Learning For Internet Of Things Research Articles

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

Bald Eagle Search Algorithm with Hierarchical Deep Learning for Internet of Things Assisted Sleep Quality Recognition

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

Device Scheduling and Assignment in Hierarchical Federated Learning for Internet of Things

A novel RPL defense mechanism based on trust and deep learning for internet of things

A novel RPL defense mechanism based on trust and deep learning for internet of things

Blockchained Federated Learning for Internet of Things: A Comprehensive Survey

The demand for intelligent industries and smart services based on big data is rising rapidly with the increasing digitization and intelligence of the modern world. This survey comprehensively reviews Blockchained Federated Learning (BlockFL) that joins the benefits of both Blockchain and Federated Learning to provide a secure and efficient solution for the demand. We compare the existing BlockFL models in four Internet-of-Things (IoT) application scenarios: Personal IoT (PIoT), Industrial IoT (IIoT), Internet of Vehicles (IoV), and Internet of Health Things (IoHT), with a focus on security and privacy, trust and reliability, efficiency, and data diversity. Our analysis shows that the features of decentralization and transparency make BlockFL a secure and effective solution for distributed model training, while the overhead and compatibility still need further study. It also reveals the unique challenges of each domain presents unique challenges, e.g., the requirement of accommodating dynamic environments in IoV and the high demands of identity and permission management in IoHT, in addition to some common challenges identified, such as privacy, resource constraints, and data heterogeneity. Furthermore, we examine the existing technologies that can benefit BlockFL, thereby helping researchers and practitioners to make informed decisions about the selection and development of BlockFL for various IoT application scenarios.

An IoT‐based multiple teaching quality evaluation method for English translation with improved deep learning

AbstractTo enhance the teaching quality (TQ) of English translation (ET) courses, multivariate evaluation methods have garnered significant attention for their ability to identify deficiencies in the teaching process and subsequently improve instructional standards. Traditional evaluation techniques have shown limited effectiveness. Therefore, this paper proposes an ET in multiple teaching quality (MTQ) evaluation method with improved deep learning in internet of things (IoT). First, the paper establishes evaluation indicators for ET in MTQ, which mainly encompass ET teaching effectiveness, pedagogic competency in ET, teaching methods, content, as well as attitudes towards ET teaching. It then utilizes IoT technology to preliminarily collect data on these indicators, and using clustering algorithm based on weighted attributes and density (CABWAD) algorithm to mine data on English teaching MTQ evaluation indicators for ET. The extracted evaluation indicator data is then denoised using a probabilistic undirected graph model. Ultimately, the multilayer perceptron in deep learning is improved through Wolfe line search optimization, and this enhanced multilayer perceptron is employed to construct an ET in MTQ evaluation model. The denoised indicator data is inputted into the model, which then outputs precise MTQ evaluation results. The results show that the absolute value of the average Pearson correlation coefficient of this method is the highest, the Spearman correlation coefficient is the lowest, the mean average precision value is 0.965, and the positive category imbalance degree and the negative category imbalance degree are the lowest, indicating that the proposed method has outstanding performance in all aspects, and has certain application value in the field of TQ evaluation.

Blockchain-based deep learning in IoT, healthcare and cryptocurrency price prediction: a comprehensive review

PurposeDeep learning (DL) is on the rise because it can make predictions and judgments based on data that is unseen. Blockchain technologies are being combined with DL frameworks in various industries to provide a safe and effective infrastructure. The review comprises literature that lists the most recent techniques used in the aforementioned application sectors. We examine the current research trends across several fields and evaluate the literature in terms of its advantages and disadvantages.Design/methodology/approachThe integration of blockchain and DL has been explored in several application domains for the past five years (2018–2023). Our research is guided by five research questions, and based on these questions, we concentrate on key application domains such as the usage of Internet of Things (IoT) in several applications, healthcare and cryptocurrency price prediction. We have analyzed the main challenges and possibilities concerning blockchain technologies. We have discussed the methodologies used in the pertinent publications in these areas and contrasted the research trends during the previous five years. Additionally, we provide a comparison of the widely used blockchain frameworks that are used to create blockchain-based DL frameworks.FindingsBy responding to five research objectives, the study highlights and assesses the effectiveness of already published works using blockchain and DL. Our findings indicate that IoT applications, such as their use in smart cities and cars, healthcare and cryptocurrency, are the key areas of research. The primary focus of current research is the enhancement of existing systems, with data analysis, storage and sharing via decentralized systems being the main motivation for this integration. Amongst the various frameworks employed, Ethereum and Hyperledger are popular among researchers in the domain of IoT and healthcare, whereas Bitcoin is popular for research on cryptocurrency.Originality/valueThere is a lack of literature that summarizes the state-of-the-art methods incorporating blockchain and DL in popular domains such as healthcare, IoT and cryptocurrency price prediction. We analyze the existing research done in the past five years (2018–2023) to review the issues and emerging trends.

An early discovery of intrusion attack using novel optimized deep learning for internet of things

In the past couple of years, neural networks have gained widespread use in network security analysis. This type of analysis is usually performed in a nonlinear and highly correlated manner. Due to the immense amount of data traffic, the current models are prone to false alarms and poor detection. Deep-learning models can help security researchers identify and extract data features that are related to an attack. They can also minimize the data’s dimensionality and detect intrusions. Unfortunately, the complexity of the network structure and hidden neurons of a deep-learning model can be set by error-prone procedures. In order to improve the performance of deep learning models, a new algorithm is proposed. This method combines a gradient boost regression and particle swarm optimization. The proposes a method called the Spark-DBN-SVM-GBR algorithm. The simulations conducted proposed algorithm revealed that it has a better accuracy rate than other deep learning models and the experiments conducted on the PSO-GBR algorithm revealed that it performed better than the current optimization technique when detecting unauthorized attack activities.

Clustered Federated Learning in Internet of Things: Convergence Analysis and Resource Optimization

Clustered Federated Learning in Internet of Things: Convergence Analysis and Resource Optimization

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

Transitioning From Federated Learning to Quantum Federated Learning in Internet of Things: A Comprehensive Survey

TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

TSGS: Two-stage security game solution based on deep reinforcement learning for Internet of Things

PrivMaskFL: A private masking approach for heterogeneous federated learning in IoT

With the arrival of the era of the Internet of Things (IoT), the rapid development of new technologies such as artificial intelligence, big data, and other advanced techniques, data is growing geometrically. These data are prone to form data silos, scattered in various places. Jointly decentralized data applications will accelerate the progress and development of the times, but also face the challenge of data privacy protection. Federated learning (FL), a new branch of distributed machine learning, is trained by collaborative training to obtain global model without direct exposure to local datasets. Some studies have shown that typically federated learning involves a larger number of participants, it can lead to a significant increase in communication overhead, resulting in issues such as higher latency and bandwidth consumption. We suggest masking a subset of diverse participants and allowing the remaining participants to proceed with the next communication round of updates. Our aim is for reducing the communication overhead and improving the convergence performance of the global model on the premise of heterogeneous data privacy protection. We design a private masking approach PrivMaskFL to address two problems. We firstly propose a dynamic participant aggregation masking approach, which adopts the greedy ideology to select the relatively important participants and mask the unimportant ones; secondly, we design an adaptive differential privacy approach, which adaptively stratifies privacy budget according to the characteristics of participant, allocates the budget in a fine-grained stratified level, and adds Gaussian noise reasonably. Specifically, in each communication round, the participant’s model needs to perform local differential privacy noise addition for uplink parameter transmission; the server aggregates to acquire global model, finds a candidate participant subset based on the smaller parameter divergence by using the greedy algorithm approximation for t+1-th communication round for downlink parameter transmission. Subsequently, the privacy budget sequence is divided and granted to the participants of the stratified level, and the Gaussian noise addition of adaptive differential privacy is completed to achieve privacy protection without compromising the model’s usability. In experiments, our approach reduces the communication overhead and improve the convergence performance. Furthermore, our approach achieves higher accuracy and robust variance on both FMNIST and FEMNIST datasets.

A Learning based Secure Routing Approach using Deep Reinforcement Learning in IoT Integrated Wireless Sensor Network

The usage of Wireless Sensor Network (WSN) is ubiquitous in nature. With the emergence of Internet of Things (IoT) technology and its unprecedented use cases, the role of sensor networks as part of IoT application became crucial. WSN became backbone of IoT to realize integration between physical and digital worlds and connectivity to Internet. However, IoT devices are resource constrained with limited computational capabilities. The entire network is distributed in nature and has increased complexity. Routing in such WSN integrated IoT network plays an important role in achieving meaningful communication among objects. In this context, it is indispensable to have more energy efficient routing method. Since the IoT integrated sensor network is highly complicated, it is very dynamic in nature. Thus routing decisions are also dynamic leading to much importance to routing in such use cases. With the emergence of Artificial Intelligence (AI), it became possible to solve complex real world problems through learning based approach which acquires desired intelligence prior to making decisions. In this paper we proposed a deep reinforcement learning based routing mechanism for energy efficient routing in WSN-IoT integrated application. We proposed novel algorithms for network setup, formation of clusters and routing. Our method adapts to network changes due to energy levels, mobility and makes learning based routing decisions. We enhanced the method further with security to ensure its Qualityof Service (QoS) in presence of attacks. Our simulation study using MATLAB has revealed that the proposed secure routing approach outperforms existing protocols.

RFedFW: Secure and trustable aggregation scheme for Byzantine-robust federated learning in Internet of Things

Federated learning is a promising approach in the Internet of Things (IoT) that enables collaborative and distributed machine learning among massive IoT devices without sharing private data, thereby constructing smart IoT applications. However, traditional federated learning approaches are unable to monitor the local training process of devices. Some malicious devices can exploit the vulnerability to conduct Byzantine attacks, which may potentially lead to the failure or compromise of shared global model. In this paper, we propose a new Byzantine-robust federated learning framework called rFedFW, which aims to achieve secure and trustable federated learning in the IoT. Specifically, we propose a dual filtering mechanism to identify and discard malicious gradients. Furthermore, we design an adaptive weight adjustment scheme that dynamically reduces the aggregated weight of potentially malicious gradients, ultimately achieving robust model aggregation. Additionally, we propose a dynamic clipping method to reduce the magnitude of various gradients, and we incorporate an additive model aggregation scheme with momentum to smooth the effects of local gradients and achieve efficient model aggregation. Extensive experimental results on various datasets demonstrate the effectiveness and robustness of rFedFW.

Lightweight Privacy and Security Computing for Blockchained Federated Learning in IoT

The development of Internet of Things (IoT) makes human life more intelligent, and the interconnection of all things has become a reality. However, the surge in the number of devices and centralized management brings severe challenges to IoT, such as single point of failure, poor security, privacy leakage, and low reliability. Due to the decentralization, verifiability and privacy protection of blockchain federated learning, some blockchain federated learning schemes have been proposed to solve these problems, but bring new challenges, such as device privacy leakage and heavy security computing load. In this paper, we propose a new decentralized, secure and verifiable consortium blockchain federated learning privacy protection scheme, named LPBFL, which realizes lightweight computing while ensuring the privacy of the local model and dataset of the device. To achieve lightweight privacy protection, LPBFL adopts the Paillier encryption and the newly designed lightweight digital signature and batch verification algorithm. Additionally, considering that devices upload invalid or even toxic local models intentionally or unintentionally, we design a device reputation selection mechanism to make blockchain federated learning more efficient. Finally, the theoretical analysis proves the security of LPBFL and verifies the unforgeability of the proposed digital signature. Comprehensive comparisons and extensive experiments demonstrate that our LPBFL has significant advantages in multiple aspects.

Dynamic multiple access based on deep reinforcement learning for Internet of Things

With the development of wireless communication technology, the rational use of spectrum resources utilizing efficient multiple access technology has become a research hotspot. The channel access strategy in dynamic spectrum access technology will directly affect the system’s spectrum utilization. Deep reinforcement learning is proposed to train users in the network to learn optimal channel access strategy to achieve reasonable allocation of spectrum resources on the channel. At the same time, the multiple access scheme adopted by users who have access to the channel will also affect the throughput of the system. Traditional multiple access schemes include Orthogonal Multiple Access scheme and Non-orthogonal Multiple Access (NOMA) scheme. In a dynamic environment, the multiple access scheme suitable for users on the channel is also dynamic. Therefore, DRL is introduced to help users learn the multiple access scheme selection strategy to maximize user throughput. Finally, we propose a dynamic multiple access algorithm based on deep reinforcement learning, which optimizes the channel access strategy and scheme selection strategy respectively, and improves the performance. The simulation results show that the proposed scheme is superior to other schemes in various scenarios, especially, the average throughput of the proposed scheme is about 0.8 Mbps and 0.5 Mbps higher than that of the FDMA scheme and NOMA scheme, respectively.

Computation and Communication Efficient Adaptive Federated Optimization of Federated Learning for Internet of Things

The proliferation of the Internet of Things (IoT) and widespread use of devices with sensing, computing, and communication capabilities have motivated intelligent applications empowered by artificial intelligence. Classical artificial intelligence algorithms require centralized data collection and processing, which are challenging in realistic intelligent IoT applications due to growing data privacy concerns and distributed datasets. Federated Learning (FL) has emerged as a privacy-preserving distributed learning framework, which enables IoT devices to train global models through sharing model parameters. However, inefficiency due to frequent parameter transmissions significantly reduces FL performance. Existing acceleration algorithms consist of two main types including local update and parameter compression, which considers the trade-offs between communication and computation/precision, respectively. Jointly considering these two trade-offs and adaptively balancing their impacts on convergence have remained unresolved. To solve the problem, this paper proposes a novel efficient adaptive federated optimization (FedEAFO) algorithm to improve the efficiency of FL, which minimizes the learning error via jointly considering two variables including local update and parameter compression. The FedEAFO enables FL to adaptively adjust two variables and balance trade-offs among computation, communication, and precision. The experiment results illustrate that compared with state-of-the-art algorithms, the FedEAFO can achieve higher accuracies faster.

Retracted: Image Classification Model Based on Deep Learning in Internet of Things

Retracted: Image Classification Model Based on Deep Learning in Internet of Things

Feature Selection and Dynamic Network Traffic Congestion Classification based on Machine Learning for Internet of Things

The network traffic congestion classifier is essential for network monitoring systems. Network traffic characterization is a methodology to classify traffic into several classes supporting various attributes. In this paper, payload-based classification is suggested for network traffic characterization. It has a broad scope of utilization like network security assessment, intrusion identification, QoS supplier, et cetera; furthermore, it has significance in investigating different suspicious movements in the network. Numerous supervised classification techniques like Support Vector Machines and unsupervised clustering methods like K-Means connected are used in traffic classification. In current network conditions, minimal supervised data and unfamiliar applications influence the usual classification procedure's performance. This paper implements a methodology for network traffic classification using clustering, feature extraction, and variety for the Internet of Things (IoT). Further, K-Means is used for network traffic clustering datasets, and feature extraction is performed on grouped information. KNN, Naïve Bayes, and Decision Tree classification methods classify network traffic because of extracted features, which presents a performance measurement between these classification algorithms. The results discuss the best machine learning algorithm for network congestion classification. According to the outcome, clustering (k-means) with network classification (Decision Tree) generates a higher accuracy, 86.45 %, than other clustering and network classification

Adaptive Resource Allocation for Blockchain-Based Federated Learning in Internet of Things

The fast development of mobile communication and artificial intelligence (AI) technologies greatly promotes the prosperity of the Internet of Things (IoT), where various types of IoT devices can perform more intelligent tasks. Considering the privacy leakage and limited communication resources, federated learning (FL) has emerged to enable devices to collaboratively train AI models based on their local data without raw data exchanges. Nevertheless, it is still challenging for guaranteeing any FL models to be effective due to the sluggish willingness of IoT devices and the model poisoning attacks in the FL. To address these issues, in this paper, we introduce blockchain technology and propose a blockchain-based FL framework for supporting a trustworthy and reliable FL paradigm in IoT. In the proposed framework, we design a committee-based participant selection mechanism that selects the aggregate node and local model updates dynamically to construct the global model. Moreover, considering the trade-off between the energy consumption and the convergence rate of the FL model, we perform the channel allocation, block size adjustment, and block producer selection jointly. Since the remaining resources, handling transactions, and channel conditions are dynamically varying (i.e., stochastic environment), we formulate the problem as a Markov decision process (MDP) and adopt a deep reinforcement learning (DRL)-based algorithm to solve it. The simulation results demonstrate the effectiveness of the proposed framework and show the superior performance of the DRL-based resource allocation algorithm compared with other baseline methods in terms of energy consumption.

DeepThink IoT: The Strength of Deep Learning in Internet of Things

DeepThink IoT: The Strength of Deep Learning in Internet of Things