Internet Of Things Gateway Research Articles

Comprehensive analysis of services towards Data Aggregation, Data Fusion and enhancing security in IoT-based smart home

Data aggregation and sensors data fusion would be very helpful in a number of developing fields, including deep learning, driverless cars, smart cities, and the Internet of Things (IoT). An advanced smart home application will test the upgraded Constrained Application Protocol (CoAP) using Contiki Cooja. Smart home can enhance people’s comfort. Secure authentication between the transmitter and recipient nodes is essential for providing IoT services. In many IoT applications, device data are critical. Current encryption techniques use complicated arithmetic for security. However, these arithmetic techniques waste power. Hash algorithms can authenticate these IoT applications. Mobile protection issues must be treated seriously, because smart systems are automatically regulated. CoAP lets sensors send and receive server data with an energy-efficient hash function to increase security and speed. SHA224, SHA-1, and SHA256 were tested by the CoAP protocol. Proposed model showed that SHA 224 starts secure sessions faster than SHA-256 and SHA-1. The ChaCha ci. This study proposed enhanced ChaCha, a stream cipher for low-duty-cycle IoT devices. For wireless connections between the IoT gateway and sensors with a maximum throughput of 1.5 Mbps, the proposed model employs a wireless error rate (WER) of 0.05; the throughput rises with an increase in the transmission data rate.

Data fusion integrated network forecasting scheme classifier (DFI-NFSC) via multi-layer perceptron decomposition architecture

The Massive Access Problem of the Internet of Things stands for the access problem of the wireless devices to the Gateway when the device population in the coverage area is excessive. We develop a hybrid model called Data Fusion Integrated Network Forecasting Scheme Classifier (DFI-NFSC) using a Multi-Layer Perceptron (MLP) Decomposition architecture specifically designed to address the Massive Access Problem. We utilize our custom error metric to display throughput and energy consumption results. These results are obtained by emulating the Joint Forecasting–Scheduling (JFS) system on a single IoT Gateway and distinguishing between ARIMA, LSTM, and MLP forecasters of the JFS system. The outcomes indicate that the DFI-NFCS method plays a notable role in improving performance and mitigating challenges arising from the dynamic fluctuations in the diversity of device types within an IoT gateway’s coverage zone.

Comparative Study of Microcontoller: ARDUINO UNO, RASPBERRY PI 4, ESP 32

Abstract: This paper provides a comparative study of three popular microcontrollers: Arduino Uno, Raspberry Pi 4, and ESP32. Arduino Uno is known for its simplicity and low power consumption, making it ideal for educational and small-scale projects. Raspberry Pi 4 offers high computational power and extensive connectivity, suitable for IoT gateways, media centers, and industrial automation. ESP32 combines robust wireless connectivity with efficient processing, perfect for IoT applications, smart home devices, and wearable electronics. The study evaluates their technical specifications, performance, development environments, and use cases, helping developers choose the best microcontroller for their projects

RADAR-IoT: An Open-Source, Interoperable, and Extensible IoT Gateway Framework for Health Research.

IoT sensors offer a wide range of sensing capabilities, many of which have potential health applications. Existing solutions for IoT in healthcare have notable limitations, such as closed-source, limited I/O protocols, limited cloud platform support, and missing specific functionality for health use cases. Developing an open-source internet of things (IoT) gateway solution that addresses these limitations and provides reliability, broad applicability, and utility is highly desirable. Combining a wide range of sensor data streams from IoT devices with ambulatory mHealth data would open up the potential to provide a detailed 360-degree view of the relationship between patient physiology, behavior, and environment. We have developed RADAR-IoT as an open-source IoT gateway framework, to harness this potential. It aims to connect multiple IoT devices at the edge, perform limited on-device data processing and analysis, and integrate with cloud-based mobile health platforms, such as RADAR-base, enabling real-time data processing. We also present a proof-of-concept data collection from this framework, using prototype hardware in two locations. The RADAR-IoT framework, combined with the RADAR-base mHealth platform, provides a comprehensive view of a user's health and environment by integrating static IoT sensors and wearable devices. Despite its current limitations, it offers a promising open-source solution for health research, with potential applications in managing infection control, monitoring chronic pulmonary disorders, and assisting patients with impaired motor control or cognitive ability.

REMOTE VEHICLE DIAGNOSTIC SYSTEM DEVELOPMENT BASED ON THE INTERNET OF THINGS TECHNOLOGY

Background. Advanced artificial intelligence and IoT gateways are working together in the automotive industry to predict potential vehicle problems by analysing sensor data and optimizing quality control processes. Manufacturers can detect anomalies, improve product reliability, and eliminate manufacturing defects or malfunctions in advance. Predictive analytics also lead to improved fuel efficiency, performance and overall vehicle reliability. Objective. The purpose of this work is to develop a model for remote diagnosis of vehicle faults using a Raspberry Pi model B microcomputer and a SIM7600G-H GSM module. Configure data modules, install the necessary software and configure it, demonstrate step-by-step actions, and perform diagnostics and testing of this module for data transmission. Methods. A prototype was created on the basis of Raspberry Pi 4. and provides monitoring of machine operation in remote mode using the SIM7600E-H LTE Cat-4 4G/3G module. The design has small dimensions, easy installation, requires only initial adjustment and has a wide range of improvements. Results. This prototype uses a diagnostic OBD-II car scanner ELM327 with Bluetooth connection support, a Raspberry PI 4 model B microcomputer with 8 GB of RAM, 4 USB connectors (2 ports type USB3 and 2 ports type USB2), a Gigabit Ethernet port, a USB-C power supply port, and two micro HDMI 4K display connectors. On top of the module there are 48 pins (contacts) with which you can connect modules of different types and directions. The SIM7600G-H communication module is connected to these pins. The last element of the prototype is the SIM card of one of the telephone service providers and the micro SD card, which will act as the main memory element on which the operating system will be written and data will be stored. Conclusions. The article proposes the development of a device model using Internet of Things technologies, which is capable of providing remote diagnosis of car malfunctions. This model is based on the use of the SIM7600G-H module, which provides data transmission through the mobile network. The developed model allows you to read data from various car sensors, as well as transfer this data to a remote device for further analysis. This makes it possible to quickly detect malfunctions and make timely decisions on their correction.

Field trial and performance evaluation of IoT poultry farm monitoring system

IoT technology revolutionizes poultry farming by enabling real-time data collection and analysis. Traditional manual methods for monitoring temperature, humidity, and AC voltage are being replaced with automated systems. The IoT setup includes three sensor nodes, CCTV, an IoT gateway, and a web server. Temperature ranges from 27 to 35°C in offfattening periods and consistently above 30°C during fattening. Humidity fluctuates between 60% to 90% in both periods. The CPU temperature remains within safe limits. Uplink data rates exceed 2 Mbps, while AC voltage initially falls below standards but improves over time.

Construction and Analysis of Intelligent Analysis and Disposal Model for Internet Security Events in Power System

The reliability of nodes is rugged to determine, and the global accuracy is low in identifying secure access nodes of ubiquitous power network gateways. Therefore, this project intends to establish a universal power grid gateway security access node identification model. Sensor technology collects secure access nodes of IoT gateways and evaluates their reliability. It is integrated with the security level of the network to form a virtual security domain. Then, the access node is searched and controlled twice. The access node identification of the universal power IoT gateway is realized according to the reliability calculation. Simulation results show that under the same parameter conditions, when the node size increases and the number of malicious nodes increases, the proposed method can obtain higher accuracy of secure access to nodes. This proves the advantages of the proposed method.

Design and Implementation of IPsec VPN IoT Gateway System in National Secret Algorithm

With the development of Internet of Things technology, the security threats faced by the industrial control field are increasing, and strengthening the security protection capabilities of intelligent systems on IoT highways is becoming increasingly important. IPSec VPN tunneling technology can achieve identity authentication and encrypted data transmission, and is an important means to achieve secure data transmission in intelligent systems on Expressway intelligent tunnel system. The commonly used IPSec VPN gateway uses a traditional Linux protocol stack-based approach for data capture, which requires multiple data copies and context switching, resulting in low efficiency of IPSec services. In addition, the commonly used IPSec VPN security gateway is implemented on the basis of the open-source IPSec framework, using internationally recognized algorithms for encryption and decryption, which poses security risks. This article is based on the IPSec protocol, and studies the high-speed network packet capture framework PFRING technology, the fusion technology of national secret algorithm and IPSec protocol. It designs and implements an IPSec VPN IoT security gateway based on national secret algorithm. After experimental verification, the IPSec VPN gateway system constructed in this article has complete functions and better performance than the common open-source IPSec frameworks OpenSwan and strongSwan, and can meet the application requirements of IoT data encryption transmission.

An Efficient Lightweight Crypto Security Module for Protecting Data Transmission Through IOT Based Electronic Sensors

The Internet of Things (IoT) devices are advanced nanoelectronics devices which has recently witnessed an explosive expansion in the field of communication and electronics, becoming ubiquitous in various applications. However, the rapid growth of IoT applications makes them prone to security threats and data breaches. Hence, cryptographic techniques are developed to ensure data confidentiality and integrity in IoT and many of the applications from optoelectronics. However, the existing cryptographic algorithms face challenges in securing the data from threats during transmission, as they lack effective key management. Therefore, we proposed a novel optimized lightweight cryptography (LWC) to resolve this challenge using the combined benefits of Grey Wolf Optimization and Hyper Elliptic Curve Cryptography (GW-HECC). The proposed LWC algorithm protects the data from attacks during data exchange by optimizing the key management process and aims to deliver greater Quality of Service (QoS) in IoT networks. An IoT network was initially created with multiple sensor devices, IoT gateways, and data aggregators. The proposed framework includes a Quantum Neural Network (QNN)-based attack prediction module to predict the malicious data entry in the IoT network. The QNN learns the attack patterns from the historical IoT data and prevents incoming malicious data entries, ensuring that only normal data is transmitted to the cloud. For secure data transmission, the sensed data from the IoT network are encrypted using the proposed GW-HECC. The presented work was designed and implemented in Python software; the experimental results demonstrate that the proposed method offers greater data confidentiality of 97.9%, improved attack prediction accuracy of 99.8%, and a reduced delay of 0.37 s. Furthermore, a comparative analysis was made with existing cryptographic algorithms, manifesting that the proposed algorithm acquired improved results.

GateKeeper: An UltraLite malicious traffic identification method with dual-aspect optimization strategies on IoT gateways

The Internet of Things (IoT) landscape is booming, and a wide range of IoT endpoints has been integrated into various aspects of life, opening opportunities for malicious attacks that compromise IoT security. Consequently, effective malicious traffic identification methods play a crucial role in ensuring IoT security. However, the current lightweight methods face two main challenges. Firstly, they often generate feature sets that are time-consuming to construct and less adaptable to various scenarios. Secondly, classification models built upon inefficient neural networks incur a significant computational overhead. To address these drawbacks, in this work, we propose GateKeeper, which is an UltraLite method for malicious traffic identification on the IoT gateway. We first propose a base model as the cornerstone and target for subsequent optimization. Then we propose dual-aspect optimization strategies for reducing the input dimension and simplifying the model structure, i.e., Key Bytes Selection (KBS) and Attention Module Simplification (AMS) strategy. Finally, we obtain the optimized UltraLite model-GateKeeper, specifically tailored for integration into IoT gateway deployments, where high-speed real-time identification is paramount. Our extensive experiments demonstrate that GateKeeper performs remarkably in identifying malicious IoT traffic, achieving over 97% accuracy in all three IoT malicious traffic classification benchmark tasks, outperforming six state-of-the-art methods. Besides, GateKeeper’s parameters and FLOPs are 65% smaller than those of existing methods. Moreover, in the case of an IoT gateway platform, GateKeeper demonstrates remarkably low time overhead and hardware resource utilization, surpassing state-of-the-art methods.

Receive wireless sensor data through IoT gateway using web client based on border gateway protocol

One of the significant topics in the field of the Internet of Things (IoT) pertains to the interaction and information sharing among people. The utilization of the Border Gateway Protocol (BGP) stack enhances the integration of web protocols and sensor networks, leading to greater accessibility. However, the BGP protocol stack introduces substantial overhead to messages transmitted at each layer, resulting in increased data overhead and energy consumption in networks by several orders of magnitude. This paper proposes a method to reduce the overhead on small and medium-sized packets. In multi-temporal networks utilizing BGP, scheduling and aggregating BGP packets at sensor nodes help achieve specific objectives. Various research methodologies and measures are employed to facilitate this, including request classification, BGP response prioritization within the network, determination of maximum acceptable delay, and overall network management. Synchronization and temporal integration of received messages at sensor nodes are performed, considering the maximum allowable delay for each message and the availability of the destination to process the accumulated messages. The evaluation results of the proposed method demonstrate a significant reduction in energy consumption and network traffic, particularly in monitoring applications within multi-stage networks. The protocol stack used is derived from the BGP standard.

Advanced Real Time Bone Fracture Monitoring with Pain Control Mechanism

Bones provide structural aid to the frame, and bone fracture recuperation is crucial for regaining functionality and mobility. The boundaries of traditional external fixators in bone fracture remedy, characterized by a loss of actual-time monitoring and capability complications, necessitate a paradigm shift. To clear up this trouble, a clever fixator design imbued with the transformative energy of Aware, Sensing, Smart, and Active (ASSA) technology has been developed. This fixator transcends its passive role, evolving into a wise IoT gateway. It continuously gathers and analyses facts from numerous incorporated sensors, supplying real-time insights into the tricky dance of fracture healing. Its analytical prowess fosters computerized identification of vital activities and milestones within the affected person's recuperation journey, empowering well-timed interventions and knowledgeable scientific selection-making. Furthermore, the fixator vigilantly monitors patient compliance, making sure adherence to prescribed behaviours and nipping non-compliance in the bud. However, its innovation extends beyond mere monitoring. Embedded within its smart framework lies a modern pain manipulation mechanism powered through a thermoelectric generator (TEG).

A Study Article of DLMS to IOT Protocol

Abstract: The DLMS (Device Language Message Specification) protocol plays a crucial role in energy metering and management. It provides a standardized communication framework for exchanging data between utility meters and data collection devices. The protocol enables interoperability, remote meter reading, and supports efficient management of energy consumption, billing, and grid operations. The DLMS protocol is essential for Internet of Things (IoT) applications as it provides a standardized method for communication between Internet of Things devices and energy meters. This facilitates the seamless integration of energy data into IoT platforms, enabling efficient monitoring, analysis, and management of energy consumption in smart grid and smart city deployments. To this end, we have developed a prototype DLMS to IoT gateway that is based on either STM32 or any other IoT module. However, we encountered a challenge in testing the prototype since we couldn't get any digital meter to conduct hands-on testing..

A Lightweight Symmetric Cryptography based User Authentication Protocol for IoT based Applications

The utilization of IoT is expanding across various domains, including telecare, intelligent home systems, and transportation networks. In these environments, IoT devices generate data gathered on remote servers, requiring external users to authenticate themselves to access the data. However, existing authentication protocols for IoT must meet the crucial requirements of speed, security against multiple attacks, and ensuring user anonymity and un-traceability. The main objective of this work is to find lightweight symmetric cryptography-based user authentication protocol tailored for IoT-based applications, focusing on MIM (Man-in-the-Middle) attack prevention, enhanced anonymity, and secure communication between IoT nodes and remote servers via IoT gateways. Existing protocols often lack sufficient defenses against MIM attacks and do not adequately address the need for enhanced user anonymity and secure communication channels within the IoT framework. Our research has identified that authentication techniques based on pairing are susceptible to attacks targeting temporary session-specific data, impersonation, privileged insiders, and offline password guessing. Furthermore, using bilinear pairing in these techniques requires significant computational and communication resources to address the security as mentioned above concerns. A new authentication mechanism must be proposed and designed explicitly for IoT scenarios. The proposed approach exclusively utilizes hash and exclusive-or operations to ensure suitability within the IoT context; thoroughly evaluated the recommended protocol against existing authentication protocols, employing both informal and formal analytical routines like BAN logic, ROR model, and AVISPA simulation. Our findings suggest protocol not only enhances performance but also enhances security. The proposed approach is a tried-and-true strategy for improving security rules in practical Internet of Things (IoT) settings addressing the inherent challenges posed by authentication requirements in IoT environments. The accuracy 98.93%, and Node detection rate 46.57% were improved which is a better outcome.

Integration of Piezoelectric Energy Harvesting Systems into Building Envelopes for Structural Health Monitoring with Fiber Optic Sensing Technology

This paper presents a study about the integration of Piezoelectric Energy Harvesting Systems (PE-EHSs) into building envelopes for powering Fiber Bragg Grating (FBG) sensors, enabling efficient and low-consumption monitoring with the objective of leveraging structural health monitoring (SHM). The research includes preliminary tests conducted in a real environment to validate the PE-EHS when fully integrated into a ventilated façade, capturing mechanical vibrations generated mainly by wind loads. Based on these activities, the final configuration of PE-EHSs is defined to provide a complete system for façade monitoring. This integrated system includes the piezoelectric generator (PEG), supercapacitor (SC), Power Conditioner Circuit (PCC), Fiber Optic Sensing (FOS) interrogator, and the IoT gateway transmitting measurement data within an Internet of Things (IoT) monitoring platform. This configuration is tailored to address the challenges related to the structural integrity of building envelopes. Results demonstrate a potential for a stand-alone solution in the façade sector but raise issues for certain limitations, requiring further investigation. In particular, the study emphasizes constraints related to the energy production of PE-EHSs for façade integration. It highlights the necessity to carefully consider these limitations within the broader context of their applicability, providing insights for the informed deployment of piezoelectric energy harvesting technology in building envelope monitoring.

A Priority-Based Self-Adaptive Random Early Detection Algorithm in IoT Gateways

Effective algorithms for queue management are crucial in place of guaranteeing maximum efficiency in gateway routers since network traffic continues to expand dramatically. An online researcher has suggested the Active Queue Management (AQM) strategy regarding the upcoming generation of gateway switches. The common active queue scheme remains (RED) Random Early Detection. Random early detection is susceptible to parameterization issues and lacks a self-adaptation mechanism. Several RED variants have been formed; nevertheless, variations in traffic load have an adverse effect on all of them. Due to the fact that each has a static drop pattern, to address the RED and its variation schemes, the SARED system, or the design of self-adaptive random early detection was created. But in order to prevent congestion, during the time when the queue length surpasses a present maximum threshold limit, SARED aggressively removes packets. This causes networks having a lot of traffic situations the average is expected to increase queue delay, so in those cases, SARED should be less aggressive. This paper develops a priority-based queuing congestion control method for IoT gateways to manage network congestion. Our method (priority-based algorithms) performs substantially better with regard to throughput, delay, and packet loss than the present methods of SARED. The outcomes of the conducted simulation experiments have shown that in scenarios with heavy traffic loads, priority-based self-adaptive random early detection (PSARED) has greatly decreased average queuing delay by 3%, minimized average throughput by 1%, and decreased the rate of packet loss by 10% in contrast to SARED.

Natural language command parsing for agricultural measurement and control based on AMR and entity recognition

There is scope to enhance agricultural measurement and control systems user interactivity, which typically necessitates training for users to perform specific operations successfully. With the continuous development of natural language semantic processing technology, it has become essential to augment the user-friendliness of multifaceted control and query operations in the agricultural measurement and control sector, ultimately leading to reduced operation costs for users. The study aims to focus on command parsing. The proposed AMR-OPO semantic parsing framework is based on the natural language understanding method of Abstract Meaning Representation of Rooted Markup Graphs (AMR). It transforms the user’s natural language inputs into structured ternary (OPO) statements (operation-place-object) and converts the corresponding parameters of the user’s input commands. The framework subsequently sends the transformed commands to the relevant devices via the IoT gateway. To tackle the intricate task of parsing instructions, we developed a BERT-BiLSTM-ATT-CRF-OPO entity recognition model. This model can detect and extract entities from agricultural instructions, and precisely populate them into OPO statements. Our model shows exceptional accuracy in instruction parsing, with precision, recall, and F-value all measuring at 92.13%, 93.12%, and 92.76%, correspondingly. The findings from our experiment reveal outstanding and precise performance of our approach. It is anticipated that our algorithm will enhance the user experience offered by agricultural measurement and control systems, while also making them more user-friendly.

Design method of laboratory equipment monitoring system based on industrial internet of things

AbstractThe IoT‐based monitoring system is used to collect, transmit, analyze, and monitor industrial data in real time, as well as to monitor and operate industrial field equipment. Reduced manufacturing costs, better managerial decision‐making, and increased production efficiency are all very important. The main research results are as follows: (1) The current development of the IoT technology at home and abroad is analyzed, the overall design of the laboratory equipment monitoring system with a three‐layer three‐dimensional architecture is proposed, and its working mode and functional composition are discussed. (2) A set of PLC control system with S7‐300 PLC as the core is designed by using the laboratory CS4000 process experiment device, which realizes the functional requirements of the laboratory equipment monitoring system on‐site monitoring. The intelligent IoT gateway suitable for the system is selected as the data collection and aggregation device of the system, which fully demonstrates the great role of the IoT technology in the monitoring field. (3) A laboratory remote monitoring platform that can remotely monitor experimental data is designed and its software and hardware functions are tested. The results show that all indicators have excellent performance.

Toward Continuous Threat Defense: in-Network Traffic Analysis for IoT Gateways

Toward Continuous Threat Defense: in-Network Traffic Analysis for IoT Gateways

Security-enhanced firmware management scheme for smart home IoT devices using distributed ledger technologies

With the increase of IoT devices generating large amounts of user-sensitive data, improper firmware harms users’ security and privacy. Latest home appliances are integrated with features to assure compatibility with smart home IoT. However, applying complex security mechanisms to IoT is limited by device hardware capabilities, making them vulnerable to attacks. Such attacks have recently become frequent. To address this issue, we developed a secure verification mechanism for firmware released by the device’s manufacturer. We proposed an IoT gateway for secure firmware verification and updating for smart home IoT devices utilizing the IOTA MAM (Masked Authenticated Messaging) protocol and a distributed file system with IPFS (Inter-Planetary File System) protocol. These two communication protocols ensure decentralized communication and firmware file distribution between the IoT device vendor and the IoT end device. The proposed scheme securely shares latest firmware content over IOTA and IPFS networks, performs a secure firmware update on IoT end devices and ensures authenticity and integrity of the firmware. Two types of validation methods were proposed for firmware updating and validation. We implemented the proposed scheme using three entities, Vendor, IoT gateway, and IoT end device. Our system yielded promising results in performing secure automated firmware updates on IoT end devices with very low computational power. The system’s functionality was implemented using IOTA’s MAM run on Raspberry Pi as an IoT gateway along with an ESP8266 Wi-Fi microcontroller, demonstrating the effectiveness of our approach. Our proposed methodology can be used for secure firmware distribution on home IoT applications.