Heterogeneous Internet Of Things Networks Research Articles

Methods of Traffic Distribution in a Heterogeneous High-Density Internet of Things Network

The paper presents a model and method for distributing traffic in a heterogeneous Internet of Things (IoT) network built on the basis of complex communication channels consisting of several subchannels using various signal transmission technologies, for example, radio, optical and acoustic. The developed methods for distributing traffic across subchannels of a heterogeneous IoT network make it possible to solve the problem of increasing network efficiency through the use of heterogeneous resources. The result of the study is methods of traffic distribution in the form of optimization problems for various solution search strategies. The results obtained can be used to build heterogeneous IoT networks and networks of robotic systems.

Clustered federated learning architecture for network anomaly detection in large scale heterogeneous IoT networks

There is a growing trend of cyberattacks against Internet of Things (IoT) devices; moreover, the sophistication and motivation of those attacks is increasing. The vast scale of IoT, diverse hardware and software, and being typically placed in uncontrolled environments make traditional IT security mechanisms such as signature-based intrusion detection and prevention systems challenging to integrate. They also struggle to cope with the rapidly evolving IoT threat landscape due to long delays between the analysis and publication of the detection rules. Machine learning methods have shown faster response to emerging threats; however, model training architectures like cloud or edge computing face multiple drawbacks in IoT settings, including network overhead and data isolation arising from the large scale and heterogeneity that characterizes these networks.This work presents an architecture for training unsupervised models for network intrusion detection in large, distributed IoT and Industrial IoT (IIoT) deployments. We leverage Federated Learning (FL) to collaboratively train between peers and reduce isolation and network overhead problems. We build upon it to include an unsupervised device clustering algorithm fully integrated into the FL pipeline to address the heterogeneity issues that arise in FL settings. The architecture is implemented and evaluated using a testbed that includes various emulated IoT/IIoT devices and attackers interacting in a complex network topology comprising 100 emulated devices, 30 switches and 10 routers. The anomaly detection models are evaluated on real attacks performed by the testbed’s threat actors, including the entire Mirai malware lifecycle, an additional botnet based on the Merlin command and control server and other red-teaming tools performing scanning activities and multiple attacks targeting the emulated devices.

Enhancing Data Security in IoT Networks with Blockchain-Based Management and Adaptive Clustering Techniques

The rapid proliferation of smart devices in Internet of Things (IoT) networks has amplified the security challenges associated with device communications. To address these challenges in 5G-enabled IoT networks, this paper proposes a multi-level blockchain security architecture that simplifies implementation while bolstering network security. The architecture leverages an adaptive clustering approach based on Evolutionary Adaptive Swarm Intelligent Sparrow Search (EASISS) for efficient organization of heterogeneous IoT networks. Cluster heads (CH) are selected to manage local authentication and permissions, reducing overhead and latency by minimizing communication distances between CHs and IoT devices. To implement network changes such as node addition, relocation, and deletion, the Network Efficient Whale Optimization (NEWO) algorithm is employed. A localized private blockchain structure facilitates communication between CHs and base stations, providing an authentication mechanism that enhances security and trustworthiness. Simulation results demonstrate the effectiveness of the proposed clustering algorithm compared to existing methodologies. Overall, the lightweight blockchain approach presented in this study strikes a superior balance between network latency and throughput when compared to conventional global blockchain systems. Further analysis of system under test (SUT) behavior was accomplished by running many benchmark rounds at varying transaction sending speeds. Maximum, median, and lowest transaction delays and throughput were measured by generating 1000 transactions for each benchmark. Transactions per second (TPS) rates varied between 20 and 500. Maximum delay rose when throughput reached 100 TPS, while minimum latency maintained a value below 1 s.

Distributed Rule-Enabled Interworking Architecture Based on the Transparent Rule Proxy in Heterogeneous IoT Networks.

Rule-enabled Internet of Things (IoT) systems operate autonomous and dynamic service scenarios through real-time events and actions based on deployed rules. For handling the increasing events and actions in the IoT networks, the computational ability can be distributed and deployed to the edge of networks. However, operating a consistent rule to provide the same service scenario in heterogeneous IoT networks is difficult because of the difference in the protocols and rule models. In this paper, we propose a transparent rule deployment approach based on the rule translator by integrating the interworking proxy to IoT platforms for operating consistent service scenarios in heterogeneous IoT networks. The rule-enabled IoT architecture is proposed to provide functional blocks in the layers of the client, rule service, IoT service, and device. Additionally, the interworking proxy is used for translating and transferring rules between IoT platforms in different IoT networks. Based on the interactions between the IoT platforms, the same service scenarios are operated in the IoT environment. Moreover, the integrated interworking proxy enables the heterogeneity of IoT frameworks in the IoT platform. Therefore, rules are deployed on IoT platforms transparently, and consistent rules are operated in heterogeneous IoT networks without considering the underlying IoT frameworks.

Network selection based on chi-square distance and reputation for internet of things

The internet of things (IoT) has become one of the most important technologies of the 21<sup>st</sup> century. The IoT environment is composed of heterogeneous IoT communication networks. These technologies are complementary and need to be integrated to meet the requirements of different types of IoT applications that require the mobility of the IoT device under different IoT communication networks. In this paper, the vertical handover decision method is considered to select the appropriate network among different IoT technologies. So, IoT devices, equipped with several radio technologies, can select the most suitable network based on several criteria like quality of service (QoS), cost, power, and security. In this work, a multi-attribute decision-making algorithm (MADM) based on techniques for order preference by similarity to an ideal solution (TOPSIS) that uses chi-square distance instead of Euclidean distance is proposed. The network reputation is added to reduce the average number of handoffs. The proposed algorithm was implemented to select the best technology depending on the requirements of the different IoT traffic classes. The obtained results showed that our proposition outperforms the traditional MADM algorithms.

Deep Model Training and Deployment in Heterogeneous IoT Networks

As a typical form of machines learning, deep learning has attracted much attention from researchers. It can independently construct (train) basic rules according to the sample data in the learning process. Especially in the field of machine vision, neural networks are usually trained by supervised learning, that is, by example data and predefined results of example data. In this paper, we firstly overview the current research progress on the deep model training and deployment on the heterogeneous Internet of Things (IoT) networks, by taking into account both the latency and energy consumption from various devices in the system. We then summarize the existing challenges on the model training and model deployment on the heterogeneous IoT devices. We further give some feasible solutions to solve the challenges on the model training and model deployment on the heterogeneous IoT devices. The study in this paper can serve as an important reference for the development of deep model training and model deployment for heterogeneous IoT networks.

Enhanced Interoperating Mechanism Between OneM2M and OCF Platform Based on Rules Engine and Interworking Proxy in Heterogeneous IoT Networks

In recent years, the Internet of Things (IoT) is growing rapidly and is being applied in a variety of industries including healthcare, smart homes, and smart cities. Many standard IoT platforms are proposed to connect and communicate with IoT devices easily and securely such as oneM2M, GoogleWeave and Apple HomeKit. However, this makes IoT application development difficult as it requires IoT devices and applications to support multiple protocols to connect different IoT platforms. Therefore, it is necessary to provide a consistent schema to support interoperability in heterogeneous IoT networks. In this paper, we propose how to design and implement interoperating schema between two edge servers oneM2M and Open Connectivity Foundation (OCF) with heterogeneous IoT devices. Specifically, we build proxies for bridging oneM2M Mobius edge server and OCF IoTivity edge server. The sensor data is collected from various IoT devices and sent to an edge server with a compatible platform. Next, the data stored in each server will be exchanged with the other server through a proposed interworking proxy. We also use a rules engine to automatically identify registered devices to support edge server interaction within the same domain and across domains. In addition, we build a web application in each edge server to provide friendly IoT services (data visualization) to clients from different environments. In order to evaluate our system, we collect the delay time of each process in the edge servers. The results show that our proposal is completely applicable in practice.

Security of federated learning with IoT systems: Issues, limitations, challenges, and solutions

Federated Learning (FL, or Collaborative Learning (CL)) has surely gained a reputation for not only building Machine Learning (ML) models that rely on distributed datasets, but also for starting to play a key role in security and privacy solutions to protect sensitive data and information from a variety of ML-related attacks. This made it an ideal choice for emerging networks such as Internet of Things (IoT) systems, especially with its state-of-the-art algorithms that focus on their practical use over IoT networks, despite the presence of resource-constrained devices. However, the heterogeneous nature of the current devices and models in complex IoT networks has seriously hindered the FL training process's ability to perform well. Thus, rendering it almost unsuitable for direct deployment over IoT networks despite ongoing efforts to tackle this issue and overcome this challenging obstacle. As a result, the main characteristics of FL in the IoT from both security and privacy aspects are presented in this study. We broaden our research to investigate and analyze cutting-edge FL algorithms, models, and protocols, with a focus on their efficacy and practical application across IoT networks and systems alike. This is followed by a comparative analysis of the recently available protection solutions for FL that can be based on cryptographic and non-cryptographic solutions over heterogeneous, dynamic IoT networks. Moreover, the proposed work provides a list of suggestions and recommendations that can be applied to enhance the effectiveness of the adoption of FL and to achieve higher robustness against attacks, especially in heterogeneous dynamic IoT networks and in the presence of resource-constrained devices.

Adaptive Caching Scheme for Jointly Optimizing Delay and Energy Consumption in Heterogeneous Digital Twin IoT

With the massive increase of mobile user and sensor data in the heterogeneous Internet of Things (IoT), the limited caching resources are challenging to meet people's increased data traffic demand. Due to ignoring the influence of uplink, the existing joint caching studies cannot reflect the actual network performance. Furthermore, some emerging delay-sensitive applications in the IoT, such as Virtual Reality, Augmented Reality, and autonomous driving, require better reliability and stability. Digital twins (DT) can provide real-time and stable wireless connectivity for heterogeneous IoT physical and virtual spaces. To address those issues, we propose an adaptive caching scheme based on the evolutionary game to jointly optimize delay and energy consumption in the uplink and downlink of heterogeneous digital twins IoT (HDT-IoT) networks. Besides, we prove the existence of evolutionary stability strategies (ESSs) in the proposed caching scheme. Based on the evolutionary stability analysis results, we derive the related expression between the content popularity and the ESS condition. The numerical results verify the existence of the content evolutionary stability caching strategy and the accuracy of the derived corresponding ESS conditional expressions and evaluate the caching performance by comparing our scheme with the other two caching schemes.

Customised Intrusion Detection for an Industrial IoT Heterogeneous Network Based on Machine Learning Algorithms Called FTL-CID

Technological breakthroughs in the Internet of Things (IoT) easily promote smart lives for humans by connecting everything through the Internet. The de facto standardised IoT routing strategy is the routing protocol for low-power and lossy networks (RPL), which is applied in various heterogeneous IoT applications. Hence, the increase in reliance on the IoT requires focus on the security of the RPL protocol. The top defence layer is an intrusion detection system (IDS), and the heterogeneous characteristics of the IoT and variety of novel intrusions make the design of the RPL IDS significantly complex. Most existing IDS solutions are unified models and cannot detect novel RPL intrusions. Therefore, the RPL requires a customised global attack knowledge-based IDS model to identify both existing and novel intrusions in order to enhance its security. Federated transfer learning (FTL) is a trending topic that paves the way to designing a customised RPL-IoT IDS security model in a heterogeneous IoT environment. In this paper, we propose a federated-transfer-learning-assisted customised distributed IDS (FT-CID) model to detect RPL intrusion in a heterogeneous IoT. The design process of FT-CID includes three steps: dataset collection, FTL-assisted edge IDS learning, and intrusion detection. Initially, the central server initialises the FT-CID with a predefined learning model and observes the unique features of different RPL-IoTs to construct a local model. The experimental model generates an RPL-IIoT dataset with normal and abnormal traffic through simulation on the Contiki-NG OS. Secondly, the edge IDSs are trained using the local parameters and the globally shared parameters generated by the central server through federation and aggregation of different local parameters of various edges. Hence, transfer learning is exploited to update the server's and edges' local and global parameters based on relational knowledge. It also builds and customised IDS model with partial retraining through local learning based on globally shared server knowledge. Finally, the customised IDS in the FT-CID model enforces the detection of intrusions in heterogeneous IoT networks. Moreover, the FT-CID model accomplishes high RPL security by implicitly utilising the local and global parameters of different IoTs with the assistance of FTL. The FT-CID detects RPL intrusions with an accuracy of 85.52% in tests on a heterogeneous IoT network.

Smart contract-based security architecture for collaborative services in municipal smart cities

The Internet of Things (IoT) can provide intelligent and effective solutions to various applications with higher accuracy that requires less or no human intervention. Smart Cities are one of the significant applications of the IoT comprising a collection of various services such as intelligent transportation, waste management, smart homes, etc. These heterogeneous services offer a wide range of collaborative applications in smart cities. A smart municipality in a smart city is a concept in which a digital municipal corporation is developed to provide comprehensive local government collaboration services based on digitization and automation aiming towards raising the living standards of citizens. Interoperability between heterogeneous services for collaborative tasks creates challenges for data security and privacy. Ensuring integrity and confidentiality of information is critical, and reliable data is essential to both the government and its citizens. In this paper, we proposed a service security architecture based on authentication and authorization for constrained environments during collaborative tasks for Software Defined Networking (SDN) and smart contract-enabled municipal smart cities. The proposed collaborative service security framework is being tested on the Multichain Blockchain networks. We present a novel method for using smart contracts in multichain blockchains for data security during collaborative tasks in smart city municipal architecture. The proposed security solution is based on the dynamism of smart contracts to govern and control all interactions and transactions securely between different heterogeneous IoT networks. We implemented a supportive use case for collaborative services in an SDN-enabled IoT architecture to evaluate the feasibility of the proposed service security architecture.

Game-Theory-Based Multimode Routing Protocol for Internet of Things

Various routing protocols have been proposed for ad hoc networks such as the Internet of Things (IoT). Most of the routing protocols introduced for IoT are specific to applications and networks. In the current literature, it is essential to configure all the network nodes with a single proposed protocol. Moreover, it is also possible for a single IoT network to consist of different kinds of nodes. Two or more IoT networks can also be connected to create a bigger heterogeneous network. Such networks may need various routing protocols with some gateway nodes installed. The role of gateway nodes should not be limited to the interconnection of different nodes. In this paper, a multi-mode hybrid routing mechanism is proposed that can be installed on all or a limited number of nodes in a heterogenous IoT network. The nodes configured with the proposed protocols are termed smart nodes. These nodes can be used to connect multiple IoT networks into one. Furthermore, a game-theory-based model is proposed that is used for intercommunication among the smart nodes to gain optimal efficiency. Various performance matrices are assessed under different network scenarios. The simulation results show that the proposed mechanism outperforms in broader heterogeneous IoT networks with diverse nodes.

A cascaded federated deep learning based framework for detecting wormhole attacks in IoT networks

The growth of the internet over the years has resulted in massive use and spread of the Internet of Things (IoT) in many areas. From home networks to industrial IoT, from medicine to transportation, IoT is everywhere. This massive usage has come with a price as attackers make use of sophisticated techniques and malware to target these heterogeneous IoT networks. Moreover, the low processing power and minimal resources of the IoT devices make security and privacy a major challenge. This weakness has motivated attackers to target IoT networks in a wide variety of ways by launching different types of attacks. One such kind of attack is the Wormhole attack which belongs to the family of routing attacks and disturbs the normal routing and flow of network packets in the IoT network. Many techniques involving rule-based, trust-based, machine learning-driven and deep learning assisted have been devised to counter wormhole attacks. The proposed research work presents a cascaded wormhole detection technique for IoT networks which is based on the federated deep learning technique and a Dynamic Trust Factor (DTF). The DTF is based on two trust attributes, whereas Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) deep learning models have been trained using the federated approach, which guarantees data security and privacy at the node level. The proposed technique achieves the accuracy of 96% and is lightweight due to the cascaded and federated learning approach.

Spectrum Efficient Communication for Heterogeneous IoT Networks

Spectrum sharing for coexistence between heterogenous IoT (Internet of Things) networks degrades data communication effectiveness severely. Cross-Technology Communication(CTC) enables that heterogeneous devices can talk to each other directly, which sheds light on spectrum allocation of heterogeneous IoT networks for efficient communication based on CTC, such as, a WiFi device, as a sophisticated leader, coordinates the spectrum allocation for achieving efficient communication on off-the-shelf devices. In this work, we propose a simple spectrum allocation strategy, which is suitable for any duty-cycles of WiFi and ZigBee devices. Depending on the scheme, heterogeneous devices arrange their working schedules and each device monopolizes its time slots to evade data collisions and reduce wireless interference for achieving efficient communication. We implement a network-layer spectrum allocation technique, called CoWBee, that can be easily deployed in existing WiFi infrastructure without modifying the firmware or hardware of both WiFi and ZigBee devices. CoWBee is evaluated based on our platform and the results show our scheme based on CTC is feasible, which provides valuable insights about the spectrum allocation on achieving desirable performance.

Multi-Armed-Bandit Based Channel Selection Algorithm for Massive Heterogeneous Internet of Things Networks

In recent times, the number of Internet of Things devices has increased considerably. Numerous Internet of Things devices generate enormous traffic, thereby causing network congestion and packet loss. To address network congestion in massive Internet of Things systems, an efficient channel allocation method is necessary. Although some channel allocation methods have already been studied, as far as we know, there is no research focusing on the implementation phase of Internet of Things devices while considering massive heterogeneous Internet of Things systems where different kinds of Internet of Things devices coexist in the same Internet of Things system. This paper focuses on the multi-armed-bandit-based channel allocation method that can be implemented on resource-constrained Internet of Things devices with low computational processing ability while avoiding congestion in massive Internet of Things systems. This paper first evaluates some well-known multi-armed-bandit-based channel allocation methods in massive Internet of Things systems. The simulation results show that an improved multi-armed-bandit-based channel selection method called Modified Tug of War can achieve the highest frame success rate in most cases. Specifically, the frame success rate can reach 95% when the numbers of channels and IoT devices are 60 and 10,000, respectively, while 12% channels are suffering traffic load by other kinds of IoT devices. In addition, the performance in terms of frame success rate can be improved by 20% compared to the equality channel allocation. Moreover, the multi-armed-bandit-based channel allocation methods is implemented on 50 Wi-SUN Internet of Things devices that support IEEE 802.15.4g/4e communication and evaluate the performance in frame success rate in an actual wood house coexisting with LoRa devices. The experimental results show that the modified multi-armed-bandit method can achieve the highest frame success rate compared to other well-known frame success rate-based channel selection methods.

An Energy-Efficient Multilevel Secure Routing Protocol in IoT Networks

In Internet of Things (IoT) applications with multihop networking, not only traditional energy efficiency but also many distinct features should be considered when designing routing protocols, including different security requirements, heterogeneity, and scalability. In this article, an energy-efficient multilevel secure routing (EEMSR) protocol in IoT networks is proposed. Considering that clustering is a reasonable solution of conserving energy, a cluster-based multihop routing protocol is utilized to reduce the high communication overhead due to the scalability of IoT networks. In particular, more reasonable analytic hierarchy process and genetic algorithms are adopted to assign accurate weight and optimize intercluster routing in which heterogeneous IoT networks are considered to support large amount of heterogeneous IoT entities and services. Moreover, multiple trust levels are adopted to defend the different attacks by calculating the trust factor on the clustering and routing, including data perception trust, data fusion trust, and communication trust. It is shown that the proposed algorithm outperforms the existing algorithms in terms of network lifetime, throughput, packet delivery ratio, network energy balance, and adaptability.

Zero knowledge proofs based authenticated key agreement protocol for sustainable healthcare

Upgradation of technologies for sustainable smart cities has led to rapid growth in Internet of Things (IoT) applications, including e-healthcare services wherein smart devices collect patient data and deliver it remotely to the servers in real-time. Despite its enormous benefits, IoT in healthcare has not received much attention primarily due to the risk of unauthorized access to confidential medical information enabled by the vulnerable wireless channel for communication. Besides, tiny IoT devices have limited computing power and storage capabilities that prevent administrators from using complex and resource-hungry security protocols. The cyber attacks on the Internet of Healthcare applications (IoHA) could result in fatalities, decreased revenue, and reputation loss, hence endangering sustainability. The existing security protocols are unsuitable due to the cost complexities that necessitate developing new security protocols for resource-constrained and heterogeneous IoT networks. We introduce a confidentiality and anonymity-preserving scheme for critical infrastructures of IoT to conquer cyber threats for sustainable healthcare. This paper proposes Zero-Knowledge Proofs (ZKP) based Authenticated Key Agreement (AKA) protocol for IoHA. ZKP-AKA uses zero-knowledge proofs, physically unclonable function, biometrics, symmetric cryptography, message digest, etc., for accomplishing the protocol’s objective at minimal computation, storage, and communication expenses. ZKP-AKA retains data integrity, confidentiality, anonymity, and safety from significant cyber threats. • This article introduces a privacy-preserving mutual authentication and key agreement protocol to protect IoT healthcare networks from unauthorized abuses. • The protocol has been implemented using Zero Knowledge Proof (ZKP) and Physically Unclonable Function (PUF) to safeguard confidentiality and prevent physical attacks. • The security protocol’s robustness against cyber-attacks has been investigated through formal (AVISPA) and informal (logical rules) analysis. • The protocol’s performance has been analyzed and compared with existing security protocols.

Evaluation and Selection Models for Ensemble Intrusion Detection Systems in IoT

Using the Internet of Things (IoT) for various applications, such as home and wearables devices, network applications, and even self-driven vehicles, detecting abnormal traffic is one of the problematic areas for researchers to protect network infrastructure from adversary activities. Several network systems suffer from drawbacks that allow intruders to use malicious traffic to obtain unauthorized access. Attacks such as Distributed Denial of Service attacks (DDoS), Denial of Service attacks (DoS), and Service Scans demand a unique automatic system capable of identifying traffic abnormality at the earliest stage to avoid system damage. Numerous automatic approaches can detect abnormal traffic. However, accuracy is not only the issue with current Intrusion Detection Systems (IDS), but the efficiency, flexibility, and scalability need to be enhanced to detect attack traffic from various IoT networks. Thus, this study concentrates on constructing an ensemble classifier using the proposed Integrated Evaluation Metrics (IEM) to determine the best performance of IDS models. The automated Ranking and Best Selection Method (RBSM) is performed using the proposed IEM to select the best model for the ensemble classifier to detect highly accurate attacks using machine learning and deep learning techniques. Three datasets of real IoT traffic were merged to extend the proposed approach’s ability to detect attack traffic from heterogeneous IoT networks. The results show that the performance of the proposed model achieved the highest accuracy of 99.45% and 97.81% for binary and multi-classification, respectively.

R-IDPS: Real Time SDN-Based IDPS System for IoT Security

The advent of the latest technologies like the Internet of things (IoT) transforms the world from a manual to an automated way of lifestyle. Meanwhile, IoT sector open numerous security challenges. In traditional networks, intrusion detection and prevention systems (IDPS) have been the key player in the market to ensure security. The challenges to the conventional IDPS are implementation cost, computing power, processing delay, and scalability. Further, online machine learning model training has been an issue. All these challenges still question the IoT network security. There has been a lot of research for IoT based detection systems to secure the IoT devices such as centralized and distributed architecture-based detection systems. The centralized system has issues like a single point of failure and load balancing while distributed system design has scalability and heterogeneity hassles. In this study, we design and develop an agent-based hybrid prevention system based on software-defined networking (SDN) technology. The system uses lite weight agents with the ability to scaleup for bigger networks and is feasible for heterogeneous IoT devices. The baseline profile for the IoT devices has been developed by analyzing network flows from all the IoT devices. This profile helps in extracting IoT device features. These features help in the development of our dataset that we use for anomaly detection. For anomaly detection, support vector machine has been used to detect internet control message protocol (ICMP) flood and transmission control protocol synchronize (TCP SYN) flood attacks. The proposed system based on machine learning model is fully capable of online and offline training. Other than detection accuracy, the system can fully mitigate the attacks using the software-defined technology SDN technology. The major goal of the research is to analyze the accuracy of the hybrid agent-based intrusion detection systems as compared to conventional centralized only solutions, especially under the flood attack conditions generated by the distributed denial of service (DDoS) attacks. The system shows 97% to 99% accuracy in simulated results with no false-positive alarm. Also, the system shows notable improvement in terms of resource utilization and performance under attack scenarios. The R-IDPS is scalable, and the system is suitable for heterogeneous IoT devices and networks.

A Tutorial on Next Generation Heterogeneous IoT Networks and Node Authentication

The extreme heterogeneity in Internet of Things (IoT) networks impacts the network performance, application service quality, and user experience, and has opened many vistas for adversaries to compromise and eventually knock down the networks. The concern for network security and network reliability will further increase in next generation networks (NGNs) such as 5G-IoT by many folds and requires a new level of security approaches. The first line of defense to secure network and its resources is node authentication. However, we note that without alleviating heterogeneity, NGNs cannot fully provide network security or meet the network performance demands. Further, a granular level of autonomy cannot be provided in NGNs, which impacts the IoT node authentication. This makes the authentication issue in heterogeneous networks a more challenging problem. In this article, we first highlight the heterogeneous NGN concept, its impact (particularly on IoT node authentication), and commonly used authentication protocols and industry standards. We also emphasize the need for enhancing NGNs' security by a novel multi-factor approach that also addresses heterogeneity. Unique research challenges and potential opportunities are highlighted.