Large-scale Internet Of Things Deployments Research Articles

A Shark Inspired Ensemble Deep Learning Stacks for Ensuring the Security in Internet of Things (IoT)-Based Smart City Infrastructure

Recent advancements in the Internet of Things (IoT) have paved the way for intelligent and sustainable solutions in smart city environments. However, despite these advantages, IoT-connected devices present significant privacy and security risks, as network attacks increasingly exploit user-centric information. To protect the network against the rapidly growing number of cyber-attacks, it is essential to employ a cognitive intrusion detection system (CIDS) capable of handling complex and voluminous network data. This research presents a novel ensemble deep learning framework designed to enhance cybersecurity in IoT-based smart city ecosystems. The proposed architecture integrates Self-Attention Convolutional Neural Networks, Bidirectional Gated Recurrent Units, and Shark Smell Optimized Feed Forward Networks to create a robust, adaptive system for detecting and mitigating cyber threats. By leveraging fog computing, the model significantly reduces latency and computational overhead, making it highly suitable for large-scale IoT deployments. Extensive experimentation using the ToN-IoT dataset demonstrates the framework's exceptional performance, achieving a 99.78% detection rate across various attack types and an AUC of 0.989. The proposed model outperforms existing state-of-the-art approaches, achieving a mean fitness function value of 0.85640 and a standard deviation of 0.037630 in binary classification outcomes. In multi-class classification, the model maintains a mean fitness function value of 0.8230 and a variance of 2.28930 × 104, significantly outperforming other meta-heuristic algorithms such as Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). The model exhibits superior accuracy and efficiency compared to existing state-of-the-art approaches, particularly in identifying complex and emerging threats. This research makes significant contributions by introducing innovative feature extraction techniques, optimizing model performance for resource-constrained environments, and providing a scalable solution for securing smart city infrastructure. The findings highlight the potential of ensemble deep learning approaches to fortify IoT networks against cyberattacks, paving the way for more resilient and secure smart cities.

Internet of Things (IoT) of Smart Homes: Privacy and Security

The Internet of Things (IoT) constitutes a sophisticated network that interconnects devices, optimizing functionality across various domains of human activity. Recent literature projections anticipate a significant increase, with estimates exceeding 50 billion connected devices by 2025. Despite its transformative potential, the IoT landscape confronts formidable privacy and security challenges, encompassing intricate issues such as data acquisition, anonymization, retention, sharing practices, and behavioural profiling. Effectively addressing these challenges mandates the development of scalable solutions, innovative management strategies, and adaptable policy frameworks. In this paper, we conduct an exhaustive examination of major IoT applications, alongside associated privacy and security concerns. We systematically categorize prevalent privacy, security, and interoperability issues within the context of the IoT layered architecture. The review highlights current research initiatives focused on developing energy-efficient devices, optimizing microprocessors, and fostering interdisciplinary collaborations to address the challenges in the IoT landscape. To efficaciously manage risks in this dynamic landscape, stakeholders must implement comprehensive strategies that span stringent data protection legislation, extensive user education initiatives, and the deployment of robust authorization and authentication frameworks. This paper aims to empower industry leaders, policymakers, and researchers by providing actionable solutions, not just insights, to navigate the complexities of the IoT landscape effectively. Future research initiatives should prioritize the fortification of security measures for large-scale IoT deployments, the formulation of user-centric privacy solutions, and the standardization of interoperability protocols. By establishing a robust foundational framework, our paper endeavours to spearhead the discourse on IoT applications, privacy paradigms, and security frameworks, paving the way towards a resilient and interconnected future.

A secure and efficient AKE scheme for IoT devices using PUF and cancellable biometrics

As the Internet of Things (IoT) continues to grow, there is an increasing need for secure and efficient authentication protocols for IoT devices. The design of IoT authentication schemes requires several essential features such as real two-factor security, Key Compromise Impersonation (KCI) resilience, and Perfect Forward Secrecy (PFS). In this paper, we perform a cryptanalysis of two state-of-the-art PUF-based Authentication and Key Exchange(AKE) schemes and show informally how they fail to achieve real two-factor security, PFS and suffer KCI, user traceability, and database attacks. To address these limitations, we propose an improved two-party AKE scheme between the user-device and the server based on a combination of a physically unclonable function (PUF) and a cancellable biometric (CB). Our proposed scheme provides real two-factor security, KCI resilience, PFS, and user untraceability, achieving all desired properties based on the Computational Diffie–Hellman (CDH) assumption in the random oracle model. To establish the security of our proposed scheme, we utilize formal analysis methods, including the RoR model and ProVerif, and perform an informal security analysis to demonstrate its resilience against potential attacks. Additionally, we analyze the computational and communication performance of our proposed scheme against related PUF-based schemes. Our proposed scheme exhibits a computational and communicational feasible overhead for deployment in the context of the IoT, with a lower magnitude than most previous related PUF-based schemes.Overall, our proposed scheme provides an efficient and secure authentication solution for IoT devices, with improved security and reduced communication overhead compared to previous PUF-based schemes. The combination of formal and informal analysis provides robust evidence of the strength and reliability of our proposed scheme, and its scalability makes it a viable solution for large-scale IoT deployments.

Intelligent multichannel cross-layer framework for enhanced energy-efficiency in 6G-IoT wireless networks

The sixth-generation (6G) communication technology is expected to enable a fully-connected world with enhanced wireless connectivity, accessibility, reliability, and performance. 6G systems will be critical in addressing the massive-connectivity requirements for Internet-of-Things (IoT) networks. Cognitive radio (CR) technology is considered a promising solution in 6G networks that can provide tremendous spectrum opportunities to support large-scale IoT deployments. Due to the IoT’s energy-constrained and massively connected nature, existing CR-based communication protocols cannot be directly implemented in 6G-IoT networks. This is because they are usually designed to enhance spectrum-utilization in networks with finite users. Therefore, energy-efficient protocols are required to support the massive-connectivity requirement for energy-constrained IoT nodes. Non-persistent Carrier-Sense-Multiple-Access (CSMA)-based protocols are recognized as energy-effective spectrum-access paradigms for networks with massive users. This paper proposes a novel energy-efficient multi-channel cross-layer MAC framework that utilizes a non-persistent CSMA protocol in CR-enabled 6G-IoT networks. Based on a joint adaptation of the physical and MAC layer parameters, our framework aims to improve IoT networks’ energy efficiency (EE). Specifically, we formulate and solve the per-bit EE maximization problem subject to several design constraints. Numerical results show that our multi-channel cross-layer framework enhances network EE by up to 50% compared to a reference single-channel cross-layer design.

Logically-Centralized SDN-Based NDN Strategies for Wireless Mesh Smart-City Networks

The Internet of Things (IoT) is a key technology for smart community networks, such as smart-city environments, and its evolution calls for stringent performance requirements (e.g., low delay) to support efficient communication among a wide range of objects, including people, sensors, vehicles, etc. At the same time, these ecosystems usually adopt wireless mesh technology to extend their communication range in large-scale IoT deployments. However, due to the high range of coverage, the smart-city WMNs may face different network challenges according to the network characteristic, for example, (i) areas that include a significant number of wireless nodes or (ii) areas with frequent dynamic changes such as link failures due to unstable topologies. Named-Data Networking (NDN) can enhance WMNs to meet such IoT requirements, thanks to the content naming scheme and in-network caching, but it necessitates adaptability to the challenging conditions of WMNs. In this work, we aim at efficient end-to-end NDN communication in terms of performance (i.e., delay), performing extended experimentation over a real WMN, evaluating and discussing the benefits provided by two SDN-based NDN strategies: (1) a dynamic SDN-based solution that integrates the NDN operation with the routing decisions of a WMN routing protocol; (2) a static one which based on SDN-based clustering and real WMN performance measurements. Our key contributions include (i) the implementation of two types of NDN path selection strategies; (ii) experimentation and data collection over the w-iLab.t Fed4FIRE+ testbed with real WMN conditions; (ii) real measurements released as open-data, related to the performance of the wireless links in terms of RSSI, delay, and packet loss among the wireless nodes of the corresponding testbed.

TinyML for Ultra-Low Power AI and Large Scale IoT Deployments: A Systematic Review

The rapid emergence of low-power embedded devices and modern machine learning (ML) algorithms has created a new Internet of Things (IoT) era where lightweight ML frameworks such as TinyML have created new opportunities for ML algorithms running within edge devices. In particular, the TinyML framework in such devices aims to deliver reduced latency, efficient bandwidth consumption, improved data security, increased privacy, lower costs and overall network cost reduction in cloud environments. Its ability to enable IoT devices to work effectively without constant connectivity to cloud services, while nevertheless providing accurate ML services, offers a viable alternative for IoT applications seeking cost-effective solutions. TinyML intends to deliver on-premises analytics that bring significant value to IoT services, particularly in environments with limited connection. This review article defines TinyML, presents an overview of its benefits and uses and provides background information based on up-to-date literature. Then, we demonstrate the TensorFlow Lite framework which supports TinyML along with analytical steps for an ML model creation. In addition, we explore the integration of TinyML with network technologies such as 5G and LPWAN. Ultimately, we anticipate that this analysis will serve as an informational pillar for the IoT/Cloud research community and pave the way for future studies.

Field Note on IoT Security: Novel JIT Security for Large-Scale Heterogeneous IoT Deployments

This article provides an overview of specific security considerations for multi-modal Internet-of-Things(IoT) use-case deployment. With the year-over-year exponential increase in smartdevice deployments, threat vectors continue to fall into a concise list of categories, all of which can be addressed with classic solution architectures. To provide increased business value from technology deployments, we look at applying novel additions to common deployment architectures to achieve high return-on-investment (ROI) on our deployments, while managing the security risk associated with heterogeneous device deployments.

An intelligent edge-enabled distributed multi-task learning architecture for large-scale IoT-based cyber–physical systems

Internet-of-Things (IoT)-based cyber–physical systems are increasingly being adopted because of the recent technological advancements in sensor technology, edge computing, machine learning, and big data. Integrating machine learning into designing IoT-based cyber–physical systems is essential. However, it is considered a challenging problem. This stems from the fact that IoT devices generate extensive data that requires extensive processing to achieve adequate learning. Relying on local learning by each IoT device is not feasible in most cases due to its limited resources. On the contrary, relying on all cloud-based learning requires transmitting a large amount of data to the cloud to perform the learning process, which is inefficient in large-scale IoT deployments. Therefore, this paper proposes a novel edge-computing architecture that employs the concept of distributed multi-task learning over EC networks in large-scale IoT-based cyber–physical systems. The architecture develops multiple distributed learning algorithms, a data placement architecture, task allocation algorithms, and a network protocol. In addition, it considers the problem of learning model parameters from IoT data distributed over different edge nodes in a large geographical area without sending raw data to the cloud. The architecture supports several distributed machine models that are trained using a combination of machine learning algorithms and population-based search algorithms to optimize the learning process. Population-based search algorithms allow for maintaining a set of candidate solutions, with each solution corresponding to a unique point in the search space for an optimal solution. Having the dataset distributed over several edge nodes, with each node having its own unique set of candidate solutions, increases the chance of finding a solution that generalizes well for the overall dataset combined. Simulation experiments with real IoT datasets are conducted to evaluate the accuracy of the proposed learning models. Results show the ability to achieve high-accuracy results that are close to single-machine models but with significantly efficient edge computing resource utilization.

Multi-radio and multi-hop LoRa communication architecture for large scale IoT deployment

Internet of Things (IoT) devices must cover large areas in the farmlands to collect vital information and avoid financial losses to improve agriculture efficiency. It is crucial to hold data acquisition in farms where network connectivity is a limitation in this context. Long-Range Wide Area Network (LoRaWAN) provides a broad coverage area tailored for IoT applications using unlicensed frequency bands with low power consumption and throughput. However, LoRaWAN might not be suitable for data acquisition on farms with hundreds of kilometers. Therefore, a multi-hop Long-Range (LoRa) network emerged as a promising solution in applications requiring extensive deployment. However, a multi-hop architecture must be designed to cope with the limitations of LoRa deployment in large-scale scenarios. This article introduces a multi-radio and multi-hop LoRa communication architecture to enhance the coverage and service for large-scale IoT deployment in rural areas, called Multi-LoRa. We present a hardware prototype for physically implementing the Multi-LoRa architecture. The results show that Multi-LoRa effectively mitigates the difficulties of multi-hop communication over LoRa for large-scale IoT deployment. Multi-LoRa reduced the delay by 60% and packet loss by 2.9% compared to different setups of Multi-LoRa on a small-scale physical testbed and large-scale simulation environment.

Decentralized Access Control using Blockchain Technology for Application in Smart Farming

The application of the Internet of Things (IoT) plays a crucial role in the fourth industrial revolution. The sophistication of technology due to the integration of heterogenous smart devices open a new threat from various aspects. Access control is the first line of defence to ensure that IoT resources are secure by preventing illegitimate users from gaining access to these resources. However, access control mechanisms face the limitation of technology in large scale IoT deployments since they are based on a centralized architecture. Significant research concerning decentralized access control solutions for securing IoT resources using combined techniques, such as blockchain, have caught much research attention in recent years. Nevertheless, research for decentralized access control for application in smart farming domain remain as a gap. Thus, this study presented a structured literature review on 81 articles related to the field of access control in IoT and blockchain technology to understand the challenges of centralized access control in securing IoT resources. This study serves as a foundation for decentralized access control using blockchain technology and its application to ensure the IoT actuators and sensors security with the aim to be applied in smart farming. This paper was deliberated based on systematic literature review that was searched from four different database platforms between 2018 and 2021. This study mostly addresses the relevant techniques/approaches including blockchain technology, access control model, key management mechanism and the combination of all three methods. The possible impacts, gap, procedures and evaluation of the decentralized access control are highlighted along with major trends and challenges.

Relay Placement Algorithms for IoT Connectivity and Coverage in an Outdoor Heterogeneous Propagation Environment

A vast majority of the Internet of Things (IoT) devices will be connected in a topology where the edge-devices push data to a local gateway, which forwards the data to a cloud for further processing. In sizeable outdoor deployment regions, the edge-devices may experience poor connectivity due to their distant locations and limited transmission power. Repeaters or relays must be placed at a few locations to ensure reliable connectivity to either a gateway or another node in the network. A big challenge in achieving reliable connectivity and coverage is the outdoor propagation environment being heterogeneous. Engineers often deploy networks based on resource-intensive field visits, detailed surveys, measurements, initial test deployments, followed by fine-tuning. For scalability to large scale IoT deployments, automated network planning tools are essential. Such tools should predict connectivity based on the edge-device locations using available Geographical Information System (GIS) data, identify the need for relays/repeaters, and, if needed, suggest the number of relays needed with their locations. Furthermore, such tools should also be extended to suggest the minimum number and locations of base stations that maximise coverage. In this paper, we propose an automated network deployment framework using a black box received signal strength estimation oracle that provides signal strength estimates between candidate pairs of transceiver locations in a heterogeneous deployment region. Our proposed methodology uses either Ant Colony Optimisation (ACO) or Differential Evolution (DE) to identify the number and locations of relays for meeting specified quality of service constraints. We discuss adaptations of our techniques to handle scenarios with multiple gateways. Further, we show the effectiveness of these algorithms to find suitable candidate base station locations to provide coverage in a heterogeneous propagation environment that meets the specified quality of service constraints. We then demonstrate the effectiveness of our algorithms in two deployment regions.

Entitlement-Based Access Control for Smart Cities Using Blockchain.

Smart cities use the Internet of Things (IoT) devices such as connected sensors, lights, and meters to collect and analyze data to improve infrastructure, public utilities, and services. However, the true potential of smart cities cannot be leveraged without addressing many security concerns. In particular, there is a significant challenge for provisioning a reliable access control solution to share IoT data among various users across organizations. We present a novel entitlement-based blockchain-enabled access control architecture that can be used for smart cities (and for any ap-plication domains that require large-scale IoT deployments). Our proposed entitlement-based access control model is flexible as it facilitates a resource owner to safely delegate access rights to any entities beyond the trust boundary of an organization. The detailed design and implementation on Ethereum blockchain along with a qualitative evaluation of the security and access control aspects of the proposed scheme are presented in the paper. The experimental results from private Ethereum test networks demonstrate that our proposal can be easily implemented with low latency. This validates that our proposal is applicable to use in the real world IoT environments.

VIoLET

Internet of Things (IoT) deployments have been growing manifold, encompassing sensors, networks, edge, fog, and cloud resources. Despite the intense interest from researchers and practitioners, most do not have access to large-scale IoT testbeds for validation. Simulation environments that allow analytical modeling are a poor substitute for evaluating software platforms or application workloads in realistic computing environments. Here, we propose a virtual environment for validating Internet of Things at large scales (VIoLET), an emulator for defining and launching large-scale IoT deployments within cloud VMs. It allows users to declaratively specify container-based compute resources that match the performance of native IoT compute devices using Docker. These can be inter-connected by complex topologies on which bandwidth and latency rules are enforced. Users can configure synthetic sensors for data generation as well. We also incorporate models for CPU resource dynamism, and for failure and recovery of the underlying devices. We offer a detailed comparison of VIoLET’s compute and network performance between the virtual and physical deployments, evaluate its scaling with deployments with up to 1, 000 devices and 4, 000 device-cores, and validate its ability to model resource dynamism. Our extensive experiments show that the performance of the virtual IoT environment accurately matches the expected behavior, with deviations levels within what is seen in actual physical devices. It also scales to 1, 000s of devices and at a modest cloud computing costs of under 0.15% of the actual hardware cost, per hour of use, with minimal management effort. This IoT emulation environment fills an essential gap between IoT simulators and real deployments.

Analysis of the interplay between RPL and the congestion control strategies for CoAP

The Constrained Application Protocol (CoAP) is gaining attention as a standardised RESTful interface for the Internet of Things (IoT). Recent studies have focused on different congestion control strategies for CoAP, in order to ensure proper operation of large-scale IoT deployments. In this paper, we carry out a performance evaluation of different congestion control policies for CoAP in a realistic environment by exploiting WiSHFUL, a platform for large-scale experimentation of network architectures. Our goal is to analyse different congestion control policies and their interplay with the routing protocol in a real environment, where unstable links and route fluctuations are frequent, due to channel variability. The results of our experiments highlight that the dynamics of the routing protocol have a noticeable impact and can impair significantly the performance of the congestion control algorithm. Specifically, the influence of the routing protocol depends on the specific congestion control policy adopted: an aggressive policy that re-transmits messages more frequently, e.g. CoCoA, is more penalised than others, in terms of throughput.

Guest Editorial Special Issue on Edge-Cloud Interplay Based on SDN and NFV for Next-Generation IoT Applications

With significant and continuing advances in information and communication technologies, the Internet of Things (IoT) will play an increasingly important role in domains, such as healthcare, transportation, finance, and energy. In an IoT system, billions of devices (e.g., sensors, wearables, and smart appliances) are connected to the global network infrastructure, and one associated phenomenon is the generation of a large volume of data. Apart from data volume, the velocity, variety, and veracity of these data will pose a significant burden on conventional networking infrastructures. However, as sensor and fifth-generation (5G) cellular technologies advance, so will the pervasiveness of IoT deployment. Parallel to this trend, cloud computing has been integrated with IoT in order to address limitations in existing IoT networks (e.g., storage and computing resources), and examples include Google cloud dataflow and Amazon IoT. However, cloud-centric IoT solutions may not be suited for delay-sensitive and computationally intensive applications, for example, due to resource availability, end-to-end latency, bandwidth, etc. Increasingly, large-scale IoT deployments demand high connectivity, interoperability, and orchestration which are necessary for minimizing latency and maximizing throughput. This highlights the importance of a distributed computing platform that can support the interactions between IoT and cloud computing systems.

Enabling Optimization-Based Localization for IoT Devices

In this paper, we propose an embedded optimization approach for the localization of Internet of Things (IoT) devices making use of range measurements from ultra-wideband (UWB) signals. Low-cost, low-power UWB radios provide time-of-arrival measurements with decimeter accuracy over large distances. UWB-based localization methods have been envisioned to enable feedback control in IoT applications, particularly, in GPS-denied environments, and large wireless sensor networks. In this paper, we formulate the localization task as a nonlinear least-squares optimization problem based on two-way time-of-arrival measurements between the IoT device and several UWB radios installed in a 3-D environment. For the practical implementation of large-scale IoT deployments we further assume only approximate knowledge of the UWB radio locations. We solve the resulting optimization problem directly on IoT devices equipped with off-the-shelf microcontrollers using state-of-the-art code generation techniques for plug-and-play deployment of the nonlinear-programming algorithms. This paper further provides practical implementation details to improve the localization accuracy for feedback control in experimental IoT applications. The experimental results finally show that subdecimeter localization accuracy can be achieved using the proposed optimization-based approach, even when the majority of the UWB radio locations are unknown.

Performance evaluation of FIWARE: A cloud-based IoT platform for smart cities

As the Internet of Things (IoT) becomes a reality, millions of devices will be connected to IoT platforms in smart cities. These devices will cater to several areas within a smart city such as healthcare, logistics, and transportation. These devices are expected to generate significant amounts of data requests at high data rates, therefore, necessitating the performance benchmarking of IoT platforms to ascertain whether they can efficiently handle such devices. In this article, we present our results gathered from extensive performance evaluation of the cloud-based IoT platform, FIWARE. In particular, to study FIWARE’s performance, we developed a testbed and generated CoAP and MQTT data to emulate large-scale IoT deployments, crucial for future smart cities. We performed extensive tests and studied FIWARE’s performance regarding vertical and horizontal scalability. We present bottlenecks and limitations regarding FIWARE components and their cloud deployment. Finally, we discuss cost-efficient FIWARE deployment strategies that can be extremely beneficial to stakeholders aiming to deploy FIWARE as an IoT platform for smart cities.

Modeling of Aggregated IoT Traffic and Its Application to an IoT Cloud

As the Internet of Things (IoT) continues to gain traction in telecommunication networks, a very large number of devices are expected to be connected and used in the near future. In order to appropriately plan and dimension the network, as well as the back-end cloud systems and the resulting signaling load, traffic models are employed. These models are designed to accurately capture and predict the properties of IoT traffic in a concise manner. To achieve this, Poisson process approximations, based on the Palm–Khintchine theorem, have often been used in the past. Due to the scale (and the difference in scales in various IoT networks) of the modeled systems, the fidelity of this approximation is crucial, as, in practice, it is very challenging to accurately measure or simulate large-scale IoT deployments. The main goal of this paper is to understand the level of accuracy of the Poisson approximation model. To this end, we first survey both common IoT network properties and network scales as well as traffic types. Second, we explain and discuss the Palm–Khintiche theorem, how it is applied to the problem, and which inaccuracies can occur when using it. Based on this, we derive guidelines as to when a Poisson process can be assumed for aggregated periodic IoT traffic. Finally, we evaluate our approach in the context of an IoT cloud scaler use case.

Deep learning and software-defined networks: Towards secure IoT architecture

Internet of Things (IoT) introduces new challenges to conventional communication model. IoT networks characteristics, such as objects heterogeneity and scalability, require revolutionary solutions. Currently, there is no universal architecture for IoT. However, several architectures were proposed based on Software Defined Networks (SDN). SDN introduces network programmability, and centralisation, these features facilitate network abstractions, simplifying network management and eases evolution. In this paper, we investigate SDN as a novel communication architecture for IoT networking to enhance the security and resilience of IoT. SDN enhances network resilience and scalability which are essential in large-scale IoT deployments, e.g., smart cities. However, security is a significant concern for IoT while SDN deepens these concerns. SDN itself presents new security threats; specifically, threats related to the controller.We propose a secure, framework for IoT based on SDN. The framework is generalization for the integration of SDN and IoT. We focus on massive IoT deployment, for instance, smart cities applications, where, security is critical, and network traffic is enormous. The study investigates the SDN architecture from a security perspective. Improving SDN security will boost the deployment of SDN-based IoT architecture.We deploy an Intrusion detection system based on Deep Learning (DL). The detection module uses Restricted Boltzmann Machines (RBM). The precision rate shows significant improvements over standard ML, e.g. SVM and PCA.

Risk-based automated assessment and testing for the cybersecurity certification and labelling of IoT devices

Nowadays, security aspects represent one of the most significant barriers for the adoption of large-scale Internet of Things (IoT) deployments. In this sense, being able to certify and communicate the security level of a certain device is crucial for their acceptance. Towards this end, we propose a security certification methodology designed for IoT to empower different stakeholders with the ability to assess security solutions for large-scale IoT deployments in an automated way. It also supports transparency on the IoT security level to the consumers because the methodology provides a label as one of the main results of the certification process. The certification approach represents an instantiation of the Risk-based Security Assessment and Testing methodologies presented by ETSI based on the ISO 31000 and ISO 29119, and it is built on top of different technologies and approaches for security testing and risk assessment adapted to the IoT landscape. As a proof of concept, the proposed methodology is applied to one of the scenarios proposed in the scope of the Horizon 2020 ARMOUR project for assessing the fulfillment of several security properties of IoT devices.