Computational Clustering Research Articles

Clustering computer mouse tracking data with informed hierarchical shrinkage partition priors.

Mouse-tracking data, which record computer mouse trajectories while participants perform an experimental task, provide valuable insights into subjects' underlying cognitive processes. Neuroscientists are interested in clustering the subjects' responses during computer mouse-tracking tasks to reveal patterns of individual decision-making behaviors and identify population subgroups with similar neurobehavioral responses. These data can be combined with neuroimaging data to provide additional information for personalized interventions. In this article, we develop a novel hierarchical shrinkage partition (HSP) prior for clustering summary statistics derived from the trajectories of mouse-tracking data. The HSP model defines a subjects' cluster as a set of subjects that gives rise to more similar (rather than identical) nested partitions of the conditions. The proposed model can incorporate prior information about the partitioning of either subjects or conditions to facilitate clustering, and it allows for deviations of the nested partitions within each subject group. These features distinguish the HSP model from other bi-clustering methods that typically create identical nested partitions of conditions within a subject group. Furthermore, it differs from existing nested clustering methods, which define clusters based on common parameters in the sampling model and identify subject groups by different distributions. We illustrate the unique features of the HSP model on a mouse tracking dataset from a pilot study and in simulation studies. Our results show the ability and effectiveness of the proposed exploratory framework in clustering and revealing possible different behavioral patterns across subject groups.

Assessing the groundwater quality of El Fahs aquifer (NE Tunisia) using multivariate statistical techniques and geostatistical modeling

This study is the first attempt to characterize the quality status of El Fahs aquifer by combining graphical tools, multivariate statistical techniques and traditional geostatistical methods. Water samples are collected from thirty-six observation wells during April 2016 to characterize the physicochemical properties of the aquifer. Subsequently, these samples are partitioned into three hydrochemically distinct water classes (i.e., C1, C2, and C3) using the K-means clustering method. Principal Component Analysis is used to reduce the dimensionality of the dataset prior performing the clustering computations, resulting in clusters of higher quality than the non-reduced case in terms of Silhouette coefficient. Piper diagram is used to display the chemical composition of the samples, revealing the dominant role of Mg–Ca–Cl water type for all three classes, whereas Sodium and Sulfate were found to be the second most important cations and anions respectively. Indicator kriging (IK) is used to identify the probability of occurrence of the hydrochemical classes beyond the sampling locations. It is found that Class 1, associated with fresh groundwater component, is most probable to occur at the central part of the plain, mainly due to the presence of a dense hydrological network, whereas Classes 2 (agricultural activities) and 3 (dissolution of evaporate geological formations) are expected to occur at the southern and northern regions respectively. IK also identified the regions associated with high levels of uncertainty, mostly occurring in a large portion of the northern area due to the absence of available hydrochemical information. The results showed that integration of graphical methods, multivariate statistical techniques and geostatistical modeling, is an efficient approach for characterizing the hydrochemical status of the aquifer system, to spatially optimize the groundwater monitoring well networks and quantify the uncertainty levels of the water classes in a systematic way.

Characterisation of phenotypic patterns in equine exercise-associated myopathies.

Equine exercise-associated myopathies are prevalent, clinically heterogeneous, generally idiopathic disorders characterised by episodes of myofibre damage that occur in association with exercise. Episodes are intermittent and vary within and between affected horses and across breeds. The aetiopathogenesis is often unclear; there might be multiple causes. Poor phenotypic characterisation hinders genetic and other disease analyses. The aim of this study was to characterise phenotypic patterns across exercise-associated myopathies in horses. Historical cross-sectional study, with subsequent masked case-control validation study. Historical clinical and histological features from muscle samples (n = 109) were used for k-means clustering and validated using principal components analysis and hierarchical clustering. For further validation, a blinded histological study (69 horses) was conducted comparing two phenotypic groups with selected controls and horses with histopathological features characterised by myofibrillar disruption. We identified two distinct broad phenotypes: a non-classic exercise-associated myopathy syndrome (EAMS) subtype was associated with practitioner-described signs of apparent muscle pain (p < 0.001), reluctance to move (10.85, p = 0.001), abnormal gait (p < 0.001), ataxia (p = 0.001) and paresis (p = 0.001); while a non-specific classic RER subtype was not uniquely associated with any particular variables. No histological differences were identified between subtypes in the validation study, and no identifying histopathological features for other equine myopathies identified in either subtype. Lack of an independent validation population; small sample size of smaller identified subtypes; lack of positive control myofibrillar myopathy cases; case descriptions derived from multiple independent and unblinded practitioners. This is the first study using computational clustering methods to identify phenotypic patterns in equine exercise-associated myopathies, and suggests that differences in patterns of presenting clinical signs support multiple disease subtypes, with EAMS a novel subtype not previously described. Routine muscle histopathology was not helpful in sub-categorising the phenotypes in our population.

Automated level-based clustering of dataflow actors for controlled scheduling complexity

Dataflow Models of Computation (MoCs) significantly enhance parallel computing by efficiently expressing application parallelism on multicore architectures, unlocking greater performance and throughput. However, the complexity of graphs within dataflow-based systems can result in a time-consuming resource allocation process. To address this issue, a solution is to cluster computations to ease heuristic solving. The information encompassing the context of computations and the constraints of the architecture plays a crucial role in determining application performance. This paper presents an automated approach that leverages this information to control graph complexity prior to the resource allocation process. Experiments demonstrate that the proposed method, driven by clustering, not only yields improved throughput but also provides better mapping decisions and data transfer efficiency, achieving a throughput up to 1.8 times higher than state-of-the-art techniques.

Enhancing online Chinese language education through intelligent computing and data clustering—A case study on personalized teaching for international students in Shanxi Province

Enhancing online Chinese language education through intelligent computing and data clustering—A case study on personalized teaching for international students in Shanxi Province

Identifying protein conformational states in the Protein Data Bank: Toward unlocking the potential of integrative dynamics studies

Studying protein dynamics and conformational heterogeneity is crucial for understanding biomolecular systems and treating disease. Despite the deposition of over 215 000 macromolecular structures in the Protein Data Bank and the advent of AI-based structure prediction tools such as AlphaFold2, RoseTTAFold, and ESMFold, static representations are typically produced, which fail to fully capture macromolecular motion. Here, we discuss the importance of integrating experimental structures with computational clustering to explore the conformational landscapes that manifest protein function. We describe the method developed by the Protein Data Bank in Europe – Knowledge Base to identify distinct conformational states, demonstrate the resource's primary use cases, through examples, and discuss the need for further efforts to annotate protein conformations with functional information. Such initiatives will be crucial in unlocking the potential of protein dynamics data, expediting drug discovery research, and deepening our understanding of macromolecular mechanisms.

Aggregation operators-based divergence measures for single-valued neutrosophic sets with their applications to pattern recognition

A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results.

Abstract 2327: MADVAR: An algorithm that improves the relevance of computational biology output, while reducing compute time and space requirements

Abstract High-throughput methods implemented in biology research produce a continuously growing array of data input that are used to produce data output with an increasing abundance of features. While growth in the volume and diversity of data input can be highly valuable for studying biological systems, it presents the challenge of managing enormous quantities of features, many of which are not relevant to the specific research question being asked. This excess data input burdens storage and computation of downstream clustering and machine learning tasks. A common approach used to manage this data input relies on filters applied to the features by their variance across the sample set, while applying random cutoffs. Our proprietary algorithm (“MADVAR”) enables the prioritization of variable features from high-throughput continuous data, by automatically finding an optimal cutoff for the distribution of the data. Based on the right-skew nature of biological data distribution, MADVAR finds and excludes the "0 variance peak" using the median of the distributions and the median absolute deviation (MAD). MADVAR enables a faster analysis with a reduced memory requirement, and dramatically improves clustering results with minimal loss of relevant features. Citation Format: Gilad Silberberg, Michael Ritchie. MADVAR: An algorithm that improves the relevance of computational biology output, while reducing compute time and space requirements [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2327.

PPPCT: Privacy-Preserving framework for Parallel Clustering Transcriptomics data

Single-cell transcriptomics data provides crucial insights into patients’ health, yet poses significant privacy concerns. Genomic data privacy attacks can have deep implications, encompassing not only the patients’ health information but also extending widely to compromise their families’. Moreover, the permanence of leaked data exacerbates the challenges, making retraction an impossibility. While extensive efforts have been directed towards clustering single-cell transcriptomics data, addressing critical challenges, especially in the realm of privacy, remains pivotal. This paper introduces an efficient, fast, privacy-preserving approach for clustering single-cell RNA-sequencing (scRNA-seq) datasets. The key contributions include ensuring data privacy, achieving high-quality clustering, accommodating the high dimensionality inherent in the datasets, and maintaining reasonable computation time for big-scale datasets. Our proposed approach utilizes the map-reduce scheme to parallelize clustering, addressing intensive calculation challenges. Intel Software Guard eXtension (SGX) processors are used to ensure the security of sensitive code and data during processing. Additionally, the approach incorporates a logarithm transformation as a preprocessing step, employs non-negative matrix factorization for dimensionality reduction, and utilizes parallel k-means for clustering. The approach fully leverages the computing capabilities of all processing resources within a secure private cloud environment. Experimental results demonstrate the efficacy of our approach in preserving patient privacy while surpassing state-of-the-art methods in both clustering quality and computation time. Our method consistently achieves a minimum of 7% higher Adjusted Rand Index (ARI) than existing approaches, contingent on dataset size. Additionally, due to parallel computations and dimensionality reduction, our approach exhibits efficiency, converging to very good results in less than 10 seconds for a scRNA-seq dataset with 5000 genes and 6000 cells when prioritizing privacy and under two seconds without privacy considerations.Availability and implementationCode and datasets availability: https://github.com/University-of-Windsor/PPPCT.

Myelodysplastic neoplasms dissected into indolent, leukaemic and unfavourable subtypes by computational clustering of haematopoietic stem and progenitor cells

Myelodysplastic neoplasms (MDS) encompass haematological malignancies, which are characterised by dysplasia, ineffective haematopoiesis and the risk of progression towards acute myeloid leukaemia (AML). Myelodysplastic neoplasms are notorious for their heterogeneity: clinical outcomes range from a near-normal life expectancy to leukaemic transformation or premature death due to cytopenia. The Molecular International Prognostic Scoring System made progress in the dissection of MDS by clinical outcomes. To contribute to the risk stratification of MDS by immunophenotypic profiles, this study performed computational clustering of flow cytometry data of CD34+ cells in 67 MDS, 67 AML patients and 49 controls. Our data revealed heterogeneity also within the MDS-derived CD34+ compartment. In MDS, maintenance of lymphoid progenitors and megakaryocytic-erythroid progenitors predicted favourable outcomes, whereas expansion of granulocyte-monocyte progenitors increased the risk of leukaemic transformation. The proliferation of haematopoietic stem cells and common myeloid progenitors with downregulated CD44 expression, suggestive of impaired haematopoietic differentiation, characterised a distinct MDS subtype with a poor overall survival. This exploratory study demonstrates the prognostic value of known and previously unexplored CD34+ populations and suggests the feasibility of dissecting MDS into a more indolent, a leukaemic and another unfavourable subtype.

Deep Learning for Clustering Single-cell RNA-seq Data

Abstract: The development of single-cell RNA sequencing (scRNA-seq) technology provides an excellent opportunity to explore cell heterogeneity and diversity. With the growing application of scRNA-seq data, many computational clustering methods have been developed to further uncover cell subgroups, and cell dynamics at the group level. Due to the characteristics of high dimension, high sparsity and high noise of the scRNA-seq data, it is challenging to use traditional clustering methods. Fortunately, deep learning technologies characterize the properties of scRNA-seq data well and provide a new perspective for data analysis. This work reviews the most popular computational clustering methods and tools based on deep learning technologies, involving comparison, data collection, code acquisition, results evaluation, and so on. In general, such a presentation points out some progress and limitations of the existing methods and discusses the challenges and directions for further research, which may give new insight to address a broader range of new challenges in dealing with single-cell sequencing data and downstream analysis.

STING signalling compensates for low tumour mutation burden to drive anti-tumour immunity

While mutation-derived neoantigens are well recognized in generating anti-tumour T cell response, increasing evidences highlight the complex association between tumour mutation burden (TMB) and tumour infiltrating lymphocytes (TILs). The exploration of non-TMB determinants of active immune response could improve the prognosis prediction and provide guidance for current immunotherapy. The transcriptomic and whole exome sequence data in The Cancer Genome Atlas were used to examine the relationship between TMB and exhausted CD8+ T cells (Tex), as an indicator of tumour antigen-specific T cells across nine major cancer types. Computational clustering analysis was performed on 4510 tumours to identify different immune profiles. NanoString gene expression analysis and single cell RNA-seq analysis using fresh human breast cancer were performed for finding validation. TMB was found to be poorly correlated with active immune response in various cancer types. Patient clustering analysis revealed a group of tumours with abundant Tex but low TMB. In those tumours, we observed significantly higher expression of the stimulator of interferon genes (STING) signalling. Dendritic cells, particularly those of BATF3+ lineage, were also found to be essential for accumulation of Tex within tumours. Mechanistically, loss of genomic and cellular integrity, marked by decreased DNA damage repair, defective replication stress response, and increased apoptosis were shown to drive STING activation. These results highlight that TMB alone does not fully predict tumour immune profiles, with STING signalling compensating for low TMB in non-hypermutated tumours to enhance anti-tumour immunity. Translating these results, STING agonists may benefit patients with non-hypermutated tumours. STING activation may serve as an additional biomarker to predict response to immune checkpoint blockades alongside TMB. Our research also unravelled the interplay between genomic instability and STING activation, informing potential combined chemotherapy targeting the axis of genomic integrity and immunotherapy. City of Hope Christopher Family Endowed Innovation Fund for Alzheimer's Disease and Breast Cancer Research in honor of Vineta Christopher; Breast Cancer Alliance Early Career Investigator Award; National Cancer Institute of the National Institutes of Health under award number R01CA256989 and R01CA240392.

A Novel Approach for Secure and Efficient VANET Communication: Integrating Clustering, Curve Fitting, and Fog Computing

A Novel Approach for Secure and Efficient VANET Communication: Integrating Clustering, Curve Fitting, and Fog Computing

Effect of a single methyl substituent on the electronic structure of cobaltocene studied by computationally assisted MATI spectroscopy.

Metallocenes represent archetypical organometallic compounds playing key roles in various fields of fundamental and applied chemistry. Many of their unique properties arise from low ionization energies (IE) which can be tuned by introducing substituents into the rings. Here we report the first mass-analyzed threshold ionization (MATI) spectrum of a methylmetallocene, (Cp')(Cp)Co (Cp' = η5-C5H4Me, Cp = η5-C5H5). The presence of a single Me group allows us to study the "pure" effect of methylation without the mutual influence of substituents. The MATI technique provides an extremely high accuracy in determining the adiabatic IE of (Cp')(Cp)Co which equals 5.2097(6) eV. The effect of a Me group on the IE of cobaltocene appears to be 36% stronger than that in bis(η6-benzene)chromium. The MATI spectrum of (Cp')(Cp)Co shows a rich vibronic structure from which vibrational frequencies of the free ion are determined. This information provides a solid basis for testing the quality of quantum chemical calculations. Various levels of the DFT and coupled cluster computations are used to describe the structural and electronic transformations accompanying the detachment of an elctron from (Cp')(Cp)Co. New aspects of the methyl substituent influence on the potential energy surfaces, as well as on the inhomogeneous changes in charge density and electrostatic potential caused by ionization, are discussed.

Retracted: Research on Digital Economy and Human Resources Based on Fuzzy Clustering and Edge Computing

Retracted: Research on Digital Economy and Human Resources Based on Fuzzy Clustering and Edge Computing

A novel efficient Rank-Revealing QR matrix and Schur decomposition method for big data mining and clustering (RRQR-SDM)

Big Data is an emerging technology with enormous potential to develop business and its administration. Due to the enormous volume, efficient data mining and clustering methods are crucial to extracting meaningful insights and patterns from large-scale datasets. Problems may arise from the need to analyze, capture, share, store, and visualize the data. Several methods have already been proposed for mining knowledge from big data. It is practically inefficient or impossible to handle these massive data using the proposed methods in a single machine because big data are frequently acquired from dispersed locations and stored on several machines. Matrix decomposition is one of the critical strategies to retrieve knowledge from diverse, noisy, huge data generated by modern applications and stored in dispersed locations. This study proposes a novel approach called the Rank-Revealing QR Matrix and Schur Decomposition Method (RRQR-SDM) specifically designed for big data mining and clustering tasks. The RRQR-SDM is designed to reveal the rank of the data matrix in a computationally efficient manner by using a modified QR decomposition, eliminating the need for expensive Singular Value Decomposition (SVD) computations. The proposed RRQR-SDM method offers several advantages over existing approaches. Firstly, exploiting the inherent low-rank structure reduces the computational complexity associated with large-scale datasets. By revealing the rank of the input matrix, it enables dimensionality reduction and efficient data compression. Secondly, the Schur decomposition enhances the interpretability of the data by providing a clear separation between the relevant and irrelevant components. This feature makes the RRQR-SDM method particularly suitable for data mining and clustering tasks where identifying the most significant features is essential. To evaluate the performance of the RRQR-SDM method, extensive experiments were conducted on various big data datasets. The results demonstrate that the proposed method outperforms state-of-the-art computational efficiency and clustering accuracy techniques.

MAIT Cell Frequencies within PBSC Grafts Are Associated with Donor CMV Serostatus and Age: An Initial Analysis from the DKMS and NMDP Graft Composition Study

Introduction We have established an international collaboration between the DKMS (Germany) and the National Marrow Donor Program (NMDP; USA) to characterize more than 2000 donor peripheral blood stem cell (PBSC) grafts. We aim to correlate graft immunophenotype with patient outcomes and perform additional analyses on biobanked aliquots of selected samples to deepen our understanding of the relationships between features of the donor graft and key patient outcomes, such as graft-versus-host disease, relapse, and overall survival. Here, we present an interim analysis, focused on the CD8 T cell compartment, describing relationships between donor characteristics and graft composition. We have developed a novel computational clustering method to integrate cytometry data from both the US and German analytical laboratories. Methods 300 donor PBSC graft samples were collected, processed, freshly stained (34-color panel for immunophenotyping of lymphocytes and hematopoietic stem cells), and analyzed using an Aurora Cytek instrument at the NMDP contract laboratory (Roswell Park, Buffalo, NY). 53 additional samples were processed in the DKMS laboratory in Dresden, Germany (equivalent antibody panel and instrument). We have developed a novel analytical pipeline for integrating these data. First, we group samples by facility, and by month run for computational convenience, to form “batches” for initial clustering. We perform Leiden clustering, and subclustering, to generate a large number of clusters per batch. To integrate data from all batches, we then cluster all subcluster centroids from all batch-specific clusterings. This gives a global clustering for each stream ( e.g. the CD8+ T cells). Cluster frequencies per sample provide features for quantifying links between immunophenotype and donor metadata ( e.g., CMV serostatus, age, and sex). Our approach offers an important complement to traditional gating and allows full exploration of the data in an unbiased fashion. Results 353 donor products were included in the analysis. The median donor age was 28 years (range 18 - 60), 59% were female and 65% were CMV positive. Initial metaclustering demonstrated that the 53 DKMS samples and the 300 NMDP samples are each well represented in all clusters, and that site-driven batch effects, while present, are mild. We initially focused on the CD8 T cell compartment (UMAP in Fig 1A) and discovered several phenotypic differences when we compared CMV seronegative and seropositive donors. CD8 T cell clusters 10 and 11 (both TCRαβ+ CD45RA+ CCR7+ HLA-DR+ CD57+; cluster 10 CCR9+; cluster 11 CCR9-) were present at higher frequency in CMV+ donors (n = 116) compared with CMV- donors (n = 220; p &amp;lt;0.0001; Fig 1B). Conversely, clusters 9 and 12, enriched for mucosal-associated invariant T (MAIT) cells, were significantly reduced in CMV+ donors (p &amp;lt;0.001). Of note, CMV serostatus was not significantly associated with age (median age in the CMV+ group was 29.4 years; vs 28.4 years in CMV- donors). We also observed an association between cluster 8 (TCRαβ+ CD45RA+ CCR7+ HLA-DR+ CD38+; consistent with an activated phenotype) and age (p = 0.0009; Fig1B). There was a trend toward decreased MAIT frequencies with age (Cluster 9; p = 0.058). When we analyzed the MAIT cell frequency as defined by traditional flow cytometry gating (CD3+ CD8+ CD161+ Vα7.2+), we observed a statistically significant relationship between younger donor age and increased MAIT cell frequency (p = 0.0002), consistent with the trend seen in cluster 9. Sex at birth was not linked with immunophenotype in our analyses thus far. Conclusions In this initial analysis of our multi-center observational study of PBSC graft products, we demonstrate the development and application of new computational tools for large-cohort flow cytometric data analysis. CMV-positive serostatus and younger donor age are both associated with an increased frequency of MAIT cells in the graft. We also found differences in CD8 T cell activation status in association with age and CMV. In the future, we will assess the relationships between these populations and patient outcomes, as well as explore cluster frequencies in association with key patient outcomes including GVHD, relapse, and overall survival. Sample collection and analysis are ongoing at both US and German sites as an ongoing collaborative effort between the NMDP and the DKMS.

Efficient time-dependent vibrational coupled cluster computations with time-dependent basis sets at the two-mode coupling level: Full and hybrid TDMVCC[2

The computation of the nuclear quantum dynamics of molecules is challenging, requiring both accuracy and efficiency to be applicable to systems of interest. Recently, theories have been developed for employing time-dependent basis functions (denoted modals) with vibrational coupled cluster theory (TDMVCC). The TDMVCC method was introduced along with a pilot implementation, which illustrated good accuracy in benchmark computations. In this paper, we report an efficient implementation of TDMVCC, covering the case where the wave function and Hamiltonian contain up to two-mode couplings. After a careful regrouping of terms, the wave function can be propagated with a cubic computational scaling with respect to the number of degrees of freedom. We discuss the use of a restricted set of active one-mode basis functions for each mode, as well as two interesting limits: (i) the use of a full active basis where the variational modal determination amounts essentially to the variational determination of a time-dependent reference state for the cluster expansion; and (ii) the use of a single function as an active basis for some degrees of freedom. The latter case defines a hybrid TDMVCC/TDH (time-dependent Hartree) approach that can obtain even lower computational scaling. The resulting computational scaling for hybrid and full TDMVCC[2] is illustrated for polyaromatic hydrocarbons with up to 264 modes. Finally, computations on the internal vibrational redistribution of benzoic acid (39 modes) are used to show the faster convergence of TDMVCC/TDH hybrid computations towards TDMVCC compared to simple neglect of some degrees of freedom.

Incremental density clustering framework based on dynamic microlocal clusters

With the prevailing development of the internet and sensors, various streaming raw data are generated continually. However, traditional clustering algorithms are unfavorable for discovering the underlying patterns of incremental data in time; clustering accuracy cannot be assured if fixed parameters clustering algorithms are used to handle incremental data. In this paper, an Incremental-Density-Micro-Clustering (IDMC) framework is proposed to address this concern. To reduce the succeeding clustering computation, we design the Dynamic-microlocal-clustering method to merge samples from streaming data into dynamic microlocal clusters. Beyond that, the Density-center-based neighborhood search method is proposed for periodically merging microlocal clusters to global clusters automatically; at the same time, these global clusters are updated by the Dynamic-cluster-increasing method with data streaming in each period. In this way, IDMC processes sensor data with less computational time and memory, improves the clustering performance, and simplifies the parameter choosing in conventional and stream data clustering. Finally, experiments are conducted to validate the proposed clustering framework on UCI datasets and streaming data generated by IoT sensors. As a result, this work advances the state-of-the-art of incremental clustering algorithms in the field of sensors’ streaming data analysis.

On the interest of artificial intelligence approaches in solving the IoT coverage problem

This survey deals with the 3D indoor deployment in IoT collection networks to identify the right locations of the IoT connected objects and, subsequently, to manage the coverage holes while guaranteeing other objectives such as localization and connectivity of IoT devices. These coverage holes result generally from the initial random distribution of the objects. Unlike the existing studies, the present survey does not only focus on the use cases and applications of the different deployment issues but, it also analyzes the problem of deployment by showing its different models and hypotheses presented in the literature and highlighting the used evaluation and performance criteria. The aim of the study is to highlight the relevance of artificial intelligence techniques; especially meta-heuristics, evolutionary optimization, machine learning and reinforcement learning; in finding coverage solutions, better than other methods. Through experimental validation, the performances of the various deployment and redeployment approaches in a 3D indoor context are compared. These approaches involve computational geometry, virtual force, clustering, mathematical modeling, and evolutionary optimization-based approaches. Afterwards, they are investigated and categorized according to their specifications into different summary tables. Statistical and complexity tests are performed to evaluate the complexity of these approaches. In addition, current 3D deployment trends are presented, and some outstanding deployment issues are discussed. Based on the experimental and simulation findings, the behaviors of evolutionary optimization algorithms are compared to those of the other deployment techniques. The obtained findings demonstrate that using artificial intelligence, specifically many-objective optimization algorithms for 3D deployment of IoT networks is more beneficial as it allows enhancing the quality of deployment compared to the other deployment approaches.