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arXiv 2023年

作者： Poorvi, Joshi Kalita, Alakesh Gurusamy, Mohan The Communications & Networks Lab Department of Electrical and Computer Engineering National University of Singapore Singapore The ISTD Pillar Singapore University of Technology and Design Singapore

Internet of Things (IoT) involves sensors for monitoring and wireless networks for efficient communication. However, resource-constrained IoT devices and limitations in existing wireless technologies hinder its full potential. Integrating Unmanned Aerial Vehicles (UAVs) into IoT networks can address some challenges by expanding its’ coverage, providing security, and bringing computing closer to IoT devices. Nevertheless, effective data collection in UAV-assisted IoT networks is hampered by factors, including dynamic UAV behaviour, environmental variables, connectivity instability, and security considerations. In this survey, we first explore UAV-based IoT networks, focusing on communication and networking aspects. Next, we cover various UAV-based data collection methods their advantages and disadvantages, followed by a discussion on performance metrics for data collection. As this article primarily emphasizes reliable and efficient data collection in UAV-assisted IoT networks, we briefly discuss existing research on data accuracy and consistency, network connectivity, and data security and privacy to provide insights into reliable data collection. Additionally, we discuss efficient data collection strategies in UAV-based IoT networks, covering trajectory and path planning, collision avoidance, sensor network clustering, data aggregation, UAV swarm formations, and artificial intelligence for optimization. We also present two use cases of UAVs as a service for enhancing data collection reliability and efficiency. Finally, we discuss future challenges in data collection for UAV-assisted IoT networks. Copyright © 2023, The Authors. All rights reserved.

关键词： Internet of things

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Irregular Honeycomb Network: Revolutionizing k-Coverage in Spatial Wireless Sensor networks

Irregular Honeycomb Network: Revolutionizing k-Coverage in S...

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International Conference on Distributed Computing in Sensor Systems (DCOSS)

作者： Dakshanya Maddala Habib M. Ammari Department of Electrical Engineering and Computer Science Frank H. Dotterweich College of Engineering Wireless Sensor and Mobile Autonomous Networks (WiSeMAN) Research Lab Texas A&M University-Kingsville Kingsville Texas USA

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Sensor deployment, and in particular coverage, is a fundamental component in the design and development of wireless sensor networks. Achieving coverage (or 1-coverage) in three-dimensional (or spatial) wireless sensor networks (WSNs), which requires that every point in a spatial field of interest (FoI), is an open challenging problem. In particular, the problem of k-coverage in spatial WSNs, where every point in a spatial FoI is k-covered (or covered by at least k sensors at the same time), is even more challenging. In this paper, we investigate the problem of connected k-coverage in spatial WSNs, where a spatial FoI is k-covered while all the sensors participating in this k-coverage process are connected to each other. Our study aims at producing an energy-efficient and optimized protocol, which helps achieve connected k-coverage in spatial WSNs with a minimum number of sensors, thus, extending the network lifespan. Precisely, this study is based on convex polyhedral space fillers that help establish an irregular hexagonal prism tessellation referred to as Irregular Honeycomb Network (IHN), which serves as a medium to achieve k-coverage in spatial WSNs. First, we propose a randomly distributed sensor placement strategy, which is designed to guarantee full k-coverage of a spatial FoI. To this end, we tile a spatial FoI with irregular hexagonal prisms, known as space fillers, which are adjacent to each other without any gaps or overlaps. Second, we exploit this tiling strategy to guarantee k-coverage of a spatial FoI. Then, we compute the spatial sensor density (i.e., number of sensors per unit volume) to k-cover a spatial FoI. Third, we determine the necessary relationship between the sensors’ communication and sensing ranges to ensure network connectivity. We corroborate our analysis with various simulation results.

关键词： Wireless communication Wireless sensor networks Sensor placement Protocols Simulation Stochastic processes Energy efficiency

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Compound-Action DRL for Timely Data Collection in UAV-Assisted IoT with Stochastic Arrival 23

Compound-Action DRL for Timely Data Collection in UAV-Assist...

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23rd IEEE International Conference on Communication Technology, ICCT 2023

作者： Yi, Mengjie Wang, Xijun Liu, Juan Zhang, Yan Hou, Ronghui School of Cyber Engineering Xidian University Xi'an710071 China School of Electronics and Information Technology Sun Yat-sen University Guangzhou510006 China School of Electrical Engineering and Computer Science Ningbo University Zhejiang 315211 China Information Science Institute Xidian University State Key Lab of Integrated Service Networks Shaanxi Xi'an710071 China

ISBN: (纸本)9798350325959

The use of unmanned aerial vehicles (UAVs) in IoT networks can provide an efficient means of collecting mission-critical data. However, ensuring the freshness of collected data is a significant challenge due to the stochastic status updates of sensor nodes (SN s), In this paper, we delve into the issue of timely data collection aided by a UAV in IoT networks, where the UAV collects status updates from SNs and offloads the data to the data center at an opportune location. SNs sample information from their surroundings at stochastic intervals to generate new status updates. To minimize the average total age of information (AoI), we describe the problem as a Markov decision process and propose a compound-action deep reinforcement learning (CADRL) approach to jointly optimize the trajectory, scheduling of SNs, offloading strategy, and offloading power of the UAV, which can handle both continuous and discrete actions of the UAV. Simulation results indicate that our algorithm effectively reduces the AoI when contrasted with baseline methods. © 2023 IEEE.

关键词： Age of information compound-action deep reinforcement learning Internet of things unmanned aerial vehicle

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Exploring the 6G Potentials: Immersive, Hyper Reliable, and Low-Latency Communication

arXiv

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arXiv 2024年

作者： Shamsabadi, Afsoon Alidadi Yadav, Animesh Gadallah, Yasser Yanikomeroglu, Halim Lab. The Department of SCE Carleton University Canada The School of EECS Ohio University AthensOH United States Egypt Systems and Computer Engineering in the Non-Terrestrial Networks Lab. Carleton University Canada

The transition towards the sixth-generation (6G) wireless telecommunications networks introduces significant challenges for researchers and industry stakeholders. The 6G technology aims to enhance existing usage scenarios through supporting innovative applications that require stringent key performance indicators (KPIs). In some critical use cases of 6G, multiple KPIs, including immersive throughput, with an envisioned peak data rate of 1 Tbps, hyper-reliability, in the range of 10−5 to 10−7, and hyper low-latency, between 0.1 and 1 ms, must be achieved simultaneously to deliver the expected service experience. However, this is challenging due to the conflicting nature of these KPIs. This article proposes a new service class of 6G as immersive, hyper reliable, and low-latency communication (IHRLLC), and introduces a potential network architecture to achieve the associated KPIs. Specifically, enhanced technologies, such as ultra-massive multiple-input multiple-output (umMIMO)-aided terahertz (THz) communications, reconfigurable intelligent surfaces (RIS), and non-terrestrial networks (NTN), are viewed as the key enablers for achieving immersive data rates and hyper reliability. Given the computational complexity involved in employing these technologies, we propose mathematical and computational enabling technologies, such as learn-to-optimize (L2O), generative-AI (GenAI), quantum computing, and network digital twin (NDT), to complement the proposed architecture and optimize the latency. © 2024, CC0.

关键词： Reconfigurable architectures

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Generating Action-conditioned Prompts for Open-vocabulary Video Action Recognition 24

Generating Action-conditioned Prompts for Open-vocabulary Vi...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Jia, Chengyou Luo, Minnan Chang, Xiaojun Dang, Zhuohang Han, Mingfei Wang, Mengmeng Dai, Guang Dang, Sizhe Wang, Jingdong School of Computer Science and Technology MOEKLINNS Lab Xi'an Jiaotong University Shaanxi Xi'an China University of Science and Technology of China Anhui Hefei China School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an China ReLER Lab AAII University of Technology Sydney SydneyNSW Australia Zhejiang University of Technology College of Computer Science and Technology China SGIT AI Lab State Grid Corporation of China Beijing China Baidu Inc Beijing China United Arab Emirates Shaanxi Province Key Laboratory of Big Data Knowledge Engineering Xi'an Jiaotong University Xi'an710049 China SGIT AI Lab State Grid Corporation of China China School of Computer Science and Technology Ministry of Education Key Laboratory of Intelligent Networks and Network Security Xi'an Jiaotong University Xi'an710049 China

ISBN: (纸本)9798400706868

Exploring open-vocabulary video action recognition is a promising venture, which aims to recognize previously unseen actions within any arbitrary set of categories. Existing methods typically adapt pretrained image-text models to the video domain, capitalizing on their inherent strengths in generalization. A common thread among such methods is the augmentation of visual embeddings with temporal information to improve the recognition of seen actions. Yet, they compromise with standard less-informative action descriptions, thus faltering when confronted with novel actions. Drawing inspiration from human cognitive processes, we argue that augmenting text embeddings with human prior knowledge is pivotal for open-vocabulary video action recognition. To realize this, we innovatively blend video models with Large Language Models (LLMs) to devise Action-conditioned Prompts. Specifically, we harness the knowledge in LLMs to produce a set of descriptive sentences that contain distinctive features for identifying given actions. Building upon this foundation, we further introduce a multi-modal action knowledge alignment mechanism to align concepts in video and textual knowledge encapsulated within the prompts. Extensive experiments on various video benchmarks, including zero-shot, few-shot, and base-to-novel generalization settings, demonstrate that our method not only sets new SOTA performance but also possesses excellent interpretability. © 2024 ACM.

关键词： Embeddings

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Design Optimisation of Electronic Nose with Multichannel Gas Sensor

Design Optimisation of Electronic Nose with Multichannel Gas...

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International Conference on Sensing Technology, ICST

作者： Yugal Pachori Richa Thakur Avinash Prashant D. Kulkarni Dinesh Singh Aditya Nigam Shubhajit Roy Chowdhury Biomedical Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi Centre for Human Computer Interaction Indian Institute of Technology Mandi Multimedia Analytics Networks and Systems Lab School of Computing and Electrical Engineering Indian Institute of Technology Mandi

ISBN: (数字)9798350374827

ISBN: (纸本)9798350374834

Human nose detects every kind of odors and our brain responds to every kind of smell that we are inhaling, so in the same way artificial nose or the electronic nose is a device that detects the smell and can enable the scope of digital smell technology. So, this paper proposes to optimize the design of an electronic nose that will eventually bring modification to the various digital platforms like internet, computers, mobile, television and tablets. We focused on design optimization of electronic nose which is built on a multichannel gas sensor array. The proposed electronic nose will enable the transmission or reception of smell over the internet and other digital platforms. The device can detect the smells of vapors coming out of volatile organic compounds. The sensing system is 3D printed in the model of a human nose. The sensing system works with an accuracy of detection of 90.45%. The response time of the electronic nose is 60 to 90 seconds, sometimes it is lesser than 60 seconds and it varies from substance to substance.

关键词： Volatile organic compounds Solid modeling TV Nose Electronic noses Three-dimensional printing Sensors Time factors Gas detectors Sensor arrays

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Domain adaptation for large-vocabulary object detectors 24

Domain adaptation for large-vocabulary object detectors

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Kai Jiang Jiaxing Huang Weiying Xie Jie Lei Yunsong Li Ling Shao Shijian Lu State Key Laboratory of Integrated Services Networks Xidian University Xi'an China S-lab School of Computer Science and Engineering Nanyang Technological University School of Electrical and Data Engineering at the University of Technology Sydney UCAS-Terminus AI Lab University of Chinese Academy of Sciences China

ISBN: (纸本)9798331314385

Large-vocabulary object detectors (LVDs) aim to detect objects of many categories, which learn super objectness features and can locate objects accurately while applied to various downstream data. However, LVDs often struggle in recognizing the located objects due to domain discrepancy in data distribution and object vocabulary. At the other end, recent vision-language foundation models such as CLIP demonstrate superior open-vocabulary recognition capability. This paper presents KGD, a Knowledge Graph Distillation technique that exploits the implicit knowledge graphs (KG) in CLIP for effectively adapting LVDs to various downstream domains. KGD consists of two consecutive stages: 1) KG extraction that employs CLIP to encode downstream domain data as nodes and their feature distances as edges, constructing KG that inherits the rich semantic relations in CLIP explicitly; and 2) KG encapsulation that transfers the extracted KG into LVDs to enable accurate cross-domain object classification. In addition, KGD can extract both visual and textual KG independently, providing complementary vision and language knowledge for object localization and object classification in detection tasks over various downstream domains. Experiments over multiple widely adopted detection benchmarks show that KGD outperforms the state-of-the-art consistently by large margins.

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A Neuro-Fuzzy Approach to Road Traffic Congestion Prediction

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computers, Materials & Continua 2022年第10期73卷 295-310页

作者： Mohammed Gollapalli Atta-ur-Rahman Dhiaa Musleh Nehad Ibrahim Muhammad Adnan Khan Sagheer Abbas Ayesha Atta Muhammad Aftab Khan Mehwash Farooqui Tahir Iqbal Mohammed Salih Ahmed Mohammed Imran BAhmed Dakheel Almoqbil Majd Nabeel Abdullah Omer Department of Computer Information System(CIS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Computer Science(CS) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Pattern Recognition and Machine Learning Lab Department of Software EngineeringGachon UniversityInchonKorea Department of Computer Science National College of Business Administration and EconomicsLahorePunjab54000Pakistan Department of Computer Science Government College University(GCU)LahorePunjab54000Pakistan Department of Computer Engineering(CE) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia College of Business Administration Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia Department of Networks and Communications(NC) College of Computer Science and Information Technology(CCSIT)Imam Abdulrahman Bin Faisal UniversityDammam31441Saudi Arabia

The fast-paced growth of artificial intelligence applications provides unparalleled opportunities to improve the efficiency of various *** as the transportation sector faces many obstacles following the implementation and integration of different vehicular and environmental aspects *** congestion is among the major issues in this regard which demands serious attention due to the rapid growth in the number of vehicles on the *** address this overwhelming problem,in this article,a cloudbased intelligent road traffic congestion prediction model is proposed that is empowered with a hybrid Neuro-Fuzzy *** aim of the study is to reduce the delay in the queues,the vehicles experience at different road junctions across the *** proposed model also intended to help the automated traffic control systems by minimizing the congestion particularly in a smart city environment where observational data is obtained from various implanted Internet of Things(IoT)sensors across the *** due preprocessing over the cloud server,the proposed approach makes use of this data by incorporating the neuro-fuzzy ***,it possesses a high level of accuracy by means of intelligent decision making with minimum error *** results reveal the accuracy of the proposed model as 98.72%during the validation phase in contrast to the highest accuracies achieved by state-of-the-art techniques in the literature such as 90.6%,95.84%,97.56%and 98.03%,*** far as the training phase analysis is concerned,the proposed scheme exhibits 99.214% accuracy. The proposed prediction modelis a potential contribution towards smart cities environment.

关键词： Neuro-fuzzy machine learning congestion prediction AI cloud computing smart cities

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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CLEO: Science and Innovations in CLEO 2024, CLEO: S and I 2024 - Part of Conference on Lasers and Electro-Optics

作者： Liu, Yuanbin Zhang, Hongyi Liu, Jiacheng Lu, Liangjun Du, Jiangbing Li, Yu He, Zuyuan Chen, Jianping Zhou, Linjie Poon, Andrew W. State Key Laboratory of Advanced Optical Communication Systems and Networks Shanghai Key Lab of Navigation and Location Services Shanghai Institute for Advanced Communication and Data Science Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China SJTU-Pinghu Institute of Intelligent Optoelectronics Pinghu314200 China Photonic Device Laboratory Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

We demonstrate wavelength-division-multiplexed data transmission and dispersion compensation of 25 Gb/s × 9 on-off-keying signals over a 20-km singlemode fiber using an integrated single-soliton microcomb and a cascaded microring resonator-based reconfigurable dispersion compensator. © Optica Publishing Group 2024

关键词： Dispersion compensation

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Wavelength-division-multiplexed Transmission and Dispersion Compensation Enabled by Soliton Microcombs and Cascaded Microrings

Wavelength-division-multiplexed Transmission and Dispersion ...

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2024 Conference on Lasers and Electro-Optics, CLEO 2024

ISBN: (纸本)9781957171395

关键词： Optical fibers

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