Heterogeneous IoT Research Articles

EM-Rhythm: An Authentication Method for Heterogeneous IoT Devices

The popularity of IoT devices has penetrated our daily life while posing new challenges in user authentication. Today’s solutions, e.g., passwords, fingerprints, and FaceIDs, primarily rely on specialized sensors or user interfaces to collect user’s identification information, which may not universally exist on heterogeneous IoT devices. In this paper, we propose a novel user authentication method that exploits the EM emanations radiated from IoT devices. Our design is motivated by the observation that human touches on the IoT device can lead to time-varying coupling between these two. Consequently, it impacts the device’s EM emanations that can be picked up by its inertial ADC (analog-to-digital converter) interfaces. We ask the user to tap on the device rhythmically following a self-determined melody, such that the human-coupled EM emanations vary accordingly. We thus extract the rhythm pattern as the user’s secure password named EM-Rhythm . To examine its effectiveness, EM-Rhythm is implemented on a wide range of IoT devices. We show that our scheme achieves authentication accuracy as high as 98.67% with less than three login attempts. Besides, it is robust against various types of attacks and maintains stable performances under various settings. EM-Rhythm also exhibits satisfactory usability in terms of memorability and time consumption.

Optimal directed acyclic graph federated learning model for energy-efficient IoT communication networks

Federated learning (FL) stimulates distributed on-device computation systems to process an optimum technique efficiency by communicating local process upgrades and global method distribution from aggregation averaging procedure. On-device FL is a standard application in wireless environments, with several mobile devices participating as nodes in the FL network. Managing extensive multi-dimensional process upgrades and resource-constrained computations in large-scale heterogeneous IoT cellular networks can be challenging. This article introduces a Lifetime Maximization using Optimal Directed Acyclic Graph Federated Learning in IoT Communication Networks (LM-ODAGFL) technique. The proposed LM-ODAGFL technique utilizes FL and metaheuristic optimization algorithms for energy-effective IoT networks. The Direct Acyclic Graph (DAG) model addresses device asynchrony in FL while minimizing additional resource usage. The Archimedes Optimization Algorithm (AOA) is designed to optimize the DAG model by reducing both user energy consumption and the training loss of the FL model. The performance validation of the LM-ODAGFL technique is performed by utilizing a series of experimentations. The obtained results of the LM-ODAGFL model demonstrate superior performance by consuming significantly less energy than SDAGFL and ESDAGFL, with values ranging from 0.373 to 0.485 kJ per round on the FMNIST-Clustered dataset and 16.27 to 20.34 kJ per round on the Poets dataset, compared to 0.000 to 1.442 kJ and 0.00 to 63.89 kJ respectively.

Performance Analysis of NOMA-Enabled Active RIS-Aided MIMO Heterogeneous IoT Networks With Integrated Sensing and Communication

Performance Analysis of NOMA-Enabled Active RIS-Aided MIMO Heterogeneous IoT Networks With Integrated Sensing and Communication

Privacy-Enhanced Personalized Federated Learning With Layer-Wise Gradient Shielding on Heterogeneous IoT Data

Privacy-Enhanced Personalized Federated Learning With Layer-Wise Gradient Shielding on Heterogeneous IoT Data

Combined localization and clustering approach for reduced energy presumption in heterogeneous IoT

The field of H-IoT is emerging with enormous potential to empower various technologies. Smart cities and advanced manufacturing are a few of the fields where H-IoT is currently used. The issue with H-IoT is its heavy energy consumption while transmitting data, which makes scaling difficult. To overcome such issues, a hybrid approach of Crayfish Optimization (CFO) with FCM and Restricted Boltzmann Machine (RBM) with Soft Sign Activation (SSA) has been proposed. Initially, Node initialization lays the foundation by configuring individual sensor nodes for network participation. After initialization, Fuzzy C Means clustering optimizes data aggregation by categorizing nodes into clusters based on similarity. Gathering Neighbor Node Traffic Data (NNTD) provides insights into communication patterns. Based on the threshold of NNTD, node localization is performed that enhances network accuracy by pinpointing sensor node locations. Integration of CFO into clustering, along with localization further improves cluster head selection for optimal data routing. Classification through the RBM with SSA function enhances anomaly detection, combining data analysis for optimizing energy utilization in heterogeneous IoT environments. The ‘combined CFO-FCM and SSA-RBM’ has been implemented in MATLAB and achieved an accuracy of 94.50%. As a result, the overall performance of the system is improved.

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

Autonomous and Intelligent Mobile Multimedia Cyber-Physical System with Secured Heterogeneous IoT Network

An optimized federated learning method based on soft label grouping for heterogeneous IoT

An optimized federated learning method based on soft label grouping for heterogeneous IoT

Distributed Multihop Task Offloading in Massive Heterogeneous IoT Systems

Distributed Multihop Task Offloading in Massive Heterogeneous IoT Systems

GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems

GOFL: An Accurate and Efficient Federated Learning Framework Based on Gradient Optimization in Heterogeneous IoT Systems

EPOPTIS: A Monitoring-as-a-Service Platform for Internet-of-Things Applications.

The technology landscape has been dynamically reshaped by the rapid growth of the Internet of Things, introducing an era where everyday objects, equipped with smart sensors and connectivity, seamlessly interact to create intelligent ecosystems. IoT devices are highly heterogeneous in terms of software and hardware, and many of them are severely constrained. This heterogeneity and potentially constrained nature creates new challenges in terms of security, privacy, and data management. This work proposes a Monitoring-as-a-Service platform for both monitoring and management purposes, offering a comprehensive solution for collecting, storing, and processing monitoring data from heterogeneous IoT networks for the support of diverse IoT-based applications. To ensure a flexible and scalable solution, we leverage the FIWARE open-source framework, also incorporating blockchain and smart contract technologies to establish a robust integrity verification mechanism for aggregated monitoring and management data. Additionally, we apply automated workflows to filter and label the collected data systematically. Moreover, we provide thorough evaluation results in terms of CPU and RAM utilization and average service latency.

Class imbalance and concept drift invariant online botnet threat detection framework for heterogeneous IoT edge

Heterogeneous networks (HetIoT) of high-capacity and resource-constrained IoT devices and their edge associations for on-device distributed critical workloads—called the edge-of-things (EoT)—attract short-burst, botnet-based zero-day attacks that exploit latent vulnerabilities due to heterogeneous device properties, dynamic operational contexts, and insufficient security scrutiny of the constituent proprietary devices. Such a scenario necessitates a device-specific network intrusion detection (NID) technique for localizing the threat space and updated rule learning through online (real-time) model retraining. Furthermore, scarce labeled knowledge base and high levels of class imbalance of NID datasets complicate the ID system design process for EoT environments, as online detection cannot afford computationally expensive data balancing techniques; this necessitates a class imbalance invariant traffic inference technique for data preprocessing. Therefore, we propound the ONIDS online NID technique, which consists of a two-fold solution for the above problems. First, we propose a Beta distribution-based inference technique for efficient traffic behavior approximation—invariant of class imbalance and capable of non-cumulative traffic processing of smaller sample sizes. Then, we put forth an online ID technique called ELMO for class imbalance invariant time-bound training of smaller sample sizes on resource-constrained device-specific network traffic. Together, they are invariant of traffic class imbalances and adaptable to resultant concept drift categories exhibited by HetIoT attack behaviors. ONIDS has low memory and compute footprints and can efficiently process large and small amounts of traffic, making it suitable for online and offline NID. It also exhibits qualitative and quantitative superiority—particularly on smaller data samples.

Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices

Intelligent resource optimization for scalable and energy-efficient heterogeneous IoT devices

Efficient Storage of Heterogeneous IoT data in a Blockchain using an Indexing Method in Metric Space

In this work, we proposed a IoT data indexing method to surpass some challenges encountered during the use of hashing in the storage of data in a blockchain. The indexing method was developed in metric space in which no dimensions are considered and only distance between objects is taken into account. The proposed method consisted on putting the index in the inner of a block. The index, called GHB-tree is based on space partitioning using hyperplane. The proposed approach was tested using two datasets of close size and different dimensions. The experimental results showed that the proposed method is efficient and competitive to other storing methods since the queries retrieve time is very reduced to be expressed by millisecond compared with that of other blockchains.

Advanced Data Processing of Pancreatic Cancer Data Integrating Ontologies and Machine Learning Techniques to Create Holistic Health Records.

The modern healthcare landscape is overwhelmed by data derived from heterogeneous IoT data sources and Electronic Health Record (EHR) systems. Based on the advancements in data science and Machine Learning (ML), an improved ability to integrate and process the so-called primary and secondary data fosters the provision of real-time and personalized decisions. In that direction, an innovative mechanism for processing and integrating health-related data is introduced in this article. It describes the details of the mechanism and its internal subcomponents and workflows, together with the results from its utilization, validation, and evaluation in a real-world scenario. It also highlights the potential derived from the integration of primary and secondary data into Holistic Health Records (HHRs) and from the utilization of advanced ML-based and Semantic Web techniques to improve the quality, reliability, and interoperability of the examined data. The viability of this approach is evaluated through heterogeneous healthcare datasets pertaining to personalized risk identification and monitoring related to pancreatic cancer. The key outcomes and innovations of this mechanism are the introduction of the HHRs, which facilitate the capturing of all health determinants in a harmonized way, and a holistic data ingestion mechanism for advanced data processing and analysis.

Integrated 2–4 Step Random Access for Heterogeneous and Massive IoT Devices

Integrated 2–4 Step Random Access for Heterogeneous and Massive IoT Devices

PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognition

PerMl-Fed: enabling personalized multi-level federated learning within heterogenous IoT environments for activity recognition

Prototype-Based Decentralized Federated Learning for the Heterogeneous Time-Varying IoT Systems

Prototype-Based Decentralized Federated Learning for the Heterogeneous Time-Varying IoT Systems

DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT

DISNET: Distributed Micro-Split Deep Learning in Heterogeneous Dynamic IoT

Collaborative Learning of Different Types of Healthcare Data From Heterogeneous IoT Devices

Collaborative Learning of Different Types of Healthcare Data From Heterogeneous IoT Devices

Comparative Analysis of Time-Slotted Channel Hopping Schedule Optimization Using Priority-Based Customized Differential Evolution Algorithm in Heterogeneous IoT Networks.

The Time-Slotted Channel Hopping (TSCH) protocol is known for its suitability in highly reliable applications within industrial wireless sensor networks. One of the most significant challenges in TSCH is determining a schedule with a minimal slotframe size that can meet the required throughput for a heterogeneous network. We proposed a Priority-based Customized Differential Evolution (PCDE) algorithm based on the determination of a collision- and interference-free transmission graph. Our schedule can encompass sensors with different data rates in the given slotframe size. This study presents a comprehensive performance evaluation of our proposed algorithm and compares the results to the Traffic-Aware Scheduling Algorithm (TASA). Sufficient simulations were performed to evaluate different metrics such as the slotframe size, throughput, delay, time complexity, and Packet Delivery Ratio (PDR) to prove that our approach achieves a significant result compared with this method.