This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pestici...
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ISBN:
(数字)9798331520540
ISBN:
(纸本)9798331520557
This paper presents a new IoT-based approach for agricultural pest control, incorporating machine learning techniques. Traditional pest monitoring methods are labor-intensive and often result in the overuse of pesticides. The proposed system solves this problem by predicting pest populations using environmental data collected via IoT sensors. We use random forest regression and random search cross-validation to predict pests and identify important factors. The system calculates the amount of pesticides based on forecasts, helping farmers optimize use and reduce environmental impact. The experimental results demonstrate the effectiveness of the system in terms of accurate prediction and optimal pesticide application, and thereby providing a scalable and cost-effective solution for sustainable agriculture.
Forest fires present a significant hazard to property, lives, and ecosystems globally, necessitating swift detection and containment measures. Historically, manual forest fire control systems relied on human observati...
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ISBN:
(数字)9798331518097
ISBN:
(纸本)9798331518103
Forest fires present a significant hazard to property, lives, and ecosystems globally, necessitating swift detection and containment measures. Historically, manual forest fire control systems relied on human observation and reporting, which often resulted in delayed responses and increased damage. The forest is divided into target areas, with each area equipped with intelligent software interfaces, automated response mechanisms, flame sensors, and sprinklers. The proposed system relies on advanced flame sensor technology for prompt and accurate fire outbreak detection. Upon detection, real-time fire data is transmitted to a cloud-based platform, ***, for comprehensive monitoring and analysis. This division allows for targeted intervention and efficient resource allocation based on the severity and location of the fire. The flame sensors used in this system have a range of up to 100 meters, allowing for extensive coverage of forest areas. Additionally, the system includes automated sprinklers that activate immediately upon fire detection, providing an immediate response to help contain and extinguish the fire. By leveraging *** for data transmission and analysis, stakeholders gain valuable insights into fire behavior, facilitating informed decision-making and long-term mitigation strategies. This integrated approach offers a comprehensive solution to combat forest fires, emphasizing early detection and efficient containment to minimize damage and protect both natural habitats and human communities.
Modern pet health management requires advanced solutions because the world demonstrates rising demand for companion animals. The current pet care systems operate without real-time data monitoring because they do not a...
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ISBN:
(数字)9798331512088
ISBN:
(纸本)9798331512095
Modern pet health management requires advanced solutions because the world demonstrates rising demand for companion animals. The current pet care systems operate without real-time data monitoring because they do not adapt to shifting diagnostic criteria. An AI-powered pet management system based on Internet of Things (IoT) links a secure cloud-edge computing system to offer these capabilities. A proposed system depends on strong machine learning techniques to deliver persistent health tracking while simultaneously analyzing behavioral patterns and carrying out automatic care operations. Through its IoT-connected smart sensors the system obtains real-time data which enables it to identify anomalies as soon as possible while providing tailored medical solutions. A modular distributed architecture design offers better system adaptability thus making possible seamless operations between multiple operational domains. Security is of utmost importance in the system so a combination of encryption layers works together with authentication protocols and monitored data channels serve to secure both privacy and data integrity. The experimental findings reveal that predictions about behavioral outcomes together with optimized resource usage and system protection function at superior rates than traditional methods do. A thorough analysis of both ethical practices and data security measures exists for automated pet supervision to facilitate responsible program execution. This recommended system gives remote accessibility together with multi-functional pet care operations to provide safe services which benefit all pet owners along with veterinarians and additional stakeholders. The future research agenda includes developing hybrid federated learning systems to deliver individualised diagnoses as well as using diversified health data from various sources to conduct more thorough examinations while striving to enable this monitoring method in various animal care settings. Such resear
Mobile crowd sensing is a technique that allows collection of real-time data from a large number of mobile users who carry a mobile device with sensing capabilities. It is widely used for data sensing applications, su...
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ISBN:
(纸本)9798350346169
Mobile crowd sensing is a technique that allows collection of real-time data from a large number of mobile users who carry a mobile device with sensing capabilities. It is widely used for data sensing applications, such as traffic monitoring, environmentalmonitoring, health and fitness, retail marketing, and emergency response. It requires individual users to perform the sensing task based on the location of the task and the user. Ensuring privacy and security of individuals and accuracy and reliability of the data collected are primary challenges in a mobile crowd sensing system. To motivate more users to collect data, it is required for the system to be built in a manner that each user is rewarded for the task done while maintaining the budget balance. As users are of heterogeneous nature, they must be rewarded for the task done based on their own true valuation of the task. The reverse auction method for mobile crowdsensing is becoming one of the widely used incentive mechanism for its choice to the mobile users, who act as the participating workers, for fixing the price for which they want to sell the sensed data. For a reverse auction system to work, it is required that there are enough users who are willing to bid in an auction round. Maintaining a participant pool with enough competition while keeping the bid values near to true values is a key challenge to be addressed. Failing to maintain enough participants can result in higher bid prices with each round and hence increasing total reward value to be distributed. This may lead to incentive explosion where the bid price is too high for the available budget. In this work, we propose a novel approach of retaining users by considering the frequency of winning and participation of users. This mechanism is built on top of RADP-VPC which is reverse auction mechanism based on reverse- auction with dynamic pricing with virtual participation credit. The experimental results show that the proposed approach using part
A minor action of human is performed with some purpose. Understanding the behavior and the way to interact of human with environment in automatic way has attained a lot of attention in research field over past few yea...
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In the rapidly advancing field of smart cities, effective traffic management is essential for improving safety, alleviating congestion, and minimizing environmental damage. This paper rigorously examines the function ...
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ISBN:
(数字)9798331509859
ISBN:
(纸本)9798331509866
In the rapidly advancing field of smart cities, effective traffic management is essential for improving safety, alleviating congestion, and minimizing environmental damage. This paper rigorously examines the function of deep learning in adaptive traffic monitoring and presents an innovative method utilizing Squeeze and Excitation Networks (SENets). SENets enhance traffic monitoring systems by dynamically recalibrating feature maps, allowing for the prioritization of important features and hence boosting detection accuracy in intricate urban settings. The suggested system combines real-time data from IoT sensors, security cameras, and vehicular networks, offering a comprehensive framework for traffic monitoring and accident prevention. Experimental findings indicate that the SENets-based model attains a 94.5% accuracy in identifying traffic abnormalities and enhances signal control optimization by 30% relative to traditional methods. These findings highlight the capacity of SENets to transform urban traffic management, providing a scalable and economical solution for intelligent transportation systems.
WSNs are a system of spatially distributed sensors that sense some environmental phenomena and communicate the information without wires to a central system. Now, integration with the IoT further strengthens WSNs in a...
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ISBN:
(纸本)9798331529352
WSNs are a system of spatially distributed sensors that sense some environmental phenomena and communicate the information without wires to a central system. Now, integration with the IoT further strengthens WSNs in aspects such as real-time data collection, analysis, and decision making across diverse network applications, fueling innovation in smart environments. Wireless Sensor Networks (WSNs) are normally groups of sensors that gather information about their surroundings and transmit it to the main system without the use of wires. The role of the Internet of Things in WSN is to provide an avenue through which the data from the sensors can be shared in real-time, hence making smart systems efficient and responsive This paper present a qualitative research paradigm where systematic literature survey has been conducted on many articles in which IoT based WSN articles published in the last five years along with a qualitative analysis of performance indicators of 20 real-world IoT-enhanced WSNs. The findings reveal that IoT integration significantly improves WSN capabilities in three key areas: It has achieved (1) network longevity, which is a 40% increase in overall sensor lifespan;(2) data accuracy, with the observation that it has cut down on false positives by 25%;and (3) real-time responsiveness, which has proven to have truncated the delay in data transmission by 60%. Nevertheless, we also pinpoint the threats in the security and privacy domains, where 75% of investigated systems are vulnerable to cyber threats. The researchers establish that IoT greatly accelerates the WSN applications in smart city and environment monitoring, but this comes with the need to enforce better security and standardized procedures for optimal use of IoT in achieving the WSN application goals. This research therefore offers a systematic map for researchers and practitioners to foster the integration of IoT in WSN applications towards intelligent and reliable sensor networks in the a
Wireless Sensor Network (WSN) has been widely recognized as one of the most important technology for low power wireless communication and also used in variety of applications like medical, military, industrial, agricu...
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Lithium battery, as an important part of new energy storage, its scientific and safe use and management has become a key factor to achieve low-carbon technology. Lithium-ion battery is a highly nonlinear system, and i...
Lithium battery, as an important part of new energy storage, its scientific and safe use and management has become a key factor to achieve low-carbon technology. Lithium-ion battery is a highly nonlinear system, and its operating state is susceptible to environmental interference and other disturbances, resulting in errors. Aiming at the error caused by the disturbance of the running state of lithium battery, this paper proposes a method of digital twin modeling of lithium-ion battery which integrates the mechanism model and the data-driven model. The adaptive particle swarm optimization support vector machine regression (SVR) algorithm was used to conduct data-driven modeling for the errors between the mechanism model and the battery entity data, and the errors were corrected to obtain a real-time and high-precision battery model, which realized condition monitoring and offline simulation display. Moreover, the accuracy of the model was verified through experiments, proving that it can lay a foundation for the safe and intelligent management and control of the battery system.
Traditional healthcare systems are based on patients approaching physically to get themselves registered and avail of medical assistance. Also, the voluminous data entry into the centralized databases makes the proces...
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