IoT Ecosystem Research Articles

Network Slicing for IoT Ecosystem in 5G Networks

Network Slicing for IoT Ecosystem in 5G Networks

ASSIST-IoT: A Modular Implementation of a Reference Architecture for the Next Generation Internet of Things

Next Generation Internet of Things (NGIoT) addresses the deployment of complex, novel IoT ecosystems. These ecosystems are related to different technologies and initiatives, such as 5G/6G, AI, cybersecurity, and data science. The interaction with these disciplines requires addressing complex challenges related with the implementation of flexible solutions that mix heterogeneous software and hardware, while providing high levels of customisability and manageability, creating the need for a blueprint reference architecture (RA) independent of particular existing vertical markets (e.g., energy, automotive, or smart cities). Different initiatives have partially dealt with the requirements of the architecture. However, the first complete, consolidated NGIoT RA, covering the hardware and software building blocks, and needed for the advent of NGIoT, has been designed in the ASSIST-IoT project. The ASSIST-IoT RA delivers a layered and modular design that divides the edge-cloud continuum into independent functions and cross-cutting capabilities. This contribution discusses practical aspects of implementation of the proposed architecture within the context of real-world applications. In particular, it is shown how use of cloud-native concepts (microservices and applications, containerisation, and orchestration) applied to the edge-cloud continuum IoT systems results in bringing the ASSIST-IoT concepts to reality. The description of how the design elements can be implemented in practice is presented in the context of an ecosystem, where independent software packages are deployed and run at the selected points in the hardware environment. Both implementation aspects and functionality of selected groups of virtual artefacts (micro-applications called enablers) are described, along with the hardware and software contexts in which they run.

Interoperability in IoT Ecosystems

Interoperability in IoT Ecosystems

A Holistic Framework for AI-Driven Cyber Risk Management in Iot Ecosystems

A Holistic Framework for AI-Driven Cyber Risk Management in Iot Ecosystems

DABAC: Smart Contract-Based Spatio-Temporal Domain Access Control for the Internet of Things

With the advent of IoT technology, the dynamic nature of IoT devices has introduced new obstacles to access control. It is essential to consider the security requirements of the actual physical environment, rendering the traditional access control approach centered on the information space. In the IoT ecosystem, there are several issues such as the dynamics of devices frequently entering and leaving, the lack of computing and storage capacity, and distributed deployment. To address these challenges, this paper proposes the Domain Attribute Based Access Control(DABAC) that incorporates domain elements to implement the physical location limitation of dynamic devices. Moreover, an intelligent gateway is utilized to divide the physical area and act as a proxy to achieve regional device management, automatic networking of devices in the domain, and the dynamic expansion of the sensor network resulting from device entry or exit. Then, given the distributed deployment of devices, smart contracts are employed to deploy access control mechanisms and construct a trusted environment to mitigate threats such as single points of failure. Finally, the DABAC is implemented on the Ethereum platform, simulating a smart medical situation. The experimental results demonstrate that the proposed solution effectively addresses the problem of access control of device dynamics in an untrusted IoT environment while maintaining system security.

Towards IoT-Big Data architecture for future education

New technology, through the IoT, Big Data and AI ecosystem, is the foundation on which tomorrow's solutions are created. Improved processes with better decision making are the actors that drive the evolution of tomorrow's services. This new technology has enabled innovations in several sectors including medical, manufacturing, automation, energy, education, and others. It should be noted that in recent years, Industry 4.0 and the COVID-19 sanitary crisis have forced some remarkable transformations and contributions to people's quality of life, safety, health and education. Currently, the education sector in all its levels (primary, secondary and university) re-quires the use of this technology and is moving more specifically towards a system known as "education 4.0" to meet the different demands of this sector. In this paper, we present a literature review of the use of Big Data analytics in the education sector in particular. We discuss the different technologies, architectures, and applications of Big Data analytics in this growing sector, and we propose a new architecture based on the new technology applied in education.

A Sustainable and Efficient IoT Approach for Fire Department Alerting System

The creation of sophisticated fire detection systems is necessary given the rising danger of fire occurrences. The system described in this abstract uses IoT components such an Arduino board, flame sensor, buzzer, SIM800L module, and SIM800L to detect fires in real time and inform users. The flame sensor is the main element of the system, and it uses infrared radiation to identify flames when they are present. The Arduino board receives the signal from the sensor and activates the alarm system when it detects a flame. A loud audible alarm is produced by the buzzer when it is linked to the Arduino board to alert residents to a potential fire threat. The system includes the SIM800L module to support distant notifications in addition to local alerts. Once a fire has been identified, the Arduino board and SIM800L module connect to send an SMS or place a call to preset emergency contacts. This makes it possible to quickly take the necessary action, even if the affected properties are unattended. The IoT device connection with the Arduino board gives the fire detection system flexibility and scalability making it reliable and sustainable. By adding more sensors or integrating with current home automation systems, it is simple to customize and expand. Real-time fire detection, remote alerting capabilities, and IoT ecosystem integration are just a few of the advantages the suggested fire detection system has over conventional fire alarm systems.

АРХІТЕКТУРА ІоТ-ЕКОСИСТЕМИ РЕАБІЛІТАЦІЙНОГО ЦЕНТРУ ВІДНОВЛЕННЯ ОПОРНО-РУХОВОГО АПАРАТУ

Background. A study of the use of IoT technologies in modern medicine, primarily in Ukraine, was conducted. Based on the analogs of the existing systems considered, taking into account the entire complex situation, the number of people in need of rehabilitation is only increasing, and there are simply not enough centers for such assistance. Rehabilitation doctors spend a lot of their time, while not everyone can get the necessary help due to the lack of specialists. As a result, there is a need to create your own IoT system to automate and optimize treatment processes. Methods. Methods of analysis, synthesis and complex systems design are employed in the research. Results. The architecture of the IoT ecosystem of the rehabilitation center for recovery after injuries, traumas, assaults and operations was developed. The project architecture consists of simulators with sensors and a Wi-Fi module for data transmission to the cloud with a database and cloud computing. The end user, i. e. the doctor, can access this data through the app, process it, monitor performance and enter a training plan for each patient. It should be noted that each patient will be issued a card with an RFI tag or an IoT beacon. The card identifies the patient on each simulator. The simulator is adjusted according to the indicators. The indicators of a specific person’s condition are transferred to a database for tracking by a doctor. Сonclusions. Implementation of the proposed solutions is expected to allow one to substantially increase the quality of medical treatment provision to the population in Ukraine.

Artificial Intelligence and Facial Recognition in an IoT Ecosystem

This article does not deepen the insight on technical aspects related to artificial intelligence and facial recognition, but it highlights how relevant privacy aspects are also inside an IoT ecosystem. Furthermore, the author points out the risks of some resources found online, exposing face images, in the compliance with GDPR and privacy rules process. Indeed, adopting artificial intelligence to realize human face images of people who do not exist but realized starting from real persons can have consequences on data protection rules and the possible misuse by everyone who can retrieve them. Artificial intelligence might foster synthetic identity, or, better, artificial intelligence synthetic identity cases, building human profiles using particulars belonging to different persons. The author proposes an approach based on the innovative data protection and privacy relationships model (DAPPREMO) to address any activity or process related to data protection and privacy due to having a broader view of any investigation's subject.

A Review of Emerging Technologies for IoT-Based Smart Cities

Smart cities can be complemented by fusing various components and incorporating recent emerging technologies. IoT communications are crucial to smart city operations, which are designed to support the concept of a "Smart City" by utilising the most cutting-edge communication technologies to enhance city administration and resident services. Smart cities have been outfitted with numerous IoT-based gadgets; the Internet of Things is a modular method to integrate various sensors with all ICT technologies. This paper provides an overview of smart cities' concepts, characteristics, and applications. We thoroughly investigate smart city applications, challenges, and possibilities with solutions in recent technological trends and perspectives, such as machine learning and blockchain. We discuss cloud and fog IoT ecosystems in the in capacity of IoT devices, architectures, and machine learning approaches. In addition we integrate security and privacy aspects, including blockchain applications, towards more trustworthy and resilient smart cities. We also highlight the concepts, characteristics, and applications of smart cities and provide a conceptual model of the smart city mega-events framework. Finally, we outline the impact of recent emerging technologies' implications on challenges, applications, and solutions for futuristic smart cities.

Towards achieving lightweight intrusion detection systems in Internet of Things, the role of incremental machine learning: A systematic literature review

While the benefits of IoT cannot be overstated, its computational constraints make it challenging to deploy security methodologies that have been deployed in traditional computing systems. The benefits and computational constraints have made IoT systems attractive to cyber-attacks. One way to mitigate these attacks is to detect them. In this study, a Systematic Literature Review (SLR) has been conducted to analyze the role of incremental machine learning in achieving lightweight intrusion detection for IoT systems. The study analyzed existing incremental machine learning approaches used in designing intrusion detection systems for IoT ecosystems, emphasizing the incremental methods used in detecting intrusions, the datasets used to evaluate these methods, and how the method achieves lightweight status. The SLR outlined the contributions of each study, focusing on their strengths and gaps, the datasets used, and the incremental machine learning model used. This study revealed that incremental learning approaches in detecting intrusion in IoT systems are in their infant stage. Over 12 years, from 2010 to 2022, a total of twenty-one (21) studies were carried out in IDSs using incremental machine learning, with eight (8) studies carried out in IoT systems. In addition to reviewing the literature, we offer suggestions for improving existing solutions and achieving lightweight IDS for IoT systems. We also discussed some problems with making lightweight IDS for IoT systems and areas where more research could be done in the future.

Using Mobile Monitoring and Atmospheric Dispersion Modeling for Capturing High Spatial Air Pollutant Variability in Cities

Air pollution is still one of the biggest environmental threats to human health on a global scale. In urban environments, exposure to air pollution is largely influenced by the activity patterns of the population as well as by the high spatial and temporal variability in air pollutant concentrations. Over the last years, several studies have attempted to better characterize the spatial variations in air pollutant concentrations within a city by deploying dense, fixed as well as mobile, low-cost sensor networks and more recently opportunistic sampling and by improving the spatial resolution of air quality models up to a few meters. The purpose of this work has been to investigate the use of properly designed mobile monitoring campaigns along the streets of an urban neighborhood to assess the capability of an operational air dispersion model as SIRANE at the district scale to capture the local variability of pollutant concentrations. To this end, an IoT ecosystem—MONICA (an Italian acronym for Cooperative Air Quality Monitoring), developed by ENEA, has been used for mobile measurements of CO and NO2 concentration in the urban area of the City of Portici (Naples, Southern Italy). By comparing the mean concentrations of CO and NO2 pollutants measured by MONICA devices and those simulated by SIRANE along the urban streets, the former appeared to exceed the simulated ones by a factor of 3 and 2 for CO and NO2, respectively. Furthermore, for each pollutant, this factor is higher within the street canyons than in open roads. However, the mobile and simulated mean concentration profiles largely adapt, although the simulated profiles appear smoother than the mobile ones. These results can be explained by the uncertainty in the estimation of vehicle emissions in SIRANE as well as the different temporal resolution of measurements of MONICA able to capture local high concentrations.

Towards SDN-based smart contract solution for IoT access control

Access control is essential for the IoT environment to ensure that only approved and trusted parties are able to configure devices, access sensor information, and command actuators to execute activities. The IoT ecosystem is subject to various access control complications due to the limited latency between IoT devices and the Internet, low energy requirements of IoT devices, the distributed framework, ad-hoc networks, and an exceptionally large number of heterogeneous IoT devices that need to be managed. The motivation for this proposed work is to resolve the incurring challenges of IoT associated with management and access control security. Each IoT domain implementation has particular features and needs separate access control policies to be considered in order to design a secure solution. This research work aims to resolve the intricacy of policies management, forged policies, dissemination, tracking of access control policies, automation, and central management of IoT nodes and provides a trackable and auditable access control policy management system that prevents forged policy dissemination by applying Software Defined Network (SDN) and blockchain technology in an IoT environment. Integration of SDN and blockchain provides a robust solution for IoT environment security. Recently, smart contracts have become one of blockchain technology’s most promising applications. The integration of smart contracts with blockchain technology provides the capability of designing tamper-proof and independently verifiable policies. In this paper, we propose a novel, scalable solution for implementing immutable, verifiable, adaptive, and automated access control policies for IoT devices together with a successful proof of concept that demonstrates the scalability of the proposed solution. The performance of the proposed solution is evaluated in terms of throughput and resource access delay between the blockchain component and the controller as well as from node to node. The number of nodes in the IoT network and the number of resource access requests were independently and systematically increased during the evaluations. The results illustrate that the resource access delay and throughput were affected neither linearly nor exponentially; hence, the proposed solution shows no significant degradation in performance with an increase in the number of nodes and/or requests.

Humanoid robotic system for social interaction using deep imitation learning in a smart city environment

IntroductionA significant resource for understanding the prospects of smart development is the smart city initiatives created by towns all around the globe. Robots have changed from purely human-serving machines to machines communicating with humans through displays, voice, and signals. The humanoid robots are part of a class of sophisticated social robots. Humanoid robots can share and coexist with people and look similar to humans.MethodsThis paper investigates techniques to uncover proposals for explicitly deploying Artificial Intelligence (AI) and robots in a smart city environment. This paper emphasis on providing a humanoid robotic system for social interaction using the Internet of Robotic Things-based Deep Imitation Learning (IoRT-DIL) in a smart city. In the context of the IoT ecosystem of linked intelligent devices and sensors ubiquitously embedded in everyday contexts, the IoRT standard brings together intelligent mobile robots. IoRT-DIL has been used to create a free mobility mode and a social interaction mode for the robot that can detect when people approach it with inquiries. In direct contact with the actuators and sensors, robotic interface control is responsible for guiding the robot as it navigates its environment and answers questions from the audience.Results and discussionFor the robots to function safely, they must be monitored and enforced by a central controller using Internet of Robotic Things (IoRT) technology in an emergency. DIL aims to facilitate robot-human interaction by integrating deep learning architectures based on Neural Networks (NN) and reinforced learning methods. DIL focuses on mimicking human learning or expertise presentation to govern robot behavior. The robot's interaction has been tracked in a smart city setting, and its real-time efficiency using DIL is 95%.

Securing communications between things against wormhole attacks using TOPSIS decision-making and hash-based cryptography techniques in the IoT ecosystem

Securing communications between things against wormhole attacks using TOPSIS decision-making and hash-based cryptography techniques in the IoT ecosystem

The Role of 5G and IoT in Smart Cities

Smart cities, one of the key application areas of 5G and IoT technologies, have at their core the goal of improving quality of life, creating collaborative solutions, and addressing resource constraints. Examples of some of the many services integrated into the network of a typical smart city are related to smart health, smart transportation, smart buildings, smart hotels, smart industry, smart universities, etc. The new era of wireless mobile telecommunication technology and the IoT ecosystem is driving the opportunities for smart cities to not only solve existing problems but also create new services and functionalities that are out of the box and invisible to the end user. The main objective of this paper is to explore the role of 5G and IoT in smart cities. The market environment, key business developments, and trends related to the key smart cities’ enablers, IoT and 5G, are presented. An in-depth literature review followed by a discussion is substantiated with synthesised research findings on the current development, 5G coverage, use and deployment of IoT in smart cities, and forecasting the evolution of its application in smart cities.

Front Cover: High Open‐Circuit Voltage Cs2AgBiBr6 Carbon‐Based Perovskite Solar Cells via Green Processing of Ultrasonic Spray‐Coated Carbon Electrodes from Waste Tire Sources (ChemSusChem 22/2022)

The Front Cover shows the transformation of waste tires into a conductive carbon material that can be employed for the additive-free preparation of hole-extracting electrodes incorporated within lead-free perovskite solar cells (an end-of-waste strategy). These highly sustainable and low-cost photovoltaic devices will pave the way to the powering of the IoT ecosystem within households, when LED illumination is turned on. More information can be found in the Research Article by F. Schmitz, N. Lago, et al.

NOSIoT NFC-based solution

One of the most perplexing discoveries from COVID-19 pandemic was the office building overall energy consumption failing to appreciably drop once staff began working from home. Also, we are witnessing an exponential growing of IoT ecosystem but most of them have been implemented for personal usage rather in an enterprise environment. The big challenge in installing a large-scale IoT ecosystem at enterprise level resides on costs, the selection of proper devices, and the management of these devices and users.NOSIoT NFC-based solution consists of a simple mobile application that will read an NFC tag in a specific workstation. The communication and management are performed through Home Assistant. All the information is stored into a database to calculate the energy consumed by each workstation.The solution provides an innovative approach for enterprise level, delivering a low-cost end-to-end flow with high flexibility, and easy “Tap&Go”. This allows companies to be more climate-friendly and positively contribute for green buildings.

Adversarial machine learning in IoT from an insider point of view

With the rapid progress and significant successes in various applications, machine learning has been considered a crucial component in the Internet of Things ecosystem. However, machine learning models have recently been vulnerable to carefully crafted perturbations, so-called adversarial attacks. A capable insider adversary can subvert the machine learning model at either the training or testing phase, causing them to behave differently. The vulnerability of machine learning to adversarial attacks becomes one of the significant risks. Therefore, there is a need to secure machine learning models enabling the safe adoption in malicious insider cases. This paper reviews and organizes the body of knowledge in adversarial attacks and defense presented in IoT literature from an insider adversary point of view. We proposed a taxonomy of adversarial methods against machine learning models that an insider can exploit. Under the taxonomy, we discuss how these methods can be applied in real-life IoT applications. Finally, we explore defensive methods against adversarial attacks. We believe this can draw a comprehensive overview of the scattered research works to raise awareness of the existing insider threats landscape and encourages others to safeguard machine learning models against insider threats in the IoT ecosystem.

Sustainable Agricultural Supply Chains for Leveraging SDGS

Agricultural crop suffers from perishability constraints. Every region cannot produce all types of required crop and food grains. There is need for cold storage to overcome the issues of perishability and at the same time efficient supply chain is essential to ensure equitable distributions of crops and food grains across the region, State as well as in the country. There lies a huge scope of earning revenues by exporting excess crops, food grains to selected regions or even abroad. Food security has become one of the biggest challenges in the modern era. This can only be solved by formulating an integrated crop planning of land for the entire region coupled with dedicated cold storage and warehousing system. This research study attempts to device a model for introducing mobile cold storage eco-system with the intervention of IoT ecosystem which is fundamentally backed by integrated crop planning of land.