Low Time Complexity Research Articles

Novel image pixel scrambling technique for efficient color image encryption in resource-constrained IoT devices

In the digital age, where data is a valuable commodity, securing sensitive information has become a growing concern. Image encryption techniques play an essential role in protecting visual data from unauthorized access and ensuring privacy. However, with limited computing capacity in Internet of Things (IoT) devices, standard encryption algorithms are not feasible, rendering lightweight methods mandatory. This study proposes a novel Corner Traversal algorithm, an alternative to existing pixel scrambling techniques. The proposed algorithm demonstrably outperforms its counterparts in both higher confusion and lower time complexity, making it remarkably efficient. Integrated with chaos-based diffusion methods, this algorithm forms a comprehensive encryption scheme. The proposed lightweight image encryption scheme utilizing the Corner Traversal algorithm successfully passed rigorous statistical and differential security analysis. Compared to similar schemes, the proposed encryption scheme employing the Corner Traversal algorithm in the confusion phase distinguishes itself through exceptional NPCR (99.6093 for Lenna) and UACI (33.4648 for Lenna) values. Combined with other evaluation criteria, this method demonstrably meets the stringent security requirements of IoT systems.

Intelligent System for Cognitive Study on Student Work Network Based on the Information of Public Opinion with the Background of IOT

Abstract Neurological, behavioral, and cognitive problems in teenage students are rising day by day. It is very important to handle these issues by gathering information from public opinion. The distribution of public opinion information in student work is unsystematic, and it is difficult to extract the needed information intelligently. An intelligent extraction algorithm of public opinion data from student work is proposed in this research and is based on human–computer interaction, machine learning, and computational techniques. In the student work, a fuzzy semantic autocorrelation mapping feature set of public opinion information is developed together with a spatial structural model of data semantic distribution features. The statistical feature quantity of semantic similarity of the public opinion data is retrieved from the student work, and the semantic ambiguity is decreased. Analyses using adaptive learning and machine understanding and human–computer interaction are used to process it. In the human–computer interaction machine understanding center, the processor adjusts the grid partition of public opinion information of the student work according to the difference of statistical features, then constructs the feature decomposition model of student behavior, and then performs context mapping. Finally, the semantic analysis is carried out to analyze the student behavior based on the cognitive study. The simulation outcomes demonstrate that the proposed method is computationally inexpensive, has low time complexity, and has real-time monitoring ability of public opinion information on student behavior.

Large-Scale Clustering on 100 M-Scale Datasets Using a Single T4 GPU via Recall KNN and Subgraph Segmentation

Despite the promising progress that has been made, large-scale clustering tasks still face various challenges: (i) high time and space complexity in K-nearest neighbors (KNN), which is often overlooked by most methods, and (ii) low recall rate caused by simply splitting the dataset. In this paper, we propose a novel framework for large-scale clustering tasks named large-scale clustering via recall KNN and subgraph segmentation (LS-RKSS) to perform faster clustering with guaranteed clustering performance, which embraces the ability of handling large-scale data up to 100 million using a single T4 GPU with less than 10% of the running time. We propose recall KNN (RKNN) and subgraph segmentation (SS) to effectively address the primary challenges in large-scale clustering tasks. Firstly, the recall KNN is proposed to perform efficient similarity search among dense vectors with lower time and space complexity compared to traditional exact search methods of KNN. Then, the subgraph segmentation is proposed to split the whole dataset into multiple subgraphs based on the recall KNN. Given the recall rate of RKNN based on traditional exact search methods, it is theoretically proved that dividing the dataset into multiple subgraphs using recall KNN and subgraph segmentation is a more reasonable and effective approach. Finally, clusters are generated independently on each subgraph, and the final clustering result is obtained by combining the results of all subgraphs. Extensive experiments demonstrate that LS-RKSS outperforms previous large-scale clustering methods in both effectiveness and efficiency.

Joint optimization of utility and privacy in mobile edge computing

In mainstream research on Mobile Edge Computing (MEC), considerations for user privacy are often overlooked. This paper introduces Differential Privacy techniques to MEC, which obfuscate specific parameter values when transmitting user data, preventing leakage of sensitive information. Simultaneously, privacy, latency, and energy are incorporated as components of the utility function. We model the task offloading process using a deep reinforcement learning model and propose the use of the Deep Deterministic Policy Gradient (DDPG) algorithm in conjunction with a Laplacian privacy budget for joint optimization of privacy and utility during offloading. After sufficient training, the DDPG model can effectively approximate the optimal solution. Compared to traditional work, the optimized framework gives ample consideration to user privacy needs and outputs the optimal task offloading ratio solution with relatively low time complexity. This approach effectively safeguards user privacy while also considering the efficiency of task offloading.

Traffic Classification and Packet Scheduling Strategy with Deadline Constraints for Input-Queued Switches in Time-Sensitive Networking

Deterministic transmission technology is a core key technology that supports deterministic real-time transmission requirements for industrial control in Time-Sensitive Networking (TSN). It requires each network node to have a deterministic forwarding delay to ensure the real-time end-to-end transmission of critical traffic streams. Therefore, when forwarding data frames, the switch nodes must consider the time-limited requirements of the traffic. In the input-queued switch system, an algorithm for clock-synchronized deterministic network traffic classification scheduling (CSDN-TCS) is proposed to address the issue of whether a higher-quality-of-service (QoS) performance can be provided under packet deadline constraints. First, the scheduling problem of the switch is transformed into a decomposition problem of the traffic matrix. Secondly, the maximum weight-matching algorithm in graph theory is used to solve the matching results slot by slot. By fully utilizing the slot resources, as many packets as possible can be scheduled to be completed before the deadline arrives. For two types of packet scheduling problems, this paper uses the maximum flow algorithm with upper- and lower-bound constraints to move packets from a larger deadline set to idle slots in a smaller deadline set, enabling early transmission, reducing the average packet delay, and increasing system throughput. When there are three or more types of deadlines in the scheduling set, this scheduling problem is an NP-hard problem. We solve this problem by polling the two types of scheduling algorithms. In this paper, simulation experiments based on the switching size and line load are designed, and the Earliest Deadline First (EDF) algorithm and the Flow-Based Iterative Packet Scheduling (FIPS) algorithm are compared with the CSDN-TCS algorithm. The simulation results show that under the same conditions, the CSDN-TCS algorithm proposed in this paper outperforms the other two algorithms in terms of success rate, packet loss rate, average delay and throughput rate. Compared with the FIPS algorithm, the CSDN-TCS algorithm has lower time complexity under the same QoS performance.

Comparative analysis of various models for image classification on Cifar-100 dataset

Nowadays, people developed various convolutional neural network (CNN) based models for computer vision. Some famous models, such as GoogLeNet, Residual Network (ResNet), Visual Geometry Group (VGG), and You Only Look Once (YOLO), have different architecture and performances. Determining which model to use may be a troublesome problem for those just starting to study image classification. To solve this problem, we introduce the GoogLeNet, ResNet-18, and VGG-16 models, comparing their architecture, features, and performance. Then we give our suggestions based on the test results to help beginners choose a suitable model. We conducted experiments to train and test GoogLeNet, ResNet-18, and VGG-16 on the Cifar-100 datasets with the same hyperparameters. Based on the test results (test accuracy, average test loss, training loss), we analyze the figures for trends, key points, increase rate, and other features. Then we combine the architecture of each model to make our conclusions. The experimental results show that ResNet-18 can be a good choice when training the model with the Cifar-100 datasets because it performs well after training and has a low time complexity. ResNet-18 also has the fastest convergence speed. GoogLeNet would be the second choice because it functions similarly to ResNet-18 and is even better. However, training GoogLeNet is a time-consuming task. VGG is not recommended in this experiment because it has the worst performance and similar training complexity compared with ResNet-18.

Waveform design through the trade-off relationship between the MI criterion and the SINR criterion

Aiming at the diversity requirements of cognitive radar monitoring tasks, a joint optimization design criterion that comprehensively considers the mutual information (MI) and signal-to-interference-to-noise ratio (SINR) between the target and the echo is proposed. In view of the challenges brought by the traditional water-filling algorithm, this paper further studies how to effectively solve the new optimization criteria to improve the overall performance of the system. Specifically, this paper proposes a PCMA-ES algorithm that combines an adaptive penalty function with the Covariance Matrix Adaptive Evolutionary Strategy (CMA-ES) algorithm. The penalty function aims to prioritize feasible solutions by assigning them the highest fitness. For infeasible solutions with lower constraint violations, the fitness is slightly lower, allowing for better utilization of information from infeasible solutions. The simulation results show that the PCMA-ES algorithm has lower time complexity or better performance than the traditional water-filling algorithm, and can solve more complex transmission waveforms. In addition, the waveform designed with a joint optimization criterion outperforms that based on a single optimization criterion. The radar detection focus can be adjusted to meet the specific requirements of diverse detection tasks.

Efficient Improved Local Time Stepping with the Leapfrog Scheme for Transient 3-D Electromagnetic Analyses

An alternate boundary local time stepping (ABLTS) method is proposed for the discontinuous Galerkin time domain method for transient electromagnetic simulations to reduce the computation complexity of the local time stepping (LTS) method. The proposed method exhibites lower storage and time complexity than the conventional LF-LTS method . The stability, accuracy, and effectiveness of the ABLTS method are verified by applying it to the simulation of a resonator cavity and multi-layer microstrip antenna. The numerical results revealed that the developed method is effective for the transient electromagnetic simulation of antennas.

Efficiency Improvisation of Large-Scale Knowledge Systems in Feature Determination using Proposed HVGAN Architecture

Knowledge Management (KM) is crucial for efficient information retrieval and forms the backbone of implementing an Information System (IS). Efficiently extracting meaningful trends from headlines aids in visualising demographic situations and addressing emergency handling needs. Understanding Information Resource Management (IRM) and Knowledge Resource Management (KRM) concepts benefits end users. This proposed KRM concept organises knowledge, ensuring global resource control through a neural decision framework. Traditional methods like Structural Equation Modelling (SEM) have higher computational steps, whereas neural network strategies in KM reduce steps and improve data prediction accuracy. The paucity of input data is a common challenge, which can be overcome by using Generative Adversarial Network (GAN) architecture for probabilistic generative processes. This research evaluates acquisition, spreading, vulnerability, and application using GAN-based knowledge systems that mitigate data scarcity through generative methods. The proposed Hierarchical Vanilla Generative Adversarial Network (HVGAN) utilises gradient functions and hierarchical computation algorithms to achieve desired knowledge extraction with lower time complexity and higher accuracy. Implementation improves efficiency in accessing stored knowledge, yielding an AUC score of 96% compared to other GAN architectures.

Lightweight Transformer Model for Mobile Application Classification.

Recently, realistic services like virtual reality and augmented reality have gained popularity. These realistic services require deterministic transmission with end-to-end low latency and high reliability for practical applications. However, for these real-time services to be deterministic, the network core should provide the requisite level of network. To deliver differentiated services to each real-time service, network service providers can classify applications based on traffic. However, due to the presence of personal information in headers, application classification based on encrypted application data is necessary. Initially, we collected application traffic from four well-known applications and preprocessed this data to extract encrypted application data and convert it into model input. We proposed a lightweight transformer model consisting of an encoder, a global average pooling layer, and a dense layer to categorize applications based on the encrypted payload in a packet. To enhance the performance of the proposed model, we determined hyperparameters using several performance evaluations. We evaluated performance with 1D-CNN and ET-BERT. The proposed transformer model demonstrated good performance in the performance evaluation, with a classification accuracy and F1 score of 96% and 95%, respectively. The time complexity of the proposed transformer model was higher than that of 1D-CNN but performed better in application classification. The proposed transformer model had lower time complexity and higher classification performance than ET-BERT.

MieWC: Medical image encryption using wavelet transform and multiple chaotic maps

AbstractRecently, digital images have been widely used in several applications through advanced wearable devices and networks. Despite several benefits of digital images, such as easy distribution, storage, and reproduction, it is difficult to prevent the issue of identity theft, privacy leakage, and ownership conflicts. We present a wavelet‐based encryption technique, MieWC, to solve the above issues using multiple chaotic keys. This work uses multiple keys for the encryption process, which ensures high security and authenticity. Lorenz and logistic maps are used for chaotic key generation for diffusion and confusion processes. Further, the technique is tested for two different wavelet transforms, including integer wavelet transform (IWT) and discrete wavelet transform, with IWT giving better results. Hence, it is being used for further analysis. The proposed technique is easy to implement and provides high security with improved results. It has low time complexity and high key bit sensitivity. The result analysis further ensures resistance against crop attacks.

Cryptanalysis of an image encryption scheme based on two-point diffusion strategy and Henon map

In recent year, an image encryption scheme was proposed based on Henon map and two-point diffusion strategy. Two-point diffusion method improved the conventional permutation–diffusion architecture by allowing two cryptographic primitives, i.e. permutation and diffusion intermingling with each other in every single round. In this paper, we show the dynamical degradation of digital Henon map using state-mapping network. By comparing to the exhaustive key search, applying algebraic cryptanalysis and chosen plaintext attack require lower time complexity of computation. The recovery of equivalent keys also reduces the possible numbers of secret keys. The keystream recovery process has been verified using simulations. We also show that the two-point diffusion strategy does not improve the efficiency of the image encryption scheme and some improvement suggestions using parallel computing are given. As a nutshell, extra care needs to be taken when selecting chaotic map and assessing the security of future proposals based on the two-point diffusion strategy schemes.

Revised eight-step feasibility checking procedure with linear time complexity for the Dial-a-Ride Problem (DARP)

In the context of Dial-a-Ride Problems (DARPs), the eight-step feasibility checking procedure is one of the most commonly used methods. The time complexity of this procedure is considered as O(n2) in the literature. In this study, a version of this procedure is proposed with O(nlogQ) time complexity. Since, in DARPs, the capacity of each vehicle is a small constant, the method introduced in this paper performs as a linear-time technique in this context. The revised scheme is just as elegant as the original one, which makes this improvement in the time complexity more noteworthy. Our proposed technique has a lower time complexity and/or is noticeably easier to comprehend and implement than the other feasibility checking methods in the literature. Considering the standard benchmark instances, experimental results show that the revised method outperforms the classic one in terms of the computational time. Moreover, it is demonstrated that the execution time of our algorithm is competitive with that of the only technique with linear time complexity in the literature.

Pre-Coded Inter-Satellite Routing Algorithm with Load Balancing for Mega-Constellation Networks

In order to achieve global multiple seamless coverage, space-based internet usually adopts low Earth orbit (LEO) mega-constellation networks structure, which has the characteristics of high network topology dynamics, limited on-board computing and storage capacity, and uneven distribution of ground traffic. Such features may cause problems such as high transmission delay, network congestion and link interruption. Establishing a stable, efficient and balanced satellite communication link can effectively alleviate the performance of the transmission delay, load balancing, and network throughput. Taking advantage of the regularity of network topology, a pre-coded inter-satellite routing algorithm with load balancing is proposed, which includes 3 parts: (a) the routing sequence coding method and the concept of gateway satellite Service Region (GSSR) are proposed; (b) the initial routing sequence of GSSR is generated based on the maximum network flow method under the ideal situation of uniform satellite traffic distribution; (c) aiming at the uneven distribution of traffic, the Sinkhorn algorithm is used to improve the load balancing performance of inter-satellite links. Simulation results show that, for the Starlink Group-4 constellation, the proposed method can maintain a low transmission delay and improve the load balancing together with the network throughout performance with minimal hops and low time complexity.

Forward feature selection: empirical analysis

Feature selection is an important preprocessing step in many data science and machine learning applications. Although there exist several sophisticated feature selection algorithms, their benefits are sometimes overshadowed by their complexity and slow execution. Therefore, in many cases, a more simple algorithm may be better suited. In this paper, we demonstrate that a rudimentary forward selection algorithm can achieve optimal performance with a low time complexity. Our study is based on an extensive empirical evaluation of the forward feature selection algorithm in the context of linear regression. Concretely, we compare the forward selection algorithm against the gold standard exhaustive search algorithm based on several datasets. The results show that the forward selection algorithm achieves high performance with relatively fast execution. Given the simplicity, accuracy, and speed of the forward feature selection algorithm, we recommend it as a primary feature selection method for most regression applications. Our results are particularly pertinent in the case of big data and real-time analysis.

An enhanced A* method incorporating an encrypted memory database for ASV efficient local path planning.

For the autonomous surface vehicle (ASV) planning problem, an enhanced A* method incorporating encrypted memory database for ASV efficient local path planning is proposed. Considering the current various path planning problems mostly use methods with high time complexity, such as neural networks, we select the A* algorithm with low time complexity as the basis. To speed up the path planning rate and further improve the real-time and realistic algorithm, this paper modifies the heuristic function of the A* algorithm by combining the motion mode of ASV. In response to the problem that the target point is far from the detection, we improve the target point design mechanism and create a new temporary target point within the detection range. In addition, the algorithm incorporates a memory database, which can record commonly used waters or retain the environmental path of navigated waters as a priori information. When the same waters are reencountered, the memory database information can be read directly to complete the navigation. Moreover, the memory database is encrypted to prevent information leakage. Finally, a simulation environment is built to verify the effectiveness of the proposed algorithm by comparison with some existing algorithms.

Low Complexity Time Reversal Imaging Methods Based on Truncated Time Reversal Operator

Low Complexity Time Reversal Imaging Methods Based on Truncated Time Reversal Operator

트랜스포머 기반의 다중 시점 3차원 인체자세추정

The technology of Three-dimensional human posture estimation is used in sports, motion recognition, and special effects of video media. Among various methods for this, multi-view 3D human pose estimation is essential for precise estimation even in complex real-world environments. But Existing models for multi-view 3D human posture estimation have the disadvantage of high order of time complexity as they use 3D feature maps. This paper proposes a method to extend an existing monocular viewpoint multi-frame model based on Transformer with lower time complexity to 3D human posture estimation for multi-viewpoints. To expand to multi-viewpoints our proposed method first generates an 8-dimensional joint coordinate that connects 2-dimensional joint coordinates for 17 joints at 4-vieiwpoints acquired using the 2-dimensional human posture detector, CPN(Cascaded Pyramid Network). This paper then converts them into 17×32 data with patch embedding, and enters the data into a transformer model, finally. Consequently, the MLP(Multi-Layer Perceptron) block that outputs the 3D-human posture simultaneously updates the 3D human posture estimation for 4-viewpoints at every iteration. Compared to Zheng[5]'s method the number of model parameters of the proposed method was 48.9%, MPJPE(Mean Per Joint Position Error) was reduced by 20.6 mm (43.8%) and the average learning time per epoch was more than 20 times faster.

The Impact of LiDAR Configuration on Goal-Based Navigation within a Deep Reinforcement Learning Framework.

Over the years, deep reinforcement learning (DRL) has shown great potential in mapless autonomous robot navigation and path planning. These DRL methods rely on robots equipped with different light detection and range (LiDAR) sensors with a wide field of view (FOV) configuration to perceive their environment. These types of LiDAR sensors are expensive and are not suitable for small-scale applications. In this paper, we address the performance effect of the LiDAR sensor configuration in DRL models. Our focus is on avoiding static obstacles ahead. We propose a novel approach that determines an initial FOV by calculating an angle of view using the sensor's width and the minimum safe distance required between the robot and the obstacle. The beams returned within the FOV, the robot's velocities, the robot's orientation to the goal point, and the distance to the goal point are used as the input state to generate new velocity values as the output action of the DRL. The cost function of collision avoidance and path planning is defined as the reward of the DRL model. To verify the performance of the proposed method, we adjusted the proposed FOV by ±10° giving a narrower and wider FOV. These new FOVs are trained to obtain collision avoidance and path planning DRL models to validate the proposed method. Our experimental setup shows that the LiDAR configuration with the computed angle of view as its FOV performs best with a success rate of 98% and a lower time complexity of 0.25 m/s. Additionally, using a Husky Robot, we demonstrate the model's good performance and applicability in the real world.

Optimization of RNN-LSTM Model Using NSGA-II Algorithm for IOT-based Fire Detection Framework

The occurrence of fire leads to unparalleled loss of resources as well as human life and hence, fire detection systems must be trustworthy and less erroneous. Real-time assessment of fire conditions through predictive learning models could lead to easier decision-making and timely rescue operations. Reported works are often restricted to the use of singular rule-based algorithms which can hardly offer a comprehensive solution by adapting to changing dynamics of fire conditions due to their static features, mostly leading to inaccurate classification. In this research, an efficient fire prediction framework has been proposed by efficiently combining the outputs of recurrent neural networks which bear the advantages of short-term predictions with long short-term memory that takes care of long-term predictions. Weights to combine multi-objective optimization functions to minimize mean absolute error and root mean square error have been designed using non-dominated sorting genetic algorithm II (NSGA-II) which offers a lower time complexity. The benchmark dataset from the NIST website has been chosen for analysis and performance validation of the proposed classifier on experimental data pertaining to different fire scenarios. A Pareto optimal front has been obtained from the proposed algorithm which represents the optimum solutions. The performance of the proposed model has been exhaustively evaluated through different factors such as accuracy, RMSE, MAE, F-Measure, binary classification rate, negative predictive value, recall and precision which justifies its contribution. The proposed model reduced RMSE by 14.95–19.88% compared to baseline machine learning models along with an enhanced accuracy of 95.05% and reduced false positive rate which is better compared to reported works along with improvement in F-Measure. Results show that the proposed NSGA-II-based RNN LSTM model accurately predicts the occurrence of fire events with reduced false alarms while maintaining a low computational overhead.