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6th International conference on Applied Computational Intelligence in information Systems, ACIIS 2023

作者： Kok Seng, Vincent Lew Thien Wan, Au Shah Newaz, S.H. Universiti Teknologi Brunei School of Computing and Informatics Bandar Seri Begawan Brunei

ISBN: (纸本)9798350317428

With the rise of quantum computers, quantum-safe digital signatures have been invented to ensure the security and verifiability of communication. Hash-based one-time signatures are one of the candidates to ensure unforgeability against the improved processing power. This signature algorithm requires tracking of secret keys to ensure one-time usage, called key state. In an Internet of Things(IoT) environment, failure to maintain key state due to network failures, hardware/software failures, implementation flaws, and improper access is possible. Consequences of failure to manage key state results in the degradation of the system's security, opening up opportunities for attackers to forge signatures. Solutions to this however are limited due to the scale and resource constraints of IoT devices. Hence, this paper proposes a solution named, Blockchain-based Key State Management to ensure the key state is maintained without affecting resources and scalability heavily. The solution utilizes blockchain and smart contracts to maintain key states by leveraging blockchain protocols to ensure key states remain updated and un-tampered. The paper also contains a qualitative survey comparing other alternative solutions for preventing key reuse to highlight the importance of the proposed solution. © 2023 IEEE.

关键词： Blockchain

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Street Smart: An intelligent Traffic Management System

Street Smart: An Intelligent Traffic Management System

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International conference on Computing for Sustainable Global Development (INDIACom)

作者： Adwait Hegde Ojas Patil Soham Belurgikar Anand Godbole Department of Computer Engineering Sardar Patel Institute of Technology Mumbai India

Traffic congestion is a critical issue not only in all major cities but also in many rural areas of India. One of the most dangerous side effects of traffic congestion is the unnecessary delay of emergency vehicles. There are several approaches to managing traffic, including wireless sensor networks, inductive loop detection, video data processing, and infrared sensors which are successful strategies for intelligent traffic management. However, the issue with these systems is that they are incredibly expensive to install, maintain, and take a long time to implement. In this paper, we propose a novel method of traffic management along with pedestrian crossing that uses Fuzzy Logic based on lane density which is sensitive to emergency vehicle in traffic. This method, when used in conjunction with the current signalling system, can be the answer to effective real-time traffic control. When compared to current techniques of reducing traffic congestion, this new plan will be easier to implement and cost less.

关键词： Wireless sensor networks Costs Roads Urban areas Process control Traffic control Infrared sensors

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Monitoring System of Wearable sensor Signal in Rehabilitation Using Efficient Deep Learning Approaches 26

Monitoring System of Wearable Sensor Signal in Rehabilitatio...

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26th International Computer Science and Engineering conference, ICSEC 2022

作者： Jantawong, Ponnipa Mekruksavanich, Sakorn Jitpattanakul, Anuchit University of Phayao School of Information and Communication Technology Department of Computer Engineering Thailand King Mongkut's University of Technology North Bangkok Intelligent and Nonlinear Dynamic Innovations Research Center Department of Mathematics Faculty of Applied Science Bangkok Thailand

ISBN: (纸本)9781665491983

Recognition of human activity has utilized inputs from wearable sensors, which has significant implications for rehabilitative medicine and cognitive neuroscience. Unfortunately, some crucial dynamic data on upper-limb movements need to be included in the feature extraction procedure for wearable sensor data. The issue is that only a few rehabilitative motions can be recognized, and classification precision is readily compromised. We study several convolution neural networks to extract valuable characteristics from multichannel wearable sensor inputs automatically and precisely identify rehabilitation operations. We gathered wearable sensor signal data for six physiotherapy exercises to assess identification effectiveness using the SPARS9x standard rehabilitation dataset. Experiments showed that the PyramidNet18 model had the highest F1-score on the benchmark dataset, 99.15%. © 2022 IEEE.

关键词： Physical therapy

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A Deep Reinforcement Learning Approach for Multi-UAV-Assisted Data Collection in Wireless Powered IoT networks 14

A Deep Reinforcement Learning Approach for Multi-UAV-Assiste...

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14th IEEE International conference on Wireless Communications and Signal processing (WCSP)

作者： Li, Zhiming Liu, Juan Xie, Lingfu Wang, Xijun Jin, Ming Ningbo Univ Sch EECS Ningbo Peoples R China Sun Yat Sen Univ Sch EIT Guangzhou Peoples R China

ISBN: (纸本)9781665450850

Unmanned aerial vehicle (UAV) has been widely deployed in efficient data collection for Internet of Things (IoT). UAV can not only act as a relay, but also as an energy source to provide information and energy transmission for ground sensor nodes (SNs). This paper studies the efficient multi-UAV-assisted data collection problem in wireless powered IoT. Specifically multiple UAVs wirelessly charge SNs using radio frequency (RF) energy transfer, and the SNs then use the harvested energy to upload the updates of the sensed information to the UAVs, thus improving the freshness of collected data and extending the service time of the SNs. The problem is modeled as a partially observed Markov decision process (POMDP) with a large observation and action space, where each UAV acts as an intelligent agent to learn the environment and make decisions independently. The value-decomposition network (VDN) algorithm is employed to find the optimal strategy in the multi-agent deep reinforcement learning framework. Simulation results validate the effectiveness of the proposed data collection approach compared to two baseline policies.

关键词： UAV wireless power transfer multi-agent deep reinforcement learning age of information

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Authentication Mechanisms used in Wireless Body Area networks: A Study 3

Authentication Mechanisms used in Wireless Body Area Network...

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3rd International conference on Distributed Sensing and intelligent Systems, ICDSIS 2022

作者： Sharma, Poonam Saini, Kamaljit Singh Sidhu, Prabhjot Kaur University Institute of Computing Chandigarh University Punjab Mohali India University Institute of Engineering Chandigarh University Punjab Mohali India IT Department MSIT GGSIP University New Delhi India

ISBN: (纸本)9781839538186

The WBANs (Collection of light-weight body sensor) of IOT and Cloud Computing provides a substantial ability to upgrade the quality of on-demand health care system. Body sensors are implanted within or outside the human being body to remotely measure the real-time parameters such as physiology signal and activity. The biggest issue originates from obtaining imprecise and inconsistent measurements when collecting these data from sensor nodes. Moreover, Privacy and Security of human sensitive information are other biggest hurdles of WBANs. In this chapter we describe some wearable devices, security requirements and some security attacks. Moreover, we also discussed various existing authentication mechanisms. Different authentication mechanisms have their own Pros and Cons. Further, based on the study we will performed some comprehensive overview between all existing authentication mechanisms and find some future scope. We also highlight some future research scope in the last section. © IET conference Proceedings 2022.

关键词： Authentication

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Energy Efficient Massive MIMO IoT Network: A Power and MSE Constrained Approach

Energy Efficient Massive MIMO IoT Network: A Power and MSE C...

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International conference on Signal processing and Communications, SPCOM

作者： Kunwar Pritiraj Rajput Ekant Sharma Prem Singh Bhavani Shankar Mysore Ramarao Interdisciplinary Centre for Security Reliability and Trust University of Luxembourg Indian Institute of Technology Roorkee India International Institute of Information Technology Bangalore India

ISBN: (数字)9798350350456

ISBN: (纸本)9798350350463

This paper considers massive multiple-input multiple-output (MIMO) Internet of Things (IoT) networks where each sensor is equipped with a single antenna, while the fusion center (FC) is equipped with a massive array of antennas. Each sensor takes the linear noisy observation of the underlying unknown random quantity, pre-processes it, and then transmits its precoded observation to the FC over a fading wireless channel for efficient post-processing. Since these sensors are battery-operated tiny devices, energy efficiency (EE) becomes such a critical factor to optimize, while individual mean square error (MSE)-based quality of service (QoS) constraints ensures an efficient estimation of the random quantities at the FC. Quadratic transform theory is used to solve the resulting non-convex EE maximization problem, and the first-order Taylor series approximation is used to linearize the non-convex quantities. Our numerical results corroborate the analytical findings of this work.

关键词： Wireless communication Wireless sensor networks Simulation Signal processing algorithms Quality of service Transforms Taylor series

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Graph Signal Restoration Using Nested Deep Algorithm Unrolling

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IEEE TRANSACTIONS ON SIGNAL processing 2022年 70卷 3296-3311页

作者： Nagahama, Masatoshi Yamada, Koki Tanaka, Yuichi Chan, Stanley H. Eldar, Yonina C. Tokyo Univ Agr & Technol Dept Elect Engn & Comp Sci Koganei Tokyo 1848588 Japan Japan Sci & Technol Agcy PRESTO Kawaguchi Saitama 3320012 Japan Tokyo Univ Sci Dept Elect Engn Tokyo 1258585 Japan Purdue Univ Sch Elect & Comp Engn W Lafayette IN 47907 USA Weizmann Inst Sci Fac Math & Comp Sci IL-7610001 Rehovot Israel

Graph signal processing is a ubiquitous task in many applications such as sensor, social, transportation and brain networks, point cloud processing, and graph neural networks. Often, graph signals are corrupted in the sensing process, thus requiring restoration. In this paper, we propose two graph signal restoration methods based on deep algorithm unrolling (DAU). First, we present a graph signal denoiser by unrolling iterations of the alternating direction method of multiplier (ADMM). We then suggest a general restoration method for linear degradation by unrolling iterations of Plug-and-Play ADMM (PnP-ADMM). In the second approach, the unrolled ADMM-based denoiser is incorporated as a submodule, leading to a nested DAU structure. The parameters in the proposed denoising/restoration methods are trainable in an end-to-end manner. Our approach is interpretable and keeps the number of parameters small since we only tune graph-independent regularization parameters. We overcome two main challenges in existing graph signal restoration methods: 1) limited performance of convex optimization algorithms due to fixed parameters which are often determined manually. 2) large number of parameters of graph neural networks that result in difficulty of training. Several experiments for graph signal denoising and interpolation are performed on synthetic and real-world data. The proposed methods show performance improvements over several existing techniques in terms of root mean squared error in both tasks.

关键词： Signal processing algorithms Image restoration Signal restoration Noise reduction Task analysis Symmetric matrices Optimization Graph signal processing signal restoration deep algorithm unrolling Plug-and-Play ADMM

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A holistic approach for an intelligent laser beam welding architecture using machine learning for the welding of metallic bipolar plates for polymer electrolyte membrane fuel cells 12

A holistic approach for an intelligent laser beam welding ar...

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12th CIRP conference on Photonic Technologies, LANE 2022

作者： Weiss, Tony Kick, Michael Grabmann, Sophie Geiger, Christian Mayr, Lukas Wudy, Katrin Zaeh, Michael F. Boltzmannstrasse 15 Garching85748 Germany Boltzmannstrasse 15 Garching85748 Germany

Laser beam welding is the state-of-the-art technology for joining micro-formed metal foils in the manufacturing of bipolar plates for proton exchange membrane fuel cells. However, the process is limited in the achievable welding speed since humping and undercut effects can occur at high feed rates. These effects significantly reduce the weld seam quality, causing scrap or subsequent failure during operation. As a result, higher manufacturing costs arise and additional quality assurance is needed. In this work, welding experiments, including a photodiode-based sensor system, were conducted on AISI 316L metal foils to evaluate the capability of this sensor for inline and online quality assurance. Based on the results, an intelligent laser beam welding architecture is proposed, representing a holistic approach for a multi-sensor-based and self-improving quality assurance system. The theoretical architecture combines a novel laser beam welding concept with different optical and acoustic sensors for determining the current weld state. It considers sensor data fusion for relevant information on the process behavior via dedicated algorithms applying deep neural networks. The approach is an idea of a predictive weld state determination for a precise and real-time capable weld seam quality assurance. © 2022 The Authors. Published by Elsevier B.V.

关键词： Polyelectrolytes

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AoI Minimizing of Wireless Rechargeable sensor Network Based on Trajectory Optimization of Laser-Charged UAV 16th

AoI Minimizing of Wireless Rechargeable Sensor Network Based...

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16th International conference on Algorithmic Aspects in information and Management, AAIM 2022

作者： Luo, Chuanwen Hou, Yunan Hong, Yi Chen, Zhibo Liu, Ning Li, Deying School of Information Science and Technology Beijing Forestry University Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China School of Information Renmin University of China Beijing100872 China

ISBN: (纸本)9783031160806

This paper investigates a new UAV-assisted Wireless Rechargeable sensor Network (WRSNs) based on wireless powered technologies, where the Unmanned Aerial Vehicle (UAV) can not only be used as wireless aerial mobile base station for gathering data from sensors but also be used as mobile charger to replenish energy for sensors, and it also can be charged energy by Laser Beam Directors (LBDs) to overcome the limitation of on-board energy shortage. In such a network, we study the average Age of information Optimization (AoIO) problem whose objective is to minimize the average AoI of data collected from sensors by UAV. We first prove that the AoIO problem is NP-hard. Since the average AoI depends on UAV’s flight trajectory, the hovering time required for data collection, energy power transfer and laser charging, we propose an approximation algorithm to solve the AoIO problem by jointly optimizing these factors. Afterwards, we conduct extensive simulation experiments to validate effectiveness of the proposed algorithm. © 2022, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Approximation algorithms

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Toward Neuromorphic Perception: Spike-driven Lane Segmentation for Autonomous Driving using LiDAR sensor

Toward Neuromorphic Perception: Spike-driven Lane Segmentati...

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International conference on intelligent Transportation

作者： Genghang Zhuang Zhenshan Bing Kai Huang Alois Knoll School of Computation Information and Technology Technical University of Munich. School of Computer Science and Engineering Sun Yat-sen University

As a prerequisite of high vehicle autonomy, lane segmentation is a significant perception task for advanced autonomous driving. In recent years, spiking neural networks (SNNs) have garnered the attention of researchers due to their appealing power efficiency, which provides the potential to improve energy consumption for the perception system on power-constrained autonomous vehicles. In this paper, we propose a spiking neural network targeted for LiDAR sensors to solve the lane segmentation problem. By encoding the LiDAR point cloud into spikes, the proposed SNN constructed in an end-to-end fully convolutional network structure is capable of processing the LiDAR input through the network to segment the lane area effectively. Experiments conducted on the KITTI dataset for urban scenes and the power consumption evaluation demonstrate the high performance and energy efficiency of the proposed SNN for LiDAR-based lane segmentation.

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