Internet Of Things Sensor Networks Research Articles

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..

Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

Active Neighbor Exploitation for Fast Data Aggregation in IoT Sensor Networks

System Design and Implementation for Machine Learning and Internet of Things Based Anomaly Detection in Patient Movement

Emerging trends in healthcare have seen an increased need for improved monitoring, and as a result, machine learning (ML) and the Internet of Things have been integrated to develop smart systems that can provide rapid and accurate health assessments. With an inclusive view on mobility anomaly detection in patients, this paper aims to improve hospital patient safety. Patients wear or place these things, and the IoT sensor network then gathers real-time motion data. The information is then moved to the central server. The system was taught the machine learning part using a large number of normal and abnormal patient movement data. It also shows a way to differentiate between common activities and those that might indicate ill health. It comprises an IoT sensor network, a data processing unit, and an ML anomaly discovery module. There are sensors on the Internet of Things that have a wide range of movement data. Then, these are run through an ML model that captures the spatial-temporal dependencies between different sets based upon more sophisticated techniques like the Naive Bayes Classifier. Where there are abnormalities discovered after analyzing them, an alert is sent out; this will help in quick emergency response by saving lives even before diseases become last-stage acute conditions

Enhancing quality-driven iot operations for smart cities: A comprehensive approach for routing, security, and optimization

This study introduces a tailored data acquisition and communication framework for IoT smart applications, focusing on enhancing efficiency and system performance. The proposed Quality-Driven IoT Routing (EQR-SC) for smart cities utilizes IoT-enabled wireless sensor networks. Additionally, a noteworthy contribution is the introduction of the Chaotic Firefly Optimization (CFOA) algorithm for IoT sensor cluster formation, potentially optimizing the organization and efficiency of IoT sensor networks in smart cities. Trust-based cluster Head Selection is enhanced by employing the Weighted Clustering Algorithm (WCA), which assigns weights to nodes based on trustworthiness and relevant metrics to select reliable cluster heads. The proposal of a lightweight data encryption technique enhances data security among IoT sensors, ensuring the privacy and integrity of transmitted information. To optimize pathfinding within the IoT platform, the research employs the Bellman-Ford algorithm, ensuring efficient data routing while accommodating negative edge weights when necessary. Finally, a thorough performance analysis, conducted through network simulation (NS2), provides insights into the effectiveness of the proposed OQR-SC technique, allowing for valuable comparisons with existing state-of-the-art methods.

A Novel Approach Integrating IoT and WSN with Predictive Modeling and Optimization for Enhancing Efficiency and Sustainability in Smart Cities

The development of smart city solutions is necessary for traffic management and environmental sustainability to meet the challenges of global urbanization. Based on this idea, the paper presents a new way to amalgamate IoT and Wireless Sensor Networks with predictive modeling and optimization methods for use in smart city management. To this end, we have installed traffic sensors (infrared, ultrasonic) and environmental sensors (air quality, humidity, gas) at strategic points throughout the city where they can collect real-time data on traffic and pollution. Then, this data is used together with machine learning models such as ANN (Artificial Neural Network), DT or Decision Trees, KNN (K-Nearest Neighbors), and RF or Random Forests. In practical terms, it is through iterative training processes that our models have achieved ever greater accuracy over time. Now they can actually learn and adapt to changing urban dynamics. The holistic solution of this approach pertains to informed decision-making in smart city infrastructure management. Urban stakeholders can make data-driven decisions by leveraging advanced sensor technologies and machine learning algorithms to deal with traffic congestion, reduce pollution and enhance the quality of life for smart cities. Urban planners, policymakers and technologists working on smart city solutions to improve urban mobility and environmental quality will all be interested in the outcomes of this study.

KRISHI MITRA – A SMART FARMING SYSTEM THROUGH AI

The concept of smart agriculture is still in its infancy, since Internet of Things (IOT) sensors may gather data about agricultural fields and behave accordingly based on human input. The development of a smart agriculture system that takes advantage of cutting edge technologies like Arduino, IOT, and wireless sensor networks is suggested in this paper. The goal of the article is to employ automation and emerging technologies, such as IOT and smart agriculture. One of the main ways to increase the production of productive crops is to monitor the surrounding conditions. This paper's highlight is the development of a system that uses sensors utilizing an Arduino board to monitor temperature, humidity, moisture, and even the movement of animals that could kill crops in agricultural fields. If there is any disparity, the system will be notified.

Authentication schemes in wireless internet of things sensor networks: a survey and comparison

The proliferation of wireless sensor networks (WSNs) fuels internet of things (IoT's) rapid global development, connecting diverse devices. IoT transforms devices into intelligent entities delivering exceptional services. This work addresses IoT authentication gaps through a comprehensive survey, analyzing recent works and exploring techniques in various applications. It includes a comparative analysis of authentication schemes, evaluating Bi-Phase authentication scheme (BAS) in WSNs. BAS outperforms sensor protocol for information via negotiation (SPIN), broadcast session key protocol (BROSK), and localized encryption and authentication protocol (LEAP), resulting in lower energy consumption and higher efficiency. With energy efficiency at 60 Kb/J for 25 nodes, BAS focuses on power optimization and lightweight security measures, reducing energy consumption, maximizing efficiency, and extending WSN lifespan. The evaluation, conducted using MATLAB/Simulink, demonstrates BAS's superiority, achieving 10 J, 12 J, 14 J, and 15 J energy consumption for 25 nodes during simulation, showcasing its effectiveness and future potential in advancing IoT authentication.

Lessons Learnt from Monitoring the Etna Volcano Using an IoT Sensor Network through a Period of Intense Eruptive Activity.

This paper describes the successes and failures after 4 years of continuous operation of a network of sensors, communicating nodes, and gateways deployed on the Etna Volcano in Sicily since 2019, including a period of Etna intense volcanic activity that occurred in 2021 and resulted in over 60 paroxysms. It documents how the installation of gateways at medium altitude allowed for data collection from sensors up to the summit craters. Most of the sensors left on the volcanic edifice during winters and during this period of intense volcanic activity were destroyed, but the whole gateway infrastructure remained fully operational, allowing for a very fruitful new field campaign two years later, in August 2023. Our experience has shown that the best strategy for IoT deployment on very active and/or high-altitude volcanoes like Etna is to permanently install gateways in areas where they are protected both from meteorological and volcanic hazards, that is mainly at the foot of the volcanic edifice, and to deploy temporary sensors and communicating nodes in the more exposed areas during field trips or in the summer season.

Optimization of Stable Energy PV Systems Using the Internet of Things (IoT)

The modern power grid faces rapid growth in load demand due to industrialization, leading to an unregulated environment and increasing adoption of renewable energy sources, which presents technical challenges, particularly in terms of stability. Hydrogen conversion technology revolutionizes clean electricity storage with renewable energy, and solar hydrogen is now available in autonomous solar systems. The efficiency of solar photovoltaic systems is closely related to using digital electronic maximum peak power tracking (MPPT) technology. The Internet of Things (IoT) is crucial for performance monitoring and real-time control of PV systems, enhancing the understanding of real-time operating parameters. IoT and wireless sensor networks for distributed solar energy devices and joint building design are essential for developing the photovoltaic construction industry. In this paper, the monitoring system that has been proposed offers a potentially effective solution for the intelligent remote and real-time monitoring of solar photovoltaic (PV) systems. It demonstrated a high level of accuracy, reaching 98.49%, and can transmit graphical representations to a smartphone application within a time frame of 52.34 seconds. Consequently, the battery's longevity was extended, energy consumption was diminished, and the quality of service (QoS) for real-time applications inside the Internet of Things (IoT) was enhanced.

Energy-efficient routing, power control and energy clustering for energy harvesting-enabled spectrum sharing IoT sensor networks

We investigate a spectrum sharing system that enables energy harvesting from distributed multi-antenna energy transmitter devices for primary dual-band device-to-device networks and secondary dual-band wireless sensor networks (WSNs), incorporating a multi-stage rectenna energy harvesting circuit model. We formulate a new multi-hop routing problem for network energy efficiency maximization of this system, which involves the interconnected tasks of WSN routing, controlling the transmit power of sensor nodes, and energy transmitter clustering, taking into account interference, power budget and rate constraints. To tackle these interconnected tasks effectively, we propose an integrated solution that includes both centralized and distributed routing schemes. Furthermore, the solution incorporates energy transmitter clustering schemes based on harvested power and channel gain. We provide an analysis of the computation complexity and signaling overhead for the proposed solution. Our simulation results demonstrate that the proposed routing and clustering schemes outperform their respective benchmark schemes, achieving significant performance gains.

Artificial Intelligence Driven Power Optimization in IOT-Enabled Wireless Sensor Networks

The widespread use of Wireless Sensor Networks (WSN) in Internet of Things (IoT) causes energy efficiency issues. This paper proposes an AI-based solution to this problem. The propose an AI-Driven Power Optimization framework for IoT-enabled WSN using Deep Q-Network (DQN) and Dynamic Voltage and Frequency Scaling (DVFS). These techniques can adapt to changing network conditions and reduce power consumption when used together. Sensor nodes provide environmental parameters, battery status, and network behavior data to the AI-driven framework DQN is implemented after data preprocessing to learn and make power management decisions using reinforcement learning. Neural network-driven agent operates in a state and action space. It optimizes energy use with rewards. Real-time hardware power adjustment is done using DVFS. DVFS precise control and AI-driven decision-making create a comprehensive power optimization strategy. AI adapts to new challenges and optimizes network lifespan by improving its power management policies. Experimental implementations of the proposed framework show significant energy savings, network lifespan extension, and QoS improvements. AI-Driven Power Optimization in IoT-enabled WSN is proven effective and flexible. This study shows the potential of AI, specifically DQN and DVFS, in IoT-enabled WSN. This AI-Driven Power Optimization framework addresses energy efficiency and improves IoT sensor networks. AI improves power optimization in IoT-enabled WSN making IoT deployments more sustainable and resilient.

A 10.31 ENOB 3.125MHz BW fully passive 2nd-order noise-shaping SAR ADC for low cost IoT sensor networks

A 10.31 ENOB 3.125MHz BW fully passive 2nd-order noise-shaping SAR ADC for low cost IoT sensor networks

Comprehensive Evaluation Model for Competitiveness of Mass Media Companies in the IoT Sensor Networks

Mass media companies can be elaborated as organizations that manipulate technological components for their various departments such as movie studios, publishing houses, radio and television station management teams that impact a large range of audience via vast communication strategies. The companies have also been referred to as media conglomerates, media groups, or media houses further illustrating their grasp over the global markets and their revenue structures. The largest media companies such as Apple, Disney, and Comcast among others, offer products and services to users that are diverse individuals as well as large organizations leading to significant revenues as well as challenges that have been further explored in a comprehensive manner in the study. IoT utilizes wireless networks that are without infrastructure to install a huge number of wireless sensors that track system, physical, and environmental conditions. If you're wanting to integrate WSN into your business, our highly driven and experienced engineers can give you an all-encompassing solution.

Color space-based autoencoder for optical camera communications

This paper proposes an end-to-end optical camera communications (OCC) system using an autoencoder neural network trained to recover the transmitted symbols. Although OCC techniques have been extensively studied in the literature, using an autoencoder that learns the transmitter and receiver functions jointly is a novel concept with significant prospects. Furthermore, we investigate the performance impact caused by the overlooked optical-to-electrical (O2E) conversion process of real-world OCC receivers. The autoencoder learning model captures these typically undesired changes in image sensors for the design of constellation symbols and reception schemes. For the simulation, we constructed an end-to-end autoencoder for a color space-based OCC system and measured the O2E performance effect. The proposed autoencoder communication system is analyzed and compared using the symbol error rate (SER) across various OCC detection systems. Despite the subtle spectral responsivity variations in image sensors, our numerical results indicate that the autoencoder model can learn to recover the transmitted data while minimizing SER and meeting the lighting requirements. These findings may interest a broad range of applications, particularly in IoT sensor networks. Among all the image sensors we studied, the OCC system with Bayer CFA-based signal detection showed superior performance.

Research on Self-Organization and Adaptive Strategy of the Internet of Things Sensor Networks

Research on Self-Organization and Adaptive Strategy of the Internet of Things Sensor Networks

IoT Integration for Enhanced Turmeric Cultivation: A Case Study in Smart Agriculture

The agricultural sector serves as a fundamental cornerstone of the economies of numerous countries, necessitating technological advancements despite limited financial resources. The Internet of Things (IoT) presents a novel aspect within the field of soil health monitoring, which has significant implications for advancing smart agriculture and farming practices. Integrating conventional agricultural practices with cutting-edge technologies, such as the IoT and Wireless Sensor Networks (WSN), can foster Smart Agriculture (SA). This paper presents IoT Integration for Enhanced Turmeric Cultivation (IoT-ETmC) in the context of SA. The TurmFox IoT and Edge-to-Cloud (ETC) technology can analyze gathered data and send it to the user through internet connectivity. The work involves the implementation of TurmFox in experiments focused on turmeric cultivation. The results demonstrate a notable improvement in the quality of turmeric as a direct outcome of this intervention. The curcumin levels in the given product are notably higher, ranging from 4450 to 5450 mg per 120g. This paper also aims to demonstrate the intuitive configuration of sensor-to-actuator connections for implementing desired SA. The real-time data obtained from Turmfox provides information on the pH values, moisture levels, and temperature, allowing for observing dynamic variations in environmental conditions within the specified period. The pH level was 6.5 at 09:00, with a moisture content of 51 g/m3 and a temperature of 293 K.

Recyclability of novel energy harvesting and storage technologies for IoT and wireless sensor networks

This paper aims to present a set of separation and recycling methods designed to recover valuable materials and components from innovative high-performance piezoelectric (PEG) and thermoelectric (TEG) generators and monolithic supercapacitors (SC) developed within the InComEss project. This project, a part of the European Horizon H2020 research program, focuses on creating environmentally friendly, cost-effective, and highly efficient Energy Harvesting Systems (EHSs) for powering wireless sensor networks. It combines advanced polymer-based composite materials for piezoelectric, thermoelectric, and supercapacitors, capable of harvesting and storing electrical energy from mechanical or waste heat sources. The authors initially identified key recoverable components. In PEG, these includes Polyvinylidene fluoride (PVDF), silver electrodes, polyimide, fiberglass/resin composite and copper. In TEG the highest-value components are single walled carbon nanotubes (SWCNTs) and polymeric matrices, along with the SWCNTs, while in SC, aluminium, polyethylene terephthalate (PET) and carbon particles are targeted. This paper proposes a range of separation and recycling techniques involving mechanical, thermal, and chemical processes. These methods include microwave-assisted and pyrolysis thermal processes, various mechanical fragmentation and separation processes, hydrometallurgical recovery processes, and solvent-based dissolution methods. However, a comprehensive understanding of the end-of-life waste stream of these devices is necessary for further advancements. The authors are currently conducting a thorough laboratory assessment of the recovery methods to pinpoint the most effective recyclable options. The outcomes of this assessment will be shared in a subsequent article.

Detecting denial of sleep attacks by analysis of wireless sensor networks and the Internet of Things

The Internet of Things (IoT) amalgamates a large number of physical objects that are distinctively identified, ubiquitously interconnected and accessible through the Internet. IoT endeavors to renovate any object in the real world into a computing device that has sensing, communicating, computing and control capabilities. There are a budding number of IoT devices and applications and this escort to an increase in the number and complexity of malicious attacks. It is important to defend IoT systems against malicious attacks, especially to prevent attackers from acquiring control over the devices. Energy utilization is significant for battery-enabled devices in the IoT and wireless sensor networks which are operated long time period. The Denial-of-Sleep attack is an explicit type of denial-of-service attack that beleaguered a battery-powered device’s power supply that results in the exhaustion of this critical resource. This paper focuses on the survey on Denial of sleep attacks in Wireless Sensor networks and the IoT.

Multi-Network Latency Prediction for IoT and WSNs

The domain of Multi-Network Latency Prediction for IoT and Wireless Sensor Networks (WSNs) confronts significant challenges. However, continuous research efforts and progress in areas such as machine learning, edge computing, security technologies, and hybrid modelling are actively influencing the closure of identified gaps. Effectively addressing the inherent complexities in this field will play a crucial role in unlocking the full potential of latency prediction systems within the dynamic and diverse landscape of the Internet of Things (IoT). Using linear interpolation and extrapolation algorithms, the study explores the use of multi-network real-time end-to-end latency data for precise prediction. This approach has significantly improved network performance through throughput and response time optimization. The findings indicate prediction accuracy, with the majority of experimental connection pairs achieving over 95% accuracy, and within a 70% to 95% accuracy range. This research provides tangible evidence that data packet and end-to-end latency time predictions for heterogeneous low-rate and low-power WSNs, facilitated by a localized database, can substantially enhance network performance, and minimize latency. Our proposed JosNet model simplifies and streamlines WSN prediction by employing linear interpolation and extrapolation techniques. The research findings also underscore the potential of this approach to revolutionize the management and control of data packets in WSNs, paving the way for more efficient and responsive wireless sensor networks.

Multi‐UAV path planning utilizing the PGA algorithm for terrestrial IoT sensor network under ISAC framework

AbstractThe integration of internet of things (IoT) technologies with unmanned aerial vehicles (UAVs) has initiated a groundbreaking revolution in data acquisition and communication systems across diverse domains. This document introduces an innovative endeavor, the integration of multi‐UAV path planning for integrated sensing and communication (ISAC) within ground‐based CAT‐M1 IoT sensor networks, accomplished through the application of the Pareto‐based genetic ant colony optimization (PGA) algorithm. The PGA algorithm is highly capable of UAV path planning due to its efficiency, adaptability, and seamless integration of domain expertise. By employing the PGA algorithm, we simultaneously minimize UAV travel distance while optimizing energy consumption, resulting in multi‐objective optimization. The synergy of ground‐based IoT sensors and seamless UAV communication, coupled with a convex optimization resource allocation algorithm, empowers real‐time data acquisition and heightens situational awareness. Our proposed UAV path planning PGA algorithm with resource allocation is crafted to maximize the efficiency of ground‐based sensor data acquisition. We stand at the forefront of advancing multi‐UAV data collection systems with this pioneering approach, promoting increased efficiency, robustness, and transformative solutions across diverse domains. The proposed system for ISAC achieves an impressive throughput of up to 95% of the system's capacity.