mechanisms that enable privacy and offline capability.
Enabling Distributed Intelligence for the Internet of…
Taking their work a step further, the authors in [42] investigated the role of Mobile
Edge Computing (MEC) to support distributed intelligence. The proposed approach is
scalable and avoids delays. However, the system lacks the ability to work in emergency
cases i.g, offline capability, and privacy is not considered in their design.
An approach named as PROTeCt–Privacy aRchitecture for the integration of the
Internet of Things and Cloud computing to enable distributed intelligence is presented
in [43]. The proposed approach consists of IoT devices and cloud platforms. IoT
devices are responsible for sensing and implementing a cryptographic mechanism
i.e., asymmetric algorithm to ensure privacy before transmitting the data to the cloud.
Similarly, in [44], the authors present an approach based on Mobile Cloud Computing
to support distributed intelligence. The main idea is to merge sensing and processing
at different levels of the network by sharing the application’s workload between the
server side and the smart things, and clouds are employed when needed. However, these
approaches are neither scalable nor suitable for time-critical applications. Furthermore,
the resiliency of the system i.e., an offline capability is outlined as future 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 architectures for processing data [41], which introduces a high cost and
delay that is not acceptable for distributed applications. In addition, such architectures
introduce inherent security vulnerabilities as data has to be transported to shared
cloud resources. Such examples include health monitoring, emergency response,
autonomous driving, and so on. In addition to that, they consume much network band-
width [2], as redundant data must be moved prior to processing using remote cloud
resources. It is suggested in previous research that future IoT systems need to move
away from central points of control [45]. Bottlenecks and delays are to be expected
from centralized systems[44]. Besides, solutions based on fog computing still have
issues regarding security and privacy [9]. 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) [16], 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 be trusted to obtain access to it (multiparty authentication scenarios), all of
which are related to privacy [46,47]. Finally, only a few of the approaches facilitate
the implementation and evaluation of their proposed solution.
7 Conclusion and future work
This paper advocates IOTA Tangle and Mobile Agents for supporting distributed
intelligence in IoT. It presents an IOTA Tangle and Mobile Agent based approach
as a solution to the problem of the limitations of traditional distributed intelligence
systems. Mobile Agents deliver an efficient way of collecting transactions. The advan-
tages of MADIT include: scalability; energy-efficiency, decentralization, elimination
of redundant transaction data, and the facilitation of node level communications (low
level intelligence).
There are a number of limitations in the work so far that need to be addressed
in the future, for example, the cost incurred by maintaining and deploying dedicated
T. Alsboui et al.
servers for performing the PoW, location privacy and constructing a static itinerary
plan for agents. As this is an emerging research field, there are a number of interesting
directions for future work that researchers in relevant fields may follow.
First, how to derive a dynamic or a hybrid itinerary plan for MAs is a critical task,
which allows each MA to decide the visiting sequence on-the-fly. This is particularly
useful for providing fault-tolerance and can be achieved by adopting an efficient clus-
tering method in which nodes will be grouped according to specific criteria, and MAs
will be directed to a particular group as described in [14].
Second, the IOTA Tangle can be used to solve the problem of offline capability. This
task is not simply a network entities configuration problem; the major issue is related
to clustering the network. However, it can be achieved by creating offline Tangles
where a certain number of nodes can effectively go offline and issue transactions
among themselves. This means that an active internet connection is not needed while
the Tangle is offline. Upon completion, it is possible to simply attach the transactions
of the offline Tangle back to the online one.
Third, it would be interesting to explore Masked Authentication Messaging with a
mixture of modes to enable multiparty authentication scenarios [8], and access policy.
Also, location privacy [47], which are fundamental issues for the maintenance of
effective IoT privacy.
Fourth, since device security is also one of the crucial fundamental challenges
that determine the successful implementation of IoT applications, cyber-security [48]
would be an important added improvement to the proposed MADIT approach. Ensur-
ing the robustness of the MADIT system against hacking is critical.
Furthermore, the benefits offered by IOTA Tangle can be explored in other areas,
such as Wireless Sensor Networks (WSN). It will not necessarily be pertinent to the
scalability and energy-efficiency issues and undoubtedly these issues will be taken
into consideration. Furthermore, how to customize IOTA Tangle to drive an efficient
routing protocol for IoT, taking into consideration various factors, such as Quality
of Service, would be promising. In addition to that, it would be interesting to inves-
tigate the possibility of adapting it to suit Information Extraction (IE) techniques in
WSNs such as event-driven (Threshold-based), time-driven (periodic), and query-
based (request-response) [33]. Therefore, not limiting the benefits of IOTA Tangle to
a specific problem or problem domain.
Finally, how to design and develop a new programming abstraction model [49] that
will suit all of the IE techniques. Consequently, it will be used as a building block in
establishing an infrastructure for a new integrated hybrid IE framework. It will be made
up of a specific, customised components and techniques along with the development
of distributed algorithms from several technologies such as Network Function Virtu-
alization (NFV) [50], Coordination Models and Languages [51], Distributed Ledger
technology [52], and Micro- services [53], wrapped up with an Application Program-
ming Interface (API).
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... This section discusses the performance outcomes and the details of the experimental design. Simulations are used to examine and verify the proposed protocol with the SoftEdgeNet model [37] and MADIT [39] with a different number of sensors and malicious nodes. The simulation environment is comprised of sensors, mobile agents, and a sink node. ...
... This section discusses the performance outcomes and the details of the experimental design. Simulations are used to examine and verify the proposed protocol with the Soft-EdgeNet model [37] and MADIT [39] with a different number of sensors and malicious nodes. The simulation environment is comprised of sensors, mobile agents, and a sink node. ...
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