Internet Of Things Research Articles

Policy-Based Smart Contracts Management for IoT Privacy Preservation

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

Puzzle sine cosine optimization-based secure communication and brain tumor classification in IoT‐healthcare system

A brain tumor (BT) refers to an irregular accumulation of cells within the brain that proliferates uncontrollably, resulting in the formation of a mass. The accurate classification and early detection are important for effective treatment. In previous researches, the BT exhibited diverse features in terms of size, shape, and location. Moreover, the images used for segmentation, which suffered from image noise, low contrast, and shifting intensities within tissues. These issues are overcome by developing an effective method in this paper named Puzzle Sine Cosine Optimization enabled Deep Kronecker Network (PSCO-DKN) for classifying BT in the Internet of Things (IoT) healthcare system. Firstly, an IoT network is simulated, where the IoT device is used to capture the patient’s Magnetic Resonance Imaging (MRI) images. Further, the images are routed to the Base Station (BS) by employing PSCO. The routing is accomplished by contemplating several fitness parameters including delay, energy, and distance. At the BS, the process for BT classification is implemented as follows. Initially, the pre-processing is done by utilizing the median filter. Afterwards, the segmentation process is done by applying Spatial Attention U-Net (SA-Unet). After that, Statistical features and Shape Local Binary Texture (SLBT) are extracted. At last, BT classification is performed by utilizing the DKN, which is structurally optimized by using PSCO developed by the hybridization of Puzzle Optimization Algorithm (POA) and Sine Cosine Algorithm (SCA). Finally, PSCO-DKN attained superior outcomes of True Negative Rate (TNR) at 90.9 %, True Positive Rate (TPR) at 92.6 %, and accuracy at 87.7 %.

Energy-efficient data routing using neuro-fuzzy based data routing mechanism for IoT-enabled WSNs

This paper proposes a novel Neuro-fuzzy-based Data Routing (NFDR) mechanism for efficient data routing and dynamic cluster formation in Internet of Things (IoT) enabled Wireless Sensor Networks (WSNs). The NFDR mechanism incorporates optimal scalability factors computed from past and present network parameter values, acting as an additional buffer factor to sustain nodes within clusters, even with partial satisfaction of network parameter values. The neural network determines cluster formation requirements, while the objective function adjusts according to the updated fuzzy logic of identified cluster members. Super heads are initially selected, and cluster member size is adjusted to sustain maximum data transmission without affecting network parameter thresholds. Simulation results demonstrate that the NFDR mechanism enhances clustering range and cluster members while sustaining evaluation metrics such as 75% energy retention, 20% end-to-end delay reduction, and a 15% reduction in dead nodes, ultimately contributing to the development of efficient and robust IoT-enabled WSNs.

Engineering an Ultrafast Ambient NO2 Gas Sensor Using Cotton-Modified LaFeO3/MXene Composites.

This work presents a room-temperature (RT) NO2 gas sensor based on cotton-modified LaFeO3 (CLFO) combined with MXene. LaFeO3 (LFO), CLFO, and CLFO/MXene composites were synthesized via a hydrothermal method. The fabricated sensor, utilizing MXene/CLFO, exhibits a p-type behavior and fully recoverable sensing capabilities for low concentrations of NO2, achieving a higher response of 14.2 times at 5 ppm. The sensor demonstrates excellent performance with a response time of 2.7 s and a recovery time of 6.2 s, along with notable stability. The sensor's sensitivity is attributed to gas interactions on the material's surface, adsorption energy, and charge-transfer mechanisms. Techniques such as in situ FTIR (Fourier transform infrared) spectroscopy, GC-MS (gas chromatography-mass spectroscopy), and near-ambient pressure X-ray photoelectron spectroscopy were employed to verify gas interactions and their byproducts. Additionally, finite-difference time-domain simulations were used to model the electromagnetic field distribution and provide insight into the interaction between NO2 molecules and the sensor surface at the nanoscale. A prototype wireless IoT (Internet of Things)-based NO2 gas leakage detection system was also developed, showcasing the sensor's practical application. This study offers valuable insight into the development of room-temperature NO2 sensors with a low detection limit.

Optimizing glycemic control in type 2 diabetes: the impact of the GLIDE program's personalized digital health intervention.

The integration of digital health applications into type 2 diabetes mellitus (T2DM) management presents promising opportunities for optimizing glycemic control, enhancing adherence, and improving health outcomes. MyTatva's Glycemic Lifestyle Intervention in Diabetes Empowerment (GLIDE) program, which integrates dietary and exercise regimens, cognitive behavioral therapy (CBT), and Internet of Things (IoT) devices, potentiates this approach. This study aimed to evaluate the effectiveness of the GLIDE program's personalized, comprehensive approach in improving glycemic control over 90 days among T2DM patients. During the study period, 30 diabetic patients completed their GLIDE journey with expert dieticians, physiotherapists, and behavior therapists. Each patient received a personalized root cause analysis based on lifestyle assessment and disease-specific parameters. Statistical analysis was conducted using a paired t-test on the deidentified HbA1c, FBS, and PPBS data at baseline and post-intervention. Throughout the study, 27 patients actively adhered to the GLIDE program. All the parameters showed statistically significant (p<0.05) changes post-intervention. HbA1c decreased by 11.79% from 8.43% ± 1.32 to 7.44% ± 0.64. Significant reductions were observed in PPBS (47.7%), decreasing from 260.89 mg/dL ± 36.31 to 136.27 mg/dL± 6.36, compared to FBS (31.1%), which decreased from 8.43 mg/dL± 1.32 to 7.44 mg/dL± 0.64. The effectiveness of the GLIDE program is based on a comprehensive root cause analysis approach. The detailed analysis of the patient's clinical journey by health experts at regular intervals enables precise goal management, resulting in expected outcomes for better glycemic control. Therefore, personalized digital health plans are vital for achieving clinically significant changes.

The use of digital technologies to automate processes in leasing companies

The article is devoted to the topic of using digital technologies to automate processes in organizations engaged in leasing activities. The relevance is due to the increasing need to improve the efficiency and competitiveness of leasing companies in the digital economy. The purpose of the study is to develop an innovative algorithm for the introduction of digital technologies in the leasing industry.The paper examines the existing contradictions between traditional business methods in the analyzed area and the opportunities provided by modern digital technologies. The author describes the impact of digitalization on the leasing industry and relies on an integrated approach to the implementation of innovative solutions.Special attention is paid to the key aspects of the use of digital technologies in leasing companies from the perspective of organizing the work of employees, their development (considered through the prism of automating routine processes, strengthening digital skills, flexibility to change, analytical capabilities, increasing motivation, engagement).Based on the conducted research, it was possible to conclude that it is necessary to create an integrated digital «ecosystem» in leasing companies, which focuses on the use of blockchain technologies, artificial intelligence, the Internet of Things, and virtual reality. The proposed algorithm is aimed at increasing the flexibility, customer orientation, and effectiveness of the leasing business. The specific examples analyzed in the article demonstrate a variety of approaches to process automation in leasing organizations; they confirm the effectiveness and positive effects of the introduction of digital developments to increase operational efficiency and improve customer experience.The article will be useful for managers and specialists of leasing companies, developers of fintech solutions, as well as researchers in the field of digital transformation of the financial sector.

Problems and prospects for livestock industry development in the context of digitalization of the agro-industrial complex in the region (based on the materials of the Republic of Bashkortostan of the Russian Federation)

The current state and prospects of livestock breeding development in the Republic of Bashkortostan in the conditions of active digitalization of the agro-industrial complex have been analyzed. Despite the desire to introduce innovative technologies, the process faces a number of obstacles that require a comprehensive solution for sustainable development of the industry. The key problems have been highlighted, such as lack of a unified digital platform, low level of digital competencies, financial constraints, lack of a unified strategy, and resistance to digital transformation. Prospects for livestock farming digitalization development in the region have been considered, such as smart livestock farms development through automated systems for climate control, feeding, milking, and animal health monitoring, use of sensors and the Internet of Things, artificial intelligence application for data analysis, yield forecasting, feeding and treatment processes optimization, development of a single digital platform for information exchange between farmers, veterinary services, fodder suppliers, and other participants, and development of a single digital platform for the exchange of information among farmers, veterinary services, feed suppliers, and other stakeholders. The important role of the state and the regions in digital livestock farming development has been emphasized, including financial support, legal framework implementation, and infrastructure and research development. Emphasis has been placed on the fact that livestock breeding digitalization in the Republic of Bashkortostan has great potential for increasing efficiency and competitiveness of the industry, but its implementation requires comprehensive measures aimed at overcoming existing problems and creating favorable conditions for new technologies implementation.

A counter mode and multi-channel based chaotic image encryption algorithm for the internet of things

To deal with the threat of image privacy leakage in the Internet of things, this paper presents a novel batch images encryption algorithm using the counter mode and a multi-channel processing scheme. We employ multi-thread technique combined with an adapter to construct a novel multi-channel processing scheme, which can encrypt four different sized images in one round. Moreover, the counter encryption mode, which can compute round keys from a plaintext related session key, is introduced to decrease the difficulty of session key management when dealing with batch images. The security tests demonstrate the exceptional performance of the proposed algorithm in terms of security, as evidenced by P-values of statistical tests far larger than 0.01, correlation coefficients and entropies of cipher images close to 0 and greater than 7.99. Additionally, the results of NPCR and UACI tests closely approximate the theoretical values 99.6094% and 33.4635%, the proposed algorithm can better resist statistical, exhaustive, differential, or even chosen plaintext attacks. Moreover, due to the novel parallel scheme with a linear time complexity of O(2W+2H), which demonstrates an acceleration of over 300% compared to existing algorithms, it only takes 2.1sto encrypt one hundred images with varying sizes. Therefore, the proposed algorithm succeeds in exceeding existing algorithms in meeting the efficiency and security requirements for encrypting batch images.

Implementation and evaluation of digital twin framework for Internet of Things based healthcare systems

AbstractThe integration of digital twins (DTs) in healthcare is critical but remains limited in real‐time patient monitoring due to challenges in achieving low‐latency telemetry transmission and efficient resource management. This paper addresses these limitations by presenting a novel cloud‐based DT framework that optimises real‐time healthcare monitoring, providing a timely solution for critical healthcare needs. The framework incorporates a Pyomo‐based dynamic optimisation model, which reduces telemetry latency by 32% and improves response time by 52%, surpassing existing systems. Leveraging low‐cost, low‐latency multimodal sensors, the system continuously monitors critical physiological parameters, including SpO2, heart rate, and body temperature, enabling proactive health interventions. A DT definition language (Digital Twin Definition Language)‐based time series analysis and twin graph platform further enhance sensor connectivity and scalability. Additionally, the integration of machine learning (ML) strengthens predictive accuracy, achieving 98% real‐time accuracy and 99.58% under cross‐validation (cv = 20) using the XGBoost algorithm. Empirical results demonstrate substantial improvements in processing time, system stability, and learning capacity, with real‐time predictions completed in 17 ms. This framework represents a significant advancement in healthcare monitoring, offering a responsive and scalable solution to latency and resource constraints in real‐time applications. Future research could explore incorporating additional sensors and advanced ML models to further expand its impact in healthcare applications.

Task offloading for Industrial Internet of Things based on evolutionary multiobjective multitasking optimization

In mobile edge computing(MEC) scenarios, offloading computation-intensive and delay-critical tasks to nearby mobile edge servers with abundant computing resources can reduce the time delay(TD) of tasks and energy consumption(EC) of terminal devices, thus improving application performance and user experience. In order to reduce the TD of tasks and the EC of factory terminal devices(FTDs), MEC is introduced into the Industrial Internet of Things(IIoT). Because TD and EC are conflicting objectives, most existing research models the task offloading problem(TOP) as a multiobjective optimization problem to optimize both TD and EC. However, as the number of FTDs and tasks in the MEC network increases, the scale and computational cost of the TOP increases, and it is very challenging to obtain the optimal task offloading scheme through multiobjective optimization. Multitasking optimization can make use of knowledge transfer between related tasks to promote the solving efficiency of each task. Based on this, we model the TOP in IIoT as a multiobjective TOP (MTOP). A cheap task that is highly similar to MTOP is constructed, and a novel evolutionary multiobjective multitasking framework is developed, which uses the positive knowledge transfer between tasks to optimize TD and EC in the network. Then, an effective evolutionary multiobjective multitasking algorithm is proposed, which includes multi-functional knowledge transfer strategy(MFKT) and adaptive cheap task update strategy(ACTU). MFKT uses decision variables with different functions in the cheap tasks for positive knowledge transfer, so as to improve the performance of MTOP. ACTU dynamically updates the cheap task to maintain knowledge transfer between tasks. In different test instances, the proposed algorithm is compared with the existing multiobjective and multitasking algorithms. The experimental results show that the proposed algorithm is more competitive in terms of TD and EC, it can guarantee the service quality of the IIoT system and has strong robustness and scalability.

Designing Smart Supply Chains Based on Digital AI Instruments and Mobile Technologies

Academic and industrial research on digital technology has been quite thorough. Nevertheless, scant information exists regarding the supply chain implementation of digital technology in industrial companies. To better understand supply chains, this article will look at why and how manufacturing companies are implementing digital technology. The goal of this paper’s literature analysis is to pinpoint current studies and perspectives on how these technologies could help digital supply chains (DSCs) operate better, faster, and more efficiently. The investigation looks at the technologies’ directions and challenges in improving the efficiency of digital help confinement, in addition to underlining their significance for supply chains and logistics. It investigates the managerial and practical issues in developing a new integrated paradigm for digital supply chains. Also, possible obstacles to DSCs and procurement will be examined, along with solutions. Consequently, this study pinpoints multiple opportunities for adding value when implementing Internet of Things (IoT) in the supply chain using mobile technology. Additionally, it suggests a method for creating business models for supply chain algorithms.

Improvement of intrusion detection system in industrial Internet of Things based on deep learning with fog computing capability

Improvement of intrusion detection system in industrial Internet of Things based on deep learning with fog computing capability

IoT Traffic Parameter Classification based on Optimized BPSO for Enabling Green Wireless Networks

The rapid expansion of artificial intelligence (AI) integrated with the Internet of Things (IoT) has fueled the development of various smart devices, particularly for smart city applications. However, the heterogeneity of these devices necessitates a robust communication network capable of maintaining a consistent traffic flow. This paper employs Machine Learning (ML) models to classify continuously received network parameters from diverse IoT devices, identifying necessary adjustments to enhance network performance. Key network traffic parameters, such as packet data, are transmitted through gateways via specialized tools. Six different ML techniques with default parameters were used: Decision Tree (DT), Random Forest (RF), Support Vector Machines (SVMs), K-Nearest Neighbors (KNN), Naive Bayes (NB), and Stochastic Gradient Descent Classifiers (SGDC), to classify the traffic of the environment (IoT / non IoT). The models' performance was evaluated in a real-time smart laboratory environment comprising 38 IoT devices from various vendors with the following metrics: Accuracy, F1-score, Recall and Precision. The RF model achieved the highest Accuracy of 95.6%. Also the Binary Particle Swarm Optimizer (BPSO) was used across the RF. The results demonstrated that the BPSO-RF with hyperparameter optimization enhanced the Accuracy from 95.6% to 99.4%.

Minimizing IoT Security Deployment Costs using the Dominating Set Approach

The rise of the Internet of Things (IoT) has generated significant interest by enabling connectivity across various objects, ranging from the smallest devices to large-scale systems. Despite its benefits, IoT poses considerable security challenges due to the many interconnected devices that collect and transmit sensitive data across networks. Therefore, ensuring robust data protection and preventing unauthorized access or misuse are essential concerns. To address this issue, strategically placing security services within IoT networks is vital for safeguarding both devices and data. One promising strategy for optimizing this placement is the use of the dominating set concept derived from graph theory, which helps in the efficient allocation of security resources. This study presents an IoT network as a simple weighted graph, considering device capabilities while focusing on adopting the dominating set concept to enhance the placement of security services in IoT networks. To achieve this, an enhanced greedy heuristic is proposed for efficiently generating the dominating set. The effectiveness and performance of the proposed approach are evaluated through a comparative analysis combined with existing methods in the recent literature.

Software-Based Security Framework for Edge andMobile IoT

With the proliferation of Internet of Things (IoT) devices, ensuring secure communications has become imperative. Due to their low cost and embedded nature, many of these devices operate with computational and energy constraints, neglecting the potential security vulnerabilities that they may bring. This work-in-progress is focused on designing secure communication among remote servers and embedded IoT devices to balance security robustness and energy efficiency. The proposed approach uses lightweight cryptography, optimizing device performance and security without overburdening their limited resources. Our architecture stands out for integrating Edge servers and a central Name Server, allowing secure and decentralized authentication and efficient connection transitions between different Edge servers. This architecture enhances the scalability of the IoT network and reduces the load on each server, distributing the responsibility for authentication and key management.

Dynamic multi-criteria scheduling algorithm for smart home tasks in fog-cloud IoT systems

The proliferation of Internet of Things (IoT) devices in smart homes has created a demand for efficient computational task management across complex networks. This paper introduces the Dynamic Multi-Criteria Scheduling (DMCS) algorithm, designed to enhance task scheduling in fog-cloud computing environments for smart home applications. DMCS dynamically allocates tasks based on criteria such as computational complexity, urgency, and data size, ensuring that time-sensitive tasks are processed swiftly on fog nodes while resource-intensive computations are handled by cloud data centers. The implementation of DMCS demonstrates significant improvements over conventional scheduling algorithms, reducing makespan, operational costs, and energy consumption. By effectively balancing immediate and delayed task execution, DMCS enhances system responsiveness and overall computational efficiency in smart home environments. However, DMCS also faces limitations, including computational overhead and scalability issues in larger networks. Future research will focus on integrating advanced machine learning algorithms to refine task classification, enhancing security measures, and expanding the framework’s applicability to various computing environments. Ultimately, DMCS aims to provide a robust and adaptive scheduling solution capable of meeting the complex requirements of modern IoT ecosystems and improving the efficiency of smart homes.

Accurate Indoor Localization with IoT Devices and Advanced Fingerprinting Methods

The Internet of things (IoT) has significantly impacted various sectors, including healthcare, environmental monitoring, transportation, and commerce, by enhancing communication networks through the integration of sensors, software, and hardware. This paper presents an accurate IoT indoor localization system based on IoT devices and fingerprinting methods. We explore indoor localization techniques using Bluetooth Low Energy (BLE) and a Radio Signal Strength Indicator (RSSI) to address the limitations of GPS in indoor environments. The study evaluates the effectiveness of iBeacon transmitters for indoor positioning, comparing the Weighted Centroid Localization (WCL) and Positive Weighted Centroid Localization (PWCL) algorithms, along with fingerprinting methods enhanced by outlier detection and mapping filters. Our methodology includes mapping a real environment onto a coordinate axis, collecting training data from 47 sampling points, and implementing four localization algorithms. The results show that the PWCL algorithm improves accuracy over the WCL algorithm, and hybrid methods further reduce localization errors. The HYBRID-MAPPED method achieves the highest accuracy, with an average error of 1.44 m.

Design of Car Based on 5G Network Control

The role of smart cars is pivotal, and this project designs and implements a four-wheel vehicle control system leveraging 5G communication technology. The system aims to enhance the portability of smart cars, reduce their costs, enable remote control functionality, and improve mobility to meet the needs of modern Internet of Things (IoT) applications. The system integrates an ESP8266 Wi-Fi module with the Blinker IoT platform to enable remote, real-time control of car movement via a smartphone app. Using Access Point (AP) mode for fast network configuration, users can input Wi-Fi credentials and a Blinker key through a web interface for easy setup. Through the custom app interface, users can send commands to control the car’s forward, backward, turning, and stopping actions, as well as adjust speed and operation delay. Additionally, the system includes Electrically Erasable Programmable Read-Only Memory (EEPROM) data storage to ensure the persistent saving of configuration information, and it features a remote wireless camera for external monitoring of the car’s surroundings. The Android-based remote control design allows users to monitor and control the car’s movement anytime and anywhere. Experimental results show that the system is stable, provides smooth control, operates at low cost and low power consumption, and offers good portability. Therefore, this intelligent car control system offers valuable insights for smart car development and application. It can also be integrated with popular smart homes, IoT, and other emerging technologies, offering broad application potential and promising development prospects.

A Framework for Smart City Traffic Management utilizing BDA and IoT

This paper explores a new approach to traffic flow optimization in smart cities, harnessing the combined power of Big Data Analytics (BDA) and the Internet of Things (IoT). The system utilizes a citywide network of connected sensors to acquire live traffic information, including vehicle speeds, density, and congestion points. These data are thereafter processed applying some top-notch BDA algorithms to identify traffic anomalies and forecast congestion levels, and generate actionable insights. By analyzing this information, the system can dynamically adjust traffic signals, recommend alternative routes, and improve traffic efficiency in real-time. The system's adaptive learning capabilities allow it to continuously enhance its predictions based on new data, ensuring its effectiveness in managing evolving traffic patterns. This intelligent traffic management solution promises to significantly reduce congestion, ameliorate overall mobility and road safety, and contribute to a more sustainable city environment.

Intelligent IoT Platform for Agroecology: Testbed

Smart farming is set to play a crucial role in the sustainable transformation of agriculture. The emergence of precision agriculture, facilitated by Internet of Things (IoT) platforms, makes effective communication among the various sensors and devices on farms essential. The development of smart sensors that utilize artificial intelligence (AI) algorithms for advanced edge computations only intensifies this need. Moreover, once data are collected, farmers frequently find it challenging to apply them effectively, especially in alignment with agroecological principles. In this context, this paper introduces an energy-efficient platform for embedded AI sensors that leverages the LoRaWAN network, along with a knowledge-based system to aid farmers in decision-making rooted in sensor data and agroecological practices. This paper focuses on the deployment of an end-to-end IoT platform that integrates a wireless sensor network (WSN), embedded AI, and a knowledge base.