IoT Ecosystem Research Articles

Efficient Zero-Knowledge Proofs for Set Membership in Blockchain-Based Sensor Networks: A Novel OR-Aggregation Approach

Blockchain-based sensor networks offer promising solutions for secure and transparent data management in IoT ecosystems. However, efficient set membership proofs remain a critical challenge, particularly in resource-constrained environments. This paper introduces a novel OR-aggregation approach (where “OR” refers to proving that an element equals at least one member of a set without revealing which one) for zero-knowledge set membership proofs, tailored specifically for blockchain-based sensor networks. We provide a comprehensive theoretical foundation, detailed protocol specification, and rigorous security analysis. Our implementation incorporates optimization techniques for resource-constrained devices and strategies for integration with prominent blockchain platforms. Extensive experimental evaluation demonstrates the superiority of our approach over existing methods, particularly for large-scale deployments. Results show significant improvements in proof size, generation time, and verification efficiency. The proposed OR-aggregation technique offers a scalable and privacy-preserving solution for set membership verification in blockchain-based IoT applications, addressing key limitations of current approaches. Our work contributes to the advancement of efficient and secure data management in large-scale sensor networks, paving the way for wider adoption of blockchain technology in IoT ecosystems.

Efficient Data Recognition and Classification in IoT Ecosystem using Optimized K-Means Algorithm and Hybrid Deep Learning Model

Efficient Data Recognition and Classification in IoT Ecosystem using Optimized K-Means Algorithm and Hybrid Deep Learning Model

HARNESSING BLOCKCHAIN WITH ENSEMBLE DEEP LEARNING-BASED DISTRIBUTED DOS ATTACK DETECTION IN IOT-ASSISTED SECURE CONSUMER ELECTRONICS SYSTEMS

Consumer electronics (CE) and the Internet of Things (IoTs) are transforming daily routines by integrating smart technology into household gadgets. IoT allows devices to link and communicate from the Internet with better functions, remote control, and automation of various complex systems simulation platforms. The quick progress in IoT technology has continuously driven the progress of further connected and intelligent CEs, shaping more smart cities and homes. Blockchain (BC) technology is emerging as a promising technology offering immutable distributed ledgers that improve the security and integrity of data. However, even with BC resilience, the IoT ecosystem remains vulnerable to Distributed Denial of Service (DDoS) attacks. In contrast, the malicious actor overwhelms the network with traffic, disrupting services and compromising device functionality. Incorporating BC with IoT infrastructure presents groundbreaking techniques to alleviate these threats. IoT networks can better detect and respond to DDoS attacks in real time by leveraging BC cryptographic techniques and decentralized consensus mechanisms, which safeguard against disruptions and enhance resilience. There must be a reliable mechanism of recognition based on adequate techniques to detect and identify whether these attacks have happened or not in the system. Artificial intelligence (A) is the most common technique that uses machine learning (ML) and deep learning (DL) to recognize cyber threats. This research presents a new Blockchain with Ensemble Deep Learning-based Distributed DoS Attack Detection (BCEDL-DDoSD) approach in the IoT platform. The primary intention of the BCEDL-DDoSD approach is to leverage BC with a DL-based attack recognition process in the IoT platform. BC technology is utilized to enable a secure data transmission process. In the BCEDL-DDoSD approach, Z-score normalization is initially employed to measure the input data. Besides, the selection of features takes place using the Fractal Wombat optimization algorithm (WOA). For attack recognition, the BCDL-DDoSD technique applies an ensemble of three models, namely denoising autoencoder (DAE), gated recurrent unit (GRU), and long short-term memory (LSTM). Lastly, an orca predator algorithm (OPA)-based hyperparameter tuning procedure has been implemented to select the parameter value of DL models. A sequence of simulations is made on the benchmark database to authorize the performance of the BCDL-DDoSD approach. The simulation results showed that the BCDL-DDoSD approach performs better than other DL techniques.

Real-Time Evaluation of the Improved Eagle Strategy Model in the Internet of Things

With the rapid expansion of cloud computing and the pervasive growth of IoT across industries and educational sectors, the need for efficient remote data management and service orchestration has become paramount. Web services, facilitated by APIs, offer a modular approach to integrating and streamlining complex business processes. However, real-time monitoring and optimal service selection within large-scale, cloud-based repositories remain significant challenges. This study introduces the novel Improved Eagle Strategy (IES) hybrid model, which uniquely integrates bio-inspired optimization with clustering techniques to drastically reduce computation time while ensuring highly accurate service selection tailored to specific user requirements. Through comprehensive NetLogo simulations, the IES model demonstrates superior efficiency in service selection compared to existing methodologies. Additionally, the IES model’s application through a web dashboard system highlights its capability to manage both functional and non-functional service attributes effectively. When deployed on real-time IoT devices, the IES model not only enhances computation speed but also ensures a more responsive and user-centric service environment. This research underscores the transformative potential of the IES model, marking a significant advancement in optimizing cloud computing processes, particularly within the IoT ecosystem.

A Proof-of-Concept Open-Source Platform for Neural Signal Modulation and Its Applications in IoT and Cyber-Physical Systems

This paper presents the design, implementation, and characterization of a digital IoT platform capable of generating brain rhythm frequencies using synchronous digital logic. Designed with the Google SkyWater 130 nm open-source process design kit (PDK), this platform emulates Alpha, Beta, and Gamma rhythms. As a proof of concept and the first of its kind, this device showcases its potential applications in both industrial and academic settings. The platform was integrated with an IoT device to optimize and accelerate research and development efforts in embedded systems. Its cost-effective and efficient performance opens opportunities for real-time neural signal processing and integrated healthcare. The presented digital platform serves as a valuable educational tool, enabling researchers to engage in hands-on learning and experimentation with IoT technologies and system-level hardware–software integration at the device level. By utilizing open-source tools, this research demonstrates a cost-effective approach, fostering innovation and bridging the gap between theoretical knowledge and practical application. Furthermore, the proposed system-level design can be interfaced with various serial devices, Wi-Fi modules, ARM processors, and mobile applications, illustrating its versatility and potential for future integration into broader IoT ecosystems. This approach underscores the value of open-source solutions in driving technological advancements and addressing skills shortages.

End User Security using Smart Devices with Ability to Access IoT services

This paper explores the security issues of smart devices in IoT environments and proposes solutions to enhance end-user protection. A qualitative approach, including a comprehensive literature review, was used to identify key security issues and best practices. Key vulnerabilities in IoT device security include insecure communication channels, weak authentication mechanisms, outdated firmware and software, and a lack of standardized security protocols. Current security practices among end-users show limited awareness and inconsistent implementation. Recommendations include adopting universal security standards, enhancing user education through regular programs, promoting advanced security tools like multi-factor authentication, and simplifying device management with user-friendly interfaces. This paper offers a comprehensive analysis of IoT security issues and practical recommendations to create a safer IoT ecosystem, ensuring technological advancements do not compromise user security.

Blockchain-assisted improved interval type-2 fuzzy deep learning-based attack detection on internet of things driven consumer electronics

The Internet of Things (IoTs) revolutionizes the consumer electronics landscape by presenting a degree of personalization and interactivity that was previously unimaginable. Interconnected devices are now familiar with user characteristics, giving custom skills that improve the user's satisfaction. Still, IoT remains to transform the consumer electronics field; security in IoT becomes critical, and it is utilized by cyber attackers to pose risks to public safety, compromise data privacy, gain unauthorized access, and even disrupt operations. Robust security measures are crucial for maintaining trust in the proliferation and adoption of interconnected technologies, mitigating those risks, protecting sensitive data, and certifying the integrity of the IoT ecosystem. An intrusion detection system (IDS) is paramount in IoT security, as it dynamically monitors device behaviours and network traffic to detect and mitigate any possible cyber threats. Using machine learning (ML) methods and anomaly detection algorithms, IDS can rapidly identify abnormal activities, unauthorized access, or malicious behaviours within the IoT ecosystem, thus preserving the integrity of interconnected devices and networks, safeguarding sensitive data, and protecting against cyber-attacks. This work presents an Improved Crayfish Optimization Algorithm with Interval Type-2 Fuzzy Deep Learning (ICOA-IT2FDL) technique for Intrusion Detection on IoT infrastructure. The main intention of the ICOA-IT2FDL technique is to utilize a hyperparameter-tuned improved deep learning (DL) method for intrusion detection, thereby improving safety in the IoT infrastructure. BC technology can be used to accomplish security among consumer electronics. The ICOA-IT2FDL technique employs a linear scaling normalization (LSN) approach for data normalization. In addition, features are selected using an improved crayfish optimization algorithm (ICOA). This is followed by the ICOA-IT2FDL technique, which applies the interval type-2 fuzzy deep belief network (IT2-FDBN) model to identify intrusions. Finally, the bald eagle search (BES) model strategy improves the intrusion recognition rate. A series of investigations is accomplished to ensure the enhanced accomplishment of the ICOA-IT2FDL model. The experimentation results specified that the ICOA-IT2FDL model shows better recognition results compared to recent models.

Securing the IoT Edge Devices Using Advanced Digital Technologies

As the IoT ecosystem continues to grow, edge computing is becoming essential for handling and analyzing the vast amount of data generated by connected devices. Unlike traditional centralized data models, where information is sent to remote centers for processing, edge computing processes data closer to where it is generated. This decentralized approach helps reduce latency, optimizes bandwidth usage, and improves both privacy and security. However, the rise in IoT devices and the spread of edge computing also increase the potential for cyberattacks, demanding more robust security measures. With AI and machine learning being utilized to analyze IoT data, edge computing facilitates this analysis directly at the data source, pointing to a future where AI and ML applications are more prevalent on edge devices.

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.

Security Challenges and Business Opportunities in the IoT Ecosystem

In the growing IoT environment, security seems to be a dominant topic, because of the broad extent of IoT devices functionalities is expressed in the different sectors. This article scrutinizes the elaborate IoT infrastructure, exposing the common flaws and traceable security incidents, which serve as a background in the appreciation of the need of having reliable security frameworks. At the same time, the research paper pinpoints the vast business prospects that will spawn from the issues, highlighting the fact that these can be tapped through the creation and provision of cutting-edge security systems and consultancy services. The study makes a prediction of the future of IoT based on its current rating and empirical evidence by reviewing literature to assess emerging technologies and market trends, which give way for future innovations in IoT security. In general, this paper proposes that interested actors who are proactive and afraid of the emerging security threats have an opportunity to tap into the growth and innovation in building strong IoT systems. Consequently, the insights offered in this paper are appropriate for both the industry stakeholders and the researchers.

Efficient Lightweight Blockchain with Hybridized Consensus Algorithm for IoT Networks

Blockchain (BC) technology has attracted a lot of interest due to its excellent security and privacy features as well as its immutability. While BC can address security and privacy challenges in IoT, it is often computationally expensive, suffers from limited scalability, and introduces bandwidth overheads and delays, making it unsuitable for IoT applications. This paper proposes a novel lightweight and scalable blockchain (LIGHT-SB) designed specifically for IoT environments. In a smart IoT ecosystem, low-resource devices benefit from a centralized manager that handles communication keys and manages requests. LIGHT-SB enhances decentralization while maintaining robust privacy and security through a hybrid consensus algorithm combining permissionless proof-of-capacity and permissioned Byzantine fault tolerance (PoC-PBFT). Secure message delivery is achieved using SHA-256 and RSA cryptographic techniques. IoT device authentication, communication, and storage are safeguarded within the network. Distributed throughput management is implemented to monitor and adjust BC usage, ensuring it remains efficient. Qualitative evaluations demonstrate that LIGHT-SB effectively mitigates several security threats.

A Security Analysis Model for IoT-ecosystem Using Machine Learning-(ML) Approach

Introduction: The attacks on IoT systems are increasing as the devices and communication networks are progressively integrated. If no attacks are found in IoT for a long time, it will affect the availability of services that can result in data leaks and can create a significant impact on the associated costs and quality of services. Therefore, the attacks and security vulnerability in the IoT ecosystem must be detected to provide robust security and defensive mechanisms for real-time applications. Method: This paper proposes an analytical design of an intelligent attack detection framework using multiple machine learning techniques to provide cost-effective and efficient security analysis services in the IoT ecosystem. Result: The performance validation of the proposed framework is carried out by multiple performance indicators. Conclusion: The simulation outcome exhibits the effectiveness of the proposed system in terms of accuracy and F1-score for the detection of various types of attacking scenarios.

A novel deep learning-based intrusion detection system for IoT DDoS security

Intrusion detection systems (IDS) for IoT devices are critical for protecting against a wide range of possible attacks when dealing with Distributed Denial of Service (DDoS) attacks. These attacks have become a primary concern for IoT networks. Intelligent decision-making techniques are required for DDoS attacks, which pose serious threats. The range of devices connected to the IoT ecosystem is growing, and the data traffic they generate is continually changing; the need for models more resistant to new attack types and existing attacks is of research interest. Motivated by this gap, this paper provides an effective IDS powered by deep learning models for IoT networks based on the recently published CICIoT2023 dataset. In this work, we improved the detection and mitigation of potential security threats in IoT networks. To increase performance, we performed preprocessing operations on the dataset, such as random subset selection, feature elimination, duplication removal, and normalization. A two-level IDS using deep-learning models containing binary and multiclass classifiers has been designed to identify DDoS attacks in IoT networks. The effectiveness of several deep-learning models in real-time and detection performance has been evaluated. We trained fully connected, convolutional, and LSTM-based deep learning models for detecting DDoS attacks and sub-classes. According to the results on a partially balanced sub-dataset, two staged models performed better than baseline models such as DNN (Deep Neural Networks), CNN (Convolutional Neural Networks), LSTM (Long Short Term Memory), RNN (Recurrent Neural Network).

An SDN-based Hybrid-DL-driven cognitive intrusion detection system for IoT ecosystem

Smart homes, healthcare, transportation, agriculture, manufacturing, and many more sectors can all benefit from IoT technology. This revolutionary way of interacting with the world could increase efficiency, convenience, and productivity. However, it also raises concerns regarding security, privacy, and data management. To mitigate security threats in IoT networks, this article presents a software-defined networking-enabled cognitive hybrid-deep learning model for intrusion detection in IoT ecosystem. The primary purpose of this framework is to continuously and efficiently identify cybersecurity threats in the IoT environment. With the influence of the cognitive computing paradigm, the designed system can analyse, understand, and even respond to various traffic approaching IoT devices. The proposed model is trained and tested using the cutting-edge N-BaIoT and CICDDoS2019 datasets. The experimental results demonstrate a high level of accuracy, yielding an acceptable false positive rate in conjunction with reasonable testing time. Furthermore, the architecture considers the limited resources of IoT devices, ensuring that they are not overburdened in the process. The proposed model obtained an acceptable accuracy rate of 99. 86 %, 99. 96 % precision, 99. 903 % recall, and 99.93 % F1-Score. The proposed model outperformed the other hybrid-DL models, such as Cu-GRU+LSTM and Cu-GRU+DNN.

A Machine Learning Based Approach for the Detection of DDoS Attacks on Internet of Things Using CICDDoS2019 Dataset - PortMap

In today's technological era, the Internet has become ubiquitous, playing a vital role in our daily lives. With the exponential growth of IoT innovation, millions of interconnected IoT-enabled devices rely on cloud services to communicate over the Internet. However, this rapid development also exposes these devices to various threats, with DDoS (Distributed Denial of Service) and DoS (Denial of Service) attacks being particularly potent and destructive. DDoS attacks present a unique challenge as they are extremely difficult to detect using conventional intrusion detection frameworks and traditional methodologies. Fortunately, advancements in machine learning have provided a promising solution by enabling accurate differentiation between DDoS attacks and other forms of data. This study proposes a DDoS detection model based on machine learning algorithms. To conduct this study, we utilized the most recent and freely available online dataset called CICDDoS2019. Various machine learning-based techniques were explored, aiming to identify the characteristics associated with accurate classification. Among the algorithms tested, AdaBoost and XGBoost demonstrated exceptional performance. As part of future work, a hybrid approach will be incorporated into this model, further improving its capabilities. It is worth noting that this model will be continuously updated with new data on DDoS attacks, ensuring its relevance and effectiveness in combating emerging threats. By leveraging machine learning techniques, this approach enhances the detection of DDoS attacks on Internet of Things networks, safeguarding the integrity and security of connected devices and the overall IoT ecosystem.

LIO-PAY: Sustainable low-cost offline payment solution

Offline payment solutions exist today to enable customers to avail themselves of the privileges of the cashless economy. Financial inclusion is visualized as a key factor in encouraging digital transactions in rural and semi-urban regions. Recently, the Government of India, along with the Reserve Bank of India, announced a framework that allows offline payments with finite transaction limits. This aims at providing access to formal financial services in an affordable manner to deepen financial inclusion. However, the remote population rarely access their digital bank accounts due to intermittent network connectivity issues. Hence, the majority of these accounts are in a dormant state, which demeans the impact of financial inclusion. The aim of this research is to design and develop a sustainable, low-cost, highly efficient offline payment solution using a LoRa-enabled IoT ecosystem to connect with Space Information Network (SIN). A novel offline payment system is designed with features of a) recharge and b) payment with offline wallet technology, which can be availed of in the absence of Internet or telecommunication services. The proposed solution, LIO-PAY, allows merchants and end users across the ecosystem to run seamless transactions. We developed a proof of concept and tested it in a lab setup with volunteers to analyze the usability of the application for initiating transactions. We examined 46 customer-initiated transactions and found that 16 (around 34.8%) failed due to merchant data entry errors, likely typos. In a separate analysis of these transactions, 4 customers (approximately 11.5%) incorrectly entered their PIN. The average time to complete a transaction was 35.42 s. Bringing satellite payments to life faces hurdles but holds immense promise for the unbanked. Getting the green light from banking authorities and the satellite network provider is no easy feat. Even if approved, launching a whole new satellite network just for low-value transactions might be too expensive. A more realistic approach could be using existing telecom satellites to bounce transaction data to banks. Regulatory approval is needed from both banking authorities and the satellite network provider. Hence, we simulated the SIN environment with seven LEO (Low Earth Orbit) satellites using the Matlab tool. These simulations revealed a transmission time range of 2 min to 1 h, indicating a reasonable timeframe for delivering information to a ground station via SIN. Despite terrestrial networks expanding their reach, satellite-based solutions hold promise for areas with no or weak internet connectivity. This becomes even more attractive if launch costs go down or if banking authorities help subsidize the service to bring financial inclusion to remote regions. Thus, we hope this approach may help researchers and policymakers visualize the possibilities of bridging the gap in financial inclusion.

Resource Control in Active IRS-Aided 6G IoT Networks with Use Case in Smart Indoor Communication

The IoT ecosystem involves connected smart devices that support massive amount of data for processing and further analysis. The proliferating massive IoT network demands network connectivity, robust communication links and computational resources which puts huge load on the network. To overcome the communication overhead in the IoT landscape while offering optimal fault tolerance is the main challenge. This paper presents an intelligent solution for improving the communication efficiency of IoT network using active IRS technology. An active IRS aided communication framework is proposed which offers enhanced network connectivity by enabling controlled reflection amplitude. An algorithm is proposed which associates optimal active IRS to each AP-node link such that signal-to-interference-plus-noise ratio (SINR) is maximized. It is observed that the proposed association scheme in active IRS system offers an improvement of 19.04% in achievable rate over random association at AP transmit power [Formula: see text] of 20[Formula: see text]dBm and IRS amplification power [Formula: see text] of 4[Formula: see text]dBm. Furthermore, the system outage probability is carried out with change in [Formula: see text] and amplification gain [Formula: see text]. The mean channel power is also evaluated for different IRS densities and AP densities under different IRS reflecting elements [Formula: see text], [Formula: see text] and [Formula: see text]. In the end, the performance comparison with passive IRS system and system with no IRS assistance is carried out. A use case scenario of active IRS-aided system in smart indoor communication is also discussed.

Error mitigation in LPWAN systems: A study on the efficacy of Hamming-coded RPW.

Rotating Polarization Wave (RPW) is a novel Low Power Wide Area Networks (LPWAN) technology for robust connectivity and extended coverage area as compared to other LPWAN technologies such as LoRa and Sigfox when no error detection and correction is employed. Since, IoT and Machine-to-Machine (M2M) communication demand high reliability, RPW with error correction can significantly enhance the communication reliability for critical IoT and M2M applications. Therefore, this study investigates the performance of RPW with single bit error detection and correction using Hamming codes to avoid substantial overhead. Hamming (7,4) coded RPW shows a remarkable improvement of more than 40% in error performance compared to uncoded RPW thereby making it a suitable candidate for IoT and M2M applications. Error performance of coded RPW outperforms coded Chirp Spread Spectrum (CSS) modulation used in LoRa under multipath conditions by 51%, demonstrating superior adaptability and robustness under dynamic channel conditions. These findings provide valuable insights into the ongoing developments in wireless communication systems whilst reporting Q-RPW model as a new and effective method to address the needs of developing LPWAN and IoT ecosystems.

A lattice-based data sharing functional encryption scheme with HRA security for IoT

In the dynamic and resource-constrained landscape of IoT, securing sensitive data access is paramount. This paper introduces an innovative access control scheme (ACPRE) blending conditional attribute-based proxy re-encryption with inner product encryption, tailored for IoT security needs. ACPRE integrates attribute-based conditions, expands plaintext space, and restricts proxy privileges, bolstering access control. Leveraging inner product encryption, it confines decrypted information to inner product values, ensuring data confidentiality. By limiting proxies’ transformation abilities, ACPRE prevents unauthorized data alterations. It offers a secure, flexible solution for data access management, allowing only entities with specific attributes to utilize inner product values. ACPRE’s lattice-based construction enables resistance against quantum attacks and achieves honestly re-encryption attacks security, fostering robust, privacy-aware IoT ecosystems. Detailed security proofs and experimental validation underscore its efficacy.

Privacy Preserving Authentication of IoMT in Cloud Computing

INTRODUCTION: The Internet of Medical Things (IoMT) blends the healthcare industry with the IoT ecosystem and enables the creation, collection, transmission, and analysis of medical data through IoT networking. IoT networks consist of various healthcare IT systems, healthcare sensors, and healthcare management software. OBJECTIVES: The IoMT breathes new life into the healthcare system by building a network that is intelligent, accessible, integrated, and effective. Privacy-preserving authentication in IoMT is difficult due to the distributed communication environment of heterogeneous IoMT devices. Although there has been numerous research on potential IoMT device authentication methods, there is still more to be done in terms of user authentication to deliver long-term IoMT solutions. However, password handling is one of the big challenges of IoMT. METHODS: In this paper, we present an IoMT-related online password-less authentication technique that is quick, effective, and safe. In order to offer cross-platform functionality, the article includes a simulation of FIDO2/WebAuthn, one of the most recent standards for a password-less authentication mechanism. RESULTS: This makes it easier to secure user credentials and improve them while preserving anonymity. The IoMT device authentication process and registration process delays are also assessed. CONCLUSION: Results and simulations show that the efficacy of the proposed mechanism with quick authentication on cloud servers may be accomplished with the fewest registration and authentication procedures, regardless of device setup.