Internet Of Things Sensor Networks Research Articles

Data Aestheticization: A Cognitively-Inspired Method for Knowledge Discovery in Cognitive IoT Sensor Network

Data Aestheticization: A Cognitively-Inspired Method for Knowledge Discovery in Cognitive IoT Sensor Network

Selective hypothesis testing in cognitive IoT sensor network

Selective hypothesis testing in cognitive IoT sensor network

Al-driven Precision Agriculture: Advancing Crop Yield Prediction

This article explores the integration of Artificial Intelligence (AI) in precision agriculture, focusing on its role in advancing crop yield prediction and improving overall agricultural productivity. It examines three key areas: satellite imagery analysis, soil health monitoring, and weather data integration. The paper discusses how AI techniques such as Convolutional Neural Networks, IoT sensor networks, and ensemble methods are revolutionizing farming practices. It highlights the technical implementations of these technologies, their applications, and their significant impact on yield optimization, resource efficiency, and decision-making in agriculture. The study emphasizes the crucial role of AI in addressing global food security challenges and improving agricultural resilience to climate change.

Transitive reasoning: A high-performance computing model for significant pattern discovery in cognitive IoT sensor network

Current research on the Internet of Things (IoT) has given rise to a new field of study called cognitive IoT (CIoT), which aims to incorporate cognition into the designs of IoT systems. Consequently, the CIoT inherits specific attributes and challenges from IoT. The CIoT applications generate vast, diverse, constantly changing, and time-dependent data due to the billions of devices involved. The efficient operation of these CIoT systems requires the extraction of valuable insights from vast data sources in a computationally efficient manner. Therefore, this study proposes transitive reasoning to glean significant concepts and patterns from a 21.25-year environmental dataset. To reduce the effects of missing entries, the proposed methodology includes a grouping of data using probabilistic clustering and applying total variance regularization in the alternate direction method of multipliers (ADMM) to regularize the sensory data. As a result, noisy entries will be less conspicuous. Afterward, it calculates the transitional plausibility value for each cluster using the transited value and then turns it into binary data to create concept lattices. In addition, each concept that is formed is assigned a weight, and the concept with the largest transitive strength value is chosen, followed by calculating the mean value. Therefore, this pattern is seen as significant. Experimental results on 21.25-year environmental data show an accuracy of over 99.5%, outperforming competing methods, as shown by cross-validation using multiple metrics.

Optimize Stormwater Management for Green Roofs Based on IoT Technology and Sensor Networks

The intensification of global climate change and the significant increase of extreme rainfall events have made traditional urban drainage systems face great adjustments. As a key component of green infrastructure to avoid urban flood risk, green roofs are receiving increasing attention due to their unique capabilities in stormwater management. This paper explores the potential of Internet of Things (IoT) technology and sensor networks to enhance green roof stormwater management. Integrated real-time monitoring of key environmental parameters, including soil moisture, temperature and rainfall, enables the system to dynamically adjust roof drainage and irrigation strategies. This greatly improves the rainwater retention capacity and reduces the pressure on the municipal sewage system. The results show that the combination of IoT technology and sensor networks not only improves the efficiency of green roof management in combating climate change, but also promotes the efficient use of water resources and enhances the adaptability and sustainability of the system. This research provides the necessary technical support for the wider adoption of green roofs in urban environments and offers viable solutions to future challenges posed by climate change.

Quality of Service-Aware Multi-Objective Enhanced Differential Evolution Optimization for Time Slotted Channel Hopping Scheduling in Heterogeneous Internet of Things Sensor Networks.

The emergence of the Internet of Things (IoT) has attracted significant attention in industrial environments. These applications necessitate meeting stringent latency and reliability standards. To address this, the IEEE 802.15.4e standard introduces a novel Medium Access Control (MAC) protocol called Time Slotted Channel Hopping (TSCH). Designing a centralized scheduling system that simultaneously achieves the required Quality of Service (QoS) is challenging due to the multi-objective optimization nature of the problem. This paper introduces a novel optimization algorithm, QoS-aware Multi-objective enhanced Differential Evolution optimization (QMDE), designed to handle the QoS metrics, such as delay and packet loss, across multiple services in heterogeneous networks while also achieving the anticipated service throughput. Through co-simulation between TSCH-SIM and Matlab, R2023a we conducted multiple simulations across diverse sensor network topologies and industrial QoS scenarios. The evaluation results illustrate that an optimal schedule generated by QMDE can effectively fulfill the QoS requirements of closed-loop supervisory control and condition monitoring industrial services in sensor networks from 16 to 100 nodes. Through extensive simulations and comparative evaluations against the Traffic-Aware Scheduling Algorithm (TASA), this study reveals the superior performance of QMDE, achieving significant enhancements in both Packet Delivery Ratio (PDR) and delay metrics.

Adaptive threshold based outlier detection on IoT sensor data: A node-level perspective

The accuracy and reliability of IoT-based sensor networks depend on validating sensed data, including detecting outliers at the node level. This study proposes an online outlier detection approach using Multiple Linear Regression-based adaptive thresholds for real-time IoT/WSN sensor nodes. IoT sensors experience two outlier types: Errors, from sensor malfunctions or low battery, and Events, from sudden environmental changes. The Adaptive Threshold Based Outlier Detection (ATBOD) approach differentiates errors from events using an adaptive threshold that adjusts to real-time data patterns. Unlike existing methods that are used in literature, which lack automated model evolution and suffer from delays and high computational time, ATBOD enhances outlier detection sensitivity without increasing false alarms, which is crucial for efficient IoT sensor board operation. It also improves sensor board lifespan by discarding errors at the node level, preventing energy wastage from transmitting error data to the cloud. ATBOD outperforms existing algorithms, which are referenced for comparison, such as Enhanced Efficient Outlier Detection and Classification Approach (EEODCA), K Nearest Neighbor approximate outlier detection (KNN), and Modified Local Outlier Factor (LOF), in Error Detection Rate, Error False Positive Rate, and Energy Saving Ratio. These advancements represent a significant leap in performance, making ATBOD a superior method for real-time outlier detection in IoT sensor networks.

Advanced authentication of IoT sensor network for industrial safety

As industries rapidly advance towards automation and intelligence, the Industrial Internet of Things (IIoT) plays a pivotal role in enhancing factory efficiency and production management. However, the advent of quantum computing introduces significant security challenges, particularly for data transmission and privacy protection, as its immense computational power can compromise the current cryptographic theories' safeguards. We introduce a novel lattice-based cryptographic authentication and key management scheme specifically designed for the IIoT within Wireless Sensor Networks (WSNs), focusing on establishing a secure industrial communication system capable of withstanding quantum threats. The proposed scheme utilizes Crystals-Kyber technology and is built around the Learning With Errors (LWE) problem, providing a robust foundation for security against both traditional and quantum threats. It features innovative methods to safeguard against password guessing, stolen verifier, and replay attacks, enhancing data confidentiality and system integrity. By integrating advanced cryptographic techniques resistant to quantum decryption, this research ensures the secure management and utilization of critical production data, supporting the ongoing transformation towards more intelligent manufacturing processes and sustaining global competitiveness for industries.

Periodic Energy Optimization Using IOT and ML

The rapid expansion of Internet of Things (IoT) applications across various sectors generates an enormous volume of continuous time-series data. However, transmitting this massive amount of sensor data from energy constrained IoT nodes poses a significant challenge. The continuous transmission of such data consumes vast amounts of energy.In this work, we present a solution to this problem by predicting the periodic behavior of sensor data through a higher-level view of continuous transmission data from nodes in IoT at server side. Our system is composed of an IoT sensor network and a data processing unit. The local sensor network: temperature and humidity data is collected using 4 different nodes, as well, which afterward this info is transferred into a data processing unit built on the Raspberry Pi device. We use the machine learning model Autoregressive Integrated Moving Average (ARIMA) on the processing unit. This model is then applied individually to the data from each of the four nodes, predicting processed sensor values in the future accurately. In short, after getting highly accurate prediction, then we settle down proper energy saving pattern which reduces the data transmission requirements hence results in energy saving pattern.By utilizing the predictive capabilities of the ARIMA model, we minimize the need for constant transmission of raw sensor data. Instead, we transmit only essential updates or deviations from the predicted values. This approach substantially reduces energy consumption by eliminating the transmission of redundant information. In summary, our project aims to overcome the energy limitations of IoT sensor nodes by leveraging predictive modelling techniques, specifically the ARIMA model. By accurately predicting periodic patterns in sensor data, we can optimize energy usage by transmitting only the necessary information, while still ensuring effective monitoring of temperature and humidity in the IoT network.

Development and Application of IoT Monitoring Systems for Typical Large Amusement Facilities.

The advent of internet of things (IoT) technology has ushered in a new dawn for the digital realm, offering innovative avenues for real-time surveillance and assessment of the operational conditions of intricate mechanical systems. Nowadays, mechanical system monitoring technologies are extensively utilized in various sectors, such as rotating and reciprocating machinery, expansive bridges, and intricate aircraft. Nevertheless, in comparison to standard mechanical frameworks, large amusement facilities, which constitute the primary manned electromechanical installations in amusement parks and scenic locales, showcase a myriad of structural designs and multiple failure patterns. The predominant method for fault diagnosis still relies on offline manual evaluations and intermittent testing of vital elements. This practice heavily depends on the inspectors' expertise and proficiency for effective detection. Moreover, periodic inspections cannot provide immediate feedback on the safety status of crucial components, they lack preemptive warnings for potential malfunctions, and fail to elevate safety measures during equipment operation. Hence, developing an equipment monitoring system grounded in IoT technology and sensor networks is paramount, especially considering the structural nuances and risk profiles of large amusement facilities. This study aims to develop customized operational status monitoring sensors and an IoT platform for large roller coasters, encompassing the design and fabrication of sensors and IoT platforms and data acquisition and processing. The ultimate objective is to enable timely warnings when monitoring signals deviate from normal ranges or violate relevant standards, thereby facilitating the prompt identification of potential safety hazards and equipment faults.

Providing Fine Temporal and Spatial Resolution Analyses of Airborne Particulate Matter Utilizing Complimentary In Situ IoT Sensor Network and Remote Sensing Approaches

This study aims to provide analyses of the levels of airborne particulate matter (PM) using a two-pronged approach that combines data from in situ Internet of Things (IoT) sensor networks with remotely sensed aerosol optical depth (AOD). Our approach involved setting up a network of custom-designed PM sensors that could be powered by the electrical grid or solar panels. These sensors were strategically placed throughout the densely populated areas of North Texas to collect data on PM levels, weather conditions, and other gases from September 2021 to June 2023. The collected data were then used to create models that predict PM concentrations in different size categories, demonstrating high accuracy with correlation coefficients greater than 0.9. This highlights the importance of collecting hyperlocal data with precise geographic and temporal alignment for PM analysis. Furthermore, we expanded our analysis to a national scale by developing machine learning models that estimate hourly PM 2.5 levels throughout the continental United States. These models used high-resolution data from the Geostationary Operational Environmental Satellites (GOES-16) Aerosol Optical Depth (AOD) dataset, along with meteorological data from the European Center for Medium-Range Weather Forecasting (ECMWF), AOD reanalysis, and air pollutant information from the MERRA-2 database, covering the period from January 2020 to June 2023. Our models were refined using ground truth data from our IoT sensor network, the OpenAQ network, and the National Environmental Protection Agency (EPA) network, enhancing the accuracy of our remote sensing PM estimates. The findings demonstrate that the combination of AOD data with meteorological analyses and additional datasets can effectively model PM 2.5 concentrations, achieving a significant correlation coefficient of 0.849. The reconstructed PM 2.5 surfaces created in this study are invaluable for monitoring pollution events and performing detailed PM 2.5 analyses. These results were further validated through real-world observations from two in situ MINTS sensors located in Joppa (South Dallas) and Austin, confirming the effectiveness of our comprehensive approach to PM analysis. The US Environmental Protection Agency (EPA) recently updated the national standard for PM 2.5 to 9 μg/m 3, a move aimed at significantly reducing air pollution and protecting public health by lowering the allowable concentration of harmful fine particles in the air. Using our analysis approach to reconstruct the fine-time resolution PM 2.5 distribution across the entire United States for our study period, we found that the entire nation encountered PM 2.5 levels that exceeded 9 μg/m 3 for more than 20% of the time of our analysis period, with the eastern United States and California experiencing concentrations exceeding 9 μg/m 3 for over 50% of the time, highlighting the importance of regulatory efforts to maintain annual PM 2.5 concentrations below 9 μg/m 3.

Plant disease detection using machine learning techniques based on internet of things (IoT) sensor network

AbstractIn recent years, smart agriculture has grown rapidly. A crop disease is generally caused by pests, insects, or pathogens and reduces the productivity of the crop by adversely affecting its yield. There is a severe loss of crops across the country due to various crop diseases, and one reason is not being able to detect the disease in its early stages keeps them from finding a solution. An Internet of Things (IOT) sensor network is used to detect and classify diseases in leaves in this paper. Precision agriculture uses machine learning techniques to increase crop growth, control the cultivation process, and enhance crop productivity with less human involvement. IOT sensor networks are being used in precision agriculture using machine learning techniques. A result of the proposed method shows an overall accuracy of 88%.

An energy efficient authentication scheme for cluster-based wireless IoT sensor networks

Wireless Internet of Things (IoT) sensor networks (WITSN) play a pivotal role in modern society, facilitating a myriad of applications ranging from smart homes to industrial automation. Therefore, safeguarding these networks against security threats is paramount to ensure the integrity, confidentiality, and availability of data transmitted within them. However, WITSNs face escalating security threats due to their diverse structures and platforms. Existing literature has identified these vulnerabilities but lacks comprehensive solutions to address them effectively. To bridge this gap, this paper proposes a novel security approach termed Randomized Bi-Phase Authentication Scheme (RBAS), which integrates digital watermarking techniques to fortify both external and internal network security. RBAS not only tackles data availability, confidentiality, and authenticity challenges prevalent in WITSNs but also strives to maintain a delicate equilibrium between robust security measures and energy efficiency. Key contributions of this work include the meticulous examination and validation of the integration of cyclic redundancy check (CRC) codes in IoT sensor network authentication, demonstrating their efficacy in error detection through simulations. Furthermore, RBAS employs advanced hashing, cryptography, and dynamic verification codes to ensure strong error detection and data authenticity, leveraging robust CRC codes and randomization to thwart potential attacks. The scheme's complexity acts as a deterrent against manipulation, while its cluster awareness enhances adaptability, and cryptographic principles bolster overall security. Extensive performance evaluation using the Network Simulator-2 (NS2) reveals significant benefits of RBAS, including an 8 % reduction in power consumption and a 7 % increase in network longevity. Notably, RBAS surpasses existing solutions with a 14 % reduction in dropping ratio and an 8 % decrease in latency. The success of RBAS stems from its innovative utilization of lightweight watermarking techniques and cluster-based routing, enabling proactive identification of data tampering and thereby enhancing the network's overall security posture. This pioneering work not only advances the state of the art in WITSN security but also holds profound implications for the practical deployment of secure and efficient IoT sensor networks in real-world scenarios.

IoT based sensor network clustering for intelligent transportation system using meta‐heuristic algorithm

SummaryInternet of Things (IoT) based sensor networks have been established as a pillar in intelligent communication systems for efficiently handling roadside congestion and accidents. These IoT networks sense, collect, and process data on a real‐time basis. However, IoT based sensor network clustering has various energy constraints such as inefficient routing due to long‐haul transmission, hot spot problem, network overhead, and unstable network whenever deployed along with the roadside that affect their architecture. In such networks, clustering techniques play a crucial role in extending the lifespan and optimizing the routes by integrating sensor devices through clusters. Therefore, a meta‐heuristic algorithm for clustering in IoT sensor networks for an intelligent transportation system is proposed. In this work, the seagull optimization algorithm is applied for clustering by considering residual and average energy, node spacing, and distance fitness parameters. Moreover, this work also considers the dynamic communication range of the cluster heads for increasing the stability period and lifetime of the proposed networks. The experiment results demonstrate that the proposed Seagull optimization algorithm for clustering in IoT networks (SOAC‐IoTNs) and Seagull optimization algorithm for clustering in IoT networks with dynamic communication range (SOAC‐IoTNs‐DR) achieve a significant increase in the stability period and network lifetime, with percentage increments of 55.68% and 71.47%, and 10.03% and 88.66% respectively, compared to the existing optimized genetic algorithm for cluster head selection with single static sink (OptiGACHS‐StSS).

Wind-driven suspended triboelectric-electromagnetic hybrid generator with vibration elimination for environmental monitoring in the high-voltage power transmission line

The sustainable development of the high-voltage power transmission system puts forward more significant requirements IoT sensor network. Currently, a large number of the IoT sensors are installed on the high-voltage transmission lines, and environmental wind energy harvesting around the outdoor power transmission lines enables a promising and feasible solution to supply distributed sensors. However, the traditional floor-mounted wind energy nanogenerator with high requirements for placement and fixation is no longer applicable to the suspension scenario of the high-voltage power transmission lines. In this work, a wind-driven suspended triboelectric-electromagnetic hybrid generator (WS-TEHG) is designed as a suspended wind cup with a three-layered integrated structure, which can be integrated with the damping structure that hanged and installed on the power transmission line, demonstrating the simultaneously achieving high efficiency wind energy harvesting and the operation stability of the device. After the structural and material optimization, WS-TEHG can achieve efficient energy collection under wide-range of the wind speed from 2.6 to 17.3 m s−1, whose TENG and EMG produced instantaneous peak power from 0.2 to 7.9 mW and 0.6–85.1 mW, respectively. Moreover, a standardized power management based on LTC 3588-based double-channel circuit was established to co-manage the outputs of the two generating modules in the hybrid generator and enhance the continuous stability of the standardized voltage output, demonstrating a shorter undervoltage charge accumulation, a faster start-up process, a lower power consumption compared with the traditional power management. Finally, a self-powered environmental monitoring system by a WS-TEHG equipped with a standardized power management circuit was developed to provide the weather and climate information around the power transmission lines, thereby demonstrating a potential and feasible for large-scale self-powered application in the field of the high-voltage power transmission lines.

Uncovering botnets in IoT sensor networks: a hybrid self-organizing maps approach

The integration of the internet of things (IoT) has revolutionized diverse industries, introducing interconnected devices and IoT sensor networks for improved data acquisition. However, this connectivity exposes IoT ecosystems to emerging threats, with botnets posing significant risks to security. This research aims to develop an innovative solution for detecting botnets in IoT sensor networks. Leveraging insights from existing research, the study focuses on designing a hybrid self-organization map (SOM) Approach that integrates lightweight deep learning (DL) techniques. The objective is to enhance detection accuracy by exploring various DL architectures. Proposed methodology aims to balance computational efficiency for resource-constrained IoT devices while improving the discriminatory power of the detection system. The study advancing IoT cybersecurity and addresses critical challenges in botnet detection within IoT sensor networks. The testing of the artificial neural networks (ANN) classifier involves three models, each represented based on parameters related to the construction of the training models. The most effective ANN achieves 86%, works on anomaly intrusion detection systems (AIDS).

Multiple hypothesis testing in cognitive IoT sensor network

Recent Internet of Things (IoT) research aims to develop generic objects to learn, reason, and perceive their environment. Therefore, a new area has emerged known as cognitive IoT (CIoT). The cognitive Internet of Things integrates IoT with intelligence and behaves as well as humans through intelligent functionality. Several inferential tasks in CIoT require multiple hypothesis testing. The situation becomes cumbersome when the data is massive and heterogeneous. Thus, this research suggests a novel technique for multiple-hypothesis testing that uses a copula function to deal effectively with massive heterogeneous data. In addition, these data may contain missing or corrupted entries. Hence, it introduced probabilistic clustering, which reduces model inefficiency and takes control over the false discovery rate (FDR). Most of the variance from each cluster was extracted using kernel principal component analysis (KPCA) to reduce the processing burden at the fusion centre. Subsequently, it computes the p-value of each cluster's first principal component data and employs the Bonferroni method for multiple hypothesis testing. Finally, this research study evaluates the performance of the proposed algorithm on six-month environmental data, revealing that the proposed technique is efficient in terms of accuracy and computation time compared to other methods in the presence of massive heterogeneous data.

An Analysis of Blockchain-Based IoT Sensor Network Distributed Denial of Service Attacks.

The Internet of Things (IoT) and blockchain are emerging technologies that have attracted attention in many industries, including healthcare, automotive, and supply chain. IoT networks and devices are typically low-powered and susceptible to cyber intrusions. However, blockchains hold considerable potential for securing low-power IoT networks. Blockchain networks provide security features such as encryption, decentralisation, time stamps, and ledger functions. The integration of blockchain and IoT technologies may address many of the security concerns. However, integrating blockchain with IoT raises several issues, including the security vulnerabilities and anomalies of blockchain-based IoT networks. In this paper, we report on our experiments using our blockchain test bed to demonstrate that blockchains on IoT platforms are vulnerable to DDoS attacks, which can also potentially lead to device hardware failures. We show that a number of anomalies are visible during either a DDoS attack or IoT device failure. In particular, the temperature of IoT hardware devices can exceed 90 °C during a DDoS attack, which could lead to hardware failure and potential fire hazards. We also found that the Block Transaction Rate (BTR) and network block loss percentage can increase due to corrupted hardware, with the BTR dropping to nearly zero blocks/sec and a block loss percentage of over 50 percent for all evaluated blockchains, and as high as 81.3 percent in one case. Our experiments demonstrate that anomalous temperature, latency, bandwidth, BTR, and network block loss percentage can potentially be used to identify DDoS attacks.

IoT Sensor Networks- Orchestrating Connectivity, Efficiency, and Intelligence Across Diverse Domains

The advent of the Internet of Things (IoT) has led to the proliferation of sensor networks, enabling a new era of connectivity, data collection, and automation across various domains. IoT-based sensor networks comprise interconnected sensors and actuators that collect, transmit, and process data to provide valuable insights and enable intelligent decision-making. This paper explores the architecture, applications, and challenges of IoT-based sensor networks. The architecture section delves into the components, layers, and communication protocols that constitute these networks, highlighting the roles and interactions of sensors, microcontrollers, gateways, and cloud services. The applications section showcases the diverse use cases of IoT-based sensor networks in smart cities, industrial automation, healthcare, agriculture, and environmental monitoring, illustrating their transformative impact. The challenges section identifies the key issues such as scalability, interoperability, security, reliability, energy efficiency, and data management that need to be addressed to realize the full potential of IoT networks. Finally, the paper discusses future directions, emphasizing the potential of edge computing, 5G, artificial intelligence, and blockchain technology to advance IoT-based sensor networks and unlock new opportunities. Through continued research, innovation, and collaboration, IoT sensor networks are poised to drive significant advancements in technology and society, creating a more connected and intelligent world.

Smart Wet and Dry Waste Management System Using IoT

Abstract: Waste management is one of the important aspects when it comes to avoiding diseases caused by unsegregated waste. Many people have lesser knowledge about what is wet and dry waste. The escalating global waste generation hasled to a pressing need for innovative waste management solutions that are efficient, sustainable, and technologically advanced. This abstract introduces a "Smart Dry and Wet Waste Management System" that leverages the Internet of Things (IoT) to revolutionize waste management practices. Traditional waste management systems often lack real-time monitoring and optimization capabilities, resultingin inefficiencies, environmental concerns, and increased operational costs. The proposed system aims to address these challenges by integrating IoT technologies, sensor networks, and data analytics into waste collection and disposal processes.The main objective is to automate the segregation system thereby removing the human intervention. The hardware components such as Arduino, sensors, LCD are used to build the system. The integration of IoT in waste management contributes to a cleaner environment, reduced operational costs, and increased overall efficiency. By preventing overflows, minimizing unnecessary collection trips, and promoting recycling practices, the system aligns with sustainable waste management goals. Moreover, the collected data can be analyzed over time to identify longterm trends, enabling continuous optimization of waste management strategies..