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  • 1.
    Ahmad, Sabtain
    et al.
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Uyanık, Halit
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Ovatman, Tolga
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    Sandıkkaya, Mehmet Tahir
    Department of Computer Engineering, Istanbul Technical University, Istanbul, Turkey.
    De Maio, Vincenzo
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Brandić, Ivona
    Institute of Information Systems Engineering, Vienna University of Technology, Vienna, Austria.
    Aral, Atakan
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Sustainable environmental monitoring via energy and information efficient multi-node placement2023Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, nr 24, s. 22065-22079Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    The Internet of Things is gaining traction for sensing and monitoring outdoor environments such as water bodies, forests, or agricultural lands. Sustainable deployment of sensors for environmental sampling is a challenging task because of the spatial and temporal variation of the environmental attributes to be monitored, the lack of the infrastructure to power the sensors for uninterrupted monitoring, and the large continuous target environment despite the sparse and limited sampling locations. In this paper, we present an environment monitoring framework that deploys a network of sensors and gateways connected through low-power, long-range networking to perform reliable data collection. The three objectives correspond to the optimization of information quality, communication capacity, and sustainability. Therefore, the proposed environment monitoring framework consists of three main components: (i) to maximize the information collected, we propose an optimal sensor placement method based on QR decomposition that deploys sensors at information- and communication-critical locations; (ii) to facilitate the transfer of big streaming data and alleviate the network bottleneck caused by low bandwidth, we develop a gateway configuration method with the aim to reduce the deployment and communication costs; and (iii) to allow sustainable environmental monitoring, an energy-aware optimization component is introduced. We validate our method by presenting a case study for monitoring the water quality of the Ergene River in Turkey. Detailed experiments subject to real-world data show that the proposed method is both accurate and efficient in monitoring a large environment and catching up with dynamic changes.

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  • 2.
    Sahoo, Shreeya Swagatika
    et al.
    deptarment of CSE, Siksha 'O' Anusandhan University, Odisha, India.
    Mohanty, Sujata
    Department of Computer Science and Engineering, National Institute of Technology Rourkela, India.
    Sahoo, Kshira Sagar
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Daneshmand, Mahmoud
    School of Engineering and Science, Stevens Institute of Technology, Hoboken, USA.
    Gandomi, Amir H.
    Faculty of Engineering and Information Technology, University of Technology Sydney, Australia.
    A three factor based authentication scheme of 5G wireless sensor networks for IoT system2023Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, nr 17, s. 15087-15099Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Internet of Things (IoT) is an expanding technology that facilitate physical devices to inter-connect each other over a public channel. Moreover, the security of the next-generation wireless mobile communication technology, namely 5G with IoT, has been a field of much interest among researchers in the last several years. Previously, Sharif et al. had suggested an IoTbased lightweight three-party authentication scheme proclaiming a secured scheme against different threats. However, it was found that the scheme could not achieve user anonymity and guarantee session key security. Additionally, the scheme fails to provide proper authentication in the login phase, and it s unable to update a new password in the password change phase. Thus, we propose an improved three-factor-based data transmission authentication scheme (TDTAS) to address the weaknesses. The formal security analysis has been proved using the Real-or-Random (RoR) model. The informal security analysis demonstrates that the scheme is secure against several known attacks and achieves more security features. In addition, the comparison of the work with other related schemes demonstrates the proposed scheme has less communicational and storage costs.

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  • 3.
    Sardar, Alamgir
    et al.
    Department of Computer Science and Engineering, Aliah University, Kolkata, India.
    Umer, Saiyed
    Department of Computer Science and Engineering, Aliah University, Kolkata, India.
    Rout, Ranjeet Kumar
    Department of Computer Science and Engineering, National Institute of Technology, Srinagar, J and K, India.
    Sahoo, Kshira Sagar
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Gandomi, Amir H.
    Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia.
    Enhanced biometric template protection schemes for securing face recognition in IoT environment2024Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    With the increasing use of biometrics in Internet of Things (IoT) based applications, it is essential to ensure that biometric-based authentication systems are secure. Biometric characteristics can be accessed by anyone, which poses a risk of unauthorized access to the system through spoofed biometric traits. Therefore, it is important to implement secure and efficient security schemes suitable for real-life applications, less computationally intensive, and invulnerable. This work presents a hybrid template protection scheme for secure face recognition in IoT-based environments, which integrates Cancelable Biometrics and Bio-Cryptography. Mainly, the proposed system involves two steps: face recognition and face biometric template protection. The face recognition includes face image preprocessing by the Tree Structure Part Model (TSPM), feature extraction by Ensemble Patch Statistics (EPS) technique, and user classification by multi-class linear support vector machine (SVM). The template protection scheme includes cancelable biometric generation by modified FaceHashing and a Sliding-XOR (called S-XOR) based novel Bio-Cryptographic technique. A user biometric-based key generation technique has been introduced for the employed Bio-Cryptography. Three benchmark facial databases, CVL, FEI, and FERET, have been used for the performance evaluation and security analysis. The proposed system achieves better accuracy for all the databases of 200-dimensional cancelable feature vectors computed from the 500-dimensional original feature vector. The modified FaceHashing and S-XOR method shows superiority over existing face recognition systems and template protection.

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  • 4.
    Seo, Eunil
    et al.
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Niyato, Dusit
    School of Computer Science and Engineering, Nanyang Technological University, Singapore, Singapore.
    Elmroth, Erik
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Resource-efficient federated learning with Non-IID data: An auction theoretic approach2022Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 9, nr 24, s. 25506-25524Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Federated learning (FL) has gained significant importance for intelligent applications, following data produced on a massive scale by numerous distributed IoT devices. From an FL perspective, the key aspect is that this data is not identically and independently distributed (IID) across different data sources and locations. This distribution-skewness leads to significant quality degradation. Moreover, an intrinsic consequence of using such non-IID data in decentralized learning is increasing costs that would be mitigated if using IID data. As a remedy, we propose a resource-efficient method for training an FL-based application with non-IID data, effectively minimizing cost through an auction approach and mitigating quality degradation through data sharing. In an experimental evaluation, we investigate the FL performance using real-world non-IID data and use the resulting ground-truth outputs to develop functions for estimating the utility of non-IID data, computation resource costs, and data generation costs. These functions are used to optimize the costs of model training, ensuring resource efficiency. It is further demonstrated that using shared-IID data significantly increases the resource efficiency of FL with local non-IID data. This holds true even when the shared IID data size is less than 1% of the size of the local non-IID data. Moreover, this work demonstrates that the profitability of the stakeholders can be maximized using the proposed auction procedure. The integration of the auction procedure and a resource-efficient training strategy allows FL service providers to create practical trading strategies by minimizing the FL clients’ resources and payments in a machine learning marketplace.

  • 5.
    Singh, Munesh
    et al.
    Department of Computer Science and Engineering, Pdpm Iiitdm Jabalpur, MP, India.
    Sahoo, Kshira Sagar
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Gandomi, Amir H.
    Faculty of Engineering and IT, University of Technology Sydney, Ultimo, Australia.
    An intelligent IoT-based data analytics for freshwater recirculating aquaculture system2023Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Smart farming is essential for a nation whose economy largely depends on agro products. In the last few years, rapid urbanization and deforestation have impacted farmers. Due to the lack of rainwater harvesting and changing weather patterns, many crop failure cases have been registered in the last few years. To prevent loss of annual crop production, many researchers propose the technology-driven smart farming method. Smart farming is a technology-driven control environment for monitoring and maintaining the crop. Smart farming increases crop production and provides an alternative source of income to small farmers. To promote smart farming in India, the government initiated many pilot projects for integrated aquaculture farming. However, the lack of technological intervention and skill-oriented process makes it difficult for most farmers to succeed in this business. In this paper, we have proposed an intelligent IoT-based freshwater recirculating aquaculture system. The proposed system has integrated sensors and actuators. The sensor system monitors the water parameters, and actuators maintain the aquaculture environment. An intelligent data analytics algorithm played a significant role in monitoring and maintaining the freshwater aquaculture environment. The analytics derived the relationship between the water parameters and identified the relative change. From the experimental evaluation, we have identified that the M5 model tree algorithm has the highest accuracy for monitoring the relative change in water parameters.

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  • 6.
    Zhang, Cheng
    et al.
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, China; Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province.
    Xu, Yang
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, China; Key Laboratory of Blockchain and Cyberspace Governance of Zhejiang Province.
    Elahi, Haroon
    Umeå universitet, Teknisk-naturvetenskapliga fakulteten, Institutionen för datavetenskap.
    Zhang, Deyu
    School of Computer Science and Engineering, Central South University, Changsha, China.
    Tan, Yunlin
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, China.
    Chen, Junxian
    School of Electronic Information, Hunan University, Changsha, China.
    Zhang, Yaoxue
    College of Computer Science and Electronic Engineering, Hunan University, Changsha, China; Department of Computer Science and Technology, Tsinghua University, Beijing, China.
    A Blockchain-based Model Migration Approach for Secure and Sustainable Federated Learning in IoT Systems2023Ingår i: IEEE Internet of Things Journal, ISSN 2327-4662, Vol. 10, nr 8, s. 6574-6585Artikel i tidskrift (Refereegranskat)
    Abstract [en]

    Model migration can accelerate model convergence during federated learning on the Internet of Things (IoT) devices and reduce training costs by transferring feature extractors from fast to slow devices, which, in turn, enables sustainable computing. However, malicious or lazy devices may migrate the fake models or resist sharing models for their benefit, reducing the desired efficiency and reliability of a federated learning system. To this end, this work presents a blockchain-based model migration approach for resource-constrained IoT systems. The proposed approach aims to achieve secure model migration and speed up model training while minimizing computation cost. We first develop an incentive mechanism considering the economic benefits of fast devices, which breaks the Nash equilibrium established by lazy devices and encourages capable devices to train and share models. Second, we design a clustering-based algorithm for identifying malicious devices and preventing them from defrauding incentives. Third, we use blockchain to ensure trustworthiness in model migration and incentive processes. Blockchain records the interaction between the central server and IoT devices and runs the incentive algorithm without exposing the devices’ private data. Theoretical analysis and experimental results show that the proposed approach can accelerate federated learning rates, reduce model training computation costs to increase sustainability, and resist malicious attacks.

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