Low Computational Complexity Research Articles

Temperature adaptive thermal storage/release wall based on dynamic spectral control

In response to global climate change, achieving sustainable development through energy conservation and emission reduction has become a common goal for humanity. In this work, we present a general model for computing the energy consumption and carbon emissions of building with low computational complexity and effort. Based on this model, we designed smart green building walls (SGBW), which consist of conventional walls and thermal storage/release films applied to their external/internal surfaces. These films, which incorporate thermochromic materials can adaptively adjust their radiation properties according to environmental temperature. At high temperatures, the thermal storage film(TSF) absorbs heat utilizing a solar absorptance of 0.604 and storing the heat within the wall. Conversely, at low temperatures, the thermal release film(TRF) unidirectionally releases heat into the interior with an infrared emissivity of 0.821. The simulation results indicate that SGBW has enhanced heat storage capacity by 18.7 % and increased heat release capacity by 30.4 % compared to conventional cement walls. In addition, calculations using the general model show that each square meter of SGBW can save 417 ∼ 805 kWh of electricity and reduces CO2 emissions by 225 ∼ 477 kg over the building’s lifecycle in various climatic zones, aligning closely with results obtained from the commercial software. Thus, this model not only simplifies intricate simulation processes but also serves as a guide for designing surface devices. The SGBW is anticipated to be particularly beneficial in buildings located in regions requiring nighttime heating, contributing significantly to indoor temperature regulation while simultaneously reducing energy consumption and carbon emissions.

Distributed dynamic scheduling algorithm of target coverage for wireless sensor networks with hybrid energy harvesting system

The integration of energy harvesting techniques has the potential to significantly prolong target monitoring in wireless sensor networks (WSNs). However, the stochastic nature of hybrid solar-wind energy arrivals poses a significant challenge to optimizing energy utilization for target coverage. To address this issue, we propose a dynamic and distributed node scheduling algorithm based on Lyapunov optimization for hybrid energy-harvesting WSNs (HEH-WSNs). By formulating the maximum long-term average coverage utility subject to peak power constraints, we utilize Lyapunov optimization theory to develop a dynamic potential game framework for target coverage optimization in HEH-WSNs. The proposed distributed dynamic target-coverage node scheduling algorithm (DTNSA) is then derived from the potential game. We present a comprehensive performance analysis of the distributed implementation and evaluate its efficiency through extensive simulations. The results demonstrate that in two distinct scenarios, specifically with different numbers of sensor nodes and target nodes, the average coverage utility of our proposed DTNSA exceeds that of existing algorithms by 10.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$10.5\\%$$\\end{document} and 11.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$11.2\\%$$\\end{document}, respectively. The performance of the average number of active sensor nodes decreased by 13.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$13.2\\%$$\\end{document} and 16.4%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$16.4\\%$$\\end{document} compared to existing algorithms, while the average coverage redundancy decreased by 23.2%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$23.2\\%$$\\end{document} and 21.6%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$21.6\\%$$\\end{document} relative to existing algorithms. Furthermore, our algorithm adapts effectively to dynamic changes in hybrid harvested energy and exhibits lower computational complexity compared to existing target coverage algorithms.

Fast Identification of Critical Nodes in Complex Network Based on Improved Greedy Algorithm

Abstract Over the past decades, many critical and complex systems, such as power grid, transportation network, and information network, have been effectively modeled using complex network. However, these networks are susceptible to cascading failure, triggered by minor failure, leading to partial or total collapse. Preventing cascading failure necessitates the protection of critical nodes within the network, making the identification of these nodes particularly crucial. In this paper, we introduce an Improved Greedy Algorithm (IGA), inspired by the traditional greedy algorithm and the relationship between the propagation mechanism of cascading failure and N-K failure. This algorithm gets rid of the shortcomings of traditional recognition algorithms for dealing with large-scale networks with long time and low accuracy, and evaluates the critical degree of nodes based on network connectivity and overload rate. The simulation is carried out in Barabsi-Albert (BA) network and IEEE 39-, 118-bus systems, and make comparisons with other different algorithms. The results show that IGA not only has low computational complexity, but also has high accuracy in identifying critical nodes in complex networks.

MFMamba: A Mamba-Based Multi-Modal Fusion Network for Semantic Segmentation of Remote Sensing Images.

Semantic segmentation of remote sensing images is a fundamental task in computer vision, holding substantial relevance in applications such as land cover surveys, environmental protection, and urban building planning. In recent years, multi-modal fusion-based models have garnered considerable attention, exhibiting superior segmentation performance when compared with traditional single-modal techniques. Nonetheless, the majority of these multi-modal models, which rely on Convolutional Neural Networks (CNNs) or Vision Transformers (ViTs) for feature fusion, face limitations in terms of remote modeling capabilities or computational complexity. This paper presents a novel Mamba-based multi-modal fusion network called MFMamba for semantic segmentation of remote sensing images. Specifically, the network employs a dual-branch encoding structure, consisting of a CNN-based main encoder for extracting local features from high-resolution remote sensing images (HRRSIs) and of a Mamba-based auxiliary encoder for capturing global features on its corresponding digital surface model (DSM). To capitalize on the distinct attributes of the multi-modal remote sensing data from both branches, a feature fusion block (FFB) is designed to synergistically enhance and integrate the features extracted from the dual-branch structure at each stage. Extensive experiments on the Vaihingen and the Potsdam datasets have verified the effectiveness and superiority of MFMamba in semantic segmentation of remote sensing images. Compared with state-of-the-art methods, MFMamba achieves higher overall accuracy (OA) and a higher mean F1 score (mF1) and mean intersection over union (mIoU), while maintaining low computational complexity.

Multi-Head Attention Affinity Diversity Sharing Network for Facial Expression Recognition

Facial expressions exhibit inherent similarities, variability, and complexity. In real-world scenarios, challenges such as partial occlusions, illumination changes, and individual differences further complicate the task of facial expression recognition (FER). To further improve the accuracy of FER, a Multi-head Attention Affinity and Diversity Sharing Network (MAADS) is proposed in this paper. MAADS comprises a Feature Discrimination Network (FDN), an Attention Distraction Network (ADN), and a Shared Fusion Network (SFN). To be specific, FDN first integrates attention weights into the objective function to capture the most discriminative features by using the proposed sparse affinity loss. Then, ADN employs multiple parallel attention networks to maximize diversity within spatial attention units and channel attention units, which guides the network to focus on distinct, non-overlapping facial regions. Finally, SFN deconstructs facial features into generic parts and unique parts, which allows the network to learn the distinctions between these features without having to relearn complete features from scratch. To validate the effectiveness of the proposed method, extensive experiments were conducted on several widely used in-the-wild datasets including RAF-DB, AffectNet-7, AffectNet-8, FERPlus, and SFEW. MAADS achieves the accuracy of 92.93%, 67.14%, 64.55%, 91.58%, and 62.41% on these datasets, respectively. The experimental results indicate that MAADS not only outperforms current state-of-the-art methods in recognition accuracy but also has a relatively low computational complexity.

Adaptive MSSRCPF filtering based on constrained optimization and fading memory for SINS/GNSS integrated navigation

To address particle degeneracy and the challenge of selecting importance density functions in traditional particle filters for complex nonlinear systems, we propose an adaptive mixed-order spherical simplex-radial cubature particle filter algorithm based on constrained optimization and fading memory. This algorithm integrates constrained optimization, an adaptive fading memory strategy, adaptive adjustment of particle numbers and weights, and the advantages of mixed-order spherical simplex-radial cubature Kalman filtering. By designing the importance density function using a mixed-order integration method, the algorithm significantly improves filtering accuracy over traditional cubature Kalman filters while maintaining lower computational complexity than high-order cubature Kalman filter methods. The adaptive fading memory strategy dynamically adjusts the covariance matrix, enhancing sensitivity to current measurement information and reducing the influence of historical data. By dynamically adjusting the noise covariance matrix and constraining the ratio between the error covariance and measurement noise covariance, the algorithm improves the convergence speed and accuracy of state estimation. An adaptive particle number and weight adjustment strategy based on effective sample size and entropy regularization dynamically adjusts the number of particles to reduce computational complexity while ensuring filtering accuracy, and employs entropy regularization to suppress weight over-concentration, thereby reducing the impact of outliers on the filtering results. Simulation results demonstrate that in complex SINS/GNSS integrated navigation systems, especially under high-noise and nonlinear conditions, the proposed algorithm significantly improves positioning accuracy compared to the CPF method, with the maximum latitude error reduced by approximately 54.8%, the maximum longitude error reduced by approximately 40.5%, and the maximum altitude error reduced by approximately 68.3%. Compared to the FMCPF method, the proposed algorithm also shows clear advantages in positioning accuracy, convergence speed, and computational efficiency, validating its effectiveness and practicality in complex environments.

A Study of Transmission Point Selection for Multi-Connectivity in Multi-Band Wireless Networks

In 5th generation mobile communication networks, the frequency band of the millimeter band of 30–100 GHz is supported to provide a transmission speed of 20 Gbps or higher. Furthermore, a huge transmission capacity, i.e., up to 1 Tbps, is one of the main requirements for the 6th generation mobile communication networks. In order to meet this requirement, the terahertz band is considered a new service band. Hence, we consider multi-band network environments, serving the sub-6Hz band, mmWave band, and additionally sub-terahertz band. Furthermore, we introduce the transmission point selection criteria with multiple connections for efficient multi-connectivity operation in a multi-band network environment, serving and receiving multiple connections from those bands at the same time. We also propose a point selection algorithm based on the selection criteria, e.g., achievable data rate. The proposed point selection algorithm attains lower computational complexity with a 2-approximation of the optimal solution. Our simulation results, applying the channel environment and beamforming in practical environments defined by 3GPP, show that selecting and serving multiple transmission points regardless of frequency band performs better than services through single-connectivity operation.

Detecting muscle fatigue among community-dwelling senior adults with shape features of the probability density function of sEMG

BackgroundPhysical exercise is an important method for both the physical and mental health of the senior population. However, excessive exertion can lead to increased risks of falls, severe injuries, and diminished quality of life. Therefore, simple and effective methods for fatigue monitoring during exercise are highly desirable, particularly in community settings. The purpose of this study was to explore the possibility of real-time detection of exercise-induced fatigue using surface Electromyogram (sEMG) features, including the kurtosis and skewness of the Probability Density Function (PDF) in the community settings to solve the issues of low sensitivity and high computational complexity of commonly used sEMG features.MethodssEMG signals from six forearm muscles were recorded during hand grip tasks at 20% maximal voluntary contraction (MVC) task-to-failure contractions from 30 healthy community-dwelling elders at their respective community centers. PDF shape features of the sEMG, namely kurtosis and skewness, were computed from 25 s of non-fatigue stable phase and 25 s of fatigue data for comparison. Statistical tests were conducted to compare and test for the significance of these features. We further proposed a novel fatigue indicator, Temporal-Mean-Kurtosis (TMK) of channel-averaged kurtosis, to detect fatigue with relatively low computational complexity and adequate sensitivity in community settings. ANOVA and post-hoc analyses were performed to examine the performance of TMK.ResultsStatistically significant differences were found between the non-fatigue period and the fatigue period for both kurtosis and skewness, with increasing values when approaching fatigue. TMK was shown to be sensitive in detecting fatigue with respect to time with lower computational complexity than the Sample Entropy.ConclusionThis study investigated PDF shape features of sEMG signals during a handgrip exercise to identify muscle fatigue in older adults in community experiments. Results revealed significant changes in kurtosis upon fatigue, indicating that PDF shape features were suitable convenient detectors of muscle fatigue in community experiments. The proposed indicator, TMK, showed potential sensitivity in tracking muscle fatigue over time in community-based settings with limited computational complexity, highlighting the promise of sEMG’s PDF features in detecting muscle fatigue among the elderly.

Stoch-IMC: A bit-parallel stochastic in-memory computing architecture based on STT-MRAM

In-memory computing (IMC) offloads parts of the computations to memory to fulfill the performance and energy demands of applications such as neuromorphic computing, machine learning, and image processing. Fortunately, the main features that stochastic computing (SC) and IMC share, which are low computation complexity and high bit-parallel computation capability, promise great potential for integrating SC and IMC. In this paper, we exploit this potential by using stochastic computation as an approximation method to present effective in-memory computations with a good trade-off among design parameters. To this end, first, commonly used stochastic arithmetic operations of applications are effectively implemented using the primitive logic gates of the IMC method. Next, the in-memory scheduling and mapping of applications are obtained efficiently by a proposed algorithm. This algorithm reduces the computation latency by enabling intra-subarray parallelism while considering the IMC method constraints. Subsequently, a bit-parallel stochastic IMC architecture, Stoch-IMC, is presented that enables bit parallelization of stochastic computations over memory subarrays/banks. To evaluate Stoch-IMC’s effectiveness, various analyses were conducted. Results show average performance improvements of 135.7X and 124.2X across applications compared to binary IMC and related in-memory SC methods, respectively. The results also demonstrate an average energy reduction of 1.5X compared to binary IMC, with limited energy overhead relative to the in-memory SC method. Furthermore, the results reveal average lifetime improvements of 4.9X and 216.3X over binary IMC and in-memory SC methods, respectively, along with high bitflip tolerance.

COMPUTATIONALLY EFFICIENT DEEP FEDERATED LEARNING WITH OPTIMIZED FEATURE SELECTION FOR IOT BOTNET DETECTION

Internet of Things (IoT) is a technology that has revolutionized various fields, offering numerous benefits, such as remote patient monitoring, enhanced energy efficiency, and automation of routine tasks in homes. However, unsecured IoT devices are susceptible to botnet-based attacks such as distributed denial of Service (DDoS). Conventional machine learning models used for detecting these attacks compromise data privacy, prompting the adoption of federated learning (FL) to improve privacy. Yet, most FL-based cyberattack detection models proposed for IoT environments do not address computational complexity to suit their deployment on resource-constrained IoT edge devices. This paper introduces an FL model with low computational complexity, designed for detecting IoT botnet attacks. The study employs feature selection and dimensionality reduction to minimize computational complexity while maintaining high accuracy. First, an extreme gradient boosting model, trained with repeated stratified k-fold cross-validation, is used to select the optimal features of the botnet dataset based on feature importance. Principal component analysis is then used to reduce the dimensionality of these features. Finally, a differentially private multi-layer perceptron is trained locally by four FL clients and aggregated through federated averaging (FedAvg) to form a global Mirai botnet attack detection model. The model achieved an accuracy, precision, recall, and F1-score of 99.93%, an area under the curve of 1.0, and 8,612 floating-point operations, contributing to 87.34% reduction in computational complexity compared to the previous work. The proposed model is well-suited for detecting botnet attacks in smart homes, smart grids, and environments where resource-constrained IoT edge devices are deployed.

Low-complexity multi-target localization via multi-BS sensing

Integrated Sensing and Communication (ISAC) is envisioned as a promising technology for Sixth-Generation (6G) wireless communications, which enables simultaneous high-rate communication and high-precision target localization. Compared to independent sensing and communication modules, dual-function ISAC could leverage the strengths of both communication and sensing in order to achieve cooperative gains. When considering the communication core network, ISAC system facilitates multiple communication devices to collaborate for networked sensing. This paper investigates such kind of cooperative ISAC systems with distributed transmitters and receivers to support non-connected and multi-target localization. Specifically, we introduce a Time of Arrival (TOA) based multi-target localization scheme, which leverages the bi-static range measurements between the transmitter, target, and receiver channels in order to achieve elliptical localization. To obtain the low-complexity localization, a two-stage search-refine localization methodology is proposed. In the first stage, we propose a Successive Greedy Grid-Search (SGGS) algorithm and a Successive-Cancellation-List Grid-Search (SCLGS) algorithm to address the Measurement-to-Target Association (MTA) problem with relatively low computational complexity. In the second stage, a linear approximation refinement algorithm is derived to facilitate high-precision localization. Simulation results are presented to validate the effectiveness and superiority of our proposed multi-target localization method.

Optimal carbon reduction and energy storage management method for new power systems considering carbon emissions

Abstract With the advancement of the national “dual carbon” strategy goals and the planning of new energy systems, the proportion of new energy connected to the power grid is rapidly increasing. Therefore, it is urgent to accelerate the planning and construction of energy storage. In this context, from the perspective of energy storage as a regulatory resource, this paper aims to build a set of energy storage state estimation and residual life prediction methods with high estimation accuracy and low computational complexity and achieve energy storage management on the energy storage management system of edge computing architecture, so as to meet the safe, reliable, lasting and efficient operation needs of the energy storage management system, while promoting wind and solar energy consumption as a means. Taking carbon emission reduction as the optimal goal, energy storage management and utilization methods are built. Through simulation experiments, it is verified that this optimal carbon reduction and energy storage management method has good experimental results.

NVM-Enhanced Machine Learning Inference in 6G Edge Computing

With the increasing popularization of smart terminals and real-time interactive applications, fast growing technical requirements push both academia and industry to look beyond 5G and conceptualize the sixth generation (6G) mobile network. Artificial intelligence (AI) with machine learning capacities at the edge is one crucial component of a 6G mobile network which makes various time-sensitive and high-stake services possible, e.g., smart security, virtual reality, self-driving vehicles. However, resource constraints, especially the memory limitation of edge servers, become major obstacles to deploying machine learning services at the edge. Fortunately, the new generation of non-volatile memory (NVM) provides new affordable memory resources that can be easily attached to existing edge servers. In this paper, we propose a novel machine learning application placement scheme using the NVM technology at the edge to reduce the end-to-end latency. Specifically, the proposed NVM-enhanced placement scheme takes into consideration the latency of various machine learning applications over NVM devices and the network. The corresponding optimization problem is exceedingly challenging, i.e., NP-hard. Therefore, we developed a novel approximation algorithm with both low computational complexity and theoretical guarantees. Experiments and extensive simulations using real-world applications highlight that our scheme provides significantly lower end-to-end latency compared with existing baselines.

Numerical analysis of a 1/2-equation model of turbulence

The recent 1/2-equation model of turbulence is a simplification of the standard Kolmogorov–Prandtl 1-equation URANS model. In tests, the 1/2-equation model produced comparable velocity statistics to a full 1-equation model with lower computational complexity. There is little progress in the numerical analysis of URANS models due to the difficulties in treating the coupling between equations and the nonlinearities in highest-order terms. The numerical analysis herein on the 1/2-equation model has independent interest and is also a first numerical analysis step to address the couplings and nonlinearities in a full 1-equation model. This report develops a complete numerical analysis of the 1/2-equation model. Stability, convergence, and error estimates are proven for a semi-discrete and fully discrete approximation. Finally, numerical tests are conducted to validate the predictions of the convergence theory.

A genetic programming Rician noise reduction and explainable deep learning model for Alzheimer's diseases severity prediction.

Degradation of magnetic resonance imaging (MRI) remains a challenging issue, with noise being a key damaging component introduced due to a variety of environmental and mechanical factors. The aim of this research work is to addresses the issue of noise reduction and to predict Alzheimer's disease detection efficiently. First, we present a genetic programming (GP) technique for reducing Rician noise in MRI images to pre-process the dataset. To effectively reduce Rician noise, this GP approach combines a Feature Extraction component, GP Optimal Expression, and an Optimum Removal Estimation component. In the second phase, we design and develop an explainable Deep Learning framework. This framework uses a local data-driven interpretation technique based on SHAP values to investigate the relationship between the neural network's estimated AD diagnosis and the input MRI images. In addition, we handle class distribution by combining an oversampling strategy with a minority approach. Several assessment metrics are used to analyze the performance of our proposed model. The proposed method is tested on a variety of medical samples, and the results are compared to those obtained using other comparable approaches. We also test and compare our model to three cutting-edge models: DenseNet169, VGGNet15, and Inceptionv3. The empirical results show that our proposed model outperforms others, particularly in handling basic structures with limited spectral features, lower computational complexity, and less overfitting. This research worked addressed Rician noise issue in MRI images and predict AD severity prediction using explainable deep learning framework.

New Proxy Signature Scheme over Elliptic Curves using Chaotic Maps Applicable during Pandemic COVID-19

Abstract: Digitalization has attracted the world to collect increasing data. Background: Proxy signature is a digital alternative for signing documents in the absence of the original signer. Methods: In this paper, we have used the mathematical methods and concepts of Chaotic maps (CMs) and elliptic curve cryptography. Results: We have proposed a new proxy signature scheme (PSS). Security of our PSS relies on "elliptic curve discrete logarithm (ECDL) and integer factorization (FAC) problems". It requires only low-complexity computation, which increases efficiency. Conclusion: It is the first PSS in such a security setting and can also be assumed to be secure in the post-quantum cryptographic world. It can be highly used digitally during thePandemic conditions like COVID-19.

Experimental study of a distributed active noise control system with multi-device nodes based on augmented diffusion strategy.

Recently, distributed active noise control (DANC) algorithms have been explored as a way to reduce computational complexity while ensuring system stability, thereby outperforming conventional centralized and decentralized algorithms. Most existing DANC algorithms assume that each node has only one pair of loudspeaker and microphone, limiting their flexibility in practical applications. In contrast, this paper proposes a DANC algorithm with general multi-device nodes based on the recently developed augmented diffusion strategy, allowing flexible and scalable ANC applications. A real-time distributed ANC system based on a multi-core digital signal processor platform is developed in order to compare the control performance of the proposed extended augmented diffusion algorithm with that of existing centralized, decentralized and augmented diffusion algorithms. Real-time experiments demonstrate that the proposed algorithm exhibits noise reduction performance consistent with that of the centralized algorithm while achieving lower global computational complexity and avoiding the system instability risk of the decentralized algorithm. Further, the new algorithm improves convergence speed and reduces the global communication cost compared to the previous augmented diffusion algorithm. Experimental results indicate the application potential of the proposed DANC algorithm for a generalized system configuration.

Acoustic and soliton propagation using fully-discrete energy preserving partially implicit scheme in homogeneous and heterogeneous mediums

This study presents an energy preserving partially implicit scheme for the simulation of wave propagation in homogeneous and heterogeneous mediums. Despite its implicit nature, the developed scheme does not require any explicit numerical or analytical inversion of the coefficient matrix. Theoretical analysis and numerical experiments are performed to validate the energy preserving properties of the fully-discrete scheme. Convergence analysis is also performed to assess the rate of convergence of the developed scheme. The efficiency and accuracy of the developed scheme are validated by numerical solutions of wave propagation in layered heterogeneous mediums. Furthermore, simulations of soliton propagation following nonlinear sine-Gordon and Klein-Gordon equations in homogeneous and heterogeneous mediums are discussed. Numerical solutions are also compared with the results available in the literature. The present method accurately resolves the physical characteristics of the chosen problems, competing well with existing multi-stage time-integration methods. Moreover, it has significantly lower computational complexity than the four-stage, fourth-order Runge-Kutta-Nyström method.

Lightweight consortium blockchain-enabled secured Vehicular ad Hoc Network using certificateless conditional privacy-preserving authentication mechanism.

Towards the intelligent transportation systems, Location Based Service (LBS) are widely engaged in Vehicular Ad Hoc Networks (VANETs) that are becoming as significant application that change the human driving experience in today's world. LBS systems facilitate the users with intelligent services by collecting an accurate location information. Due to the frequent exchange rate of the location information in an open environment, VANETs are inherently susceptible to privacy and security attacks. In past, many schemes have been proposed to ensure the privacy and security of exchanged location information; but fail to deploy in practical VANETs. At the same time, system efficiency is compromised which is another primary requirement of VANETs. Leveraging the semi-decentralized and lightweight nature of consortium blockchain technology, and Certificateless conditional privacy protection scheme to reduce the node authentication overhead, this paper introduces Consortium Blockchain assisted Certificateless Conditional Privacy Protection scheme to address the aforementioned challenges. Additionally, the proposed scheme has ability to develop anonymous regions for a particular time stamp ensuring the location privacy of vehicles. Rigorous security analysis and experiments show the practicality and resilience to various attack models, and achieve ADP 83% with maximum malicious attacks. Comparing with existing state of the art methods, the proposed scheme exhibits the privacy improvement and low computational complexity.

Barrier-free tomato fruit selection and location based on optimized semantic segmentation and obstacle perception algorithm.

With the advancement of computer vision technology, vision-based target perception has emerged as a predominant approach for harvesting robots to identify and locate fruits. However, little attention has been paid to the fact that fruits may be obscured by stems or other objects. In order to improve the vision detection ability of fruit harvesting robot, a fruit target selection and location approach considering obstacle perception was proposed. To enrich the dataset for tomato harvesting, synthetic data were generated by rendering a 3D simulated model of the tomato greenhouse environment, and automatically producing corresponding pixel-level semantic segmentation labels. An attention-based spatial-relationship feature extraction module (SFM) with lower computational complexity was designed to enhance the ability of semantic segmentation network DeepLab v3+ in accurately segmenting linear-structured obstructions such as stems and wires. An adaptive K-means clustering method was developed to distinguish individual instances of fruits. Furthermore, a barrier-free fruit selection algorithm that integrates information of obstacles and fruit instances was proposed to identify the closest and largest non-occluded fruit as the optimal picking target. The improved semantic segmentation network exhibited enhanced performance, achieving an accuracy of 96.75%. Notably, the Intersection-over-Union (IoU) of wire and stem classes was improved by 5.0% and 2.3%, respectively. Our target selection method demonstrated accurate identification of obstacle types (96.15%) and effectively excluding fruits obstructed by strongly resistant objects (86.67%). Compared to the fruit detection method without visual obstacle avoidance (Yolo v5), our approach exhibited an 18.9% increase in selection precision and a 1.3% reduction in location error. The improved semantic segmentation algorithm significantly increased the segmentation accuracy of linear-structured obstacles, and the obstacle perception algorithm effectively avoided occluded fruits. The proposed method demonstrated an appreciable ability in precisely selecting and locating barrier-free fruits within non-structural environments, especially avoiding fruits obscured by stems or wires. This approach provides a more reliable and practical solution for fruit selection and localization for harvesting robots, while also being applicable to other fruits and vegetables such as sweet peppers and kiwis.