This paper reviews big data and Internet of Things (IoT)-based applications in smart environments. The aim is to identify key areas of application, current trends, data architectures, and ongoing ...challenges in these fields. To the best of our knowledge, this is a first systematic review of its kind, that reviews academic documents published in peer-reviewed venues from 2011 to 2019, based on a four-step selection process of identification, screening, eligibility, and inclusion for the selection process. In order to examine these documents, a systematic review was conducted and six main research questions were answered. The results indicate that the integration of big data and IoT technologies creates exciting opportunities for real-world smart environment applications for monitoring, protection, and improvement of natural resources. The fields that have been investigated in this survey include smart environment monitoring, smart farming/agriculture, smart metering, and smart disaster alerts. We conclude by summarizing the methods most commonly used in big data and IoT, which we posit to serve as a starting point for future multi-disciplinary research in smart cities and environments.
Video streaming-based real-time vehicle identification and license plate recognition systems are challenging to design and deploy in terms of real-time processing on edge, dealing with low image ...resolution, high noise, and identification. This paper addresses these issues by introducing a novel multi-stage, real-time, deep learning-based vehicle identification and license plate recognition system. The system is based on a set of algorithms that efficiently integrate two object detectors, an image classifier, and a multi-object tracker to recognize car models and license plates. The information redundancy of Saudi license plates' Arabic and English characters is leveraged to boost the license plate recognition accuracy while satisfying real-time inference performance. The system optimally achieves real-time performance on edge GPU devices and maximizes models' accuracy by taking advantage of the temporally redundant information of the video stream's frames. The edge device sends a notification of the detected vehicle and its license plate only once to the cloud after completing the processing. The system was experimentally evaluated on vehicles and license plates in real-world unconstrained environments at several parking entrance gates. It achieves 17.1 FPS on a Jetson Xavier AGX edge device with no delay. The comparison between the accuracy on the videos and on static images extracted from them shows that the processing of video streams using this proposed system enhances the relative accuracy of the car model and license plate recognition by 13% and 40%, respectively. This research work has won two awards in 2021 and 2022.
Satellite images have drawn increasing interest from a wide variety of users, including business and government, ever since their increased usage in important fields ranging from weather, forestry ...and agriculture to surface changes and biodiversity monitoring. Recent updates in the field have also introduced various deep learning (DL) architectures to satellite imagery as a means of extracting useful information. However, this new approach comes with its own issues, including the fact that many users utilize ready-made cloud services (both public and private) in order to take advantage of built-in DL algorithms and thus avoid the complexity of developing their own DL architectures. However, this presents new challenges to protecting data against unauthorized access, mining and usage of sensitive information extracted from that data. Therefore, new privacy concerns regarding sensitive data in satellite images have arisen. This research proposes an efficient approach that takes advantage of privacy-preserving deep learning (PPDL)-based techniques to address privacy concerns regarding data from satellite images when applying public DL models. In this paper, we proposed a partially homomorphic encryption scheme (a Paillier scheme), which enables processing of confidential information without exposure of the underlying data. Our method achieves robust results when applied to a custom convolutional neural network (CNN) as well as to existing transfer learning methods. The proposed encryption scheme also allows for training CNN models on encrypted data directly, which requires lower computational overhead. Our experiments have been performed on a real-world dataset covering several regions across Saudi Arabia. The results demonstrate that our CNN-based models were able to retain data utility while maintaining data privacy. Security parameters such as correlation coefficient (−0.004), entropy (7.95), energy (0.01), contrast (10.57), number of pixel change rate (4.86), unified average change intensity (33.66), and more are in favor of our proposed encryption scheme. To the best of our knowledge, this research is also one of the first studies that applies PPDL-based techniques to satellite image data in any capacity.
Distributed denial-of-service (DDoS) attacks persistently proliferate, impacting individuals and Internet Service Providers (ISPs). Deep learning (DL) models are paving the way to address these ...challenges and the dynamic nature of potential threats. Traditional detection systems, relying on signature-based techniques, are susceptible to next-generation malware. Integrating DL approaches in cloud-edge/federated servers enhances the resilience of these systems. In the Internet of Things (IoT) and autonomous networks, DL, particularly federated learning, has gained prominence for attack detection. Unlike conventional models (centralized and localized DL), federated learning does not require access to users' private data for attack detection. This approach is gaining much interest in academia and industry due to its deployment on local and global cloud-edge models. Recent advancements in DL enable training a quality cloud-edge model across various users (collaborators) without exchanging personal information. Federated learning, emphasizing privacy preservation at the cloud-edge terminal, holds significant potential for facilitating privacy-aware learning among collaborators. This paper addresses: (1) The deployment of an optimized deep neural network for network traffic classification. (2) The coordination of federated server model parameters with training across devices in IoT domains. A federated flowchart is proposed for training and aggregating local model updates. (3) The generation of a global model at the cloud-edge terminal after multiple rounds between domains and servers. (4) Experimental validation on the BoT-IoT dataset demonstrates that the federated learning model can reliably detect attacks with efficient classification, privacy, and confidentiality. Additionally, it requires minimal memory space for storing training data, resulting in minimal network delay. Consequently, the proposed framework outperforms both centralized and localized DL models, achieving superior performance.
The Industrial Internet of Things (IIoT) refers to the use of smart sensors, actuators, fast communication protocols, and efficient cybersecurity mechanisms to improve industrial processes and ...applications. In large industrial networks, smart devices generate large amounts of data, and thus IIoT frameworks require intelligent, robust techniques for big data analysis. Artificial intelligence (AI) and deep learning (DL) techniques produce promising results in IIoT networks due to their intelligent learning and processing capabilities. This survey article assesses the potential of DL in IIoT applications and presents a brief architecture of IIoT with key enabling technologies. Several well-known DL algorithms are then discussed along with their theoretical backgrounds and several software and hardware frameworks for DL implementations. Potential deployments of DL techniques in IIoT applications are briefly discussed. Finally, this survey highlights significant challenges and future directions for future research endeavors.
Medical images possess significant importance in diagnostics when it comes to healthcare systems. These images contain confidential and sensitive information such as patients’ X-rays, ultrasounds, ...computed tomography scans, brain images, and magnetic resonance imaging. However, the low security of communication channels and the loopholes in storage systems of hospitals or medical centres put these images at risk of being accessed by unauthorized users who illegally exploit them for non-diagnostic purposes. In addition to improving the security of communication channels and storage systems, image encryption is a popular strategy adopted to ensure the safety of medical images against unauthorized access. In this work, we propose a lightweight cryptosystem based on Henon chaotic map, Brownian motion, and Chen’s chaotic system to encrypt medical images with elevated security. The efficiency of the proposed system is proved in terms of histogram analysis, adjacent pixels correlation analysis, contrast analysis, homogeneity analysis, energy analysis, NIST analysis, mean square error, information entropy, number of pixels changing rate, unified average changing intensity, peak to signal noise ratio and time complexity. The experimental results show that the proposed cryptosystem is a lightweight approach that can achieve the desired security level for encrypting confidential image-based patients’ information.
The Vision Transformer (ViT) architecture has been remarkably successful in image restoration. For a while, Convolutional Neural Networks (CNN) predominated in most computer vision tasks. Now, both ...CNN and ViT are efficient approaches that demonstrate powerful capabilities to restore a better version of an image given in a low-quality format. In this study, the efficiency of ViT in image restoration is studied extensively. The ViT architectures are classified for every task of image restoration. Seven image restoration tasks are considered: Image Super-Resolution, Image Denoising, General Image Enhancement, JPEG Compression Artifact Reduction, Image Deblurring, Removing Adverse Weather Conditions, and Image Dehazing. The outcomes, the advantages, the limitations, and the possible areas for future research are detailed. Overall, it is noted that incorporating ViT in the new architectures for image restoration is becoming a rule. This is due to some advantages compared to CNN, such as better efficiency, especially when more data are fed to the network, robustness in feature extraction, and a better feature learning approach that sees better the variances and characteristics of the input. Nevertheless, some drawbacks exist, such as the need for more data to show the benefits of ViT over CNN, the increased computational cost due to the complexity of the self-attention block, a more challenging training process, and the lack of interpretability. These drawbacks represent the future research direction that should be targeted to increase the efficiency of ViT in the image restoration domain.
Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) have emerged as transforming technologies, bringing the potential to revolutionize a wide range of industries such as environmental ...monitoring, agriculture, manufacturing, smart health, home automation, wildlife monitoring, and surveillance. Population expansion, changes in the climate, and resource constraints all offer problems to modern IoT applications. To solve these issues, the integration of Wireless Sensor Networks (WSNs) and the Internet of Things (IoT) has come forth as a game-changing solution. For example, in agricultural environment, IoT-based WSN has been utilized to monitor yield conditions and automate agriculture precision through different sensors. These sensors are used in agriculture environments to boost productivity through intelligent agricultural decisions and to collect data on crop health, soil moisture, temperature monitoring, and irrigation. However, sensors have finite and non-rechargeable batteries, and memory capabilities, which might have a negative impact on network performance. When a network is distributed over a vast area, the performance of WSN-assisted IoT suffers. As a result, building a stable and energy-efficient routing infrastructure is quite challenging in order to extend network lifetime. To address energy-related issues in scalable WSN-IoT environments for future IoT applications, this research proposes EEDC: An Energy Efficient Data Communication scheme by utilizing “Region based Hierarchical Clustering for Efficient Routing (RHCER)”—a multi-tier clustering framework for energy-aware routing decisions. The sensors deployed for IoT application data collection acquire important data and select cluster heads based on a multi-criteria decision function. Further, to ensure efficient long-distance communication along with even load distribution across all network nodes, a subdivision technique was employed in each tier of the proposed framework. The proposed routing protocol aims to provide network load balancing and convert communicating over long distances into shortened multi-hop distance communications, hence enhancing network lifetime.The performance of EEDC is compared to that of some existing energy-efficient protocols for various parameters. The simulation results show that the suggested methodology reduces energy usage by almost 31% in sensor nodes and provides almost 38% improved packet drop ratio.
In applications of the Internet of Things (IoT), where many devices are connected for a specific purpose, data is continuously collected, communicated, processed, and stored between the nodes. ...However, all connected nodes have strict constraints, such as battery usage, communication throughput, processing power, processing business, and storage limitations. The high number of constraints and nodes makes the standard methods to regulate them useless. Hence, using machine learning approaches to manage them better is attractive. In this study, a new framework for data management of IoT applications is designed and implemented. The framework is called MLADCF (Machine Learning Analytics-based Data Classification Framework). It is a two-stage framework that combines a regression model and a Hybrid Resource Constrained KNN (HRCKNN). It learns from the analytics of real scenarios of the IoT application. The description of the Framework parameters, the training procedure, and the application in real scenarios are detailed. MLADCF has shown proven efficiency by testing on four different datasets compared to existing approaches. Moreover, it reduced the global energy consumption of the network, leading to an extended battery life of the connected nodes.
Computer networks face vulnerability to numerous attacks, which pose significant threats to our data security and the freedom of communication. This paper introduces a novel intrusion detection ...technique that diverges from traditional methods by leveraging Recurrent Neural Networks (RNNs) for both data preprocessing and feature extraction. The proposed process is based on the following steps: (1) training the data using RNNs, (2) extracting features from their hidden layers, and (3) applying various classification algorithms. This methodology offers significant advantages and greatly differs from existing intrusion detection practices. The effectiveness of our method is demonstrated through trials on the Network Security Laboratory (NSL) and Canadian Institute for Cybersecurity (CIC) 2017 datasets, where the application of RNNs for intrusion detection shows substantial practical implications. Specifically, we achieved accuracy scores of 99.6% with Decision Tree, Random Forest, and CatBoost classifiers on the NSL dataset, and 99.8% and 99.9%, respectively, on the CIC 2017 dataset. By reversing the conventional sequence of training data with RNNs and then extracting features before applying classification algorithms, our approach provides a major shift in intrusion detection methodologies. This modification in the pipeline underscores the benefits of utilizing RNNs for feature extraction and data preprocessing, meeting the critical need to safeguard data security and communication freedom against ever-evolving network threats.
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