Adaptive Clustering Research Articles

An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

An Adaptive Clustering Algorithm Based on Local-Density Peaks for Imbalanced Data Without Parameters

ECO-LEACH: A Blockchain-Based Distributed Routing Protocol for Energy-Efficient Wireless Sensor Networks

This paper proposes a novel architecture based on blockchain technology to enhance the dependability and safety of wireless sensor networks (WSN) by authenticating WSN nodes. In a WSN, sensor nodes collect and transmit data to cluster heads (CHs) for further processing. The proposed model employs the distance and residual energy-based low-energy adaptive clustering hierarchy (ECO-LEACH) protocol to replace CHs with ordinary nodes and the Interplanetary File System (IPFS) for storing data. In addition, consensus based on proof of authority (PoA) is used to validate transactions, reducing the computational cost associated with proof of work. The proposed system was evaluated using simulations with 300 sensor nodes and compared with other protocols, including LEACH, DDR-LEACH, PEGASIS, and LEACH-PSO. The simulation results showed that the proposed ECO-LEACH outperformed the other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement. Specifically, the proposed system consumed 23.5J for 300 sensor nodes, achieved 687.5 kbps, and improved the network's lifetime by 4.12 seconds for 50 rounds. Overall, this paper provides a reliable and secure solution for authenticating WSN nodes, enhancing data transfer safety, and dependability. The proposed architecture offers a promising approach for addressing the challenges of WSN design using blockchain technology and PoA consensus. The comparative analysis shows that the proposed ECO-LEACH protocol outperforms other protocols in terms of energy consumption, throughput achieved, and network lifetime improvement for 300 sensor nodes

Invertible linear transforms based adaptive multi-view subspace clustering

Constructing tensor with low-rank prior is the crucial issue of tensor based multi-view subspace clustering methods, but there are still some shortcomings. First, they cannot adaptively allocate the contribution of different views, resulting in the learned tensor being suboptimal. Second, they pursue low-rank tensor for different data through Discrete Fourier Transform based tensor nuclear norm, which lacks generality. To overcome these problems, we propose an invertible linear transforms based adaptive multi-view subspace clustering method, named ILTMSC. Firstly, we mine the potential low-order representation of each view through self-representation subspace learning. Then, we capture high-order representation by integrating low-order representations with adaptive weights into a tensor and then rotated. This strategy can integrate tensor adaptively and handle the noise effectively. Finally, we approximate the low-rank tensor with a recently proposed invertible linear transforms based tensor nuclear norm. Such a new tensor nuclear norm makes our model more general because it can use different invertible linear transforms for different tensor data. Moreover, an adaptive weighted tensor singular value thresholding operator is proposed for capturing the new tensor nuclear norm. Our model could be solved by convex optimization efficiently. Extensive experiments on multi-view datasets validate the effectiveness and robustness of our method.

The Switch to Online Learning during the COVID-19 Pandemic: The Interplay between Personality and Mental Health on University Students

The switching from traditional to online learning during the COVID-19 pandemic was challenging for students, determining an increase in physical and mental health problems. The current paper applied a two-step cluster analysis in a large sample of n = 1028 university students (Mage = 21.10 years, SD = 2.45 years; range: 18-30 years; 78.4% females). Participants responded to an online survey exploring neuroticism, trait/state anxiety, general self-efficacy, academic motivation, fear of COVID-19, the impact of the COVID-19 pandemic on physical and mental health, and the help requests. Results showed two significant clusters of students having a Maladaptive Academic Profile (n = 456; 44.4%) or an Adaptive Academic Profile (n = 572; 55.6%). Significant differences were found between the two clusters, where students belonging to the Maladaptive Academic Profile reported higher levels of neuroticism, higher dispositional and situational anxiety, and fear of COVID-19, and lower self-efficacy and academic motivation than students of the Adaptive Academic Profile cluster. In addition, more physical or mental health problems and help requests, mainly to partners during the COVID-19 pandemic, were found in the Maladaptive Academic Profile cluster compared to the Adaptive Academic Profile. Finally, the practical implications of the study's results in implementing university counseling services as protective measures to contrast psychological distress in the long-term COVID-19 pandemic are discussed.

Fast retrieval and efficient identification of monument images using features based adaptive clustering and optimized deep belief network

ABSTRACT Content-Based Image Retrieval (CBIR) is an extensively used image processing technique for retrieving similar images (general, images, monument images, etc) from large datasets. Some existing approaches' accuracy and retrieval time are ineffective for monument image retrieval. To overcome these challenges, the monument images are identified and retrieved efficiently using the visual feature extraction and adaptive density-based clustering approach (ADCA). Features required for efficient retrieval is obtained and the obtained multiple features are fused into a single feature using the hybrid weighted and average weighted methods. An optimized Deep belief network (ODBN) is designed to label the clusters based on the grouped images and then retrieve the images relevant to query images. From the experimental results, the overall accuracy achieved by the proposed framework are 99.63% (Architectural heritage dataset), 98.88% (synthetic dataset) and 99.1% (Kaggle dataset), respectively. The experimental results proved the efficiency of proposed in retrieving monument images.

Energy-Efficient LoRa Routing for Smart Grids.

Energy-efficient routing protocols in Internet of Things (IoT) applications are always of colossal importance as they improve the network's longevity. The smart grid (SG) application of the IoT uses advanced metring infrastructure (AMI) to read and record power consumption periodically or on demand. The AMI sensor nodes in a smart grid network sense, process, and transmit information, which require energy, which is a limited resource and is an important parameter required to maintain the network for a longer duration. The present work discusses a novel energy-efficient routing criterion in an SG environment realised using LoRa nodes. Firstly, a modified LEACH protocol-cumulative low-energy adaptive clustering hierarchy (Cum_LEACH) is proposed for cluster head selection among the nodes. It uses the cumulative energy distribution of the nodes to select the cluster head. Furthermore, for test packet transmission, multiple optimal paths are created using the quadratic kernelised African-buffalo-optimisation-based LOADng (qAB_LOADng) algorithm. The best optimal path is selected from these multiple paths using a modified version of the MAX algorithm called the SMAx algorithm. This routing criterion showed an improved energy consumption profile of the nodes and the number of active nodes after running for 5000 iterations compared to standard routing protocols such as LEACH, SEP, and DEEC.

Energy efficient clustering with Heuristic optimization based Ro/uting protocol for VANETs

Vehicle networks have been the subject of increasing amounts of study from both academia and industry as a way of enhancing traffic safety and providing real-time data to motorists and passengers. But beyond that, the need for a safe and secure network in moving vehicles is what's pushing the spread of VANETs. Intelligent, on-the-road vehicles that can exchange data with one another and with fixed roadside infrastructure make up what are known as ad hoc vehicular networks. It's anticipated that it will soon provide a wide variety of exciting new services. There are a lot of distinguishing features of ad hoc networks in autos, such as their adaptability to a wide range of situations. Vehicular Ad hoc Networks (VANET) use a network of moving automobiles as nodes to create a wireless network. Clustering and routing can be thought of as a multi-objective minimization issue that can be handled with metaheuristic optimization methods. With this in view, this work proposes the ANFC-QGSOR protocol for VANET, which combines adaptive neural fuzzy clustering (ANFC) and quantum glowworm swarm optimization-based routing (QGSOR). The presented ANFC-QGSOR technique initially allows the vehicles to communicate with one another. For effective cluster head (CH) selection and cluster assembly, the ANFC technique is used with three input parameters: residual energy, distance, and node degree. In addition, by developing a fitness function, the QGSOR approach is used to select the best routes to the destination. Network Simulator is used to simulate the proposed ANFC-QGSOR method (NS3 tool). The experimental results demonstrated that the ANFC-QGSOR technique surpassed earlier state-of-the-art technologies in a variety of evaluation variables.

Adaptive type2-possibilistic C-means clustering and its application to microarray datasets

Adaptive type2-possibilistic C-means clustering and its application to microarray datasets

An Energy-Efficient Data Aggregation Clustering Algorithm for Wireless Sensor Networks Using Hybrid PSO

Extending the lifetime of wireless sensor networks (WSNs) and minimizing energy costs are the two most significant concerns for data transmission. Sensor nodes are powered by their own battery capacity, allowing them to perform critical tasks and interact with other nodes. The quantity of electricity saved from each sensor together in a WSN has been strongly linked to the network’s longevity. Clustering conserves the most power in wireless transmission, but the absence of a mechanism for selecting the most suitable cluster head (CH) node increases the complexity of data collection and the power usage of the sensor nodes. Additionally, the disparity in energy consumption can lead to the premature demise of nodes, reducing the network’s lifetime. Metaheuristics are used to solve non-deterministic polynomial (NP) lossy clustering problems. The primary purpose of this research is to enhance the energy efficiency and network endurance of WSNs. To address this issue, this work proposes a solution where hybrid particle swarm optimization (HPSO) is paired with improved low-energy adaptive clustering hierarchy (HPSO-ILEACH) for CH selection in cases of data aggregation in order to increase energy efficiency and maximize the network stability of the WSN. In this approach, HPSO determines the CH, the distance between the cluster’s member nodes, and the residual energy of the nodes. Then, ILEACH is used to minimize energy expenditure during the clustering process by adjusting the CH. Finally, the HPSO-ILEACH algorithm was successfully implemented for aggregating data and saving energy, and its performance was compared with three other algorithms: low energy-adaptive clustering hierarchy (LEACH), improved low energy adaptive clustering hierarchy (ILEACH), and enhanced PSO-LEACH (ESO-LEACH). The results of the simulation studies show that HPSO-ILEACH increased the network lifetime, with an average of 55% of nodes staying alive, while reducing energy consumption average by 28% compared to the other mentioned techniques.

Performance Analysis of Adaptive RIS-Assisted Clustering Strategies in Downlink Communication Systems

Reconfigurable intelligent surfaces (RISs)-assisted Internet of Things (IoT) networks have attracted a great deal of interest due to the potential contributions to the next generation wireless networks. In this article, an adaptive RIS clustering system with weak channels or obstacles is investigated. Ideally, there is a suitable RIS that adopts the self-organized RIS-assisted (SORA) scheme between a base station (BS) and a user equipment (UE) for reflection transmission. However, due to the nonideal channel scenarios, a single RIS may not be capable of connecting the BS with the UE. To tackle these nonideal cases, we propose a complementary RIS-cluster-assisted (C-RCA) strategy by reflecting signals among clusters to improve the performance of the overall system. The performances of the two strategies in terms of outage probability (OP) and ergodic capacity (EC), derived using approximate closed-form expressions, are analyzed. By increasing the numbers of complementary RISs and the reflecting elements on each RIS simultaneously, these performance metrics as well as the effective throughput and energy efficiency (EE) are evaluated under different signal-to-noise ratios (SNRs). The obtained results demonstrated that, in contrast to the ideal case, the network EE of the proposed C-RCA strategy is higher than the SORA scheme under the larger target spectral efficiency (SE).

Simultaneous Wireless Information and Power Transfer in mmWave Networks Under User-Centric Base Station Clustering

User-centric base station (BS) deployment has been designed for the fifth-generation (5G) dense millimeter wave (mmWave) networks for alleviating the inter-cell interference and improving the cell-edge user experience. However, the system power consumption increases sharply with the network density. In this paper, we investigate a user-centric simultaneous wireless information and power transfer (SWIPT) mmWave system employing a time-switching protocol at users to allow both energy harvesting (EH) and data decoding. To enable user-centric BS cooperation, adaptive BS clustering model is used to determine the user’s serving cluster based on its channel condition. Considering both linear and non-linear EH models, we analyze the joint coverage, namely, the probability that the user harvests enough energy in a given time slot and receives the required data from its serving cluster. The random serving clusters and the correlation between the amount of harvested energy and received data rate make the joint coverage analysis more challenging. A tractable tight approximation of the joint coverage probability is thus derived for ultra-dense networks. A mathematical optimization model for the time switching coefficient is also developed to maximize the system joint rate and energy coverage performance. All mathematical expressions are validated by Monte-Carlo simulations. Our results show that the proposed analytical framework is accurate and efficient for the design and deployment of SWIPT-enabled user-centric mmWave networks.

Optimization of wireless sensor networks energy consumption by the clustering method based on the firefly algorithm

Wireless sensor networks (WSNs) contain an inordinate number of sensor nodes that are spatially distributed. The network is composed of entities that determine its lifetime. The WSN nodes are equipped with a battery whose autonomy is limited in duration. In this paper, different solutions are introduced to improve the overall energy consumption of the network in order to improve its lifetime. Contrary to many works considering the clustering algorithm as one potential candidate to improve the network's lifetime, this study has investigated the firefly algorithm optimization where an optimal cluster head is selected from a group of nodes. The set-up process of the cluster head is based on a set of conditions. To measure the performance of the proposed approach, the number of dead nodes and data packets received by the base station (BS) or sink node are considered. The results are tested on 100 nodes for 5000 transmission rounds, the amount of data transported is 20 million bits a little more than the other methods. It has been shown that the proposed solution outperformed the traditional low energy adaptive clustering hierarchy (LEACH), threshold sensitive energy efficient sensor network (TEEN), and developed distributed energy-efficient clustering (DEEC) approaches.

Enhancing Parameters of LEACH Protocol for Efficient Routing in Wireless Sensor Networks

The increasing popularity of wireless sensor networks can be attributed to their unique characteristics, which include high density, low power consumption, software programmability, and long-term accuracy. This paper presents a thorough analysis of key parameters for sensor network applications, including network lifetime, packet delivery ratio, energy-efficient transmission, and dead node ratio. This paper examines the effectiveness of clustering in wireless sensor networks for achieving efficiency. The research focuses on the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol and proposes an enhanced algorithm that improves network lifetime, throughput, and the number of living nodes. This paper evaluates the performance of the improved LEACH protocol and compares it to other protocols currently in use. The results indicate that the proposed method significantly improves the performance of the protocol, making it a promising method for efficient routing in wireless sensor networks. This study is an important contribution to the field of wireless sensor networks and can aid in the creation of more efficient and effective protocols for various sensor network applications.

Early Forest Fire Detection Using a Protocol for Energy-Efficient Clustering with Weighted-Based Optimization in Wireless Sensor Networks

Wireless sensor networks (WSNs) have proven to be incredibly useful for forest applications that rely on sensing technologies for event detection and monitoring. This radical sensing technology has revolutionized data gathering, analysis, and application. Despite the many advantages of this technology, one key drawback is the rapid drain on sensor batteries caused by their intensive processing activities and communication processes. The effectiveness of sensor nodes is strongly influenced by two factors: the amount of energy they consume and the length of their coverage lifetimes. Using our proposed method, we can find fire zones in a forest, detect and monitor battlefield surveillance, combat monitoring and intruder detection, and then wirelessly send all the information to a central station. So, extending the life of WSNs is essential to ensure that Sensor Nodes (SN) will always be available. Our proposed EEWBP (energy-efficient weighted-based protocol) technique uses a composite weighted metric that includes system elements such as the node degree, residual energy, the number of neighbors’ nodes, average flying speed, and trust value, which are evaluated separately and then added together to help in cluster-building and node-scheduling processes. Our proposed protocol makes it easy to set up many clusters of SNs, each with their own cluster head (CH). This way, data can be sent between clusters in a way that uses the least amount of energy and makes coverage last longer. After putting our cluster-based routing strategy in place, we tested how it worked and evaluated it with different network parameters. The simulation results show that EEWBP consumes less energy and maintains a higher level of consistency in the CH than coverage preserving clustering protocol (CPCP), coverage clustering protocol (CACP), coverage aware unequal clustering algorithm (CUCA), and low-energy adaptive clustering hierarchy (LEACH). EEWBP also shows a better packet delivery rate and an improvement in first-node death.

Self-paced Adaptive Bipartite Graph Learning for Consensus Clustering

Consensus clustering provides an elegant framework to aggregate multiple weak clustering results to learn a consensus one that is more robust and stable than a single result. However, most of the existing methods usually use all data for consensus learning, whereas ignoring the side effects caused by some unreliable or difficult data. To address this issue, in this article, we propose a novel self-paced consensus clustering method with adaptive bipartite graph learning to gradually involve data from more reliable to less reliable ones in consensus learning. At first, we construct an initial bipartite graph from the base results, where the nodes represent the clusters and instances, and the edges indicate that an instance belongs to a cluster. Then, we adaptively learn a structured bipartite graph from this initial one by self-paced learning, i.e., we automatically determine the reliability of each edge with adaptive cluster similarity measuring and involve the edges in bipartite graph learning in order of their reliability. At last, we obtain the final consensus result from the learned structured bipartite graph. We conduct extensive experiments on both toy and benchmark datasets, and the results show the effectiveness and superiority of our method. The codes of this article are released in http://Doctor-Nobody.github.io/codes/code_SCCABG.zip.

Analisis Perbandingan Teknik Pemilihan Cluster Head Pada Wireless Sensor Network Berbasis Clustering Menggunakan Algoritma Routing Protocol LEACH dan G-LEACH

Wireless Sensor Network (WSN) is a series of wireless networks consisting of several nodes in a limited area. Low Energy Adaptive Clustering Hierarchy (LEACH) and Grid-Based Low Energy Adaptive Clustering Hierarchy (G-LEACH) methods are clustering methods used to collet data by clustering nodes in the WSN area. The clustering process for both methods is the same, namely by select node as the Cluster head (CH) whose job is to collet data from other nodes to the sink. The selection of CH is carried out in each round by taking into account the probability formula used and the largest the remaining energy at that node. This study aims to determine the quality comparison of CH selection techniques in clustering-based routing protocol on WSN. The result of the study show that the G-LEACH routing protocol has good quality CH selection techniques and a long network lifetime compared to LEACH but requires a long time to reach the CH selection process

A Video Target Tracking and Correction Model with Blockchain and Robust Feature Location

In this paper, a cutting-edge video target tracking system is proposed, combining feature location and blockchain technology. The location method makes full use of feature registration and received trajectory correction signals to achieve high accuracy in tracking targets. The system leverages the power of blockchain technology to address the challenge of insufficient accuracy in tracking occluded targets, by organizing the video target tracking tasks in a secure and decentralized manner. To further enhance the accuracy of small target tracking, the system uses adaptive clustering to guide the target location process across different nodes. In addition, the paper also presents an unmentioned trajectory optimization post-processing approach, which is based on result stabilization, effectively reducing inter-frame jitter. This post-processing step plays a crucial role in maintaining a smooth and stable track of the target, even in challenging scenarios such as fast movements or significant occlusions. Experimental results on CarChase2 (TLP) and basketball stand advertisements (BSA) datasets show that the proposed feature location method is better than the existing methods, achieving a recall of 51% (27.96+) and a precision of 66.5% (40.04+) in the CarChase2 dataset and recall of 85.52 (11.75+)% and precision of 47.48 (39.2+)% in the BSA dataset. Moreover, the proposed video target tracking and correction model performs better than the existing tracking model, showing a recall of 97.1% and a precision of 92.6% in the CarChase2 dataset and an average recall of 75.9% and mAP of 82.87% in the BSA dataset, respectively. The proposed system presents a comprehensive solution for video target tracking, offering high accuracy, robustness, and stability. The combination of robust feature location, blockchain technology, and trajectory optimization post-processing makes it a promising approach for a wide range of video analytics applications, such as surveillance, autonomous driving, and sports analysis.

Automatic detection and update of landslide inventory before and after impoundments at the Lianghekou reservoir using Sentinel-1 InSAR

There is an urgent demand for continuous detection and monitoring of active slopes in wide reservoir areas, as reservoir impoundments may activate unstable slopes. Although satellite time-series InSAR has been widely used in mapping active landslides, the tedious artificial interpretation of InSAR results limits the efficiency and reliability of landslides detection. We propose a set of procedures for deformation monitoring and continuous automatic landslide identification in wide reservoir areas. The local time window estimation can extract the nonlinear deformation in the time series InSAR signal, thus enhancing the deformation field. Spatial adaptive clustering enables the effective extraction of unstable slopes. The abnormal deformation trends of landslides can be updated through the continuous identification of new results and comparison with historical results. The procedure is used to continuously detect unstable slopes in the Lianghekou reservoir area before and after the impoundment. Combining the ascending and descending SAR data of Sentinel-1, 109 historically active landslides were found and 18 new landslides were activated within one year after the first water storage. The distribution and types of unstable slopes were analyzed, followed by two case studies. The first case shows the different effects of the two-stage water storage on slope deformations, indicating rising water levels' critical role in landslide activation. The second case shows the consolidation settlement of the embankment dam and the influence of water storage on slope deformation monitoring. The results demonstrate the effectiveness of the InSAR automatic landslide identification and this study provides a technical reference for similar reservoir area.

A CLUSTERING OPTIMIZATION FOR ENERGY EFFICIENCY IN WIRELESS SENSOR NETWORK USING K-MEANS ALGORITHM

The limitation of sensors energy make energy efficiency still a priority issue in Wireless Sensor Network (WSN) technology. One effort that can be done to overcome this problem is to design the right data transmission path (or better known as routing). Low Energy Adaptive Clustering Hierarchy (LEACH) is one of the most widely used cluster-based routing protocols because it is considered capable of minimizing the amount of energy consumption through the formation of clusters or groups of nodes. Unfortunately, this protocol will experience a significant decrease in energy as the amount of data transmission increases. This is partly due to the clustering process which is carried out randomly and causes an imbalance in the distribution of the number of nodes between clusters. This study proposed a method to optimize the clustering process in the LEACH protocol by integrating the K-Means algorithm, which is called LEACH-KMe. A simulation was conducted to determine the effectiveness of the proposed method by considering 4 main parameters, namely total energy consumption, number of alive nodes, number of dead nodes, and residual energy. The test results proved that the LEACH-KMe protocol provides better performance than the conventional LEACH protocol (more even distribution of nodes, less total energy consumption and number of dead nodes, as well as a larger number of alive nodes and residual energy).

Energy-Efficient Bi-Objective Optimization Based on the Moth–Flame Algorithm for Cluster Head Selection in a Wireless Sensor Network

Designing an efficient wireless sensor network (WSN) system is considered a challenging problem due to the limited energy supply per sensor node. In this paper, the performance of several bi-objective optimization algorithms in providing energy-efficient clustering solutions that can extend the lifetime of sensor nodes were investigated. Specifically, we considered the use of the Moth–Flame Optimization (MFO) algorithm and the Salp Swarm Algorithm (SSA), as well as the Whale Optimization Algorithm (WOA), in providing efficient cluster-head selection decisions. Compared to a reference scheme using the Low-Energy Adaptive Clustering Hierarchy (LEACH) protocol, the simulation results showed that integrating the MFO, SSA or WOA algorithms into WSN clustering protocols could significantly extend the WSN lifetime, which improved the nodes’ residual energy, the number of alive nodes, the fitness function and the network throughput. The results also revealed that the MFO algorithm outperformed the other algorithms in terms of energy efficiency.