Aiming at the energy loss in underwater sensornetworks, a routing algorithm based on improved Northern Goshawk Optimisation (NGO) algorithm is proposed. Withthe determination of the optimal number of cluster heads, ...
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ISBN:
(纸本)9798350386783;9798350386776
Aiming at the energy loss in underwater sensornetworks, a routing algorithm based on improved Northern Goshawk Optimisation (NGO) algorithm is proposed. Withthe determination of the optimal number of cluster heads, the individual fitness function of the northern goshawk is constructed by considering the energy and distance factors. Cluster head selection is optimized using the NGO *** position update rate of candidate cluster head nodes is adjusted using convex lens and positive cosine strategies to broaden local search and expedite global search *** MATLAB simulation, the improved algorithm improves the uptime by 23.4% and 85% compared to the LEACH-improved algorithm and the classical LEACH algorithm, which effectively cuts down the network energy consumption and increases the network working time.
this study presents an optimized real-time monitoring and fault diagnosis system tailored for industrial robots that integrates sensor data for improved performance. Industrial robots, as complex interdisciplinary pro...
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Data aggregation is a fundamental and inherent technique for increasing the lifespan of wireless sensornetworks. By combining multiple data into a single one, it conserves the bandwidth and energy of sensor nodes. To...
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ISBN:
(纸本)9798350386813;9798350386820
Data aggregation is a fundamental and inherent technique for increasing the lifespan of wireless sensornetworks. By combining multiple data into a single one, it conserves the bandwidth and energy of sensor nodes. To preserve the privacy of aggregated data, security features are added during the aggregation process. Nevertheless, the addition of security features increases the computational cost of data aggregation algorithms. To overcome this issue, this paper proposes a lightweight Signcryption protocol for Multihop Data Aggregation (SMDA) in wireless sensornetworks. the proposed protocol, due to its homomorphic feature, preserves privacy and significantly reduces the computational overhead of sensor nodes. Also, the proposed SMDA protocol is oracle free. Without executing signing algorithms, it can generate signatures on aggregated ciphertext during transmissions. In addition, the proposed scheme validates the signatures in batches and the signature verification cost does not depend on the number of messages. Extensive security analysis demonstrates that the proposed protocol meets the security requirements of WSNs. Furthermore, the results of performance evaluation proves that the computational efficiency of the proposed protocol outperforms other existing secure aggregation schemes.
WBA networks support various medical applications consisting of heterogeneous requirements. For reliable data transfer, an effective media access control protocol must be used. Here in this project, a dynamic MAC prot...
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WBA networks support various medical applications consisting of heterogeneous requirements. For reliable data transfer, an effective media access control protocol must be used. Here in this project, a dynamic MAC protocol based on Super frame structure is proposed by extending the standard principles from IEEE 802.15.6 which is a standard protocol, a mechanism for allocating reserved slots with prioritization is used which is known to be Importance criteria through Inter-criteria Correlation, to assign a slot allocation for each sensor device. the sensor device values are calculated based on various sensor parameters using the CRITIC method. Here, we compare our proposed work withthe IEEE 802.15.6 MAC standard and other MAC protocols. the simulation shows that our proposed MAC protocol performed better in reliability, throughput, energy efficiency as well as packet delivery delay. the results show that data transmission reliability is increased by more than 60% compared to the IEEE 802.15.6 MAC standard.
this research study introduces a novel Field Programmable Gate Array (FPGA) design for autonomous robotics that integrates data from multiple sensors to improve their operational efficiency and decision-making. the pr...
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Smart technologies have revolutionized home automation, enhancing the way people interact withthe environment. this study introduces an innovative home automation system that combines voice and gesture recognition te...
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Smart transportation systems rely heavily on integrating Internet of things (IoT) devices, especially roadside sensornetworks, to significantly improve road safety, traffic control, and the overall quality of the dri...
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this study investigates optimizing computational design for multi-modal perception computation of tourism landscape images. the research aims to enhance the accuracy and efficiency of multi-modal perception computatio...
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this research study examines modern intelligent systems for analyzing the security of corporate networks including the analysis of the Internet Scanner and System Security Scanner, RealSecure. the functionality and me...
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Since the advent of the transformer neural network architecture, there has been a rapid adoption and investigation of its applicability in various domains, such as computer vision, speech processing, and natural langu...
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ISBN:
(纸本)9798400710810
Since the advent of the transformer neural network architecture, there has been a rapid adoption and investigation of its applicability in various domains, such as computer vision, speech processing, and natural language processing, withthe latter most notably exemplified by the rise of Large Language Models. these accomplishments have also led to increased interest in other network architectures that rely on attention mechanisms, one of the building blocks of transformers. Transformers and other attention-based networks are being applied to the quantitative analysis, management, and trading of financial assets, be it for price movement prediction, discovery of trading strategies, portfolio optimization, and risk management. the applications range across different asset categories, including equity markets, foreign exchange pairs, cryptocurrencies, and futures markets. this survey aims to provide a comprehensive overview of the applications of attention-based networks within the field of quantitative analysis, management, and trading of financial assets. After a brief overview of transformers and attention mechanisms, we analyze the existing applications of these architectures for quantitative finance in a taxonomy of four specializations: Alpha Seeking, Risk Management, Portfolio Construction, and Execution. After comparing the literature in light of the research problems, modeling approaches, and complementary results, we discuss current challenges and research opportunities.
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