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IEEE Internet of Things Journal Publication Information
Year: 2024 | Volume: 11 | Issue: 3
A Lightweight-Window-Portion-Based Multiple Imputation for Extreme Missing Gaps in IoT Systems
Deepak Adhikari;Wei Jiang;Jinyu Zhan;Maregu Assefa;Hadi A. Khorshidi;Uwe Aickelin;Danda B. Rawat
Publication Year: 2024,Page(s):3676 - 3689
Intelligent techniques, including artificial intelligence and deep learning, normally perform on complete data without missing data. Multiple imputation is indispensable for addressing missing data resulting in unbiased estimates and dealing with uncertainty by providing more valid results. Most state-of-the-art techniques focus on high-missing rates (around 50%–60%) and short missing gaps, while ...Show More
Partially Observed Visual IoT Data Reconstruction Based on Robust Half-Quadratic Collaborative Filtering
Publication Year: 2024,Page(s):3690 - 3701
Cited by: Papers (1)
Recent advances and applications in the Visual Internet of Things (VIoT) have witnessed significant progress in various areas, such as smart sensing and environmental monitoring, which rely on sensors to capture data to build analytic models. However, when VIoT devices are deployed in harsh environments, such as the deep ocean and underground, the physical integrity and functionality of these devi...Show More
MODSC: Many-Objective-Optimization-Driven Data-Balancing Strategy in Cross-Architectural Malware Classification for Extreme IoT
Zhihua Cui;Zhigang Zhang;Zhixia Zhang;Wensheng Zhang;Jinjun Chen
Publication Year: 2024,Page(s):3702 - 3710
Cited by: Papers (7)
Stable operation of IoT systems in extreme environments is critical for infrastructures, including energy, aerospace, and healthcare. But resource-constrained devices are vulnerable to malware exploiting known weaknesses due to complex operating conditions and low-frequency device maintenance, and systems are at serious risk of failure or information leakage. However, automated security defense ba...Show More
ATWR-SMR: An Area-Constrained Truthful-Worker Recruitment-Based Sensing Map Recovery Scheme for Sparse MCS in Extreme-Environment Internet of Things
Publication Year: 2024,Page(s):3711 - 3724
Cited by: Papers (5)
Sparse mobile crowdsensing (SMCS) is a prospective solution for large-scale data sensing through mobile devices of Internet of Things (IoT) systems where IoT systems cannot obtain the sensing data of the area under extreme environments. The unsensed area data can be obtained by the data inference algorithm trained by the sensed data of recruited workers. However, recruited workers may upload false...Show More
Underwater Visibility Enhancement IoT System in Extreme Environment
Yujie Li;Xu Zhu;Yuchao Zheng;Huimin Lu;Jianru Li;Zhengxiang Shen
Publication Year: 2024,Page(s):3725 - 3732
Cited by: Papers (2)
Imagery captured in extreme underwater environments often presents unique challenges, including blurred details, color distortion, and reduced contrast. These discrepancies largely emanate from the intricate interplay of light absorption and scattering within the aquatic medium. Predominant restoration techniques, rather simplistically, apply a static attenuation coefficient, neglecting the dynami...Show More
BOOM: Bottleneck-Aware Opportunistic Multicast Strategy for Cooperative Maritime Sensing
Xiao Chen;Chao Zhu;Jie Ma;Guanju Shi;Zhenjie Yang;Xiang Gao;Yong Cui
Publication Year: 2024,Page(s):3733 - 3748
Cited by: Papers (1)
With the advancements in sensing technologies, maritime sensing has become indispensable in various domains, including logistics, weather forecasting, and marine ranching. However, transmitting large volumes of sensing data faces many challenges in the maritime environment. First, the transmissions purely depend on satellite links often costly and suffer from long propagation latency. On the other...Show More
AGRIC: Artificial-Intelligence-Based Green Routing for Industrial Cyber–Physical System Pertaining to Extreme Environment
Sandeep Verma;Satnam Kaur;Sahil Garg;Ajay K. Sharma;Mubarak Alrashoud
Publication Year: 2024,Page(s):3749 - 3756
Cited by: Papers (7)
Industrial cyber–physical systems (ICPSs) can play a crucial role in damage assessment during extreme conditions by leveraging their integration of physical infrastructure, sensing capabilities, and advanced analytics. However, due to the wireless sensing devices that are made to operate in ICPS, there is a dire need to address the green routing (energy-efficient) challenges through an optimized s...Show More
An Improved Congestion-Controlled Routing Protocol for IoT Applications in Extreme Environments
Muhammad Adil;Muhammad Usman;Mian Ahmad Jan;Hussein Abulkasim;Ahmed Farouk;Zhanpeng Jin
Publication Year: 2024,Page(s):3757 - 3767
Cited by: Papers (3)
The Internet of Things (IoT) has shown its presence in applications that require monitoring extreme environments, such as wildfires, military operations, and coastal areas, among others. In these applications, the IoT nodes are deployed in hazardous terrains where humanistic access is hard or not possible. Hence, to ensure reliable data transmission in these applications, novel routing protocols n...Show More
Energy-Efficient Secure Offloading System Designed via UAV-Mounted Intelligent Reflecting Surface for Resilience Enhancement
Publication Year: 2024,Page(s):3768 - 3778
Cited by: Papers (3)
With increasing interest in mmWave and THz communication systems, an unmanned aerial vehicle (UAV)-mounted intelligent reflecting surface (IRS) has been suggested as a key enabling technology to establish robust Line-of-Sight (LoS) connections with ground nodes owing to their free mobility and high altitude, especially for emergency and disaster response. This article investigates a secure offload...Show More
Orchestration and Management of Adaptive IoT-Centric Distributed Applications
Sehrish Amjad;Ahmed Akhtar;Muhammad Ali;Ayesha Afzal;Basit Shafiq;Jaideep Vaidya;Shafay Shamail;Omer Rana
Publication Year: 2024,Page(s):3779 - 3791
Cited by: Papers (1)
Current Internet of Things (IoT) devices provide a diverse range of functionalities, ranging from measurement and dissemination of sensory data observation, to computation services for real-time data stream processing. In extreme situations, such as emergencies, a significant benefit of IoT devices is that they can help gain a more complete situational understanding of the environment. However, th...Show More
Bidirectional LSTM Model for Accurate and Real-Time Landslide Detection: A Case Study in Mawiongrim, Meghalaya, India
J. Sharailin Gidon;Jintu Borah;Smrutirekha Sahoo;Shubhankar Majumdar;Masahiro Fujita
Publication Year: 2024,Page(s):3792 - 3800
Cited by: Papers (12)
This article presents a bidirectional long short-term memory (LSTM) model for the detection of landslides. Previous uses of machine learning (ML) in this setting have demonstrated its general potential, which necessitates the implementation of a suitable algorithm. Landslides are natural disasters that can cause significant destruction and disruption in the affected areas. Early detection is the k...Show More
General System Architecture and COTS Prototyping of an AIoT-Enabled Sailboat for Autonomous Aquatic Ecosystem Monitoring
André Paulo Dantas de Araújo;Dickson H. J. Daniel;Raphael Guerra;Diego N. Brandão;Eduardo Charles Vasconcellos;Alvaro P. F. Negreiros;Esteban W. Gonzalez Clua;Luiz M. G. Gonçalves;Philippe Preux
Publication Year: 2024,Page(s):3801 - 3811
Cited by: Papers (2)
Unmanned vehicles keep growing attention as they facilitate innovative commercial and civil applications within the Internet of Things (IoT) realm. In this context, autonomous sailing boats are becoming important marine platforms for performing different tasks, such as surveillance, water, and environmental monitoring. Most of these tasks heavily depend on artificial intelligence (AI) technologies...Show More
General System Architecture and COTS Prototyping of an AIoT-Enabled Sailboat for Autonomous Aquatic Ecosystem Monitoring
André Paulo Dantas de Araújo;Dickson H. J. Daniel;Raphael Guerra;Diego N. Brandão;Eduardo Charles Vasconcellos;Alvaro P. F. Negreiros;Esteban W. Gonzalez Clua;Luiz M. G. Gonçalves;Philippe Preux
Year: 2024 | Volume: 11 | Issue: 3
An Internet of Things and AI-Powered Framework for Long-Term Flood Risk Evaluation
Publication Year: 2024,Page(s):3812 - 3819
Cited by: Papers (5)
Integrating Internet of Things (IoT) and artificial intelligence (AI) techniques have found widespread application in various fields, including smart cities, agriculture, and environmental monitoring. With the increasing availability of satellite imagery and other remote sensing data, deep learning algorithms can be used and trained to detect, classify, and segment flood regions in real time. In a...Show More
A Real-Time Bearing Fault Diagnosis Model Based on Siamese Convolutional Autoencoder in Industrial Internet of Things
Publication Year: 2024,Page(s):3820 - 3831
Cited by: Papers (10)
The extreme environment refers to the abnormal temperature, pressure, or vibration in the environment within a certain period of time, which will cause the fault of bearing equipment. Bearing fault diagnosis model can accurately identify the health status of bearing equipment, which can deal with the influence of extreme environments on the normal operation of bearings in a timely manner. However,...Show More
Edge-Centric Intelligent Early Warning System for Residual Soil Stability Prediction in Slope
Sudarshan Nandy;Mainak Adhikari;Arunava Ray;Rajesh Rai;T. N. Singh
Publication Year: 2024,Page(s):3832 - 3839
Cited by: Papers (1)
Different geospatial and geotechnical parameters change over time and can affect the residual soil stability on a slope. Thus, it is essential to analyze the stability of slopes continuously to identify the potential landslide sections. The stability of slopes is defined by the factor of safety (FOS). To track the immediate changes in soil stability, it is essential to monitor multiple environment...Show More
Analysis of Quantum Machine Learning Algorithms in Noisy Channels for Classification Tasks in the IoT Extreme Environment
Sritam Kumar Satpathy;Vallabh Vibhu;Bikash K. Behera;Saif Al-Kuwari;Shahid Mumtaz;Ahmed Farouk
Publication Year: 2024,Page(s):3840 - 3852
Cited by: Papers (9)
By 2050, there will be a 50% rise in energy demand, and existing natural and renewable resources will be under extreme scrutiny. Optimizing current power generation and transmission to reduce energy consumption, cost, and other factors is equally vital to upgrading methods for effectively harvesting renewable energy. However, it gets more challenging for conventional computers to perform optimizat...Show More
Deep-Federated-Learning-Based Threat Detection Model for Extreme Satellite Communications
Sara Salim;Nour Moustafa;Mohamed Hassanian;David Ormod;Jill Slay
Publication Year: 2024,Page(s):3853 - 3867
Cited by: Papers (12)
Satellite communications (Satcoms), whether ground-to-space or intersatellite, have, to date, primarily combined the two cutting-edge fields of communication and space, in which cybersecurity and space activities are intricately connected. The susceptibility of Satcoms and other space assets to cyberattacks, as a means of targeting critical infrastructure, is sometimes overlooked. Neither space no...Show More
Multikeyword-Ranked Search Scheme Supporting Extreme Environments for Internet of Vehicles
Dequan Xu;Changgen Peng;Weizheng Wang;Kapal Dev;Sunder Ali Khowaja;Youliang Tian
Publication Year: 2024,Page(s):3868 - 3880
Cited by: Papers (5)
In recent years, the cloud infrastructure has been developed as a promising sharing system for the Internet of Vehicles (IoV) communication. During the information exchange, search service over ciphertext called searchable encryption (SE) is an extraordinary method to prevent data breaches. However, two open problems still need to be solved for ranked search, which hinders the application practica...Show More
Integrating Blockchain and Deep Learning Into Extremely Resource-Constrained IoT: An Energy-Saving Zero-Knowledge PoL Approach
Heyi Zhang;Jun Wu;Xi Lin;Ali Kashif Bashir;Yasser D. Al-Otaibi
Publication Year: 2024,Page(s):3881 - 3895
Cited by: Papers (5)
The convergence of blockchain and deep learning (DL) drives the intelligence of the Internet of Things (IoT) with security guarantees. However, the soaring resource consumption resulting from blockchain mining and DL model training has overwhelmed the extremely resource-constrained IoT. In this article, we first build a blockchain and DL-empowered cloud–edge orchestrated framework for an extremely...Show More
Secure Task Distribution With Verifiable Re-Encryption in Mobile-Crowdsensing-Assisted Emergency IoT System
Publication Year: 2024,Page(s):3896 - 3908
Cited by: Papers (2)
Extreme events (such as earthquakes, hurricanes, etc.) pose a dual challenge to the reliability and serviceability of Internet of Things (IoT) systems. With regard to this challenge, by publishing some tasks and then encouraging the public to assist in real-time data collection through their mobile terminals (namely, the mobile crowdsourcing-assisted IoT systems), is expected to play an important ...Show More
On-Device Indoor Positioning: A Federated Reinforcement Learning Approach With Heterogeneous Devices
Publication Year: 2024,Page(s):3909 - 3926
Cited by: Papers (7)
The widespread deployment of machine learning techniques in ubiquitous computing environments has sparked interests in exploiting the vast amount of data stored on mobile devices. To preserve data privacy, federated learning (FL) has been proposed to learn a shared model by performing distributed training locally on participating devices and aggregating the local models into a global one. Reinforc...Show More
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