Cyber-attacks pose a significant challenge to the security of Internet of Things(IoT)sensor networks,necessitating the development of robust countermeasures tailored to their unique characteristics and *** prevention ...
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Cyber-attacks pose a significant challenge to the security of Internet of Things(IoT)sensor networks,necessitating the development of robust countermeasures tailored to their unique characteristics and *** prevention and detection techniques have been proposed to mitigate these *** this paper,we propose an integrated security framework using blockchain and Machine Learning(ML)to protect IoT sensor *** framework consists of two modules:a blockchain prevention module and an ML detection *** blockchain prevention module has two lightweight mechanisms:identity management and trust *** management employs a lightweight Smart Contract(SC)to manage node registration and authentication,ensuring that unauthorized entities are prohibited from engaging in any tasks,while trust management uses a lightweight SC that is responsible for maintaining trust and credibility between sensor nodes throughout the network’s lifetime and tracking historical node *** and transaction validation are achieved through a Verifiable Byzantine Fault Tolerance(VBFT)mechanism to ensure network reliability and *** ML detection module utilizes the Light Gradient Boosting Machine(LightGBM)algorithm to classify malicious nodes and notify the blockchain network if it must make decisions to mitigate their *** investigate the performance of several off-the-shelf ML algorithms,including Logistic Regression,Complement Naive Bayes,Nearest Centroid,and Stacking,using the WSN-DS *** is selected following a detailed comparative analysis conducted using accuracy,precision,recall,F1-score,processing time,training time,prediction time,computational complexity,and Matthews Correlation Coefficient(MCC)evaluation metrics.
It is with great enthusiasm that I introduce this focused issue of IEEE Microwave Magazine, focusing on state-of-the-art advances in the field of signal generation. As an active member of the IEEE Microwave Theory and...
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Virtual reality (VR) simulations have been adopted to provide controllable environments for running augmented reality (AR) experiments in diverse scenarios. However, insufficient research has explored the impact of AR...
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The growing demand for advanced beyond 5G connectivity solutions explores the deployment of end-to-end 5G Non- Terrestrial Networks (NTNs) in cloud-native environments. With the increasing reliance on mobile communica...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks ofte...
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Robots are increasingly being deployed in densely populated environments, such as homes, hotels, and office buildings, where they rely on explicit instructions from humans to perform tasks. However, complex tasks often require multiple instructions and prolonged monitoring, which can be time-consuming and demanding for users. Despite this, there is limited research on enabling robots to autonomously generate tasks based on real-life scenarios. Advanced intelligence necessitates robots to autonomously observe and analyze their environment and then generate tasks autonomously to fulfill human requirements without explicit commands. To address this gap, we propose the autonomous generation of navigation tasks using natural language dialogues. Specifically, a robot autonomously generates tasks by analyzing dialogues involving multiple persons in a real office environment to facilitate the completion of item transportation between various *** propose the leveraging of a large language model(LLM) through chain-of-thought prompting to generate a navigation sequence for a robot from dialogues. We also construct a benchmark dataset consisting of 625 multiperson dialogues using the generation capability of LLMs. Evaluation results and real-world experiments in an office building demonstrate the effectiveness of the proposed method.
Recent growth in the number of drones has made traffic management unworkable, particularly in urban areas. The safe operation and optimized navigation of drone swarms are now growing concerns. In this article, we use ...
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In offshore aquaculture operations, personnel equipped with diving gear are often necessary to inspect the underwater net cages for damage, particularly on the sea floor. This manual inspection process is time-consumi...
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Instruction-tuned large language models have demonstrated remarkable capabilities in following human instructions across various domains. However, their proficiency remains notably deficient in many low-resource langu...
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Hyperspectral sensing is a valuable tool for detecting anomalies and distinguishing between materials in a scene. Hyperspectral anomaly detection (HS-AD) helps characterize the captured scenes and separates them into ...
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LiDAR sensors play a crucial role in autonomous driving (AD) systems because of their high-definition 3D sensing capabilities. While LiDAR spoofing attacks that project fake ranging signals pose a critical security th...
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