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Reward-Based Energy-Aware Routing Protocols in Wireless Sensor Networks

Reward-Based Energy-Aware Routing Protocols in Wireless Sens...

International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software and Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Smart cities, industrial automation, healthcare, and environmental monitoring are just a few of the industries that have seen revolutionary changes as a result of Wireless Sensor Networks’ (WSNs) explosive growth. WSNs are significantly hampered by the limited energy resources of their sensor nodes, which are usually battery-powered and placed in inaccessible locations, despite their wide-ranging applications. As a result, energy efficiency is now a crucial component of these networks’ operation and design. Though these protocols frequently fail to adjust to the dynamic and unpredictable nature of WSNs, traditional energy-aware routing methods have been created to decrease energy consumption by choosing the best paths for data transfer. By allowing sensor nodes to learn from their surroundings and make wise routing decisions, the machine learning field of reinforcement learning (RL) provides a viable answer to these problems. In contrast to conventional techniques, RL-based protocols provide continuous energy optimization by dynamically modifying their routing strategies in response to real-time feedback. This paper investigates how to include RL methods—specifically, Q-learning and SARSA—into WSN energy-aware routing protocols. The suggested method creates a more distributed and scalable solution by enabling each node to independently learn and modify its routing choices. The effectiveness of RL-based routing protocols is compared to traditional methods through comprehensive simulations. The findings show that by slowing down the rate at which energy is being used up by each node, RL not only increases energy efficiency but also lengthens the network's operational life. In particular, Q-learning demonstrates a notable improvement in sustaining data delivery rates and network connectivity under various circumstances. Although a little more computationally demanding, the SARSA algorithm offers greater flexibility in situations where network topology changes often. Th

关键词： Multi agent systems

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A distributed approach for solving the position inverse kinematics of mobile parallel manipulators

A distributed approach for solving the position inverse kine...

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2014 International Conference on robotics and Emerging Allied Technologies in engineering, iCREATE 2014

作者： Shah, Z.A. AbdulQayyum, T. Shah, S.I.A. Robotics and AI Department School of Mechanical and Manufacturing Engineering NUST H-12 Islamabad Pakistan

ISBN: (纸本)9781479951321

This paper presents a modified method for solving the Inverse Kinematics problem for Parallel Manipulators. The manipulator assumed consists of two 6R serial chains in parallel (biped configuration) with multiple orthogonally intersecting joint axes. The problem question is to determine whether or not is it possible to attain a certain goal configuration of the base and if yes then how to solve for the unknown joint angles. In principle this method can be extended to applications for mobile parallel manipulators with more than 2 serial chains. © 2014 IEEE.

关键词： Inverse kinematics

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Using Gamification to Increase Healthcare Professionals’ Cybersecurity Awareness

Using Gamification to Increase Healthcare Professionals’ Cy...

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International Conference on Smart Applications and Sustainability in the Artificial Intelligence of Things, SAS-aioT 2024

作者： Salama, Ramiz Alturjman, Sinem Altrjman, Chadi Al-Turjman, Fadi Department of Computer Engineering AI and Robotics Institute Research Center for AI and IoT Near East University Nicosia Cyprus Artificial Intelligence Software Information Systems Engineering Departments AI and Robotics Institute Near East University Nicosia Cyprus

ISBN: (纸本)9783031782756

Cybersecurity knowledge is essential in today’s increasingly digital healthcare environment to protect sensitive patient data and guarantee the integrity of healthcare systems. But conventional cybersecurity training approaches frequently fail to hold healthcare professionals’ attention, which results in inadequate application and retention of the material. In order to increase cybersecurity awareness among healthcare professionals, this study investigates the use of gamification—a technological technique that incorporates game design aspects into non-gaming contexts—as an innovative method. A mixed-methods research methodology is used in the study to assess the effectiveness of a cybersecurity training program that is gamified. To encourage user involvement and motivation, the program incorporates aspects like leaderboards, interactive scenarios, badges, and points. The study included a total of 100 healthcare professionals from various departments, offering a thorough understanding of the program’s effects across a range of positions and experience levels. Pre- and post-training surveys were used to gather quantitative data on participant changes in knowledge, attitudes, and behaviors pertaining to cybersecurity procedures. Furthermore, semi-structured interviews yielded qualitative insights that further elucidated the participants’ perspectives and experiences with the gamified training method. Comparing gamification to conventional training approaches, the results show that gamification dramatically improves engagement and knowledge retention. The willingness of the participants to acquire and use cybersecurity techniques in their daily lives has reportedly increased. Additionally, the gamified components strengthened the training objectives by fostering a competitive yet cooperative learning environment. The potential of gamification as a game-changing instrument for cybersecurity education in healthcare settings is highlighted by this study. Healthcare busines

关键词： Cybersecurity

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Decentralized Fusion-Based Ego Velocity Estimation Using Multiple FMCW Radars 21

Decentralized Fusion-Based Ego Velocity Estimation Using Mul...

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21st International Conference on Advanced robotics, ICAR 2023

作者： Mukherjee, Moumita Banerjee, Avijit Koval, Anton Nikolakopoulos, George Robotics and AI Luleå University of Technology Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342291

This article presents a decentralized fusion-based ego velocity estimation framework utilizing multiple Frequency-Modulated Continuous-Wave (FMCW) radar sensors and an Inertial Measurement Unit (IMU). This method relies on the collaborative exchange of information among radar sensors, employing a decentralized estimation approach. This approach enhances system robustness and incorporates a unique and effective noise reduction technique to diagnose and eliminate undesirable measurements. Traditional robotics missions, relying on visual data, often face challenges, particularly in scenarios where Global Navigation Satellite System (GNSS) signals are either not accessible or unreliable and when visibility is hindered by factors such as darkness, direct sunlight, fog, or smoke. The proposed framework holds immense potential for enhancing the precision of navigation systems in autonomous vehicles and advanced driver assistance systems, particularly in challenging conditions like adverse weather and complex urban environments. Comparative analysis against traditional centralized fusion methods in experimental studies reveals sub-stantial enhancements in accuracy and reliability, highlighting the potential of decentralized fusion. © 2023 IEEE.

关键词： Smoke

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Identifying Research Hotspots and Trends in Psoriasis Literature: Autotuned Topic Modeling with Agent

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Journal of Investigative Dermatology 2025年

作者： Wu, Sunsi Wang, Dan Gu, Xinpei Xiao, Ruiheng Gao, Hongzhi Yang, Bo Kang, Yanlan Department of Dermatology Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai China Department of Rehabilitation Zhongshan Hospital Fudan University Shanghai China Shanghai Institute of Rehabilitation with Integrated Western and Chinese Traditional Medicine Shanghai China School of Pharmaceutical Sciences Guangdong Provincial Key Laboratory of New Drug Screening Southern Medical University Guangzhou China School of Literature Shandong University Jinan China Department of Nephrology Longhua Hospital Shanghai University of Traditional Chinese Medicine Shanghai China Institute of AI and Robotics Academy for Engineering & Technology Fudan University Shanghai China

The rapid expansion of psoriasis research presents challenges in efficient analysis and trend identification, necessitating advanced approaches. We propose AgenTopic, an interactive topic modeling framework that integrates Bidirectional Encoder Representations from Transformers embeddings, dimensionality reduction, clustering, and a language model feedback loop to analyze the psoriasis research literature from 2000 to 2023. Applied to PubMed articles, AgenTopic extracted 158 psoriasis-related topics across 8 categories, outperforming traditional methods in handling complex medical literature. Further trend analysis using multiple modeling techniques, including a support vector regression-Linear model, revealed nonlinear patterns in research growth across categories (R² values = 0.75–0.97). Key trends identified include focus on nail psoriasis and spondyloarthritis, shift from TNF-α to IL-17 in pathogenesis understanding, rapid development of biologics and small-molecule inhibitors, and increased attention to comorbidities. We developed an interactive web tool to facilitate literature retrieval and identification of trends. To the best of our knowledge, this application of an agent-based interactive topic modeling framework to dermatological literature has not been previously reported. Using only topic-modeled data, our framework achieved a performance comparable with that of expert manual reviews in identifying research trends. AgenTopic performed better than several state-of-the-art topic modeling methods and demonstrated the potential of artificial intelligence for advancing medical literature analyses. © 2025 The Authors

关键词： Agent Dynamic feedback Psoriasis Research trends Topic modeling

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Virtual Reality Hand Tracking for Immersive Telepresence in Rehabilitative Serious Gaming 15th

Virtual Reality Hand Tracking for Immersive Telepresence in...

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15th International Conference on Social robotics, ICSR 2023

作者： Mazhar, Noaman Gaballa, Aya Pandey, Amit Kumar Cabibihan, John-John Mechanical and Industrial Engineering Department Qatar University Doha Qatar Socients AI and Robotics Toulouse France

ISBN: (纸本)9789819987146

Intelligent systems face an increasingly complex array of challenges in the rapidly evolving landscape of human-device interactions. To address these challenges, there arises a pressing need for the development of refined methods of information transfer, capable of capturing subtle nuances such as body language and tonal subtleties. The system presented in this paper is designed to highlight a novel method of refined human-device interactions and its potential impact on the medical domain. Leveraging the Oculus SDK in the Unity Game Engine alongside the Oculus VR headset, our system allows users to interact with virtual objects naturally, mirroring real-world hand movements while simultaneously promoting upper limb rehabilitation through engaging gameplay scenarios. The integration of haptic feedback enriches the immersive experience, enabling users to not only visualize but also feel their virtual interactions. The system accommodates varying levels of mobility, adapting its complexity to individual progress. Furthermore, our VR rehabilitation system has garnered positive outcomes from user assessments, demonstrating the system’s effectiveness and user satisfaction. While limited in addressing severe upper limb impairments, the system’s flexibility allows for modular improvements and broader clinical integration. © 2024, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Mixed reality

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Learning to Solve a Rubik's Cube with a Dexterous Hand

Learning to Solve a Rubik's Cube with a Dexterous Hand

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2019 IEEE International Conference on robotics and Biomimetics, ROBIO 2019

作者： Li, Tingguang Xi, Weitao Fang, Meng Xu, Jia Meng, Max Q.-H. Robotics Perception and AI Laboratory Electronic Engineering Department Chinese University of Hong Kong Hong Kong Tencent AI Lab Shenzhen China

ISBN: (纸本)9781728163215

We present a learning-based approach to solving a Rubik's cube with a multi-fingered dexterous hand. Despite the promising performance of dexterous in-hand manipulation, solving complex tasks which involve multiple steps and diverse internal object structure has remained an important, yet challenging task. In this paper, we tackle this challenge with a hierarchical deep reinforcement learning method, which separates planning and manipulation. A model-based cube solver finds an optimal move sequence for restoring the cube and a model-free cube operator controls all five fingers to execute each move step by step. To train our models, we build a high-fidelity simulator which manipulates a Rubik's Cube, an object containing high-dimensional state space, with a 24-DoF robot hand. Extensive experiments on 1400 randomly scrambled Rubik's cubes demonstrate the effectiveness of our method, achieving an average success rate of 90.3%. To benefit the community, we make our simulator and source code publicly available to advance future research1. © 2019 IEEE.

关键词： Deep learning

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Autonomous Rescue Hexapod Robot with ai Human Detection and Tracking

Autonomous Rescue Hexapod Robot with AI Human Detection and ...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Saengsint, Chissanupong Juntacheevakul, Kidtipod Chalermkitpaisan, Kirawut Suthapintu, Napak Tomaneenilrat, Pavares Jungwongsuk, Navavee Yanyong, Sarucha Konghuayrob, Poom Dissanyaka, Supun King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Robotics and Ai Bangkok Thailand

ISBN: (纸本)9798350325706

The occurrence of catastrophic caused tons of thousands of lives. To enhance the efficacy of search and rescue operations in challenging to access region, we have introduced an autonomous hexapod robot equipped with a partial human detection and tracking system that is capable to identify fragments of the human body. The chosen hexagonal arrangement of six legs enables the robot to navigate through rough terrains. This configuration provides stable mobility, with an average acceleration of 0.8 m/s2 and a peak acceleration of 3.1 m/s2 during walking, making the camera of the robot remains steady for human identification. Due to the employed DeeplabV3+ architecture for the training process, partial human bodily structure detection has achieved 94% accuracy. Moreover, the robot can track the detected individual by maintaining 1m distance. © 2023 IEEE.

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Autonomous Residual Threshold Detection for Decentralized Radar Inertial Odometry in Hostile Environments

Autonomous Residual Threshold Detection for Decentralized Ra...

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2024 International Conference on Control, Automation and Diagnosis, ICCAD 2024

作者： Mukherjee, Moumita Banerjee, Avijit Koval, Anton Nikolakopoulos, George Luleå University of Technology Robotics and Ai Group Department of Computer Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350361025

The article introduces an adaptive threshold detection mechanism aimed at enhancing a decentralized radar inertial odometry (RIO) framework, enabling resilient localization in challenging hostile environments. Considering that frequency-modulated continuous-wave (FMCW) radars possess characteristics enabling measurements in challenging indoor and outdoor environments, the fusion of multiple radars in an ensemble configuration, along with an inertial measurement unit (IMU), holds promise in surpassing individual sensor limitations. This approach thereby enhances robust perception. The proposed adaptive autonomous residual threshold mechanism employs a real-time residual analysis to dynamically adjust the sensor fusion process by comparing the variance between two extended Kalman filters. This adaptive approach addresses irregularities in data samples from multiple sensors, thereby enhancing the decentralized smoothing estimator's precision in providing localization while navigating through hostile environments marked by limited visibility, extreme weather, or high interference. Consequently, it contributes to the resilience and adaptability of autonomous systems in real-world scenarios. The proposed framework effectively showcases precise localization through decentralized radar inertial odometry(RIO). © 2024 IEEE.

关键词： Kalman filters

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Autonomous Modular Harvesting System for Vertical Aeroponic Farming

Autonomous Modular Harvesting System for Vertical Aeroponic ...

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2023 IEEE International Conference on robotics and Biomimetics, ROBIO 2023

作者： Asnapetch, Papinwich Ruangrit, Chanet Tongsri, Nabhatara Chuenyoo, Napat Poonthongpan, Peeradon Konghuayrob, Poom Dissanayaka, Supun Yanyong, Sarucha King Mongkut's Institute of Technology Ladkrabang School of Engineering Department of Robotics and Ai Bangkok Thailand

ISBN: (纸本)9798350325706

As the global population grows exponentially, food consumption rises at a drastic rate. However, the limited availability of agricultural lands and inadequate traditional farming techniques have led to an emergence of alternatives such as hydroponic and aeroponic farming. Nonetheless, these systems still have limits, especially in terms of their constrained expansion when reliant on machinery, and the restricted vertical reach for human labor-driven harvesting. Therefore, we proposed an autonomous modular harvesting system for vertical farming of green oak lettuce. The structure is designed as an expandable cartesian gantry. With the initial base area of 40 x 40 cm, the system could be expanded to 120 cm in length and 240 cm in height, potentially hosting up to 96 lettuces. The system is also integrated with advanced computer vision to detect the presence of a plant and evaluate its readiness, primarily through color detection. Additionally, a specialized mechanism is installed to harvest the ripe lettuce. The system shows a notably high level of accuracy and impressive success rate in both detecting and harvesting. © 2023 IEEE.

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