Things Ecosystem Research Articles

Nature-inspired routing and optical communication for next-generation IoT systems

Abstract In this paper, we explore the interconnection of natural processes and current technologies to satisfy the emerging requirements of next-generation Internet of Things (IoT) ecosystems. Motivated by biological networks, we propose a novel routing and optical communication approach that leverages nature’s ability for self-organization, flexibility and resilience. This paper presents a model for bio-inspired routing protocols to be used in Internet of Things networks in order to improve scalability, reduce latency and maximize data transmission efficiency. Moreover, we investigate the potential of optical communication technologies, which are fast, low-latency and energy-efficient, to support the immense data transmission requirements of Internet of Things systems. The research finds key barriers and opportunities for integrating nature-based approaches.

An Enhanced LSTM Approach for Detecting IoT-Based DDoS Attacks Using Honeypot Data

One of the widening perils in network security is the Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) ecosystem. This paper presents an enhanced Intrusion Detection System (IDS) through the proposal of an enhanced version of the long short-term memory (LSTM) model to detect DDoS attacks using honeypot-generated data. The proposed model aggregates the Conv1D, Bidirectional Long Short-Term Memory (Bi-LSTM), Bidirectional Gated Recurrent Unit (Bi-GRU), and dropout layers to extract temporal and spatial features from IoT traffic effectively. We tested the efficacy of the proposed system on a real-world IoT-DH dataset, which showed a remarkable accuracy of 99.41%, with an AUC score of 0.9999. A comparative analysis with other baseline models, such as LSTM, Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), Recurrent Neural Network (RNN), Feedforward Neural Network (FNN), and Temporal Convolutional Network (TCN), proved that enhanced LSTM outperformed the other models. This indicates the robustness of the proposed model in correctly detecting DDoS attacks with high generalization capability for unseen traffic data. The contribution of this paper will be an addition to the deep learning techniques applied for the solution of intrusion detection systems (IDS), which will also allow the building and implementation of more efficient security mechanisms in IoT environments.

A Sustainable Hydrogel‐Based Dye‐Sensitized Solar Cell Coupled to an Integrated Supercapacitor for Direct Indoor Light‐Energy Storage

The rapid growth of the Internet of Things ecosystem has increased the need for sustainable, cost‐effective energy sources for indoor low‐power devices. Indoor photovoltaics offer a solution by harnessing ambient indoor lighting, with dye‐sensitized solar cells (DSSCs) emerging as strong candidates for these applications. When it comes to indoor environments, there is an increased demand for nontoxic and nonflammable solvents for electrolytes. The use of water‐based electrolytes is a promising way to address these issues, while ensuring the eco‐friendliness and sustainability of these devices. Herein, a DSSC system is employed featuring an aqueous gel electrolyte composed of xanthan gum, a biosourced polymer, and an iodide/triiodide redox couple. The performances of the cells are characterized under LED lighting, reaching efficiencies up to 3.5% in indoor conditions, and then integrated with an electric double‐layer capacitor, also based on a xanthan gum gel electrolyte, resulting in a fully aqueous device for indoor light‐energy harvesting and storage with an overall photoelectric conversion and storage efficiency of 1.45%.

A 5G LAN Testbed for Hybrid Industrial IoT Scenarios

ABSTRACTIn this paper, we describe the conception and implementation of a high‐fidelity 5G testbed for 5G Local Area Networks (LANs). This testbed comprises a combination of real, emulated and virtualized components that enable R&D activities such as exploring use cases, integrating components, and infrastructure management. This environment has undergone an experimental assessment of its functional characteristics and performance, using an Industrial Internet of Things (IoT) ecosystem over 5G.

Optimized Deep Neuro-Fuzzy Networks for Enhanced Cyber Forensic Analysis in Iot-Driven Big Data Ecosystems

The rapid expansion of Internet of Things (IoT) ecosystems has significantly increased the complexity and scale of cyber forensic investigations, necessitating innovative and efficient analytical methods. This study proposes an Optimized Deep Neuro-Fuzzy Network (DNFN) framework designed to enhance forensic capabilities in IoT-driven big data environments. The framework synergizes the feature extraction strengths of deep learning with the interpretative advantages of fuzzy logic, while employing optimization algorithms to fine-tune its parameters for improved performance. The DNFN framework addresses key challenges such as data heterogeneity, dynamic threat landscapes, and resource constraints in IoT ecosystems. Extensive experimentation on benchmark datasets demonstrates the framework's ability to achieve high accuracy in detecting cyber anomalies, reduce false positive rates, and support comprehensive forensic analysis. The proposed approach is a significant step toward enabling scalable, interpretable, and robust cyber forensic solutions tailored for the evolving complexities of IoT-driven networks.

Monitoring Technologies in Industrial Systems

Monitoring technologies, which are at the heart of the industrial Internet of Things ecosystem, promise significant transactional efficiencies by making it easier to track product performance and contract compliance. These efficiencies are particularly compelling in industrial multivendor, multicomponent systems, where complex component interdependencies often cause disputes around liability in the case of product failures. Drawing on transaction cost theory, fieldwork, and a national survey of industrial original equipment manufacturers (OEMs), we estimate how product performance contracts are specified in these contexts, and how these monitoring technologies can impact ex post exchanges between OEMs and their suppliers. We find that systems architecture associated with multicomponent systems, as well as the presumed efficiencies of monitoring technologies, drive the contract designs through their potential impact on the disputes, monitoring, and contract writing costs faced by the OEM. However, we also find there are clear limits to the benefits offered by these monitoring technologies, as the greater monitoring facilitated by these technologies appears to exacerbate disputes. This counterintuitive finding comports with the view that when unforeseen interdependent failures occur, detailed data from monitoring may trigger a spate of disputes over new and unexpected information, as well as over the appropriateness of existing protocols for failure metrics and remedies.

Policy-Based Smart Contracts Management for IoT Privacy Preservation

This paper addresses the challenge of preserving user privacy within the Internet of Things (IoT) ecosystem using blockchain technology. Several approaches consider using blockchain and encryption to enhance the privacy of IoT applications and constrained IoT devices. However, existing blockchain platforms such as Ethereum and Hyperledger Fabric already use encryption to store data blocks and secure communication. Therefore, introducing an additional cryptographic layer on top of these platforms could potentially increase processing overhead and reduce response time. In this work, we investigate the integration of IoT and blockchain for privacy preservation. More specifically, we propose a new model that leverages the properties of private blockchain and smart contracts to ensure user data privacy when shared with others. We define policy-based algorithms and notations to assist users in managing smart contracts responsible for registering and controlling their IoT devices. We also specify multiple smart contracts designed to enhance privacy by creating a private channel for communication between the user and the blockchain network.

Integrating Connected Vehicles into IoT Ecosystems: A Comparative Study of Low-Power, Long-Range Communication Technologies.

Integrating road vehicles into broader Internet of Things (IoT) ecosystems is an important step in the development of fully connected and smart transportation systems. This research explores the potential of using communication technologies that achieve a balance between low-power and long-range (LPLR) capabilities while remaining cost-effective, specifically Bluetooth Classic BR-EDR, Bluetooth LE, ZigBee, nRF24, and LoRa-for Vehicle-to-Infrastructure (V2I) and Vehicle-to-IoT (V2IoT) ecosystem interactions. During this research, several field tests were conducted employing different types of communication modules, across three distinct environments: an open-field inter-urban road, a forest inter-urban road, and an urban road. The modules were evaluated based on the communication range, messaging rate, error rate, and geographical data from GNSS (Global Navigation Satellite System) coordinates, using point-to-point communication between a roadside unit (RSU) and a moving vehicle equipped with an onboard unit (OBU). The results demonstrate the usability of these technologies for integrating vehicles into both public infrastructure (for V2I services) and private IoT systems, highlighting their potential for scalable, cost-effective deployment in smart transportation systems.

Correction to “[Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems

Correction to “[Securing smart cities through machine learning: A honeypot‐driven approach to attack detection in Internet of Things ecosystems

Hybridization of Learning Techniques and Quantum Mechanism for IIoT Security: Applications, Challenges, and Prospects

This article describes our point of view regarding the security capabilities of classical learning algorithms (CLAs) and quantum mechanisms (QM) in the industrial Internet of Things (IIoT) ecosystem. The heterogeneity of the IIoT ecosystem and the inevitability of the security paradigm necessitate a systematic review of the contributions of the research community toward IIoT security (IIoTsec). Thus, we obtained relevant contributions from five digital repositories between the period of 2015 and 2024 inclusively, in line with the established systematic literature review procedure. In the main part, we analyze a variety of security loopholes in the IIoT and categorize them into two categories—architectural design and multifaceted connectivity. Then, we discuss security-deploying technologies, CLAs, blockchain, and QM, owing to their contributions to IIoTsec and the security challenges of the main loopholes. We also describe how quantum-inclined attacks are computationally challenging to CLAs, for which QM is very promising. In addition, we present available IIoT-centric datasets and encourage researchers in the IIoT niche to validate the models using the industrial-featured datasets for better accuracy, prediction, and decision-making. In addition, we show how hybrid quantum-classical learning could leverage optimal IIoTsec when deployed. We conclude with the possible limitations, challenges, and prospects of the deployment.

Edge Computing and Cloud Computing for Internet of Things: A Review

The rapid expansion of the Internet of Things ecosystem has created an urgent need for efficient data processing and analysis technologies. This review aims to systematically examine and compare edge computing, cloud computing, and hybrid architectures, focusing on their applications within IoT environments. The methodology involved a comprehensive search and analysis of peer-reviewed journals, conference proceedings, and industry reports, highlighting recent advancements in computing technologies for IoT. Key findings reveal that edge computing excels in reducing latency and enhancing data privacy through localized processing, while cloud computing offers superior scalability and flexibility. Hybrid approaches, such as fog and mist computing, present a promising solution by combining the strengths of both edge and cloud systems. These hybrid models optimize bandwidth use and support low-latency, privacy-sensitive applications in IoT ecosystems. Hybrid architectures are identified as particularly effective for scenarios requiring efficient bandwidth management and low-latency processing. These models represent a significant step forward in addressing the limitations of both edge and cloud computing for IoT, offering a balanced approach to data analysis and resource management.

Enhancing internet of things security and efficiency through advanced elliptic curve cryptography-based strategies in fog computing

<p>Fog computing (FC) has evolved as a significant paradigm within the internet of things (IoT) ecosystem, serving as a crucial link between edge devices and centralised cloud computing resources. This research paper investigates advanced methodologies for improving the security and efficiency of FC in the IoT domain. The primary emphasis is placed on the utilisation of elliptic curve cryptography (ECC) to accomplish these goals. This study examines the difficulties encountered in ensuring the security of IoT deployments based on FC. It also presents novel solutions based on ECC to mitigate these obstacles. Moreover, this study investigates techniques for enhancing the efficiency and allocation of resources in IoT applications within a FC environment. This study seeks to offer significant insights into the application of ECC-based techniques for enhancing the security and efficiency of FC in the context of the IoTs. These insights are derived through a combination of theoretical analysis and practical implementations. To evaluate the effectiveness of the proposed system, an analysis is conducted to examine the encryption time, decryption time, and correlation coefficients. These metrics are then compared to those of existing state-of-the-art approaches.</p>

Framework towards critical event classification of bipolar disorder in internet of things ecosystem

<p><span lang="EN-US">Bipolar disorder is quite a challenging mental illness which encounters substantial degree of challenges in confirmed diagnosis irrespective of modernized increasing pace of development in medical science. With the evolving standards of automation in healthcare section integrated with advanced technology, it is imperative to anticipate a reliable on-line diagnosis of mental illness for a given scenario of internet of things (IoT). Review of existing methodologies showcases a wide gap between enormous research work towards identification of bipolar disorder and only few studies towards on-line diagnosis considering patients residing in smart city. Therefore, the proposed scheme introduces a novel computational framework of an underlying architecture of an IoT that not only facilities an effective and simplified transmission of multimodal data autonomously from the patient forwarded to clinical analytical unit but also perform a multitier classification using deep neural network. The study outcome exhibits proposed scheme to offer better data transmission with higher accuracy performance in contrast to existing prevalent schemes.</span></p>

Advanced Encryption Techniques in Healthcare IoT: Securing Patient Data in Connected Medical Devices

As a result of the fast development of Internet of Things (IoT) technology, the healthcare sector has undergone a transformation. This transformation has been brought about by the deployment of linked medical devices that provide immediate monitoring and data collecting. Despite the fact that these innovations promise to bring about considerable gains in patient care and operational efficiency, they also bring about major security issues, especially with regard to the protection of personally identifiable information about patients. Within the context of the Internet of Things (IoT) ecosystem for healthcare, this study investigates sophisticated encryption approaches that are aimed to protect patient data stored in linked medical equipment. One of the first things that the research does is investigate the specific security needs and risks that are linked with Internet of Things (IoT) devices in the healthcare industry. These include concerns around data privacy, integrity, and authentication. Advanced Encryption Standard (AES), Elliptic Curve Cryptography (ECC), and Quantum Key Distribution (QKD) are some of the encryption standards and protocols that are often used in the process of protecting Internet of Things (IoT) connections. This article presents a complete examination of these encryption standards and protocols. Following that, it digs into sophisticated encryption approaches that are especially targeted to the limits and needs of healthcare IoT systems. These techniques include lightweight cryptographic algorithms, key management strategies, and secure multi-party computing (MPC) frameworks.

A review of data analytic algorithms for outlier detection on the internet of things ecosystem

A review of data analytic algorithms for outlier detection on the internet of things ecosystem

Exploring the fusion of lattice‐based quantum key distribution for secure Internet of Things communications

AbstractThe integration of lattice‐based cryptography principles with Quantum Key Distribution (QKD) protocols is explored to enhance security in the context of Internet of Things (IoT) ecosystems. With the advent of quantum computing, traditional cryptographic methods are increasingly susceptible to attacks, necessitating the development of quantum‐resistant approaches. By leveraging the inherent resilience of lattice‐based cryptography, a synergistic fusion with QKD is proposed to establish secure and robust communication channels among IoT devices. Through comprehensive Qiskit simulations and theoretical analysis, the feasibility, security guarantees, and performance implications of this novel hybrid approach are thoroughly investigated. The findings not only demonstrate the efficacy of lattice‐based QKD in mitigating quantum threats, but also highlight its potential to fortify IoT communications against emerging security challenges. Moreover, the authors provide valuable insights into the practical implementation considerations and scalability aspects of this fusion approach. This research contributes to advancing the understanding of quantum‐resistant cryptography for IoT applications and paves the way for further exploration and development in this critical domain.

QS-Trust: An IoT ecosystem security model incorporating quality of service and social factors for trust assessment

In the rapidly growing and increasingly complex Internet of Things (IoT) ecosystem, securing communication and data exchanges between devices is a major concern. To address this, we proposed QS-Trust, a trust-based security model considering both Quality of Service (QoS) and social parameters. QS-Trust uses a trust value to determine the trust level between devices and employs a QoS-aware trust-based algorithm to improve the security of data transmissions. Additionally, the model incorporates intelligence parameters such as computing power, memory capacity, device behavior and context information to enhance the accuracy of trust evaluation. Our simulation results demonstrated that QS-Trust effectively improved the security of the IoT ecosystem while maintaining the high level of QoS. The execution time of QS-Trust was in the range of 21 to 128 milliseconds, which is efficient for real-time IoT applications. QS-Trust offers a promising solution for securing the IoT ecosystem. The QS-Trust model effectively addresses the challenges of maintaining accurate and up-to-date trust levels in dynamic IoT environments through its decentralized approach, multi-factor evaluations, and adaptive algorithms. By continuously monitoring device performance and interactions and dynamically adjusting trust scores, QS-Trust ensures that the IoT network remains secure and reliable.

A differential privacy aided DeepFed intrusion detection system for IoT applications

AbstractIn the rapidly‐developing Internet of Things (IoT) ecosystem, safeguarding the privacy and accuracy of linked devices and networks is of utmost importance, with the challenge lying in effective implementation of intrusion detection systems on resource‐constrained IoT devices. This study introduces a differential privacy (DP)‐aided DeepFed architecture for intrusion detection in IoT contexts as a novel approach to addressing these difficulties. To build an intrusion detection model, we combined components of a convolutional neural network with bidirectional long short‐term memory. We apply this approach to the Bot‐IoT dataset, which was rigorously curated by the University of New South Wales (UNSW) and N‐BaIoT dataset. Our major goal is to create a model that delivers high accuracy while protecting privacy, an often‐overlooked aspect of IoT security. Intrusion detection tasks are distributed across multiple IoT devices using federated learning principles to protect data privacy, incorporating the DP framework to gauge and minimize information leakage, all while investigating the intricate relationship between privacy and accuracy in pursuit of an ideal compromise. The trade‐off between privacy preservation and model accuracy is investigated by adjusting the privacy loss and noise multiplier. Our research enhances IoT security by introducing a deep learning model for intrusion detection in IoT devices, explores the integration of DP in federated learning framework for IoT and offers guidance on minimizing the accuracy‐privacy trade‐off based on specific privacy and security needs. Our study explores the privacy‐accuracy trade‐off by examining the effects of varying epsilon values on accuracy for various delta values for a range of clients between 5 and 25. We also investigate the influence of several noise multipliers on accuracy and find a consistent accuracy curve, especially around a noise multiplier value of about 0.5. The findings of this study have the possibilities to enhance IoT ecosystem security and privacy, contributing to the IoT landscape's trustworthiness and sustainability.

A hybrid encryption approach for efficient and secure data transmission in IoT devices

Security is a crucial concern in the Internet of Things (IoT) ecosystem. Due to IoT devices' constrained processing and storage resources, providing reliable security solutions is challenging. Encryption is one of the most commonly used techniques to secure user data against unauthorized access. Therefore, it is essential to develop encryption solutions that have minimal impact on the performance of IoT devices. This study introduces a hybrid encryption approach that combines symmetric blowfish encryption with asymmetric elliptic curves. Blowfish encryption is used to encrypt large volumes of data, which could otherwise affect the execution time.In contrast, elliptic curve cryptography is utilized to ensure the security of the private key, which has a small size and does not increase the execution time significantly. The suggested approach provides advantages of both asymmetric and symmetric encryption methods, leading to an improvement in throughput and a reduction in execution time. The proposed approach was evaluated, yielding promising results in comparison to other cryptographic algorithms. The results show the optimization of more than 15% in the execution time and the efficiency increase by the proposed solution. This improvement represents security with the least impact on processing resources.

New paradigms of water‐enabled electrical energy generation

AbstractNanotechnology‐inspired small‐sized water‐enabled electricity generation (WEG) has sparked widespread research interest, especially when applied as an electricity source for off‐grid low‐power electronic equipment and systems. Currently, WEG encompasses a wide range of physical phenomena, generator structures, and power generation environments. However, a systematic framework to clearly describe the connections and differences between these technologies is unavailable. In this review, a comprehensive overview of generator technologies and the typical mechanisms for harvesting water energy is provided. Considering the different roles of water in WEG processes, the related technologies are presented as two different scenarios. Moreover, a detailed analysis of the electrical potential formation in each WEG process is presented, and their similarities and differences are elucidated. Furthermore, a comprehensive compilation of advanced generator architectures and system designs based on hydrological cycle processes is presented, along with their respective energy efficiencies. These nanotechnology‐inspired small‐sized WEG devices show considerable potential for applications in the Internet of Things ecosystem (i.e., microelectronic devices, integrated circuits, and smart clothing). Finally, the prospects and future challenges of WEG devices are also summarized.