# Challenges in Integration of Heterogeneous Internet of Things

Authors: Muhammad Noaman; Muhammad Sohail Khan; Muhammad Faisal Abrar; Sikandar Ali; Atif Alvi; Muhammad Asif Saleem
Journal: Scientific Programming
 (2022)

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## Abstract

Internet of Things (IoT) is considered the upcoming industrial and academic revolution in the technological world having billions of things and devices connected to the Internet. These connected devices are heterogeneous. They have different standards and technologies which communicate through different protocols. Therefore, the implementation of IoT on a large scale is difficult due to these heterogeneity challenges. This motivated us to overcome the scaling problem of IoT by identifying the challenges from the literature and providing solutions. This study is based on the identification of the heterogeneous challenges with solutions via a systematic literature review. A total of 81 primary sources were selected. After extracting and synthesizing the data, we identified 14 different IoT heterogeneity challenges. Some of the identified challenges are “heterogeneity of devices,” “heterogeneity in formats of data,” “heterogeneity in communication,” and “interoperability issue due to heterogeneity.” The identified challenges have been analyzed from digital libraries and timeframe perspectives. Furthermore, we have found a total of 81 solutions for those challenges, with at least 5 unique solutions for each challenge. In the future, we will categorize the challenges and prioritize the solutions by using a multi-criteria decision-making problem.


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## Body

## 1. Motivation

IoT is the expansion of current Internet services to provide connectivity to each object of this world. IoT has become the most prominent technology across the globe. It is an emerging technology that is under development process where everyone is trying to interpret it according to their needs. The implementation and interpretation of IoT face some serious challenges like security, virtualization, and heterogeneity. Heterogeneity itself is a multifaceted challenge hindering the large-scale implementation of IoT vision. It is due to these challenges that so far only limited implementations of IoT systems have become a reality. This motivated us to perform a systematic literature review to identify those IoT heterogeneity challenges and their solutions. Another contribution of this study is conducting a depth analysis of those challenges using the chi-square test based on digital libraries and timeframe.


## 2. Introduction

In today’s technological world, IoT is considered an important advancement among the trending technologies. The term IoT can be simply defined as the devices that can be connected with sources of the Internet [1]. In past years, these devices have been constantly growing. Reference [2] reported that around 500 billion devices will be connected to the Internet by 2030. In the physical as well as in the virtual world, these IoT devices will further interconnect with other devices in a large number, which will give new birth to the forms of interaction. This will enable us to connect all objects of our surroundings in every corner of the world in a single period. These objects can be sensors, smartphones, automobiles, industrial robots, refrigerators, thermostats, tablets, etc. The IoT is widespread in both academia and industry. It is producing business opportunities in multiple fields of industrial markets, in both public and private sectors in a very broad range. The industrial revolution of IoT will have billions of heterogeneous devices on the Internet of Things in the near future.At the other extreme, the IoT vision of a large-scale implementation faces serious challenges across many dimensions. One of the main obstructions in IoT is its inclination toward heterogeneity, and the heterogeneous nature might be in form of protocols, device data format, communication capabilities of the devices, technologies, hardware, etc. [3, 4]. It is due to these types of challenges that so far only limited implementations of IoT systems have become a reality. For IoT to evolve toward its vision of global implementation, these obstructions need to be reduced on different levels. To activate and provide the service, devices must be connected to the Internet. The identification of heterogeneity-based challenges that exist at different levels is needed, and the current solutions adopted and/or implemented by different studies for handling heterogeneity in IoT systems need to be highlighted. The objective of this study is to conduct a systematic literature review to identify those IoT heterogeneity challenges and find out the solutions implemented by different studies to handle IoT heterogeneity challenges. The significance of this study is that it will provide the identification and analysis of heterogeneity challenges in IoT systems and provide a summary of different studies that implemented various solutions to handle the heterogeneity of IoT systems. Another significant contribution is that it will provide a future direction to researchers to make a better stand-alone architecture aiming to tackle the heterogeneity challenges at different levels of IoT systems. As a result, IoT systems can be utilized and implemented in a wide range of industrial fields.The organization of this paper is as follows: Section3 presents a literature review related to IoT history, challenges, and heterogeneity concerns. Section 4 explains the research methodology used to achieve the objectives of this research. Section 5 provides the result and discussion of IoT heterogeneity challenges and the solutions to those challenges found in this study. Section 6 concludes this review and suggests future work.


## 3. Literature Review

Because of the broad and complex nature of IoT, it has not yet got a single unique definition that is acceptable to the whole global community of users. Many researchers, practitioners, academicians, developers, and corporate people have defined IoT in their terms, but the credit must be attributed to Kevin Ashton, an expert on digital innovation who for the first time used and defined it. According to [5], IoT could be nicely defined as follows: a very comprehensive and accessible network of intelligent devices which can act and react in accord with situations; self-organize; share information, data, and resources; and be subject to change in the environment.IoT is growing and maturing day by day. It is the latest, most fine, and excellent concept in information technology. It is a new paradigmatic shift in information technological advancement. The expression “Internet of Things,” concisely shortened to IoT, is comprised of two words, “Internet” and “Things.” Internet uses a standard set of Internet protocols (TCP/IP) to connect and serve a large number of users around the globe [6, 7]. It is a global system of interconnected computer networks. Internet is interconnected networks that includes a large number of local to regional commercial networks that may be private, government-owned, public, or academic networks. These networks are connected through a wide range of electronic, wireless, and optical network technologies [8]. The Internet is generally defined as a global network that connects millions of computers. About 190 states of the world are linked through the Internet that constantly shares data, opinions, and news [9]. According to [10], there is an estimated 5,080,388,142 Internet users around the world. This large sum of users indicates that about 40% of the world’s total population uses the Internet.The word “Things” in the “Internet of Things” can be any object or person identifiable in the real world. Daily necessities include electronic devices that we come across as well as daily used advanced technological items like equipment and gadgets. In the near future, other certain everyday objects are expected to connect with the Internet, which will lead to a period of extreme expansion of the Internet known as the Internet of Things. IoT system is based on devices to sense, actuate, control, and monitor activities [11]. IoT devices that are connected to other devices and applications can exchange data with each other, they may receive information from some other IoT devices. To process data, they may send the data either to centralized serves locally or it may send data for processing to cloud.The National Intelligence Council (NIC) of the United Nations (UN) has considered IoT as one of the six “Disruptive Civil Technologies” [12]. In this respect, we can list many fields that are already benefiting from the services of different architectural forms of IoT like transportation, e-governance, smart city, smart health, life support, education, retail, logistics, agriculture, automation, manufacturing of industrial products, and management of businesses.Ericsson [13] and Evans [14] have conducted surveys and estimated that the use of the Internet will further increase, grow, and be boosted tenfold in the coming days. According to their estimation, about 50 billion devices would have connected by 2020. This expected number of new Internet devices shall be supposedly called constricted devices [15]. These devices are small in size, are enclosed in nature, and have a low cost. They are specifically designed for the purpose of executing specific tasks like monitoring the physical environment. IoT devices are limited in terms of communication capabilities, processing power, and energy consumption due to their low cost. That is why these constrained devices are very heterogeneous in terms of their essential communication protocols, device data formats, and technologies.Due to the heterogeneity of IoT, devices on the market nowadays have diversity in communication protocols, methods of network connectivity, and resulting models of application. It is not feasible to support such kinds of diversities in IoT, because developers typically lack the proper resources required to have a grip on the specifics of the constrained devices and network [16]. The main objective is to decrease, hide, or eradicate such a broad range of diversity of the technologies, applications models, and protocols from the users of IoT [17]. Because of the heterogeneous nature of IoT, it is one of the newly emerging research areas which has robust potential to bring a paradigm shift in the understanding of fundamental computer science principles and standards of our future living [18]. The demand for constrained IoT devices is expected to increase; this problem is expected to get worse in the future. Therefore, there will be a need to improve the integration of a large number of constrained devices in IoT. In this study, we have performed SLR aiming to identify the heterogeneity challenges and provide a summary of the solutions adopted for those challenges.


## 4. Research Methodology

A systematic literature review (SLR) methodology is adopted to identify the heterogeneity challenges in IoT systems, hindering a global IoT vision, and to find solutions to the identified heterogeneity challenges.


### 4.1. Research Questions

The first step of a systematic literature review is to define research questions. The research questions of this study are mentioned in Table1.Table 1
Research questions.
No.
Research questions
RQ1
What are the challenges in heterogeneous IoT in the literature?
RQ2
What are the solutions to these challenges in heterogeneous IoT in the literature?


### 4.2. Search String

The second phase of a systematic literature review is to find relevant studies on the research topic. We identified digital libraries in which primary search was carried out: IEEE Xplore, SpringerLink, Google Scholar, ScienceDirect, and ACM. We then defined a set of keywords related to our research topic: “Internet of Things,” “IoT,” “heterogeneity,” “heterogeneous,” and “challenges.” Finally, search strings were defined and used to collect published articles related to the research topic. Search strings are provided in Table2.Table 2
Search strings.
Sources
Search string
Context
Google Scholar
(“Heterogeneous Internet of Things”) AND (“issues OR challenges”)
IoT heterogeneity
IEEE Explore
“Heterogeneous” AND (“IoT” OR “Internet of Things”) AND “challenges”
ScienceDirect
(“IoT” OR “Internet of Things”) AND (“heterogenous”) AND (“challenges”)
ACM
(“IoT”) AND (“heterogenous”) AND (“challenges”)
SpringerLink
(“IoT”) AND (“heterogeneity”) AND (“challenges”)


### 4.3. Study Selection

The research selection process is to perform search in digital libraries based on the tollgate approach considering the search strings. Figure1 shows a selection of articles using the tollgate approach.Figure 1
Selection of articles using the tollgate approach.In snowballing process, we selected 9 papers from journals (IEEE TMC, TPDS, JSAC, ToN, TWC) and conferences (SIGCOMM, MobiCom, MobiSys, INFOCOM), the content of which is analyzed and discussed in Section.Initially, 3854 papers were selected by applying search protocol to the selected digital libraries. A selection process has been applied based on keywords, titles, duplication removal, abstracts, and full text of selected papers. We excluded the papers of the following types:(1)
Studies published in sources other than conferences, journals, patents, and technical reports(2)
Research papers not published in the English language(3)
Studies published before 2010(4)
Studies that are not related to the defined search stringsTo evaluate the quality of the included research papers, we assessed the following aspects:(1)
The study provides information about any challenge related to IoT heterogeneity(2)
The study represents a clear solution to the identified challenge of heterogeneity(3)
The published study is from a stable and recognized publication sourceFigure2 shows a summary of paper selection based on digital libraries, and Figure 3 shows the paper selection on a yearly basis.Figure 2
Paper selection based on digital libraries.Figure 3
Paper selected on a yearly basis.


## 4.1. Research Questions

The first step of a systematic literature review is to define research questions. The research questions of this study are mentioned in Table1.Table 1
Research questions.
No.
Research questions
RQ1
What are the challenges in heterogeneous IoT in the literature?
RQ2
What are the solutions to these challenges in heterogeneous IoT in the literature?


## 4.2. Search String

The second phase of a systematic literature review is to find relevant studies on the research topic. We identified digital libraries in which primary search was carried out: IEEE Xplore, SpringerLink, Google Scholar, ScienceDirect, and ACM. We then defined a set of keywords related to our research topic: “Internet of Things,” “IoT,” “heterogeneity,” “heterogeneous,” and “challenges.” Finally, search strings were defined and used to collect published articles related to the research topic. Search strings are provided in Table2.Table 2
Search strings.
Sources
Search string
Context
Google Scholar
(“Heterogeneous Internet of Things”) AND (“issues OR challenges”)
IoT heterogeneity
IEEE Explore
“Heterogeneous” AND (“IoT” OR “Internet of Things”) AND “challenges”
ScienceDirect
(“IoT” OR “Internet of Things”) AND (“heterogenous”) AND (“challenges”)
ACM
(“IoT”) AND (“heterogenous”) AND (“challenges”)
SpringerLink
(“IoT”) AND (“heterogeneity”) AND (“challenges”)


## 4.3. Study Selection

The research selection process is to perform search in digital libraries based on the tollgate approach considering the search strings. Figure1 shows a selection of articles using the tollgate approach.Figure 1
Selection of articles using the tollgate approach.In snowballing process, we selected 9 papers from journals (IEEE TMC, TPDS, JSAC, ToN, TWC) and conferences (SIGCOMM, MobiCom, MobiSys, INFOCOM), the content of which is analyzed and discussed in Section.Initially, 3854 papers were selected by applying search protocol to the selected digital libraries. A selection process has been applied based on keywords, titles, duplication removal, abstracts, and full text of selected papers. We excluded the papers of the following types:(1)
Studies published in sources other than conferences, journals, patents, and technical reports(2)
Research papers not published in the English language(3)
Studies published before 2010(4)
Studies that are not related to the defined search stringsTo evaluate the quality of the included research papers, we assessed the following aspects:(1)
The study provides information about any challenge related to IoT heterogeneity(2)
The study represents a clear solution to the identified challenge of heterogeneity(3)
The published study is from a stable and recognized publication sourceFigure2 shows a summary of paper selection based on digital libraries, and Figure 3 shows the paper selection on a yearly basis.Figure 2
Paper selection based on digital libraries.Figure 3
Paper selected on a yearly basis.


## 5. Analysis and Discussion

In this section, we have discussed the results relevant to our RQs. For the analysis, we used the linear chi-square test of association. For categorical values of predictor and outcome variables, the chi-square test is counted as more significant than other statistical tests. To answer RQ1, identified challenges/critical issues through the SLR are presented in Table3. We have found a significant difference in heterogeneity challenges.Table 3
Challenges identified through SLR.
S. No
Challenges
Frequency, 81
Percentage (%)
Papers ID
1
Fragmentation in connectivity, protocols
10
14
PiD4, PiD2, PiD4, PiD5, PiD36, PiD54, PiD60, PiD66, PiD74, PPiD78
2
Diversity in network technologies
9
13
PiD3, PiD6, PiD7, PiD8, PiD9, PiD10, PiD46, PiD54, PiD63
3
Management of networks
6
8
PiD8, PiD11, PiD13, PiD14, PiD15, PiD16
4
Heterogeneous communication issues
26
36
PiD3, PiD10, PiD11, PiD13, PiD15, PiD17, PiD20, PiD21, PiD26, PiD28, PiD29, PiD49, PiD57, PiD58, PiD61, PiD62, PiD64, PiD65, PiD68, PiD70, PiD72, PiD73, PiD75, PiD80, PiD81
5
Heterogeneity of devices issues
35
49
PiD2, PiD3, PiD8, PiD11, PiD12, PiD14, PiD16, PiD19, PiD20, PiD21, PiD22, PiD23, PiD24, PiD25, PiD26, PiD27, PiD28, PiD45, PiD46, PiD47, PiD50, PiD51, PiD56, PiD57, PiD58, PiD62, PiD63, PiD65, PiD68, PiD70, PiD71, PiD72, PiD77
6
Communication between heterogeneous devices
15
21
PiD3, PiD7, PiD10, PiD18, PiD28, PiD29, PiD30, PiD31, PiD32, PiD33, PiD47, PiD50, PiD53, PiD69, PiD71
7
Management and configuration of devices
13
18
PiD11, PiD14, PiD16, PiD30, PiD33, PiD34, PiD35, PiD36, PiD37, PiD54, PiD61, PiD62
8
Heterogeneous data/data formats
26
36
PiD1, PiD6, PiD10, PiD18, PiD25, PiD26, PiD27, PiD28, PiD32, PiD36, PiD38, PiD39, PiD40, PiD41, PiD42, PiD57, PiD58, PiD62, PiD64, PiD65, PiD68, PiD69, PiD70, PiD72, PiD79
9
Data security
12
17
PiD23, PiD31, PiD43, PiD44, PiD45, PiD46, PiD49, PiD52, PiD53, PiD76, PiD77
10
Communication security
16
22
PiD19, PiD23, PiD31, PiD32, PiD35, PiD43, PiD44, PiD47, PiD48, PiD49, PiD50, PiD51, PiD52, PiD53, PiD54, PiD55
11
Device security
14
20
PiD8, PiD19, PiD23, PiD24, PiD31, PiD43, PiD48, PiD49, PiD52, PiD53, PiD54, PiD55, PiD67
12
Heterogeneity in standards, platform
10
14
PiD1, PiD34, PiD36, PiD56, PiD57, PiD58, PiD59, PiD60, PiD66, PiD72
13
Integration of devices and data
14
20
PiD12, PiD18, PiD21, PiD23, PiD38, PiD41, PiD53, PiD56, PiD60, PiD61, PiD62, PiD63, PiD64, PiD65
14
Interoperability issue
16
22
PiD5, PiD9, PiD23, PiD27, PiD30, PiD37, PiD51, PiD59, PiD65, PiD66, PiD67, PiD68, PiD69, PiD70, PiD71, PiD72We set an occurrence percentage threshold of 30%. Accordingly, “Heterogeneity of devices,” “heterogeneity in formats of data,” and “heterogeneity in communication” are the most critical identified challenges.


### 5.1. Comparison of Challenges Based on Digital Libraries

Table4 shows the analysis of the identified challenges based on digital libraries. We have Google Scholar, IEEE Xplore, SpringerLink, ScienceDirect, and ACM as digital libraries. From the analysis, we have found the following:(1)
Heterogeneity in communication is critical in Google Scholar and SpringerLink(2)
Heterogeneity of devices is critical in Google Scholar, IEEE Xplore, and SpringerLink(3)
Heterogeneity in formats of data is critical in ScienceDirect, SpringerLink, and IEEE Xplore(4)
Interoperability issue is critical in ACM and SpringerLinkTable 4
Summary of challenges based on digital libraries.
Challenges
Google Scholar,n = 36
IEEE Xplore,n = 29
ScienceDirect,n = 4
ACM,n = 7
SpringerLink,n = 5
Chi-square test,α =0.05
f
%
f
%
f
%
f
%
f
%
x
2
p
Fragmentation in connectivity, protocols
5
14
3
10
0
0
2
29
0
0
4.1393
0.0419
Diversity in network technologies
4
11
3
10
1
25
0
0
1
20
1.6701
0.1962
Management of networks
3
8
3
10
0
0
0
0
0
0
1.6683
0.1965
Heterogeneity in communication
12
33
7
24
1
25
1
14
2
40
0.3876
0.5336
Heterogeneity of devices
16
44
10
34
1
25
2
29
5
100
1.6342
0.2011
Communication between heterogeneous devices
7
19
6
21
1
25
0
0
1
20
1.4601
0.2269
Management and configuration of devices
8
22
3
10
0
0
1
14
0
0
2.9163
0.0877
Heterogeneity in formats of data
9
25
10
34
3
75
2
29
2
40
4.4138
0.0357
Data security
4
11
5
17
0
0
0
0
1
20
1.9809
0.1593
Communication security
8
22
5
17
1
25
1
14
1
20
0.5067
0.4766
Device security
5
14
6
21
0
0
1
14
1
20
1.3884
0.2387
Heterogeneity in standards, platform
7
19
1
2
0
0
1
14
1
20
3.4381
0.0637
Integration of devices and data
6
17
6
21
0
0
1
14
1
20
1.0421
0.3073
Interoperability issue
6
17
4
14
1
25
2
29
3
60
3.4303
0.0640


### 5.2. Comparison of Challenges Based on Timeframe

Table5 shows the analysis of the identified challenges based on timeframe. We have divided the duration into two timeframes: Timeframe I from 2011 to 2016 and Timeframe II from 2017 to 2021. From the analysis, we have found the following:Table 5
Summary of challenges based on timeframe.
Challenges
Timeframe I (2011–2016),n = 23
Timeframe II (2017–2021),n = 58
Chi-square test,α = 0.05
f
%
f
%
x
2
p
Fragmentation in connectivity, protocols
2
9
8
14
0.4918
0.4831
Diversity in network technologies
5
22
4
7
2.7502
0.0972
Management of networks
3
13
3
5
1.1185
0.2902
Heterogeneity in communication
8
35
18
31
0.0042
0.9478
Heterogeneity of devices
13
57
22
38
0.8052
0.3695
Communication between heterogeneous devices
4
17
11
19
0.0878
0.7669
Management and configuration of devices
3
13
10
17
0.3112
0.5769
Heterogeneity in formats of data
8
35
18
31
0.0042
0.9478
Data security
2
9
10
17
1.0399
0.3078
Communication security
3
13
13
22
0.9920
0.3192
Device security
2
9
12
20
1.6784
0.1951
Heterogeneity in standards, platform
3
13
7
12
0.0001
0.9901
Integration of devices and data
6
26
8
14
1.0677
0.3015
Interoperability issue
5
22
11
19
0.0086
0.9257Heterogeneity in communication is critical in Timeframe I from 2011 to 2016 as shown in Table5.(1)
Heterogeneity of devices is critical in Timeframe I and Timeframe II(2)
Heterogeneity in formats of data is critical in Timeframe I and Timeframe II


### 5.3. Proposed Solutions

To answer RQ2, solutions to the identified challenges are presented in Table6. We have found a total of 81 solutions for those challenges, with at least 5 unique solutions for each challenge.Table 6
Summary of solutions for identified challenges.
Challenge addressed
Proposed solutions
Ref
Year
Approach
Solution
Fragmentation in connectivity, data formats, and protocols
[4]
2015
Framework
ConnectOpen, providing a flexible communication agent deployed at the gateway
[19]
2016
Platform
SPOT, a smartphone-based platform that makes use of open device driver models using XML
[15]
2015
Platform
Cloud-based platform to integrate the services with communication models and constrained devices
[20]
2020
Architecture
The recursive Inter-network architecture-based approach that reduces the protocol complexity and improves standardization
[21]
2019
Protocol
Coexistent routing and flooding (CRF) using unique features of the physical layer technology-to-physical communication method for concurrent routing
[22]
2020
Platform
iArk, a universal tracking platform for all types of IoT devices operating in the very high-frequency band
[23]
2020
Mechanism
New roaming mechanism for LoRaWAN (low bandwidth wide area network) protocol based on reliable 5G network
Diversity in network technologies
[24]
2019
Architecture
Fog computing-based, multi-technology service architecture for IoT devices
[25]
2011
Framework
IDRA, reconfigurable network framework to directly connect the devices that are correlated to each other
[26]
2019
Framework
SDN-IoT, a framework that provides the functionality of convertingm heterogeneous controllers to n homogeneous controllers
[27]
2017
Middleware
A smartphone-based mobile gateway that provides an interface between devices and the Internet, being flexible and transparent
[28]
2020
Proposed system
A decentralized, blockchain-based cloud solution for creating complex services of the network at the edge using IoT devices
Management of networks
[29]
2016
Architecture
A solution based on the utilization of dockers implemented on devices
[30]
2016
Architecture
Combining both direct and indirect current management approaches
[31]
2020
Model
Message-based communication model consists of a dictionary of services for devices and servers to interact
[32]
2019
Platform
M4DN.IoT, a platform for the management of IoT networks with a user-friendly interface
[33]
2014
Architecture
Extending the multi-network information architecture (MINA) middleware with SDN multilayer IoT controller
[34]
2014
Framework
Framework for managing and configuring the network dynamically based on SDN
Heterogeneity in communication
[35]
2016
Algorithm
Hierarchal clustering algorithm for dynamic and heterogeneous IoT
[36]
2016
Framework
Relying on a device and distributed SDN connectivity to overcome the issue of heterogeneous communication methods used in IoT
[37]
2016
Proposed system
TACIoT, a flexible and reliable IoT access control system
[38]
2020
Framework
Knowledge-based framework using edge computing for heterogeneous connectivity in the Internet of Things networks
[39]
2021
Algorithm
Distributed online optimization algorithm based on game theory and optimization theory. The algorithm works online and jointly decides to offload heterogeneous tasks, allocate computing resources, and manage battery power
[40]
2020
Model
Optimal geographic distribution across heterogeneous networks with caching support. Extending optimization to heterogeneous networks using simulated user distributions
[41]
2020
Framework
Elastic zoom algorithm for cells based on the end-user quality of service and traffic load
[42]
2019
Platform
MINOS, multi-protocol software that defines a networking platform
Heterogeneity of devices
[43]
2017
Platform
ThingsJS, a JavaScript-based middleware platform and runtime environment that bypasses system-specific complexities
[44]
2014
Architecture
Architecture, combined with cognitive capabilities, that supports intelligent decision-making and automates service creation
[45]
2017
Platform
IoTOne, software platform to support heterogeneous Internet of Things devices and allow robust control of devices
[46]
2020
Middleware
Cuttlefish, lightweight and flexible middleware having unified APIs for application development for heterogeneous device utilization
[47]
2020
Middleware
MSOAH-IoT is based on a service-oriented architecture that handles various networking interfaces and collects data using REST API
[48]
2017
Architecture
A middleware architecture and edge-based protocol that enables heterogeneous edge devices to dynamically exchange data and resources to improve application performance and privacy
[49]
2019
Framework
A novel communication framework that enables simultaneous N-Way communication between Wi-Fi and bluetooth low energy (BLE) devices
[50]
2020
Mechanism
eWoT, a semantically interactive ecosystem of IoT devices that provides SPARQL query-based mechanism for transparent discovery and access to IoT devices
Communication between heterogeneous devices
[51]
2012
Framework
Resource-oriented middleware framework using blockchain technology
[52]
2018
Architecture
Use of a multimodald employing a variety of heterogeneous wireless networks
[53]
2019
Model
Ontology-based device semantic web rule language between multiple devices in a heterogeneous system
[54]
2020
Protocol
A device-to-device lightweight security protocol based on a symmetric key scheme to ensure secure communication between devices
[55]
2019
Middleware
PICO, REST web service-based data-centric middleware for real-time communication and storage of data
[56]
2018
Architecture
A fully decentralized IoT access control system, based on blockchain technology architecture
Management and configuration of devices
[32]
2019
Platform
M4DN.IoT, a platform for the management of IoT networks with a user-friendly interface
[57]
2016
Framework
EC-IoT, making use of an open standard upon IoT communication protocol (COAP)
[58]
2019
Architecture
An improved architecture for managing, monitoring, and configuring IoT devices, based on private blockchain
[59]
2020
Platform
An open and scalable IoT platform having edge computing characteristics
[60]
2020
Framework
DIAM-IoT, a framework for IoT device decentralized identification and access management
Heterogeneity in formats of data
[61]
2016
Framework
SIGHTED, a framework based on the semantic web and connected data principles
[62]
2017
Framework
A solution consisting of Internet gateway device functions, NoSQL database, web services, and IoT application
[63]
2011
Framework
SeaCloudDM, a novel sea-cloud-based heterogeneous data management framework
[64]
2020
Proposed system
MusQ, a solution that provides a multi-storing query system for IoT data using a formal unified query language (MQL)
[68]
2020
Mechanism
Ethereum blockchain consisting of generic and constrained devices that connect to the blockchain via a wired and wireless heterogeneous network
[65]
2020
Architecture
Software architecture for processing and analyzing data from heterogeneous sources with different structures in IoT scopes
Data security
[66]
2021
Framework
Distributed multiparty secure computing framework for data authentication of devices
[67]
2018
Framework
Framework for access control in IoT using block chain technology
[69]
2020
Proposed system
PEIoT, a data streaming system with enhanced privacy
(i) Providing users with the ability to subscribe to/unsubscribe from data
(ii) Enabling data controllers to invoke various privacy-enhancing technologies
[70]
2019
Model
Two modules:
(i) Secure data processing system to maintain IoT data security and integrity
(ii) Drone-based data analytic system using edge computing and on-device computing
[71]
2016
Architecture
A novel architecture consisting of a distributed interface having e-nodes—inexpensive, simple, and embedded nodes
Communication security
[72]
2019
Protocol
Multigroup key management protocol to ensure upstream and downstream secrecy, recovery from collision attacks, and network security
[73]
2019
Mechanism
A physical layer security mechanism to validate the single source for heterogeneous IoT
[74]
2020
Protocol
Using proxy re-signature, a privacy-preserving authentication protocol for heterogeneous system
[75]
2019
Method
SMER, a method for exchanging resources between heterogeneous devices securely
[76]
2015
Algorithms
Optimized elliptic curve cryptography (ECC) algorithms for the devices based on NXP/Jennic JN5148
Device security
[77]
2019
Proposed system
Novel and lightweight authentication and key agreement scheme for heterogeneous IoT devices
[78]
2018
Algorithm
ECC-based algorithm to authenticate and authorize new devices in a network
[79]
2021
Framework
MECshield, a DDoS prevention framework, based on mobile edge computing (MEC)
[80]
2020
Proposed system
Novel decentralized authentication mechanism based on blockchain technology
Heterogeneity in standards, platform
[81]
2018
Method
Smart governance approach for heterogeneous IoT system management
[82]
2019
Framework
BRAIN-IoT, a framework and methodology for interconnecting heterogeneous platforms and automation
[83]
2016
Model
The data model used in the VITAL project (open source IoT system of systems) for rapid development of IoT-based systems
[84]
2021
Platform
Data Spine, federated platform for bridging interoperability gaps between heterogeneous IoT platforms
[85]
2017
Model
A concept of generic driver injection for developing mobile applications that can be deployed in a variety of environments using different middleware
Integration of devices and data
[86]
2019
Proposed system
SensPnP, a novel plug-and-play solution which combines hardware with firmware
[87]
2020
Architecture
Blockchain-based architecture to improve integration and reduce computation overhead and energy consumption
[88]
2015
Proposed system
A solution based on management distribution of devices among gateways and making use of web service delegation
[89]
2016
Architecture
Architecture for data integration from heterogeneous data sources like government agencies and unreliable sources
[90]
2020
Model
Point-to-point integration model in IoT applications, with three layers: hardware, communication, and integration
Interoperability issue
[91]
2019
Model
Three internetworking models based on the status of the city
[92]
2020
Framework
AFaaS, authorization framework as a services to support interoperability challenge
[93]
2018
Framework
SHIOT, an SDN-based framework which is based on ontology and applies SDN controller
[94]
2018
Platform
New design of decentralized IoT platform having capabilities of edge computing, fog computing, and cloud computing
[95]
[96]
2014
Framework
A lightweight, middleware-independent development framework for interoperability monitoring
[90]
2022
[91]
2022
[92]
2022
[93]
2019
[97]
2021
Framework
Trust-based middleware framework for managing interoperability challenges in heterogeneous IoT
[98]
2016
Method
Model-based engineering methods to ensure those complex software systems are interoperable with each other
2020
2021
2022


## 5.1. Comparison of Challenges Based on Digital Libraries

Table4 shows the analysis of the identified challenges based on digital libraries. We have Google Scholar, IEEE Xplore, SpringerLink, ScienceDirect, and ACM as digital libraries. From the analysis, we have found the following:(1)
Heterogeneity in communication is critical in Google Scholar and SpringerLink(2)
Heterogeneity of devices is critical in Google Scholar, IEEE Xplore, and SpringerLink(3)
Heterogeneity in formats of data is critical in ScienceDirect, SpringerLink, and IEEE Xplore(4)
Interoperability issue is critical in ACM and SpringerLinkTable 4
Summary of challenges based on digital libraries.
Challenges
Google Scholar,n = 36
IEEE Xplore,n = 29
ScienceDirect,n = 4
ACM,n = 7
SpringerLink,n = 5
Chi-square test,α =0.05
f
%
f
%
f
%
f
%
f
%
x
2
p
Fragmentation in connectivity, protocols
5
14
3
10
0
0
2
29
0
0
4.1393
0.0419
Diversity in network technologies
4
11
3
10
1
25
0
0
1
20
1.6701
0.1962
Management of networks
3
8
3
10
0
0
0
0
0
0
1.6683
0.1965
Heterogeneity in communication
12
33
7
24
1
25
1
14
2
40
0.3876
0.5336
Heterogeneity of devices
16
44
10
34
1
25
2
29
5
100
1.6342
0.2011
Communication between heterogeneous devices
7
19
6
21
1
25
0
0
1
20
1.4601
0.2269
Management and configuration of devices
8
22
3
10
0
0
1
14
0
0
2.9163
0.0877
Heterogeneity in formats of data
9
25
10
34
3
75
2
29
2
40
4.4138
0.0357
Data security
4
11
5
17
0
0
0
0
1
20
1.9809
0.1593
Communication security
8
22
5
17
1
25
1
14
1
20
0.5067
0.4766
Device security
5
14
6
21
0
0
1
14
1
20
1.3884
0.2387
Heterogeneity in standards, platform
7
19
1
2
0
0
1
14
1
20
3.4381
0.0637
Integration of devices and data
6
17
6
21
0
0
1
14
1
20
1.0421
0.3073
Interoperability issue
6
17
4
14
1
25
2
29
3
60
3.4303
0.0640


## 5.2. Comparison of Challenges Based on Timeframe

Table5 shows the analysis of the identified challenges based on timeframe. We have divided the duration into two timeframes: Timeframe I from 2011 to 2016 and Timeframe II from 2017 to 2021. From the analysis, we have found the following:Table 5
Summary of challenges based on timeframe.
Challenges
Timeframe I (2011–2016),n = 23
Timeframe II (2017–2021),n = 58
Chi-square test,α = 0.05
f
%
f
%
x
2
p
Fragmentation in connectivity, protocols
2
9
8
14
0.4918
0.4831
Diversity in network technologies
5
22
4
7
2.7502
0.0972
Management of networks
3
13
3
5
1.1185
0.2902
Heterogeneity in communication
8
35
18
31
0.0042
0.9478
Heterogeneity of devices
13
57
22
38
0.8052
0.3695
Communication between heterogeneous devices
4
17
11
19
0.0878
0.7669
Management and configuration of devices
3
13
10
17
0.3112
0.5769
Heterogeneity in formats of data
8
35
18
31
0.0042
0.9478
Data security
2
9
10
17
1.0399
0.3078
Communication security
3
13
13
22
0.9920
0.3192
Device security
2
9
12
20
1.6784
0.1951
Heterogeneity in standards, platform
3
13
7
12
0.0001
0.9901
Integration of devices and data
6
26
8
14
1.0677
0.3015
Interoperability issue
5
22
11
19
0.0086
0.9257Heterogeneity in communication is critical in Timeframe I from 2011 to 2016 as shown in Table5.(1)
Heterogeneity of devices is critical in Timeframe I and Timeframe II(2)
Heterogeneity in formats of data is critical in Timeframe I and Timeframe II


## 5.3. Proposed Solutions

To answer RQ2, solutions to the identified challenges are presented in Table6. We have found a total of 81 solutions for those challenges, with at least 5 unique solutions for each challenge.Table 6
Summary of solutions for identified challenges.
Challenge addressed
Proposed solutions
Ref
Year
Approach
Solution
Fragmentation in connectivity, data formats, and protocols
[4]
2015
Framework
ConnectOpen, providing a flexible communication agent deployed at the gateway
[19]
2016
Platform
SPOT, a smartphone-based platform that makes use of open device driver models using XML
[15]
2015
Platform
Cloud-based platform to integrate the services with communication models and constrained devices
[20]
2020
Architecture
The recursive Inter-network architecture-based approach that reduces the protocol complexity and improves standardization
[21]
2019
Protocol
Coexistent routing and flooding (CRF) using unique features of the physical layer technology-to-physical communication method for concurrent routing
[22]
2020
Platform
iArk, a universal tracking platform for all types of IoT devices operating in the very high-frequency band
[23]
2020
Mechanism
New roaming mechanism for LoRaWAN (low bandwidth wide area network) protocol based on reliable 5G network
Diversity in network technologies
[24]
2019
Architecture
Fog computing-based, multi-technology service architecture for IoT devices
[25]
2011
Framework
IDRA, reconfigurable network framework to directly connect the devices that are correlated to each other
[26]
2019
Framework
SDN-IoT, a framework that provides the functionality of convertingm heterogeneous controllers to n homogeneous controllers
[27]
2017
Middleware
A smartphone-based mobile gateway that provides an interface between devices and the Internet, being flexible and transparent
[28]
2020
Proposed system
A decentralized, blockchain-based cloud solution for creating complex services of the network at the edge using IoT devices
Management of networks
[29]
2016
Architecture
A solution based on the utilization of dockers implemented on devices
[30]
2016
Architecture
Combining both direct and indirect current management approaches
[31]
2020
Model
Message-based communication model consists of a dictionary of services for devices and servers to interact
[32]
2019
Platform
M4DN.IoT, a platform for the management of IoT networks with a user-friendly interface
[33]
2014
Architecture
Extending the multi-network information architecture (MINA) middleware with SDN multilayer IoT controller
[34]
2014
Framework
Framework for managing and configuring the network dynamically based on SDN
Heterogeneity in communication
[35]
2016
Algorithm
Hierarchal clustering algorithm for dynamic and heterogeneous IoT
[36]
2016
Framework
Relying on a device and distributed SDN connectivity to overcome the issue of heterogeneous communication methods used in IoT
[37]
2016
Proposed system
TACIoT, a flexible and reliable IoT access control system
[38]
2020
Framework
Knowledge-based framework using edge computing for heterogeneous connectivity in the Internet of Things networks
[39]
2021
Algorithm
Distributed online optimization algorithm based on game theory and optimization theory. The algorithm works online and jointly decides to offload heterogeneous tasks, allocate computing resources, and manage battery power
[40]
2020
Model
Optimal geographic distribution across heterogeneous networks with caching support. Extending optimization to heterogeneous networks using simulated user distributions
[41]
2020
Framework
Elastic zoom algorithm for cells based on the end-user quality of service and traffic load
[42]
2019
Platform
MINOS, multi-protocol software that defines a networking platform
Heterogeneity of devices
[43]
2017
Platform
ThingsJS, a JavaScript-based middleware platform and runtime environment that bypasses system-specific complexities
[44]
2014
Architecture
Architecture, combined with cognitive capabilities, that supports intelligent decision-making and automates service creation
[45]
2017
Platform
IoTOne, software platform to support heterogeneous Internet of Things devices and allow robust control of devices
[46]
2020
Middleware
Cuttlefish, lightweight and flexible middleware having unified APIs for application development for heterogeneous device utilization
[47]
2020
Middleware
MSOAH-IoT is based on a service-oriented architecture that handles various networking interfaces and collects data using REST API
[48]
2017
Architecture
A middleware architecture and edge-based protocol that enables heterogeneous edge devices to dynamically exchange data and resources to improve application performance and privacy
[49]
2019
Framework
A novel communication framework that enables simultaneous N-Way communication between Wi-Fi and bluetooth low energy (BLE) devices
[50]
2020
Mechanism
eWoT, a semantically interactive ecosystem of IoT devices that provides SPARQL query-based mechanism for transparent discovery and access to IoT devices
Communication between heterogeneous devices
[51]
2012
Framework
Resource-oriented middleware framework using blockchain technology
[52]
2018
Architecture
Use of a multimodald employing a variety of heterogeneous wireless networks
[53]
2019
Model
Ontology-based device semantic web rule language between multiple devices in a heterogeneous system
[54]
2020
Protocol
A device-to-device lightweight security protocol based on a symmetric key scheme to ensure secure communication between devices
[55]
2019
Middleware
PICO, REST web service-based data-centric middleware for real-time communication and storage of data
[56]
2018
Architecture
A fully decentralized IoT access control system, based on blockchain technology architecture
Management and configuration of devices
[32]
2019
Platform
M4DN.IoT, a platform for the management of IoT networks with a user-friendly interface
[57]
2016
Framework
EC-IoT, making use of an open standard upon IoT communication protocol (COAP)
[58]
2019
Architecture
An improved architecture for managing, monitoring, and configuring IoT devices, based on private blockchain
[59]
2020
Platform
An open and scalable IoT platform having edge computing characteristics
[60]
2020
Framework
DIAM-IoT, a framework for IoT device decentralized identification and access management
Heterogeneity in formats of data
[61]
2016
Framework
SIGHTED, a framework based on the semantic web and connected data principles
[62]
2017
Framework
A solution consisting of Internet gateway device functions, NoSQL database, web services, and IoT application
[63]
2011
Framework
SeaCloudDM, a novel sea-cloud-based heterogeneous data management framework
[64]
2020
Proposed system
MusQ, a solution that provides a multi-storing query system for IoT data using a formal unified query language (MQL)
[68]
2020
Mechanism
Ethereum blockchain consisting of generic and constrained devices that connect to the blockchain via a wired and wireless heterogeneous network
[65]
2020
Architecture
Software architecture for processing and analyzing data from heterogeneous sources with different structures in IoT scopes
Data security
[66]
2021
Framework
Distributed multiparty secure computing framework for data authentication of devices
[67]
2018
Framework
Framework for access control in IoT using block chain technology
[69]
2020
Proposed system
PEIoT, a data streaming system with enhanced privacy
(i) Providing users with the ability to subscribe to/unsubscribe from data
(ii) Enabling data controllers to invoke various privacy-enhancing technologies
[70]
2019
Model
Two modules:
(i) Secure data processing system to maintain IoT data security and integrity
(ii) Drone-based data analytic system using edge computing and on-device computing
[71]
2016
Architecture
A novel architecture consisting of a distributed interface having e-nodes—inexpensive, simple, and embedded nodes
Communication security
[72]
2019
Protocol
Multigroup key management protocol to ensure upstream and downstream secrecy, recovery from collision attacks, and network security
[73]
2019
Mechanism
A physical layer security mechanism to validate the single source for heterogeneous IoT
[74]
2020
Protocol
Using proxy re-signature, a privacy-preserving authentication protocol for heterogeneous system
[75]
2019
Method
SMER, a method for exchanging resources between heterogeneous devices securely
[76]
2015
Algorithms
Optimized elliptic curve cryptography (ECC) algorithms for the devices based on NXP/Jennic JN5148
Device security
[77]
2019
Proposed system
Novel and lightweight authentication and key agreement scheme for heterogeneous IoT devices
[78]
2018
Algorithm
ECC-based algorithm to authenticate and authorize new devices in a network
[79]
2021
Framework
MECshield, a DDoS prevention framework, based on mobile edge computing (MEC)
[80]
2020
Proposed system
Novel decentralized authentication mechanism based on blockchain technology
Heterogeneity in standards, platform
[81]
2018
Method
Smart governance approach for heterogeneous IoT system management
[82]
2019
Framework
BRAIN-IoT, a framework and methodology for interconnecting heterogeneous platforms and automation
[83]
2016
Model
The data model used in the VITAL project (open source IoT system of systems) for rapid development of IoT-based systems
[84]
2021
Platform
Data Spine, federated platform for bridging interoperability gaps between heterogeneous IoT platforms
[85]
2017
Model
A concept of generic driver injection for developing mobile applications that can be deployed in a variety of environments using different middleware
Integration of devices and data
[86]
2019
Proposed system
SensPnP, a novel plug-and-play solution which combines hardware with firmware
[87]
2020
Architecture
Blockchain-based architecture to improve integration and reduce computation overhead and energy consumption
[88]
2015
Proposed system
A solution based on management distribution of devices among gateways and making use of web service delegation
[89]
2016
Architecture
Architecture for data integration from heterogeneous data sources like government agencies and unreliable sources
[90]
2020
Model
Point-to-point integration model in IoT applications, with three layers: hardware, communication, and integration
Interoperability issue
[91]
2019
Model
Three internetworking models based on the status of the city
[92]
2020
Framework
AFaaS, authorization framework as a services to support interoperability challenge
[93]
2018
Framework
SHIOT, an SDN-based framework which is based on ontology and applies SDN controller
[94]
2018
Platform
New design of decentralized IoT platform having capabilities of edge computing, fog computing, and cloud computing
[95]
[96]
2014
Framework
A lightweight, middleware-independent development framework for interoperability monitoring
[90]
2022
[91]
2022
[92]
2022
[93]
2019
[97]
2021
Framework
Trust-based middleware framework for managing interoperability challenges in heterogeneous IoT
[98]
2016
Method
Model-based engineering methods to ensure those complex software systems are interoperable with each other
2020
2021
2022


## 6. Conclusion and Future Work

This research is a systematic literature review that reviews the literature in the domain of IoT heterogeneity. The review has been implemented using a systematic methodology to select different studies addressing the challenges faced by heterogeneous IoT. To conduct this study, a total of 81 research papers that are published in different digital libraries from 2011 to 2021 were selected. For analysis purposes, we divided this duration into two timeframes. One is from 2011 to 2016, and the other is from 2017 to 2021. In this SLR, we have identified 14 different heterogeneity challenges that need to be addressed to implement IoT on a large scale. Challenges with occurrence percentage of more than 30% are defined as the most critical ones. In this study, we analyze the occurrence of those challenges based on digital libraries as well as timeframe. In this analysis, we found out that some challenges were more critical in the earlier timeframe than in the recent timeframe.We also found out that some challenges are still critical in both timeframes. After identifying those challenges, we found at least 5 solutions for each identified challenge. The summary of those solutions is given in Table6. In future work, we want to categorize the challenges and prioritize the solutions by using a multi-criteria decision-making problem.



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*Source: 8626882-2022-08-16.xml*