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CHMP-CNN: To Implement the IoT Based Crops Health Monitoring and Prediction Using Deep Learning

CHMP-CNN: To Implement the IoT Based Crops Health Monitoring...

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Advances in Computing Research on Science Engineering and Technology (ACROSET), International conference on

作者： Vivek Pandey Yukta Patil Ramya Kanagaraj Kaustubh Dabhilkar Ritesh Darade Hitesh Patil Department of Computer Engineering University of Mumbai Mumbai India Department of Information Technology University of Mumbai Mumbai India

ISBN: (数字)9798350388800

ISBN: (纸本)9798350388817

Agriculture is entering a new era marked by the Internet of Things (IoT), which is completely changing how we monitor and manage crops. The adoption of remote monitoring and control has resulted in reduced operational expenses and increased production. The significance of agriculture in maintaining human existence makes it necessary to adopt technical innovations to guarantee its effectiveness and sustainability. This research provides a novel approach to IoT-based smart agriculture that addresses multiple agricultural characteristics and maximizes crop productivity. The sophisticated network of sensors that make up the hardware part of the proposed system includes the DHT11 temperature and humidity sensor, raindrop sensors that sense the presence of rain, ultrasonic sensors that monitor water level, and the ESP32-CAM wireless image transmission sensor. Real-time data gathering and analysis are made possible by the combination of these sensors and the Arduino Mega processing unit. In the proposed model uses chili plant disease dataset and apply a convolutional neural network on which tune the performance and improve the accuracy more than the traditional existing model. The software component for plant disease identification uses Convolutional Neural Network (CNN) techniques to improve farmers' predictive analytics and decision-making.

关键词： Temperature sensors Temperature measurement Image sensors Wireless sensor networks Crops sensor phenomena and characterization sensor systems Convolutional neural networks Monitoring intelligent sensors

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Virtual Sample Generation Using Conditional Adversarial Network with Latent Spaces as Noise Inputs 5

Virtual Sample Generation Using Conditional Adversarial Netw...

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5th International conference on Industrial Engineering and Artificial Intelligence, IEAI 2024

作者： Zhang, Jie-Long Zhu, Qun-Xiong Song, Xiao-Lu He, Yan-Lin Xu, Yuan Beijing University of Chemical Technology Engineering Research Center of Intelligent PSE Ministry of Education in China College of Information Science & Technology Beijing China

ISBN: (纸本)9798350386363

Data-driven soft sensors are widely applied in the chemical industry. However, due to the production conditions and resource limitations, the soft sensor models faced the challenge of poor prediction effect caused by the small sample. To solve this challenge, this paper puts forward a novel virtual sample generation (VSG) method based on conditional generative adversarial networks with latent spaces as noise inputs (CGAN-LSNI). In the CGAN-LSNI, we use the latent space of the Variational Auto-Encoder (VAE) as the noise input to the CGAN. In addition, utilizing the samples generated by VAE and the original samples to co-train the conditional generative adversarial nets (CGAN) by semi-supervised learning method. This method aims to improve generalization ability of the CGAN predictions. To better apply the numerical data obtained from sampling in the chemical industry, we incorporate the mean square error into the existing loss function of CGAN. The benchmark function and industrial purified terephthalic acid (PTA) data are used to verify the performance of the proposed CGAN-LSNI. The results show that the CGAN-LSNI has better prediction accuracy compared to the related methods of generating virtual samples. © 2024 IEEE.

关键词： Generative adversarial networks

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Comprehensive Stride Analysis Through Plantar Pressure

Comprehensive Stride Analysis Through Plantar Pressure

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International conference on Wireless Communications, Signal processing and Networking (WiSPNET)

作者： V Harsha Vardhan B Senthilnaathan Venkatasubramanian K Dept. of ECE Amrita Vishwa Vidyapeetham Chennai India

ISBN: (数字)9798331524227

ISBN: (纸本)9798331524234

The use of stride analysis extends to various domains, particularly in studying human plantar pressure. Most importantly, plantar pressure measurement is vital in the management and treatment of several conditions, including diabetes, trauma, stroke, ankle instability, sports injuries, and Parkinson’s disease. This study presents a comprehensive system for stride analysis using a smart insole embedded with piezoelectric sensors, capturing real-time pressure variations across different regions of the foot. The voltage signals generated by piezoelectric sensors in response to foot pressure are collected and then transmitted via Bluetooth to a Serial Bluetooth Terminal application for further processing, calibration, and mapping into meaningful pressure values. This system collects foot pressure changes from four essential regions, which it converts into heat maps to assist with the detection of gait and stride abnormalities. The results show that the developed system successfully measures foot pressure fluctuations as well as displays real-time information on walking patterns and weight distribution patterns. This system demonstrates its potential to support ongoing monitoring applications, which further benefits rehabilitation programs and helps reduce injuries and enhance athletic performance.

关键词： Wireless sensor networks Bluetooth sensor systems Real-time systems Pressure measurement Heat maps intelligent sensors Injuries Foot Diseases

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Enhancing Reliability in Wireless sensor networks through Cluster Based Optimization

Enhancing Reliability in Wireless Sensor Networks through Cl...

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Science and information Technology in Smart Administration (ICSINTESA), International conference of

作者： Yaqin Jasim Abd Hashim Saad Talib Hasson University of Babylon Iraq

ISBN: (数字)9798350376111

ISBN: (纸本)9798350376128

This study delves into enhancing the reliability of Wireless sensor networks (WSNs) through the implementation of the Low-Energy Adaptive Clustering Hierarchy (LEACH) algorithm. WSNs play a vital role in various domains, including environmental monitoring and industrial automation, necessitating dependable data transmission. Initially, the research evaluates the reliability of WSNs without clustering, serving as a baseline. Subsequently, LEACH is applied to organize sensors into clusters, optimizing energy usage and network efficiency. The focus lies on improving reliability metrics, such as packet delivery ratio (PDR), which quantifies the proportion of successfully transmitted data packets. Through simulation and analysis, the effectiveness of LEACH in enhancing reliability is thoroughly examined. The results demonstrate a significant enhancement in reliability, indicating a substantial reduction in packet loss and data inconsistency. This reliability enhancement is attributed to the intelligent clustering of sensors, which minimizes energy consumption and optimizes data routing. The findings underscore the importance of employing advanced algorithms like LEACH to bolster the reliability of WSNs, ensuring accurate and timely data delivery in critical applications.

关键词： Wireless sensor networks Adaptive systems Scalability Clustering algorithms Packet loss Routing Energy efficiency Delays Reliability intelligent sensors

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Resource Allocation Scheme for Wireless sensor networks: Machine Learning Approach 2

Resource Allocation Scheme for Wireless Sensor Networks: Mac...

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2nd International conference on Signal and information processing, IConSIP 2022

作者： Kori, Gururaj S. Kakkasageri, Mahabaleshwar S. Biluru Gurubasava Mahaswamiji Institute of Technology Dept. of Electronics & Communication Engineering Karnataka Mudhol587313 India Dept. of Electronics & Communication Engineering Karnataka Bagalkot587102 India

ISBN: (纸本)9781728168852

Wireless sensor Network is a distributed and decentralized adhoc network comprising of powerful sensing, computing and processing nodes. sensor nodes are restricted in terms of energy i.e. battery power, communication range, bandwidth, computational latency, and storage. Effective usage of Wireless sensor networks (WSN) resources is a challenging task, to enhance network lifespan, increase throughput, reduce computational delay and minimize the control overheads. Several intelligent strategies are proposed by adopting intelligent resource management schemes. In WSN, intelligent resource management involves resource discovery, resource scheduling, and resource (bandwidth) allocation. In this paper, a Classification and Regression Tree (CART), Machine Learning (ML) protocol is applied to deal with incomplete information about the WSN i.e., uncertainty during effective bandwidth allocation. The scheme operates as follows: 1) k-means clustering algorithm is applied to the network, clusters are formed & cluster head is selected. 2) k-NN algorithm is applied to find the number of neighbor nodes in the cluster. 3) The attributes of the Cluster Members (CM) % Cluster Head (CH) like distance from base station, their degree of connectivity, congestion rate of the network, type of data & size of data aggregated at CH after performing task, and channel quality etc are calculated. 4) Aggregation and classification of CM & CH attributes (data sets) using intelligent search and feature selection algorithm. 5) Data set are further processed for training phase, predication phase and decision tree model is built to achieve target attributes i.e. bandwidth allocation. 6) Heat matrix & confusion matrix are generated and performance evaluation of proposed scheme is done. From simulation results we observe that the proposed CART scheme enhances the performance of WSN in terms of resource allocation accuracy, allocation computational delay, and data transmission efficiency etc. © 2022 IEE

关键词： Resource allocation

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Attack Detection in Wireless sensor networks using Novel Artificial Intelligence Algorithm 7

Attack Detection in Wireless Sensor Networks using Novel Art...

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7th International conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), I-SMAC 2023

作者： Aneja, Aseem Chitkara University Institute of Engineering and Technology Chitkara University Centre for Interdisciplinary Research in Business and Technology Punjab India

ISBN: (纸本)9798350341485

The Wireless sensor Network (WSN) is a concept of an Internet of Things network (IoT) with limited energy resources. When many sensors are used despite their limitations, security becomes a significant concern in the network, necessitating the practical design of detection and mitigation techniques. Using Artificial Intelligence (AI) algorithms is a very effective way to develop cyber-attack detection systems in WSN. Squirrel Search Optimized Probabilistic Neural Network (SSOPNN), a unique classification approach for the detection of cyberattacks in WSN, is presented in this research. Gaussian Naïve Bayes (Gaussian NB), K-Nearest Neighbors (KNN), and Random Forest (R.F.) are three well-known methods against which their results are compared. The study utilizes Pearson's correlation and mutual information to choose features for the ensemble and to reduce the number of dimensions. T Pipeline optimization technique with a tree structure is used to fine-tune hyperparameters. Specifically, one-of-a-kind WSNDS dataset is used, which provides adequate material for four distinct attacks (Grayhole, Blackhole, Flooding, and TDMA scheduling). The study compares three approaches using a variety of metrics, including detection rate, false positive rate, false negative rate, average prediction time per sample, and memory and processing time requirements. © 2023 IEEE.

关键词： Wireless sensor networks

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Toward Reliable Mobile CrowdSensing Data Collection: Image Splicing Localization Overview 19

Toward Reliable Mobile CrowdSensing Data Collection: Image S...

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19th IEEE International Wireless Communications and Mobile Computing (IEEE IWCMC)

作者： Abou-Zbiba, Wahiba Benbrahim, Houda El Bakkali, Hanan El Gadi, Hajar Maqour, Zaina Benhaddou, Driss Mohammed V Univ IRDA ENSIAS Rabat Morocco Mohammed V Univ SSL ENSIAS Rabat Morocco Univ Houston Engn Technol Dept Houston TX 77204 USA Alfaisal Univ Dept Elect Engn Coll Engn Riyadh Saudi Arabia

ISBN: (纸本)9798350333398

With the advancement of technology, the collection of data used in intelligent Transportation Systems has become increasingly easy, notably with the emergence of the Mobile CrowdSensing paradigm. This paradigm could provide insights into traffic situation, road condition, pedestrians' behaviours, public transportation situation, and so on. Through the use of the powerful sensors in the mobile, different types of data can be generated, such as Gyroscope data, light sensors data, Magnetometer data, GPS data, Accelerometer data, videos, and images. However, the use of MCS raises issues of data reliability, as it involves the participation of several mobile owners with different 'levels' of trustworthiness. An important related issue is image forgery, i.e. the manipulation of images in order to deliberately provide misleading information, which constitutes a threat to the decision-makers of the MSC-based applications in intelligent Transportation Systems. In this paper, an overview on image forgery is presented. We examine the workflows, approaches and techniques employed for image forgery detection and localization. Then, we provide a brief review of some image splicing localization techniques. Finally, we provide a comparative analysis of handcrafted feature extraction-based techniques and deep learning-based techniques. The aim of this paper is to draw attention to the image forgery problem that could threaten any Mobile CrowdSensing application dedicated to image collection, and to provide an overview of different existing techniques that could be used to overcome this problem.

关键词： intelligent Transportation Systems Mobile CrowdSensing Image Forgery Image Splicing Localization

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Green Wireless Powered sensor Network through Joint Power Beacon and sensor Activation

Green Wireless Powered Sensor Network through Joint Power Be...

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IEEE/CIC International conference on Communications in China (ICCC)

作者： Jiang, Yiping Chet, Yue Ling Luo, Sheng Wu, Kaishun Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (数字)9781665484800

ISBN: (纸本)9781665484800

The technology of wireless powered communications is essential for the energy-sustainable sixth generation (6G) wireless networks. However, due to the generally low end-to-end wireless energy transmission efficiency from the power beacons (PBs) to the wireless sensors (WSs), the PBs are required to adopt high transmit power or deploy densely to meet the WSs' energy demands, which may result in intolerantly high energy consumption at the PBs. To investigate the optimal design tradeoff between the WSs' energy-sustainable communications and the PBs' energy-efficient wireless energy transmissions, this paper jointly optimizes the PBs' activation probabilities and the WSs' duty cycles in a wireless powered sensor network (WPSN), to minimize the overall energy consumption at the PBs. Using tools from stochastic geometry, we derive the WSs' successful transmission probability in each frame, which is a joint probability of being sufficiently charged by the PBs in the idle phase and sending information successfully in the transmission phase. However, the PBs' energy consumption minimization problem under the WSs' successful information transmission probability constraint is shown to be non-convex, and thus is difficult to solve. By studying the problem structure, we manage to find the optimal solutions efficiently. Numerical results are also provided to show the energy consumption performance of our proposed scheme.

关键词： Geometry Wireless sensor networks Energy consumption Wireless networks Stochastic processes Wireless power transfer information processing

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YONO: Modeling Multiple Heterogeneous Neural networks on Microcontrollers 21

YONO: Modeling Multiple Heterogeneous Neural Networks on Mic...

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21st ACM/IEEE International conference on information processing in sensor networks (IPSN)

作者： Kwon, Young D. Chauhan, Jagmohan Mascolo, Cecilia Univ Cambridge Cambridge England Univ Southampton Southampton Hants England

ISBN: (纸本)9781665496247

Internet of Things (IoT) systems provide large amounts of data on all aspects of human behavior. Machine learning techniques, especially deep neural networks (DNN), have shown promise in making sense of this data at a large scale. Also, the research community has worked to reduce the computational and resource demands of DNN to compute on low-resourced microcontrollers (MCUs). However, most of the current work in embedded deep learning focuses on solving a single task efficiently, while the multi-tasking nature and applications of IoT devices demand systems that can handle a diverse range of tasks (such as activity, gesture, voice, and context recognition) with input from a variety of sensors, simultaneously. In this paper, we propose YONO, a product quantization (PQ) based approach that compresses multiple heterogeneous models and enables in-memory model execution and model switching for dissimilar multi-task learning on MCUs. We first adopt PQ to learn codebooks that store weights of different models. Also, we propose a novel network optimization and heuristics to maximize the compression rate and minimize the accuracy loss. Then, we develop an online component of YONO for efficient model execution and switching between multiple tasks on an MCU at run time without relying on an external storage device. YONO shows remarkable performance as it can compress multiple heterogeneous models with negligible or no loss of accuracy up to 12.37x. Furthermore, YONO's online component enables an efficient execution (latency of 16-159 ms and energy consumption of 3.8-37.9 mJ per operation) and reduces model loading/switching latency and energy consumption by 93.3-94.5% and 93.9-95.0%, respectively, compared to external storage access. Interestingly, YONO can compress various architectures trained with datasets that were not shown during YONO's offline codebook learning phase showing the generalizability of our method. To summarize, YONO shows great potential and opens further d

关键词： Multi Task Learning Product Quantization Microcontrollers

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A sensor-Based Deep Learning Approach for Recognizing Daily and Work Activities in Open Environments for Sanitation Workers 9

A Sensor-Based Deep Learning Approach for Recognizing Daily ...

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Joint 9th International conference on Digital Arts, Media and Technology with 7th ECTI Northern Section conference on Electrical, Electronics, Computer and Telecommunications Engineering, ECTI DAMT and NCON 2024

作者： Mekruksavanich, Sakorn Jantawong, Ponnipa Phaphan, Wikanda Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Phayao Thailand King Mongkut's University of Technology North Bangkok Research Group in Statistical Learning and Inference Department of Applied Statistics Faculty of Applied Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9798350318241

Sanitation workers play a crucial role in maintaining public hygiene and cleanliness. Understanding their daily routines and job responsibilities is essential for improving their work environment, task distribution, and overall supervision. However, tracking sanitation workers in open environments remains a persistent challenge. This paper introduces a deep learning model named CNN-BiGRU-CBAM, designed to recognize sanitation workers' daily and working activities using smartwatch sensors. The model leverages advanced convolutional neural networks to extract highly informative features from multi-modal sensor data, including acceleration information, collected from smartwatches. The dataset used in this study is carefully curated and collected from sanitation workers during their daily work routines, encompassing activities such as walking, running, sweeping, lifting, and driving vehicles. These activities are systematically categorized to cover both work-related tasks and non-work activities. Experimental results demonstrate that the proposed model consistently achieves an average F1-score of over 94.50% across seven activity groups, outperforming baseline deep learning methods by an impressive margin of 5.62%. The model excels in accurately identifying operations in diverse and dynamic work settings. This research highlights the potential of smartwatches and deep learning in continuously analyzing sanitation workers' duties and developing improved working regulations that consider their well-being and daily tasks. © 2024 IEEE.

关键词： Sanitation

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