Blar i Institutt for IKT og realfag på tidsskrift "Sensors"

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    • A Novel Friendly Jamming Scheme in Industrial Crowdsensing Networks against Eavesdropping Attack 

      Li, Xuran; Wang, Qiu; Dai, Hong-Ning; Wang, Hao (Journal article; Peer reviewed, 2018)
      Eavesdropping attack is one of the most serious threats in industrial crowdsensing networks. In this paper, we propose a novel anti-eavesdropping scheme by introducing friendly jammers to an industrial crowdsensing network. ...

    • An Adaptive Real-Time Malicious Node Detection Framework Using Machine Learning in Vehicular Ad-Hoc Networks (VANETs) 

      Rashid, Kanwal; Saeed, Yousaf; Ali, Abid; Jamil, Faisal; Alkanhel, Reem; Muthanna, Ammar (Peer reviewed; Journal article, 2023)
      Modern vehicle communication development is a continuous process in which cutting-edge security systems are required. Security is a main problem in the Vehicular Ad Hoc Network (VANET). Malicious node detection is one of ...

    • Application-Oriented Retinal Image Models for Computer Vision 

      Silva, Ewerton; Torres, Ricardo Da Silva; Pinto, Allan; Li, Lin; Vianna, José; Azevedo, Rodolfo; Goldenstein, Siome (Peer reviewed; Journal article, 2020)
      Energy and storage restrictions are relevant variables that software applications should be concerned about when running in low-power environments. In particular, computer vision (CV) applications exemplify well that ...

    • Cognitive control-loop for elastic optical networks with space-division multiplexing 

      Trindade, Silvana; Torres, Ricardo Da Silva; Zhu, Zuqing; da Fonseca, Nelson L. S. (Peer reviewed; Journal article, 2021)
      This paper introduces a new solution to improve network performance by decreasingspectrum fragmentation, crosstalk interference, blocking of virtual networks, cost, and link loadimbalance. These problems degrade the ...

    • A Dual-Attention Recurrent Neural Network Method for Deep Cone Thickener Underflow Concentration Prediction 

      Yuan, Zhaolin; Hu, Jinlong; Wu, Di; Ban, Xiaojuan (Peer reviewed; Journal article, 2020)
      This paper focuses on the time series prediction problem for underflow concentration of deep cone thickener. It is commonly used in the industrial sedimentation process. In this paper, we introduce a dual attention neural ...

    • Eemds: An effective emergency message dissemination scheme for urban vanets 

      Ullah, Sami; Abbas, Ghulam; Waqas, Muhammad; Abbas, Ziaul Haq; Tu, Shanshan; Hameed, Ibrahim A. (Peer reviewed; Journal article, 2021)
      In Vehicular Adhoc Networks (VANETs), disseminating Emergency Messages (EMs) to a maximum number of vehicles with low latency and low packet loss is critical for road safety. However, avoiding the broadcast storm and dealing ...

    • Link Connectivity and Coverage of Underwater Cognitive Acoustic Networks under Spectrum Constraint 

      Wang, Qiu; Dai, Hong-Ning; Cheang, Chak Fong; Wang, Hao (Journal article; Peer reviewed, 2017)
      Extensive attention has been given to the use of cognitive radio technology in underwater acoustic networks since the acoustic spectrum became scarce due to the proliferation of human aquatic activities. Most of the recent ...

    • Litter Detection with Deep Learning: A Comparative Study 

      Cordova, Manuel; Pinto, Allan; Hellevik, Christina Carrozzo; Alaliyat, Saleh Abdel-Afou; Hameed, Ibrahim A.; Pedrini, Helio; Torres, Ricardo da S. (Peer reviewed; Journal article, 2022)
      Pollution in the form of litter in the natural environment is one of the great challenges of our times. Automated litter detection can help assess waste occurrences in the environment. Different machine learning solutions ...

    • A Machine Learning and Blockchain Based Efficient Fraud Detection Mechanism 

      Ashfaq, Tehreem; Khalid, Rabiya; Yahaya, Adamu Sani; Aslam, Sheraz; Azar, Ahmad Taher; Alsafari, Safa; Hameed, Ibrahim A. (Peer reviewed; Journal article, 2022)
      In this paper, we address the problems of fraud and anomalies in the Bitcoin network. These are common problems in e-banking and online transactions. However, as the financial sector evolves, so do the methods for fraud ...

    • On Modeling Eavesdropping Attacs in Underwater Acoustic Sensor Networks 

      Wang, Qiu; Dai, Hong-Ning; Li, Xuran; Wang, Hao; Xiao, Hong (Journal article; Peer reviewed, 2016)
      The security and privacy of underwater acoustic sensor networks has received extensive attention recently due to the proliferation of underwater activities. This paper proposes an analytical model to investigate the ...

    • On performance analysis of protective jamming schemes in wireless sensor networks 

      Li, Xuran; Dai, Hong-Ning; Wang, Hao; Xiao, Hong (Peer reviewed; Journal article, 2016)
      Wireless sensor networks (WSNs) play an important role in Cyber Physical Social Sensing (CPSS) systems. An eavesdropping attack is one of the most serious threats to WSNs since it is a prerequisite for other malicious ...

    • Predictive Maintenance of Norwegian Road Network Using Deep Learning Models 

      Hassan, Muhammad Umair; Steinnes, Ole-Martin Hagen; Gustafsson, Eirik Gribbestad; Løken, Sivert; Hameed, Ibrahim A. (Journal article; Peer reviewed, 2023)

    • Towards Intelligent Zone-Based Content Pre-Caching Approach in VANET for Congestion Control 

      Nazar, Khola; Saeed, Yousaf; Ali, Abid; Algarni, Abeer D.; Soliman, Naglaa F.; Ateya, Abdelhamied A.; Muthanna, Mohammed Saleh Ali; Jamil, Faisal (Peer reviewed; Journal article, 2022)
      In vehicular ad hoc networks (VANETs), content pre-caching is a significant technology that improves network performance and lowers network response delay. VANET faces network congestion when multiple requests for the same ...