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Lightweight and privacy-preserving device-to-device authentication to enable secure transitive communication in IoT-based smart healthcare systems

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Journal of Ambient Intelligence and Humanized Computing 2024年第9期15卷 3331-3345页

作者： Das, Sangjukta Singh, Maheshwari Prasad Namasudra, Suyel Department of Computer Science and Engineering National Institute of Technology Patna Bihar India Department of Computer Science and Engineering National Institute of Technology Agartala Tripura India

Internet of Things (IoT) devices are often directly authenticated by the gateways within the network. In complex and large systems, IoT devices may be connected to the gateway through another device in the network. In such a scenario, new device should be authenticated with the gateway through the intermediate device. To address this issue, an authentication process is proposed in this paper for IoT-enabled healthcare systems. This approach performs a privacy-preserving mutual authentication between the gateway and an IoT device through intermediate devices, which are already authenticated by the gateway. The proposed approach relies on the session key established during gateway-intermediate device authentication. To emphasizes lightweight and efficient system, the proposed approach employs lightweight cryptographic operations, such as XOR, concatenation, and hash functions within IoT networks. This approach goes beyond the traditional device-to-device authentication, allowing authentication to propagate across multiple devices or nodes in the network. The proposed work establishes a secure session between an authorized device and a gateway, preventing unauthorized devices from accessing healthcare systems. The security of the protocol is validated through a thorough analysis using the AVISPA tool, and its performance is evaluated against existing schemes, demonstrating significantly lower communication and computation costs. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Internet of things

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Analysis of the effect of different scattering mechanisms on the excess noise behavior of Sb-based avalanche photodiodes

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Optics Express 2025年第4期33卷 7337-7344页

作者： Karimi, Hannaneh Herrera, Daniel J. Dadey, Adam A. Wei, Dongxia Mcarthur, Andrew Bank, Seth R. Campbell, Joe. C. Department of Electrical and Computer Engineering University of Virginia CharlottesvilleVA22903 United States Department of Electrical and Computer Engineering University of Texas AustinTX78758 United States

In optical applications where avalanche photodiodes (APDs) provide the benefit of high sensitivity, Sb-based materials systems such as AlInAsSb and AlGaAsSb have shown extremely low excess noise factors. The Monte Carlo (MC) model described in this work reveals the effect of high alloy scattering on the excess noise factor in these material systems. A comparison between the excess noise factor of different combinations of scattering rates is investigated. Furthermore, the ionization path length probability distribution is presented for AlInAsSb APDs with different scattering mechanisms. Good agreement is achieved between simulation results and experimental measurements. © 2025 Optica Publishing Group under the terms of the Optica Open Access Publishing Agreement.

关键词： III-V semiconductors

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An empirical study of contact data distribution of mobile social networks (MSN): findings and implications

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Multimedia Tools and Applications 2025年 1-29页

作者： Moradi, Saeed Mohasefi, Bagherzadeh Kashani, Mostafa Haghi Department of Computer Engineering Maku Branch Islamic Azad University Maku Iran Cert Center Urmia University Urmia Iran Department of Computer Engineering Urmia University Urmia Iran Department of Computer Engineering Shahr-e-Qods Branch Islamic Azad University Tehran Iran

The dynamics of Mobile Social Networks (MSN) in terms of contact opportunities are critical in the new generation of networks for improving many applications such as routing, data transmission, video surveillance, community detection, and offloading. It was confirmed that utilizing the probability distribution to model the contact data could lead to improving the relevant performance metrics. The more accurate the distribution model is, the better the prediction of the contact opportunity is obtained. Two ignored challenges are addressed by this study, which also aim to empower network nodes to automate their contact data modeling. First, it is empirically demonstrated that the contact data of network nodes follow heterogeneous distributions. Second, it is mathematically proven that some generalizations of different contact type distributions do not justify even under the homogeneity assumption of distributions. By using our developed theorems and propositions, a framework called valid distribution generalization is provided. A statistical analysis is conducted to substantiate the first claim using a state-of-the-art modeling framework GAMLSS. Due to the rapid rise in the automation of operational and management systems, the feasibility of empowering nodes for adaptably model selection, rather than static or manually setting them, is investigated as well. The test of proposed hypotheses on two well-known real traces verified that using homogeneous distributions, for any reason such as simplicity or a compromise between complexity and accuracy, does not make sense in real networks. The implications of our findings on multimedia routing, data transmission and surveillance systems efforts are also discussed. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2025.

关键词： Heterogeneous contact model Mobile social network Multimedia transmission Network dynamics Opportunistic routing Surveillance systems

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A learning automata based edge resource allocation approach for IoT-enabled smart cities

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Digital Communications and Networks 2024年第5期10卷 1258-1266页

作者： Sampa Sahoo Kshira Sagar Sahoo Bibhudatta Sahoo Amir H.Gandomi Department of Computer Science and Engineering C.V.Raman Global UniversityBhubaneswarIndia Department of Computer Science and Engineering SRM UniversityAmaravatiIndia Department of Computing Science Umea UniversityUmea90187Sweden Department of Computer Science and Engineering NIT RourkelaIndia Faculty of Engineering and Information Technology University of Technology SydneyAustralia University Research and Innovation Center(EKIK) Obuda University1034 BudapestHungary

The development of the Internet of Things(IoT)technology is leading to a new era of smart applications such as smart transportation,buildings,and smart ***,these applications act as the building blocks of IoT-enabled smart *** high volume and high velocity of data generated by various smart city applications are sent to flexible and efficient cloud computing resources for ***,there is a high computation latency due to the presence of a remote cloud *** computing,which brings the computation close to the data source is introduced to overcome this *** an IoT-enabled smart city environment,one of the main concerns is to consume the least amount of energy while executing tasks that satisfy the delay *** efficient resource allocation at the edge is helpful to address this *** this paper,an energy and delay minimization problem in a smart city environment is formulated as a bi-objective edge resource allocation ***,we presented a three-layer network architecture for IoT-enabled smart ***,we designed a learning automata-based edge resource allocation approach considering the three-layer network architecture to solve the said bi-objective minimization *** Automata(LA)is a reinforcement-based adaptive decision-maker that helps to find the best task and edge resource *** extensive set of simulations is performed to demonstrate the applicability and effectiveness of the LA-based approach in the IoT-enabled smart city environment.

关键词： Edge computing IoT Learning automata Resource allocation Smart city

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Using fog computing (FC) and optimization techniques for tasks migration and resource allocation in the internet of things (IoT)

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International Journal of computers and Applications 2024年第2期46卷 113-121页

作者： Arvaneh, Fatemeh Zarafshan, Faraneh Karimi, Abbas Department of Computer Engineering Arak Branch Islamic Azad University Arak Iran Department of Computer Engineering Ashtian Branch Islamic Azad University Ashtian Iran

The present paper proposes a novel algorithm with the highest accuracy and efficiency for resource allocation in the IoT, considering the fog computing technique. To meet the need for resource allocation, including central processing unit, bandwidth, and main memory, the Genetic Algorithm, Particle Swarm Optimization, Adaptive Weighted Particle Swarm Optimization, Crystal Structure Algorithm, and Simulated Annealing were examined. The obtained results emphasize the better performance of Simulated Annealing compared to the rest. The findings of this paper can benefit optimizing the tasks migration in internet of things. The major innovation of this research lies in the heart of the comparison made between the selected algorithms to see which one conducted the migration tasks with the highest accuracy and remarkable convergence rate. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： Fog computing

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Gradient Propagation Based DenseNet121 with ResNet50 Feature Extraction for Lymphoma Classification

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Journal of The Institution of Engineers (India): Series B 2024年 1-13页

作者： Srinivasan, Deepthi Kalaiarasan, C. Department of Computer Science and Engineering Presidency University Karnataka Bengaluru India Department of Computer Science and Engineering and Information Science Presidency University Karnataka Bengaluru India

Lymphoma is a type of malignant tumor that develops from lymphoid hematopoietic tissues. The precise diagnosis of lymphomas is one of the challenging tasks because of the similarity within the morphological features across lymphoma classes. Therefore, an effective classification of lymphoma is required to be developed for taking a timely action for the patients. This paper aims to propose the Gradient Propagation based DenseNet121 namely GPDN for an effective multiclass lymphoma classification. The dense connections of GPDN is used to mitigate the vanishing gradient issue by confirming an effective gradient propagation over the network for enhancing stability and avoiding overfitting issue. An effective reutilization of features from previous layers, the GPDN obtains the intricate for enhanced differentiation of lymphoma. Different pre-processing approaches such as gaussian filter, min-max normalization and data augmentation are developed for denoising, rescaling and augmenting the input images. Here, multiscale features from the image are obtained by using ResNet50-based feature extraction. This research uses a dataset from Kaggle named Multi cancer dataset. The performance of the proposed ResNet50-GPDN is analyzed using accuracy, precision, recall and F-measure. Simulation results show that the GPDN achieves higher accuracy of 99.90% than the VGG16, InceptionV3, MobileNet and AlexNet. Moreover, existing researches HPC and FFNN-ResNet50, are used for the comparison of the ResNet50-GPDN. The accuracy of ResNet50-GPDN is 99.90% which is clearly higher than those of HPC and FFNN-ResNet50. © The Institution of Engineers (India) 2024.

关键词： Convolutional neural networks

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Deep reinforcement learning for online scheduling of photovoltaic systems with battery energy storage systems

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Intelligent and Converged Networks 2024年第1期5卷 28-41页

作者： Yaze Li Jingxian Wu Yanjun Pan the Department of Electrical Engineering University of ArkansasFayettevilleAR 72701USA the Department of Computer Science and Computer Engineering University of ArkansasFayettevilleAR 72701USA

A new online scheduling algorithm is proposed for photovoltaic(PV)systems with battery-assisted energy storage systems(BESS).The stochastic nature of renewable energy sources necessitates the employment of BESS to balance energy supplies and demands under uncertain weather *** proposed online scheduling algorithm aims at minimizing the overall energy cost by performing actions such as load shifting and peak shaving through carefully scheduled BESS charging/discharging *** scheduling algorithm is developed by using deep deterministic policy gradient(DDPG),a deep reinforcement learning(DRL)algorithm that can deal with continuous state and action *** of the main contributions of this work is a new DDPG reward function,which is designed based on the unique behaviors of energy *** new reward function can guide the scheduler to learn the appropriate behaviors of load shifting and peak shaving through a balanced process of exploration and *** new scheduling algorithm is tested through case studies using real world data,and the results indicate that it outperforms existing algorithms such as Deep *** online algorithm can efficiently learn the behaviors of optimum non-casual off-line algorithms.

关键词： photovoltaic(PV) battery energy storage system(BESS) Markov decision process(MDP) deep deterministic policy gradient(DDPG)

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A Hybrid Regularized Multilayer Perceptron for Input Noise Immunity

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第1期5卷 115-126页

作者： Mondal, Rahul Pal, Tandra Dey, Prasenjit National Institute of Technology Durgapur Department of Computer Science and Engineering Durgapur713209 India Cooch Behar Government Engineering College Department of Computer Science and Engineering Cooch Behar736170 India

The immunity of multilayer perceptron (MLP) is less effective toward input noise. In this article, we have focused on the robustness of MLP with respect to input noise where noise can be additive or multiplicative. Here, we have proposed a DropConnect-based regularized MLP to reduce the coadaptation among the neurons of the hidden layer. At first, we have empirically and statistically shown that by reducing coadaptation among the hidden neurons, an MLP can achieve better noise immunity. We have also empirically shown that an MLP with input noise injection and $l_{2}$ regularizer is an effective approach to improve its noise immunity. However, the results indicate that it does not adjust the coadaptation among the hidden neurons. Therefore, for further improvement, we have proposed a hybrid regularized MLP (HRMLP), where DropConnect is combined with the noise injection and $l_{2}$ regularizer. In addition to input noise, we have also verified the robustness of HRMLP with respect to $\mathbf {20\%}$ outliers in the dataset. To justify the effectiveness of the proposed HRMLP, we have compared it with MLP, MLP with noise injection, MLP with $l_{2}$ regularizer, MLP with noise injection and $l_{2}$ regularizer, and MLP with DropConnect along with two state-of-the-art works based on $\mathbf {20}$ standard datasets. The experimental results for both noisy inputs and outliers confirm that the performance of HRMLP is significant compared to other methods. © 2020 IEEE.

关键词： Neurons

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Performance optimization in ddos prediction with ensemble based approach

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Multimedia Tools and Applications 2025年第12期84卷 10631-10644页

作者： Dogra, Amit Taqdir Department of Computer Science and Engineering SoET BGSB University Rajouri India Department of Computer Science Engineering and Technology Guru Nanak Dev University Regional Campus Gurdaspur India

Distributed Denial of Service (DDoS) attacks pose a significant threat to network infrastructures, leading to service disruptions and potential financial losses. In this study, we propose an ensemble-based approach for DDoS attack detection, leveraging the strengths of three different classifiers: Adaboost, K-Nearest Neighbors (KNN), and Random Forest. We apply data normalization during pre-processing, utilize a Multi-Layer Perceptron (MLP) for feature extraction, and combine the classifiers using an ensemble approach. The performance of each individual classifier and the ensemble is extensively evaluated, and the results demonstrate the effectiveness of the proposed ensemble in accurately identifying and mitigating DDoS attacks. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Denial-of-service attack

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An innovative Telugu text summarization framework using the pointer network and optimized attention layer

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Multimedia Tools and Applications 2024年第37期83卷 84539-84564页

作者： M, Varaprasad Rao Chakma, Kunal Jamatia, Anupam Rudrapal, Dwijen Department of Computer Science & amp Engineering National Institute of Technology Agartala Tripura799046 India Department of Computer Science & amp Engineering CVR College of Engineering Telangana Hyderabad501510 India

Summarizing lengthy text involves distilling crucial information into a concise form by covering the key events in the source text. Previous researchers mostly explored the supervised approaches for the task, but due to its strong reliance on the quality of text features, the resulting summaries often lack precision and coherence. The performance of the state-of-the-art summarizers becomes poor when applied to various Indian languages due to several challenges. The current research paper proposes a summarization approach for Telugu text based on a Pointer Network with an Optimised Attention Layer (PN-OAL). In this approach, the attention layer’s weights are adjusted using a hybrid optimization method that combines the Fusion of Coyote Optimization and Squirrel Search method (FCO-SSA). Weight optimization is conducted in order to address the goal function, which involves the maximization of the cosine similarity between the source text and the summary text. A novel loss function is incorporated within the framework of the constructed network architecture to generate a robust summary. The proposed approach is experimented with alternative baseline approaches in order to validate its efficacy for summarizing Telugu documents. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network architecture

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