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Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

computer Systems Science & engineering 2023年第8期46卷 2319-2335页

作者： Saud Yonbawi Sultan Alahmari B.R.S.S.Raju Chukka Hari Govinda Rao Mohamad Khairi Ishak Hend Khalid Alkahtani JoséVarela-Aldás Samih M.Mostafa Department of Software Engineering College of Computer Science and EngineeringUniversity of JeddahJeddahSaudi Arabia King Abdul Aziz City for Science and Technology RiyadhKingdom of Saudi Arabia Aditya Engineering College Affiliated to JNTUK(Kakinada) SurampalemIndia Department of MBA Vignan’s Institute of Information and Technology(A)DuvvadaVisakhapatnamAP530049India School of Electrical and Electronic Engineering Engineering CampusUniversiti Sains Malaysia(USM)Nibong TebalPenang14300Malaysia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11564Saudi Arabia SISAu Research Group Universidad Tecnológica IndoaméricaAmbato180103Ecuador Faculty of Computers and Information South Valley UniversityQena83523Egypt

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual *** the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart *** irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation *** learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an *** this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent *** MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination ***,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not *** MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

关键词： Agriculture smart farming hyperparameter tuning artificial intelligence irrigation management sensors deep learning

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Optimizing Packet Delivery in Underwater Wireless Sensor Networks: A Comparative Study of Genetic Bee Colony and Ant Colony Algorithms

Optimizing Packet Delivery in Underwater Wireless Sensor Net...

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International Conference on communication and electronics Systems (ICCES)

作者： A. Kannappan Nagesh Kolagani Vijay Anand Kandaswamy J. Vishnupriyan Department of Electronics and Communication Engineering Saveetha School of Engineering SIMATS Chennai India Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Chennai India Department of Electrical and Electronics Engineering Chennai Institute of Technology Chennai India

ISBN: (数字)9798350377972

ISBN: (纸本)9798350377989

Physically Underwater communication adopt the bio inspired genetic bee colony formed network communication structures. Underwater environment condition communication is established between the Transmitting node and in between several clustering relay nodes to reach receiver nodes. Transmitting nodes are placed on sea bed areas at certain depth and clustering relay nodes are placed in various underwater depth conditions dipping the sensor nodes. Also, underwater battery life is very hard and battery replacement is a very tough task. Hence, an alternate solution of optimization battery capacity utilization is used. This optimization algorithm improves Packet delivery ratio by comparing genetic bee algorithm with Ant colony algorithm. Hence, equivalence of two algorithms implemented in underwater conditions. The genetic bee colony Algorithm produces better output results more than 4 to 6 percentage compared to ant colony parameter output. The genetic bee colony algorithm offers a maximum clustering node technique compared to ant colony algorithm. Its greater support for saving energy consumption extending battery life.

关键词： Energy consumption Wireless sensor networks Underwater communication Simulation Clustering algorithms Genetics Throughput Batteries Reliability Relays

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge Computing Networks

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IEEE Transactions on Consumer electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge Computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Artificial Intelligence in Next-Generation Networking: Energy Efficiency Optimization in IoT Networks Using Hybrid LEACH Protocol

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SN computer Science 2024年第5期5卷 546页

作者： Khan, Surbhi Bhatia Kumar, Ankit Mashat, Arwa Pruthviraja, Dayananda Imam Rahmani, Mohammad Khalid Mathew, Jimson School of Science Engineering and Environment University of Salford Salford United Kingdom Department of Information Technology Guru Ghasidas Vishwavidyalaya Chhattisgarh Bilaspur India Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Rabigh 21911 Saudi Arabia Department of Information Technology Manipal Institute of Technology Bengaluru Manipal Academy of Higher Education Karnataka Manipal 576104 India Department of Computer Science College of Computing and Informatics Saudi Electronic University Riyadh 11673 Saudi Arabia Department of Computer Science and Engineering Indian Institute of Technology Patna Bihar Patna India

The convergence of the Internet of Things (IoT) and Artificial Intelligence (AI) is significantly transforming the landscape of future networking. The Internet of Things (IoT) is a technological paradigm that encompasses embedded systems, wireless sensors, and automation, facilitating the integration of various applications ranging from smart homes to wearable devices. In addition, the advent of artificial intelligence (AI) amplifies this influence by providing data-driven analytics, optimising processes, and presenting novel opportunities for growth. Nevertheless, the widespread adoption of devices within Internet of Things (IoT) networks gives rise to apprehensions regarding increased energy consumption. In order to ensure the longevity of network operations, it is imperative to employ energy-efficient protocols for sensor nodes that possess limited power resources. One example of a protocol that demonstrates this concept is the Low Energy Adaptive Clustering Hierarchy (LEACH) protocol. This protocol effectively divides networks into clusters and dynamically adjusts the cluster heads to optimise the transmission of data to the base stations. Our study enhances the LEACH protocol by incorporating digital twin simulation, thereby enhancing the efficiency of IoT systems. Virtual network models and AI analytics are employed to assess energy consumption and performance. Cache nodes play a crucial role within this framework as they collect data from cluster heads in order to transmit it to the base station. By leveraging artificial intelligence (AI) and simulation techniques, we are able to improve the energy efficiency and reliability of the Internet of Things (IoT) systems. The findings indicate a significant reduction of 83% in non-functioning nodes and a notable increase of 1.66 times in energy levels of nodes compared to conventional approaches. This study highlights a potential direction for energy-efficient, AI-enhanced Internet of Things (IoT) networking through

关键词： Cluster head Clustering Data aggregation Gateway Internet of things LEACH Lifetime

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MULTIPLE DIMENSION COLLABORATIVE OPERATION TECHNIQUES AND NETWORKING FOR AIR-SPACE-GROUND INTEGRATED SYSTEM:TECHNOLOGIES AND APPLICATIONS

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China communications 2021年第9期18卷 I0003-I0004页

作者： Qinyu Zhang Xuejun Sha Fei Ji Ning Ge Harbin Institute of Technology(Shenzhen) system of MOE China communication research center Electronic Engineering South China University of Technology Department of Electronics Engineering at Tsinghua University

Along with the swift growth of communication services,various networks have undergone multidimensional technical evolution,*** transmission speed and large bandwidth for ground cellular network,high mobility for aviation network,and wide-area coverage for satellite *** the industry demands for joint operation arise,an integrated space-air-ground collaboration network will be formed.

关键词： network SPACE aviation

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Computational Microuidic Channel for Separation of Escherichia coli from Blood-Cells

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computers, Materials & Continua 2021年第5期67卷 1369-1384页

作者： Chinnapalli Likith Kumar A.Vimala Juliet Bandaru Ramakrishna Shubhangi Chakraborty Mazin Abed Mohammed Kalakanda Alfred Sunny Department of Electronics and Instrumentation Engineering SRM Institute of Science and TechnologyKattankulathur603203India Department of Electronics and Communication Engineering SRM Institute of Science and TechnologyKattankulathur603203India College of Computer Science and Information Technology University of AnbarAnbarIraq Department of Mechanical Engineering Karunya Institute of Technology and SciencesCoimbatore641114India

Microuidic channels play a vital role in separation of analytes of interest such as bacteria and platelet cells,etc.,in various biochemical diagnosis procedures including urinary tract infections(UTI)and bloodstream *** paper presents the multi physics computational model specifically designed to study the effects of design parameters of a microuidics channel for the separation of Escherichia coli(***)from various blood constituents including red blood cells(RBC)and platelets.A standard two inlet and a two outlet microchannel of length 805µm with a channel width of 40µm is *** effect of electrode potentials and the effect of electrode placement along the channel length and also the levitation of electrodes from the channel wall are studied to optimize the selective particle separation *** results show the efcient separation of E-coli with a mean diameter 0.68µm is achieved at low voltages(less than 20 V)when electrodes placed near to the micro channel and also noticed that the applied electric potential is inversely proportional to the number of electrodes placed along the microuidic *** computer aided multi physics simulations with multiple governing parameters could be advantage in design optimization of microuidics channels and support precise bioparticle separation for better diagnosis.

关键词： Microuidics dielectrophoresis cell separation electro kinetic force platelets E.coli

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Research on Majorization Algorithm and Application of Geotechnical engineering Structure Based on Granule Swarm Majorization 2

Research on Majorization Algorithm and Application of Geotec...

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2nd International Conference on Smart Technologies for Smart Nation, SmartTechCon 2023

作者： Gnana Soundari, A. Al-Fatlawy, Mohammed Hussien Omkar, Jamvant Arun Balaji, N. Suseela, D. Asha, V. Department of Computer Science and Engineering Saveetha School of Engineering SIMATS Tamil Nadu Chennai India College of Technical Engineering The Islamic University Najaf Iraq Department of Electronics & Communication IES College Of Technology MP Bhopal462044 India New Prince Shri Bhavani College of Engineering and Technology Tamil Nadu India Department of Artificial Intelligence and Machine Learning Bannari Amman Institute of Technology Tamil Nadu Sathyamangalam India Department of Master of Computer Applications New Horizon College of Engineering Bellandur Post Bengaluru560103 India

ISBN: (纸本)9798350305418

The traditional majorization design method makes too many restrictions on the objective function to be optimized and its constraints, which brings a lot of inconvenience to solving majorization problems in practical projects. A new stochastic Optimization algorithm based on swarm intelligence - granule swarm algorithm (PSO) was put forward in, and it has been widely concerned by researchers. When the algorithm is initialized, the granules are randomly divided into several sub-granule groups, each sub-granule group evolves independently according to a given strategy, and random migration and adaptive mutation of granules are carried out in the specified period of evolution to maintain the diversity of the entire population., to avoid premature convergence. The inverse analysis of geotechnical engineering majorization is essentially a typical majorization problem of complex nonlinear functions. The use of global majorization algorithm is an ideal way to solve this problem. low productivity. The new algorithm is applied to the elastic-plastic parameter inversion of geotechnical materials. The results show that compared with the conventional granule swarm majorization algorithm, the improved algorithm significantly improves the search efficiency of parameters, and the results that meet the accuracy requirements can be obtained with less iterations, which reduces the amount of calculation of elastic-plastic back analysis of geotechnical engineering. It is a feasible parameter inversion method. © 2023 IEEE.

关键词： Efficiency Geotechnical engineering Granule Swarm Particle swarm majorization Structural majorization

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Complete Hierarchy Modelling of Motor Functions: A Design and Management Strategy

Complete Hierarchy Modelling of Motor Functions: A Design an...

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2023 International Conference on Power Energy, Environment and Intelligent Control, PEEIC 2023

作者： Singh, Vijay Bhagwan, Shri Sharma, Sanghmitra Kumar, V. Mathan Gopinath, S. Dedgaonkar, Suruchi Gaurav Graphic Era Deemed to Be University Department of Computer Science & Engineering Uttarakhand Dehradun248002 India Tmu Department of Me India School of Basic Scieences & Technology Iimt University Meerut India Karpagam Academy of Higher Education Department of Computer Science Coimbatore641021 India Karpagam Institute of Technology Department of Electronics and Communication Engineering Coimbatore641105 India It Vishwakarma Institute of Information Technology Pune India

ISBN: (纸本)9798350357769

We show a system for organizing and controlling the execution of different manual tasks by physiologically generated figures. Our approach employs a tiered collection of popular because of its low to produce mechanically locomotion programs. As a result, software components are concentrated on areas where they're many needed: momentum plans with shorter time spans are produced using increased products, because when kinematic plans with longer intervals are produced using rougher configurations. Our paradigm can be applied to a variety of motor abilities, as well as those requiring exact coordination between the movements of the limbs and legs. We show tools for actions like stepping out of a chair, ascending steps using a railing, and slithering onto an upper floor. Without the necessity of gesture recognition data, every single one of the movements are generated using just a black-box game bryo engine from high-ranking user instructions. © 2023 IEEE.

关键词： Complete hierarchy modeling Coordination Design strategy Gesture recognition data Industrial automation Management strategy Manual tasks Motion planning Motor abilities Motor functions Muscle strength Optimization Robotics Synchronized action

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Traffic Surveillance System Through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation

Traffic Surveillance System Through Capsule Gated Graph Atte...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Mahesh Prasanna K Devashree Marotka Muralidharan J Rachit Adhvaryu Vani V Ramya Maranan Department of Electronics and Communication Engineering Vivekananda College of Engineering and Technology Puttur Department of Electronics and Telecommunication Engineering G H Raisoni College of Engineering and Management Nagpur Maharashtra Department of Electronics and Communication Engineering KPR Institute of Engineering and Technology Coimbatore Tamil Nadu Computer science and Engineering Faculty of Engineering and Technology Parul institute of Engineering and Technology Parul University Gujarat Department of Information Science and Engineering Bangalore Institute of Technology Bangalore Karnataka Department of Research and Innovation Saveetha School of Engineering SIMATS Chennai Tamil Nadu

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Internet of Things and Industrial Internet of Things have seen rapid adoption and have transformed industries through smart automation, constant monitoring and better connected systems. The progressive inclusion of such solutions has contributed to the growth in exposure to complex and changing global threats. The primary challenge lies in accurately detecting and classifying cyber threats from high-volume, unstructured network traffic data in real-time. Existing methods are not very accurate and cannot be easily scaled due to constraints in computational resources and the nature of IIoT network systems. This paper introduces Traffic Surveillance System through Capsule Gated Graph Attention Network for IoT/IIoT Cyberthreat Detection and Mitigation (Cyber-CapG). The system uses raw traffic data from Edge-IIoTset and uses the Tunable Transformer for feature extraction. The extracted features are passed through the Capsule Gated Graph Attention Network to model the traffic as a graph based on communication hierarchy and being selective to essential traffic patterns. Furthermore, weights and loss functions are tuned based on the Progressive Crested Porcupine Optimization so as to achieve maximum optimization. The proposed system exhibits outstanding performance with 99.21 % detection rate, 99.07% precision, 99.15% recall, 99.18% F1 measure, and it provides an effective way of identifying threats like DDoS, SQL Injection, and Ransomware. In conclusion, Cyber-CapG provides a solid solution for traffic surveillance in IoT/IIoT, providing the high detection and prevention of various cyber threats, as well as optimizing the cybersecurity and reliability of industrial networks.

关键词： Surveillance Prevention and mitigation Telecommunication traffic Traffic control Logic gates SQL injection Feature extraction Real-time systems Optimization Industrial Internet of Things

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GNN-Assisted BiG-AMP: Joint Channel Estimation and Data Detection for Massive MIMO Receiver

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IEEE Transactions on Wireless communications 2025年第6期24卷 4631-4646页

作者： Liu, Zishen Wu, Nan He, Dongxuan Yuan, Weijie Li, Yonghui Quek, Tony Q. S. Beijing Institute of Technology School of Information and Electronics Beijing100081 China Southern University of Science and Technology School of System Design and Intelligent Manufacturing Shenzhen Key Laboratory of Robotics and Computer Vision Shenzhen518055 China The University of Sydney School of Electrical and Information Engineering SydneyNSW2008 Australia Singapore University of Technology and Design Information Systems Technology and Design Pillar Tampines 487372 Singapore Kyung Hee University Department of Electronic Engineering Yongin17104 Korea Republic of

In this paper, we develop a graph neural network (GNN)-assisted bilinear inference approach to enhance the receiver performance of the MIMO system through message passing-based joint channel estimation and data detection (JCD). Specifically, based on the bilinear generalized approximate message passing (BiG-AMP) framework and conditional correlation of signal, we propose a GNN-assisted BiG-AMP (GNN-BiGAMP) approach, which integrates a GNN module into the data-detection-loop to compensate the inaccurate marginal likelihood approximation. By leveraging the coupling between the channel and received symbols, a bilinear GNN-assisted BiG-AMP (BiGNN-BiGAMP) JCD receiver is further proposed. This method incorporates two GNNs with similar graph representation into the bilinear posterior estimation loops, which not only compensates for approximation errors but also alleviates performance loss due to premature variance convergence, thereby enhancing the receiver performance significantly. To fully exploit the supervised information from channel estimation and data detection, we propose a multitask learning based training scheme, which coordinates GNNs with different tasks in two loops. Simulation results show that our proposed GNN-assisted JCD receivers significantly outperform other JCD counterparts in terms of both channel estimation and data detection. © 2002-2012 IEEE.

关键词： Graph neural networks

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