IoT Communication Research Articles

Retraction Note: Quantum-enhanced OFDM signal processing: advancing 5G mobile network and IoT communication for healthcare application

Retraction Note: Quantum-enhanced OFDM signal processing: advancing 5G mobile network and IoT communication for healthcare application

Ensuring the Correctness and Reliability of CBPS System Using Event‐B

ABSTRACTDuring the early phases of software system development, error detection can be challenging due to the complexity of both the requirements and the operating environments. This paper advocates for the utilization of formal modelling and verification throughout the first phases of systems development to promptly detect and correct errors. The formalism employed throughout is Event‐B, which is backed by the Rodin toolset. To conquer requirements complexity, the frameworks of set theory and first‐order logic are employed, which provide the necessary tools for formalizing and analysing the properties and behaviours associated with Event‐B. Also, we detail the way in which modelling may be used to achieve abstraction, as well as the way in which refinement can be used to manage complexity through layering. Furthermore, we emphasize the significance of model validation and verification in improving the precision of formal models and requirements in IoT communication systems. The model is exemplified using a Content‐Based Publish Subscribe System (CBPS), with a special emphasis on a fire alarm system as a motivating example.

IOT Based Mining, Tracking and Worker Safety Helmet

Abstract: This project addresses a cost-effective, flexible solution of underground mine workers safety. A module of MEMS and temperature-based sensors are used for underground environment monitoring and automating progression of measurement data through IOT communication technique is proposed with high accuracy, smooth control and reliability. Arduino microcontroller is used for collecting data and making decision, based on which the mine worker is informed through server. Sensor data transforms into digital signal and effectively communicate wirelessly with the ground control centre computer.

SMART JACKET PROTOTYPE FOR ONLINE OJEK DRIVERS BASED ON THE INTERNET OF THINGS (IOT) IN SURABAYA CITY

The research aims to design an automatic cooling jacket based on the Internet of Things (smart jacket) that can be used when riding motorbikes, including online motorcycle taxis. The methods used in this research include user needs analysis, system design, prototypes, and fan speed performance testing. In the user needs analysis stage, a survey was conducted among motorcyclists to identify problems faced when riding in hot weather conditions. Next, a system design consisting of a temperature sensor and an IoT communication module was carried out. After designing the system, an automatic cooling jacket prototype was developed. This prototype is equipped with a temperature sensor that will detect the rider's body temperature level, and an IoT communication module to allow users to control the jacket via an application on a smartphone. The results of this research show that an IoT-based automatic cooling jacket can effectively reduce the body temperature of motorcyclists when riding in hot weather. In speed performance testing, this jacket succeeded in reducing the rider's body temperature efficiently. This research is expected to contribute to the development of wearable technology that can increase comfort and safety for motorcyclists. Apart from that, this research can also be the basis for developing IoT-based automatic cooling jacket products that can be mass-produced and sold on the market. Keywords: jacket, fan cooling, IOT, motorbike, temperature sensor.

Super Low Profile 5G mmWave Highly Isolated MIMO Antenna with 360∘ Pattern Diversity for Smart City IoT and Vehicular Communication

This study introduces a planar folded super low profile 5G mmWave monopole antenna for small IoT devices with superior efficiency and gain. Low-loss Rogers RT-5880 substrate material has been utilized to achieve higher efficiency, gain, and diversity performance in a relatively compact size. The unit element planar monopole antenna has been designed within 6.15×5.13×0.8mm3 with a partial ground. Four unit element antenna was orthogonally arranged on alternating sides of the substrate plane with narrow inter-component spacing in the multiple input multiple output (MIMO) system. The integration of the double negative (DNG) metamaterial array on the antenna led to a cumulative improvement in the overall performance of the antenna, which includes gain, efficiency, and isolation. The minimum isolation of the antenna increased by 11 dB with substrate-loaded metamaterial. The proposed MIMO antenna model with substrate-loaded metamaterial demonstrates 8 GHz instantaneous bandwidth (33.1 GHz - 41.1 GHz), 0.001 envelope correlation coefficient (ECC), diversity gain (DG) greater than 9.99 dB, channel capacity loss (CCL) less than 0.2bits/s/Hz, and a total active reflection coefficient (TARC) of 25 dB, which suggests that the diversity characteristics of the MIMO antenna system is impressive. Moreover, a densely packed 12-element antenna model has been introduced in this study with 360∘ pattern diversity, capable of receiving signals from any direction. Three 4-element MIMO systems have been arranged in an architecture to develop the 12-element MIMO system, and the size of the 12-element MIMO architecture is 15×15×15mm3. Despite having this compact size and dense packing, the 12-element MIMO antenna model shows similar diversity performance as the 4-element antenna model. Analog equivalent circuit models or resistor-inductor-capacitor (RLC) equivalent systems for the antenna models were created. A concept of a 128-element massive MIMO antenna has been presented to support the end devices, creating an application scenario of an IoT-interconnected smart city.

The Possibility of Improving the Range and Quality of Air-to-Ground WebRTC-Based IoT Communication in Emergency Situations by Employing an External Transceiver.

The proliferation of new services, either interpersonal or machine-oriented, has generated new demands concerning the flexibility and efficiency of transmission. The ubiquity of multimedia communication in the current internet is seamlessly and successfully supported by the WebRTC concept. This paper reports on the study of the usage of a solution employing a proxy transmission unit for air-to-ground delivery of video streaming multiplexed with sensor data in the UAV-IoT system when using the WebRTC protocol stack. The comparative experiments were carried out for two cases employing the 802.11ac network with WebRTC: the first scenario (S1) without an external transceiver and the second scenario (S2) with an external transceiver working as a proxy of the ground receiver. The presented results compare the transmission conditions without (scenario S1) and with (scenario S2) the external transceiver in terms of the RSSI, the available data rate, and total throughput of transmission of multimedia data (video stream from the UAV camera and bursty data coming from employed sensors. The usefulness of the external transceiver used in a wide range of transmission conditions is clearly proven.

Green Communication in IoT for Enabling Next-Generation Wireless Systems

Recent developments and the widespread use of IoT-enabled technologies has led to the Research and Development (R&D) efforts in green communication. Traditional dynamic-source routing is one of the well-known protocols that was suggested to solve the information dissemination problem in an IoT environment. However, this protocol suffers from a high level of energy consumption in sensor-enabled device-to-device and device-to-base station communications. As a result, new information dissemination protocols should be developed to overcome the challenge of dynamic-source routing, and other similar protocols regarding green communication. In this context, a new energy-efficient routing protocol (EFRP) is proposed using the hybrid adopted heuristic techniques. In the densely deployed sensor-enabled IoT environment, an optimal information dissemination path for device-to-device and device-to-base station communication was identified using a hybrid genetic algorithm (GA) and the antlion optimization (ALO) algorithms. An objective function is formulated focusing on energy consumption-centric cost minimization. The evaluation results demonstrate that the proposed protocol outperforms the Greedy approach and the DSR protocol in terms of a range of green communication metrics. It was noticed that the number of alive sensor nodes in the experimental network increased by more than 26% compared to the other approaches and lessened energy consumption by about 33%. This leads to a prolonged IoT network lifetime, increased by about 25%. It is evident that the proposed scheme greatly improves the information dissemination efficiency of the IoT network, significantly increasing the network’s throughput.

Enhancing smart city IoT communication: A two-layer NOMA-based network with caching mechanisms and optimized resource allocation

With advancements in next-generation communication systems, large-scale Internet of Things device (IoTDs) deployments in smart cities face challenges like limited bandwidth, high latency, and network congestion. To address this, we propose a two-layer network architecture utilizing non-orthogonal multiple access (NOMA) and caching to enhance IoT communications’ performance, efficiency, and reliability. Our primary objective is to optimize resource allocation and solve the association problem in a two-layer network. We formulated a joint optimization problem to maximize system utility through device association, power, and bandwidth allocation, considering constraints like channel quality and interference. We decoupled the non-linear, non-convex problem using block coordinate descent (BCD) and inner approximation techniques to maximize the aggregated data rate in high-density IoT scenarios. This approach reduced computational complexity while proving the scheme’s theoretical and numerical convergence. To evaluate the proposed scheme, we compared its performance with an ideal backhauling approach, an exhaustive search (upper bound), and a Genetic algorithm-based heuristic. Our scheme outperformed the others, achieving 98.04% of the ideal backhauling and 99.60% of the upper bound. Statistical analysis confirmed its robustness and consistent performance across various conditions. The two-layer NOMA-based network with caching and optimized resource allocation significantly enhances IoT communication efficiency and resilience, offering a solid framework for future smart city deployments.

Experimental study and techno-economic evaluation of an active fault detection kit in the prospect of future zero energy building installations

This article presents an active fault detection kit, applicable to low voltage single-phase electrical installations, in the prospect of the Zero Energy Building concept, integrating on-site renewable energy generation and energy storage. This simple, flexible, self-powered and compact kit is capable of detecting faults, such as power theft (meter tampering), unintentional islanding and neutral conductor loss. Its operation is based on harmonic voltage injection, in series with the electrical installation, through a low-power H-bridge inverter and a current transformer, along with the corresponding harmonic current measurement, to estimate the impedance and effectively detect faulty conditions; the fast and robust Goertzel algorithm is utilized. Moreover, it features IoT communication capabilities, employing the ESP32 microcontroller, to exchange data and information with the installation meters. The functionality and effective fault detection of the proposed device are validated, through experimental tests on a custom-developed hardware prototype; IoT connectivity and data uploading are experimentally tested and verified, too. Finally, a sustainability assessment study is performed, using the Life Cycle Cost Analysis tool.

Beacon-Based Uplink Transmission for LoRaWAN Direct to LEO Satellite Internet of Things

Direct-to-satellite IoT (DtS-IoT) communication structure is a promising solution to provide connectivity and extend the coverage of traditional low-power and long-range technologies, especially for isolated and remote areas where deploying traditional infrastructure is impracticable. Despite their bounded visibility, the Low Earth Orbit (LEO) satellites complement the terrestrial networks, offering broader gateway coverage and terrestrial network traffic offloading. However, the dynamics of LEO and the nature of such integration come with several challenges affecting the efficacy of the network. Therefore, this paper proposes Beacon-based Uplink LoRaWAN (BU-LoRaWAN) to enhance satellite-terrestrial communication efficiency. The proposed scheme exploits the LoRaWAN class B synchronization mechanism to provide efficient uplink transmission from LoRaWAN devices placed on the ground to satellite gateways. BULoRaWAN proposes an uplink transmission slot approach to synchronize ground devices’ uplink traffic with LEO-based orbiting gateways. It also uses a queue data structure to buffer end devices’ ready-to-send packets until the appropriate moment. BU-LoRaWAN avoids possible transmission collision by optimizing a random transmission slot for an end device within the beacon window. The proposed system is implemented and evaluated using OMNeT++ network simulator and FLoRaSat framework. The result demonstrates the feasibility of the proposed system. BU-LoRaWAN achieves better performance compared to the standard LoRaWAN, which manages to deliver almost double the traffic delivered by the standard one.

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.

Comparative analysis of lattice-based cryptographic schemes for secure IoT communications

This paper presents comparative analysis of lattice-based cryptographic schemes, focusing on their runtime and memory performance to evaluate their suitability for secure IoT communications. Specifically, analyzing FrodoKEM, Kyber, and sntrup761, assessing their key generation, encapsulation, and decapsulation operations across various IoT processors. Kyber schemes, particularly Kyber512 and Kyber1024, demonstrate the highest efficiency in terms of battery utilization and computational speed, making them ideal for highly constrained IoT devices. FrodoKEM variants, while providing robust security, show moderate performance, suitable for devices with more processing capacity. sntrup761, however, is less efficient, highlighting the need for careful selection of cryptographic schemes based on specific IoT requirements. The findings underscore the importance of balancing security, speed, and resource usage in PQC implementations, with Kyber schemes emerging as the most promising candidates for secure, resource-constrained IoT environments.

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.

Delta compression correction method for covert communication in IoT

Delta compression correction method for covert communication in IoT

OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications

OFDM-Standard Compatible SC-NOFS Waveforms for Low-Latency and Jitter-Tolerance Industrial IoT Communications

EMAKAS: An efficient three-factor mutual authentication and key-agreement scheme for IoT environment

The adoption of IoT in healthcare revolutionizes remote patient monitoring and healthcare efficiency. Yet, it brings notable security and privacy challenges, particularly in resource-constrained environment. We propose a secure and efficient three-factor lightweight mutual authentication and key agreement scheme, designed for IoT-based smart healthcare systems, addressing these critical concerns. The scheme employs a fuzzy extractor, a one-way hash function, Elliptic Curve Discrete Logarithm and XOR operations for efficient cryptographic transformations, creating a robust framework for secure data handling. The scheme's design focuses on security and privacy while minimizing computational demands, making it ideal for resource-constrained IoT devices. We utilized both informal and formal security analyses to validate our scheme, employing the Random Oracle Model (ROM), Scyther tool and Burrows-Abadi-Needham (BAN) logic. The security and performance analysis showed that our scheme offers more security features across 15 defined criteria with minimal communication and computational costs compared to other related schemes. The scheme is not only robust against security threats but also practical for implementation in IoT healthcare environment, offering a solution for secure IoT communication by achieving mutual authentication and key agreement with minimized computational requirements.

A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

A Cross-Layer Survey on Secure and Low-Latency Communications in Next-Generation IoT

Maximizing intrusion detection efficiency for IoT networks using extreme learning machine

Intrusion Detection Systems (IDSs) are crucial for safeguarding modern IoT communication networks against cyberattacks. IDSs must exhibit exceptional performance, low false positive rates, and significant flexibility in constructing attack patterns to efficiently identify and neutralize these attacks. This research paper discusses the use of an Extreme Learning Machine (ELM) as a new technique to enhance the performance of IDSs. The study utilizes two standard IDS-based IoT network datasets: NSL-KDD 2009 via Distilled-Kitsune 2021. Both datasets are used to assess the effectiveness of ELM in a conventional supervised learning setting. The study investigates the capacity of the ELM algorithm to handle high-dimensional and unbalanced data, indicating the potential to enhance IDS accuracy and efficiency. The research also examines the setup of ELM for both NSL_KDD and Kitsune using Python and Google COLAB to do binary and multi-class classification. The experimental evaluation revealed the proficient performance of the proposed ELM-based IDS among other implemented supervised learning-based IDSs and other state-of-the-art models in the same study area.

Research and Application of a Generalized Power Communication Framework with Joint Security Protection Mechanism

Background: The rapid development of Industry 4.0 has opened up new power systems, which face a more complex network topology and a broader range of security threats. This has led to an urgent demand for a coordinated, lightweight, and secure communication management method. Methods: This paper presents a general power communication framework with a joint security protection mechanism that is suitable for coordinated management and transmission safety. The framework manages the protocol’s thread management of the device by converting it to the framework’s process management. It formulates specifications for protocols and equipment that access the framework to achieve support for multiple protocols and equipment. This reduces the difficulty of system operation and maintenance, as well as system complexity, and improves management efficiency. Additionally, the joint security protective mechanism for plugin authentication and protocol encryption is introduced to enable secure data transmission and guarantee the framework's security management. Finally, two experiments have been conducted to discuss and confirm the effectiveness and safety of the framework. Results: The results of the two experiments and the following analysis show that our framework is superior to the chosen well-known industrial commercial communication platforms, and the time delay cost by our mechanism doesn’t significantly affect the real-time data transmission in the power industry, proving its feasibility in both IoT and power applications. Conclusion: Our framework demonstrates exceptional performance in terms of time lag and packet loss during the experiment, surpassing other IoT communication frameworks. Furthermore, it maintains its high standards even under rigorous security analysis.

Empowering Digital Resilience: Machine Learning-Based Policing Models for Cyber-Attack Detection in Wi-Fi Networks

In the wake of the COVID-19 pandemic, there has been a significant digital transformation. The widespread use of wireless communication in IoT has posed security challenges due to its vulnerability to cybercrime. The Indonesian National Police’s Directorate of Cyber Crime is expected to play a preventive role in supervising these attacks, despite lacking a specific cyber-attack prevention function. An Intrusion Detection System (IDS), employing artificial intelligence, can differentiate between cyber-attacks and non-attacks. This study focuses on developing a machine learning-based policing model to detect cyber-attacks on Wi-Fi networks. The model analyzes network data, enabling quick identification of attack indications in the command room. The research involves simulations and analyses of various feature selection methods and classification models using a public dataset of cyber-attacks on Wi-Fi networks. The study identifies mutual information with 20 features such as the optimal feature reduction method and the Neural Network as the best classification method, achieving a 94% F1-Score within 95 s. These results demonstrate the proposed IDS’s ability to swiftly detect attacks, aligning with previous research findings.