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The Cluster-based Cognitive Radio sensor Networks that are Wireless Aware of PUEA and SSDF Attacks 1

The Cluster-based Cognitive Radio Sensor Networks that are W...

引用

1st ieee international conference on Applied Intelligence and Sustainable computing, ICAISC 2023

作者： Thakkar, Hina Goswami, Sachin ATLAS Skill Tech University ISME - School of Management & Entrepreneurship Mumbai India IIMT University School of Media Film & Television Studies Meerut India

ISBN: (纸本)9798350323795

This paper presents a secure Cognitive RadioWireless sensor Network (CR-WSN) architecture. CR-WSN is a cluster based network in which the sensors are hierarchically structured. Each cluster is chosen with single cluster head that operates on ISM band frequencies. sensor data from cluster members are aggregated by cluster head. Cluster heads forwards the collected data to sink based on selection of a shortest path using Dijkstra algorithm. Due to the problem of spectrum scarcity, Cognitive Radios are subjected to two main attacks for utilizing the unlicensed spectrum. We resolve Spectrum Sensing Data Falsification attack and Primary User Emulation attack. Dynamic Spectrum Sensing Technique is involved for performing spectrum sensing. We propose a regression based spectrum sensing data falsification attack detection technique and mathematical SHA with Digital Signature is applied for primary user emulation attack detection. Extensive implementation is handled in Network Simulator 3 and comparative results are illustrated for betterment of throughput and end-to-end delay efficiencies. © 2023 ieee.

关键词： Cognitive radio

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Smart Manufacturing: Integrating IoT and AI for Agile Production Management 1

Smart Manufacturing: Integrating IoT and AI for Agile Produc...

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1st international conference on Advances in Computer Science, Electrical, Electronics, and Communication Technologies, CE2CT 2025

作者： Annavarapu, Babu Jagadeesh Bastray, Tejaswini Rusho, Maher Ali Long, Choi Sang Channi, Arvinder Singh Kumar, Vishwa Vijay Hyderabad India St Joseph's University Department of Management India University of Colorado Lockheed Martin Engineering Management BoulderCO United States Raffles University Malaysia Guru Kashi University Talwandi Sabo Punjab Bathinda India Stuart School of Business Illinois Institute of Technology ChicagoIL United States

ISBN: (纸本)9798331518578

Production management is being revolutionised by smart manufacturing, which is fuelled by the combination of artificial intelligence (AI) and the Internet of Things (IoT) and allows for quick, data-driven decision-making. Through this interface, producers may use cutting-edge machine learning models and real-time sensor data for process optimisation, quality assurance, and predictive maintenance. Model performance is evaluated using evaluation metrics like accuracy, recall, ROC curves, and AUC in conjunction with machine learning approaches including classification, regression, and time series forecasting. AI-driven systems can enhance production scheduling, anticipate equipment problems, and optimise resource allocation by utilising IoT data. This makes it possible to create agile manufacturing environments that can minimise downtime and react swiftly to changes in demand. The robustness and dependability of these AI models are further improved by the addition of machine learning assessment techniques including learning curves, feature importance, and cross-validation. In the end, this IoT and AI synergy enables manufacturers to attain increased productivity, cost reductions, and adaptability in dynamic production environments. © 2025 ieee.

关键词： Smart manufacturing

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distributed Control strategy in Energy Cell systems Based on Blockchain Technology 1

Distributed Control Strategy in Energy Cell Systems Based on...

引用

1st international conference on Cyber-Energy systems and Intelligent Energy, ICCSIE 2022

作者： Li, Zhengping Yu, Fei Guan, Qi State Grid Anshan Power Supply Company Anshan China

ISBN: (纸本)9781665452151

To tackle the problems of cumbersome computation and information security of centralized scheduling under energy cell structure, this paper suggests a distributed and synchronized scheduling technique for energy cell-tissue systems that considers privacy protection based on blockchain technology. First, blockchain technology is used to realize the privacy protection of the system. Then, a two-stage model of distributed optimal dispatch is established, with the pre-dispatch stage forming generation and consumption plans to achieve global economic optimality;and the intra-day real-time dispatch regulating errors to achieve real-time optimal performance. Finally, the feasibility of the proposed approach is demonstrated through simulation experiments, providing a feasible reference solution for the operation model of the energy cell-tissue system. © 2023 ieee.

关键词： Blockchain

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Modeling and Prediction of Environmental Parameters for Implementation of Smart Agriculture Using LoRa 1

Modeling and Prediction of Environmental Parameters for Impl...

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1st international conference on Intelligent Technologies for Sustainable Electric and Communications systems, iTech SECOM 2023

作者： Hariprasath, G. Monishkumar, A. Senthil Vadivu, M. Sona College of Technology Department of Ece Tamilnadu India

ISBN: (纸本)9798350371185

Precision agriculture, driven by IoT(Internet Of Things) technologies, has revolutionized the farming industry by enabling real-time monitoring and data-driven decision-making. This study proposes an enhanced data transmission system for LoRa- (Long Range)based precision agriculture, leveraging the capabilities of dual LoRa devices. The system aims to overcome communication challenges in large agricultural areas and ensure reliable and extended-range connectivity between sensor nodes and the central gateway. The proposed system architecture includes IoT sensor nodes deployed throughout the farm, equipped with various environmental sensors to monitor crucial parameters such as soil moisture, temperature, humidity, light intensity, and pressure. Two LoRa devices are introduced within the system. A local LoRa device is placed in proximity to the sensor nodes, and a main LoRa gateway serves as the central hub for data aggregation. The local LoRa device acts as a relay point, receiving sensor data from the nearby nodes and transmitting it wirelessly to the main LoRa gateway. This dual LoRa device setup extends the communication range and enhances signal strength, enabling seamless and reliable transmission of sensor data over long distances, even in challenging terrains. The main LoRa gateway collects the transmitted data from the local LoRa device and forwards it to the network server for further analysis and decision-making. This data-driven approach empowers farmers to gain real-time insights into the farm's conditions, optimize resource allocation, and improve agricultural practices. In our project, we transfer the collected sensor data using LoRa and we implement it into Machine Learning for rainfall prediction. © 2023 ieee.

关键词： sensor nodes

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Towards On-demand Gas Sensing 17

Towards On-demand Gas Sensing

引用

17th Annual ieee international conference on distributed computing in sensor systems (ieee DCOSS)

作者： Gherman, Markus-Philipp Cheng, Yun Gomez, Andres Saukh, Olga Graz Univ Technol Inst Tech Informat Graz Austria Swiss Fed Inst Technol Comp Engn & Networks Lab Zurich Switzerland Univ St Gallen Interact & Commun Based Syst St Gallen Switzerland Complex Sci Hub Vienna Vienna Austria

ISBN: (纸本)9781665439299

Low-power operation of power-hungry MOX sensors is usually achieved by duty cycling them periodically. However, a difficulty arises if a sensor is operated at irregular time intervals due to intermittent energy availability in energy-neutral or batteryless applications. In this work, we propose a compensation method which re-maps on-demand measurements to virtually duty-cycled readings in the value domain by taking the duration of the last off-time into account. We evaluate our compensation algorithm based on Sensirion's SGP30 sensor and achieve up to 79% accuracy improvement compared to uncompensated measurements.

关键词： sensor systems sensors distributed computing

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Detection of distributed Denial of Service Attack using Random Forest Algorithm 1

Detection of Distributed Denial of Service Attack using Rand...

引用

1st ieee international conference on Automation, computing and Renewable systems, ICACRS 2022

作者： Murukesh, C. Kishore Kannan, B. Thilak kumar, A. Venkat, B. Haris kumar, V. Department of ECE Velammal Engineering College Tamil Nadu Chennai India

ISBN: (纸本)9781665460842

The distributed Denial of Service (DDoS) attack entails flooding an online service with traffic from multiple sources such that it is rendered unavailable. These attacks have been identified by many researchers using machine learning algorithms. In this paper, Ping of death attack is executed and their detection was performed using random forest algorithms. A DDoS attack is detected by Splunk software, which collects attack details about the data packets. Data from Kaggles's dataset is used to train the machine learning algorithm. An algorithm based on the random forest is used to visually differentiate between the normal and attacked samples whose accuracy is equal to 99.8%. During the attack on the network, the Central Processing Unit and Wi-Fi performances are also analyzed. © 2022 ieee

关键词： Denial-of-service attack

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Edge Sensing of Agricultural Layers with Signals and TS Protocol and PS with PAPRAware Transmission 1

Edge Sensing of Agricultural Layers with Signals and TS Prot...

引用

1st ieee international conference on Networking and Communications, ICNWC 2023

作者： Maheswari, G. Uma Lakshmi, A. Anna Valli, K. Kirubanantha Rajkumar, G. Vinoth Ramesh, S.P. RMK College of Engineering and Technology Department of Computer Science and Engineering Tamil Nadu Chennai India Thamirabharani Engineering College Department of Computer Science and Engineering Tamil Nadu Tirunelveli India Unnamalai Institute of Technology Department of Computer Science and Engineering Tuticorin Tamil Nadu Kovilpatti India JP College of Engineering Department of Electronics and Communication Engineering Ayikkudi Tamil Nadu Tenkasi India AMET University Department of Computer Science and Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350336009

Efficient data collection using wireless sensor networks (WSN) today focuses on the hot topics of smart agriculture and precision farming. Agricultural WSNs, however, often face many problems, including multitasking, variability, and transmission rate. In this paper, we provide a practical solution that takes advantage of the many data collection tasks in smart farming enabled by wireless sensor networks created enabled by edge computing. The next phase is a cutting-edge data collection system that combines edge computing and wireless sensor technology. The data collection system is made by modeling a variety of sensors and processes. The optimal workstation and wireless sensor are then selected on the edges of the network server, taking into consideration each unique job and the relationship between both the task and the sensor, to satisfy the accuracy of the information and the time constraints of information-gathering activities. We also design a data collection procedure that takes into consideration established criteria for information accuracy. Eventually, when the new technique is implemented, a simulation model is built and effects are evaluated and contrasted with conventional approaches. The idea outperforms the conventional approaches in metrics, according to the comparative results. © 2023 ieee.

关键词： Wireless sensor networks

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Multi-Criteria Mesh Partitioning for an Explicit Temporal Adaptive, Tasl-Distribnted Finite-volume Solver

Multi-Criteria Mesh Partitioning for an Explicit Temporal Ad...

引用

1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Lasserre, Alice Couteyen-Carpaye, Jean-Marie Guermouche, Abdou Namyst, Raymond Inria Le Chesnay Rocquencourt France Airbus CRT Toulouse France Univ Bordeaux Bordeaux France

ISBN: (纸本)9798350364613;9798350364606

The aerospace industry is one of the largest users of numerical simulation, which is an essential tool in the field of aerodynamic engineering, where many fluid dynamics simulations are involved. In order to obtain the most accurate solutions, some of these simulations use unstructured finite volume solvers that cope with irregular meshes by using explicit time-adaptive integration methods. Modern parallel implementations of these solvers rely on task-based runtime systems to perform fine-grained load balancing and to avoid unnecessary synchronizations. Although such implementations greatly improve performance compared to a classical fork-join MPI+OpenMP variants, it remains a challenge to keep all cores busy throughout the simulation loop. In this article, we first investigate the origins of this lack of parallelism. We emphasize that the irregular structure of the task graph plays a major role in the inefficiency of the computation distribution. Our main contribution is to improve the shape of the task graph by using a new mesh partitioning strategy. The originality of our approach is to take the temporal level of mesh cells into account during the mesh partitioning phase. We evaluate our approach by integrating our solution in an ArianeGroup production code used by Airbus. We show that our partitioning method leads to a more balanced task graph. The resulting task scheduling is up to two times faster for meshes ranging from 200,000 to 12,000,000 components.

关键词： scheduling DAG task-based applications heterogeneous systems

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Smart Traffic Management Solution integrating Machine Learning and Internet of Things 1

Smart Traffic Management Solution integrating Machine Learni...

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1st international conference on Innovative Engineering Sciences and Technological Research, ICIEstR 2024

作者： Batra, Isha Malik, Arun Sharma, Shamneesh Gochhait, Saikat Lovely Professional University School of Computer Science and Engineering Punjab India UpGrad Education Private Limited Delivery and Student Success-upGrad Campus Bangalore India Symbiosis International Deemed University Sidtm Maharashtra Pune India Samara State Medical University Nri Russia

ISBN: (纸本)9798350348637

Increase in population and trend for luxurious lifestyle has led to huge increase in the number of vehicles. This change has further fascinated the researchers to focus on smart transportation systems. Numerous applications are offered in smart transportation systems like smart parking, detecting event of accidents, smart lights, and many more. Detecting event of accidents and informing vehicles on the move is the most prominent area of research now days. Technologies such as Internet of Things (IoT), Machine Learning (ML) can help achieve goals of smart transportation system. In this research paper, a smart architecture for accident detection and information dissemination is proposed. Initially, the data is collected using IoT sensor. After data collection, decision making is made to find out whether accident has occurred or not and the associated congestion in network using ML techniques. This information is finally disbursed to the neighboring vehicles using a secure method for data communication. The vehicles after receiving this information will decide whether to continue moving or choose some alternate path for communication. This research work emphasizes results after applying ML method and evaluates the performance of method in terms of accuracy, precision, and recall. © 2024 ieee.

关键词： Decision making

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Towards Fine-grained Parallelism in Parallel and distributed Python Libraries

Towards Fine-grained Parallelism in Parallel and Distributed...

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1st international conference on Smart Energy systems and Artificial Intelligence (SESAI)

作者： Kerney, Jamison Raicu, Joan Raicu, John Chard, Kyle IIT Coll Comp Chicago IL 60616 USA Univ Chicago Dept Comp Sci Chicago IL 60637 USA

ISBN: (纸本)9798350364613;9798350364606

There is a growing need, for example in machine learning and analytics, to decompose applications into smaller schedulable units. Such decomposition can improve performance, reduce energy consumption, and increase resource utilization. Unfortunately, enabling fine-grained parallelism comes with significant overheads and requires improvements at all layers of the programming stack. We consider the challenges of supporting fine-grained parallelism in the increasingly popular Python-based programming libraries. Specifically, we focus on Parsl, a Python library that is widely used to parallelize the execution of fine-grained Python functions. Parsl's Python-based runtime supports a maximum throughput of around 1200 tasks per second insufficient to meet modern application needs. We perform a comprehensive analysis of Parsl and identify areas that prohibit it from achieving higher throughput. We first profile Parsl components and identify that, with fine-grained tasks workers are often not saturated. We find that tasks spend a majority of their time in the components between the scheduler and worker, however, we also learned that the scheduler is capable of submitting thousands of tasks per second. We then focused on developing new optimizations and implementing crucial components in C to improve throughput. Our new implementation increases Parsl's throughput 6 fold.

关键词： Python

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