Streptococcus pneumoniae pneumonia. There is evidence that early antibiotics treatment decreases the inflammatory response during pneumonia. Here, we investigate mechanistically this strategy using a multi-level mathematical model, which describes the 24 first hours after infection of a single alveolus from the key signaling networks behind activation of the epithelium to the dynamics of the local immune response. With the model, we simulated pneumonia in aged and young patients subjected to different antibiotics timing. The results show that providing antibiotics to elderly patients 8 h in advance compared to young patients restores in aged individuals the effective response seen in young ones. This result suggests the use of early, probably prophylactic, antibiotics treatment in aged hospitalized people with high risk of pneumonia.Bacterial pneumonia is one of the most prevalent infectious diseases and has high mortality in sensitive patients . Although an infection, the disease alters the alveolar epithelium homeostasis and hinders normal breathing, often with fatal consequences. A special case is hospitalized aged patients, which present a high risk of infection and death because of the community acquired version of the  Streptococcus pneumoniae infection being the most common cause [Pneumonia is the cause of 20% of deaths in children worldwide , with Ston cause ,4. Localon cause , which mon cause ,7.S. pneumoniae inside the alveoli alters the tissue homeostasis and promotes liquid accumulation inside the alveolar tissue, thereby hindering normal breathing. Alveolar tissue regeneration to restore the damaged epithelium is activated only when bacterial infection is controlled. However, since the process can get delayed in time, acute, long-lasting lung infection jeopardizes tissue recovery and patient survival.During the first hours of the infection, passive and innate immunity lead the response against bacteria and inflammation. Resident alveolar macrophages keep surveillance of agents entering the alveolus together with the epithelial cells. These cells activate inflammation together with other immune cells recruited during the activation process. In the early infection, monocytes act as phagocytes supporting alveolar macrophages and neutrophils to produce an acute inflammatory and bactericide response. The immune system produces and secretes several cytokines to facilitate cell recruitment and modulate inflammatory response. Upon bacteria sensing, the epithelial cells of the alveolus synthesize and secrete the chemokine MCP-1, which controls phagocyte recruitment, and CXCL5, which recruits neutrophils ,9. FurthS. pneumoniae alveolar infection is mostly controlled with antibiotics. Complications due to the bacterial infection  are less frequent in patients than those linked to lung inflammation. Although vaccines against S. pneumoniae are available, their efficiency is low in high-risk populations due to the limited immune response triggered by the vaccine in these patients [S. pneumoniae during pneumonia. We talk about early antibiotics treatment when the decision criteria are based on the individual patient prognosis instead of on general clinical recommendations [patients ,12. Thusndations . This stndations . Early aS. pneumoniae infection inside a single lung alveolus. The objective of the present work is to use this computational model to investigate in silico the relevance of early antibiotics treatment in elderly patients.Mathematical model simulations allow testing multiple hypotheses in order to select optimal treatments for their validation in vivo ,15. CaliS. pneumoniae infection during the very first stages of pneumonia, which cannot be entirely tracked by experimentation. The model considers both the barrier defenses and innate immune system against S. pneumoniae, while the adaptive immune system response is assumed to play a relevant role in later phases of the infection. To cope with all the relevant spatiotemporal features, we derived and characterized a hybrid, multi-level mathematical model, which combines agent-based, partial and ordinary differential equations.We proposed a model able to simulate the first 24 h after bacterial lung alveolus infection. This model aims to predict the most relevant cellular and molecular mechanisms driving In this work, we analyze the effect of the neutrophil load on alveolar infection. Neutrophils play a major role during the early phases of pneumonia that are controlled by the innate response . It has S. pneumoniae in a young, healthy individual . The nominal condition was obtained by combining quantitative information from the literature and calibration of given model parameters using in vivo [We defined the nominal solution of the model as the starting point of the perturbation analysis, which represents the average condition that happens during an infection of a lung alveolus with  in vivo  experime in vivo . Model s in vivo . In Figu in vivo .In our simulations we distinguish between resident alveolar macrophages and monocytes-derived phagocytes. In physiological conditions, a single alveolar macrophage patrols several interconnected alveoli, which leads to inclusion of a single macrophage in the alveolus at the beginning of the simulation . InteresNext to the nominal solution, we included a second scenario in our model to simulate the effect of aging in the fitness of the innate immune system ,32. To tS. pneumoniae under antibiotic administration, we simulated the continuous administration of penicillin as described in the Material and Methods section. Next, we performed perturbation simulations in which we modified the time after infection in which antibiotic administration starts in both the young and aged scenarios in conjunction with changes in the initial amount of bacteria infecting the alveolus . Each of these metrics provides a different view of the bacteremia dynamics during infection. R0 provides an estimate of the potential dispersion of the infection through the lungs, Dif is the average of the increase rate of the infection and Inc measures the bacteremia increase between the initial time and 24 h later. In In order to quantify the dynamics of the infection during the simulation, we computed three different magnitudes that account for bacteria dynamics in the course of the infection inside the alveolus  of 20 h. In this scenario these two measurements reach their maximum, even above the values they get for the higher number of initial bacteria. Our analysis suggests that, since this effect is only observed in the two measurements that normalize by the initial number of bacteria, it is linked to the interplay between initial and maximum bacterial load in the alveolus in this scenarios and metrics.S. pneumoniae. In the model, the lung alveolus is represented as a truncated frequency two icosahedron. Bacteria and immune cells move through the inner surface of the icosahedron and chemical signals like chemokines and cytokines diffused on the inner surface.The mathematical model presented accounts for the first 24 h of infection of a lung alveolus by S. pneumoniae experiments in vitro and in vivo. In the model, epithelial cells can recognize and react to bacteria producing chemokines (MCP-1) that attract the resident macrophages to the point of infection. The model also considers the dynamics of the alveolar lining liquid that can wash out bacteria from the alveolus. The model further simulates the recruitment of immune cells to the infection site. We here focus on the innate immune system response, which precedes the adaptive response by one or more days. In our model, monocytes appear to support the function of resident macrophages, while neutrophils act as the main destructive agent against the increasing infection. Both cell types are recruited following chemical signals secreted by both local macrophages and epithelial cells in response to bacteria. Macrophages produce interleukin 8 (IL-8), which attracts neutrophils from the bloodstream to the alveolar tissue, while the epithelial cells chemokine MCP-1 recruit monocytes. Another signal coming from the epithelial cells included in our model is CXCL5, which also recruits neutrophils. In the model we integrated in each epithelial, monocyte and macrophage cell agent, an NF-κB-centered intracellular network is responsible for the triggering of cytokines and chemokines secretion. In case of the IL-8 produced by macrophages and monocytes in response to bacteria, we assumed for IL-8 the same model parameter values than for MCP-1 based on the fact that both ligands show similar expression profiles in relevant in vitro experiments [Our model is built on previous models developed by our group and others describing the signaling pathways and the cell-to-cell interactions between host and bacteria triggered during early time after infection ,34. Thiseriments . Furthereriments .S. pneumoniae, primarily responsible for the bacteria-associated tissue damage. In order to fit the model to experimental observations concerning pneumolysin [S. pneumoniae lung infection is the existence of immunomodulatory effects promoted by proteins and other compounds of the bacteria surface and capsule [We also included the production of the virulence factor pneumolysin by umolysin , we set  capsule ,38,39. I capsule .S. pneumoniae (MOI = 10 bacteria per resident macrophage). This condition produces acute pneumonia that a subset of the exposed animals is able to overcome [Our model is a multi-level hybrid model, which utilizes agent-based modelling to represent the movement of cells in the tissue, partial differential equations for the gradients of cytokines and ordinary differential equations for the intracellular pathways. The agents and processes included in the model are described in overcome .The model presents a stochastic behavior on the movement of cells through the inner surface of the alveolus. Due to its stochasticity, the same set of parameters can produce different outcomes every time the model is executed. In order to deal with this stochastic process, we simulated every solution 10 times. As we can see in We used data from in vitro experiments with penicillin to calculate the clearance rate of pneumococci  and we iAnother parameter that presents variability in vivo is the initial load of pneumococci that initiate the infection inside the alveolus. To account for this, we allowed values of initial bacteria from 11 to 151 per alveolus in each group . Eleven bacteria per alveolus represent the nominal situation of a standard primary infection, calculated from mice experiments data see . We usednominal and aged). Each solution is represented by the average of 10 simulations with the same parameter values, which produced a total amount of 480 simulations inside each group . Each simulation is run from 0 to 24 h after infection onset.Combining modifications in both timing for antibiotics administration and bacterial load, we obtain 48 solutions inside each group . The simulations were performed in a cluster with 32 CPUs and 128 GB RAM memory. Each solution took on average 10 h. The files containing model equations and rules, seeds for stochastic simulations and simulations are provided here.Streptococcus pneumonia, among others. There is a need for finding early diagnostics and therapeutic strategies able to reduce the risk of these patients entering the acute phase of pneumonia.Controlling bacterial pneumonia in patients that have reached the acute phase is complicated due to the involvement of an uncontrolled inflammatory response. This severity occurs often in immunocompromised patients like those from the aged population. Moreover, aged patients in hospitals are exposed to nosocomial infections like To this end, according to our simulations, during the first hours of infection the activation and recruitment of neutrophils is one of the main agents involved in this overwhelming inflammation in aged individuals. There is experimental evidence suggesting an improvement in patients by acting on the antibiotics timing , but to S. pneumoniae. We have focused on S. pneumoniae because it is the most prevalent bacterial lung infection. However, the current implementation of our model could be used with little changes to simulate the infection dynamics associated with similar bacterial species.To this end, we utilized a multi-level mathematical model to predict the effect of early antibiotics treatment on an elderly clinical model of the disease. We base our predictions on a multi-level mathematical model calibrated with a clinically accepted mouse model of CAP pneumonia . Our modS. pneumoniae infection of an alveolar tissue in both aged and young individuals  compared to the one accounting for the instant bacteremia values at the end of the simulation (Inc). This suggests that the scenarios analyzed cover the saturation on bacterial growth inside the alveolus for a high amount of initial bacteria. Our simulations also suggest substantial differences in the dynamics of the infection between the two clinical scenarios. Younger individuals respond better to the alveolar infection producing far better measurements concerning bacteremia.To reproduce the situation of the elderly model we modified the parameters to fit the immunological differences observed on aged mice with pneumonia . This pruals see . We compThe maximal similarity between the simulations for young and aged individuals occurs for very early administration of antibiotics in aged individuals (τ = 4 h) compared to mid-time administration in young individuals (τ = 12 h). Although younger individuals respond better to the very early administration of antibiotics, the improvement can be observed in both age groups . These rIn this sense, profiling blood levels of neutrophils could be a surrogate to decide for preventive of early antibiotics treatment in different patients. Further clinical assays based on these results could identify neutrophils as biomarkers to identify sensitive patients that would require early antibiotics after symptoms first appear during hospitalization. The main limitation for an early antibiotic treatment is the delay observed from diagnosis . Our resOur multi-level model only reproduces the evolution of the infection in a single alveolus. To generate with the model a spectrum of alveoli infection based on the level of exposure to bacteria, we systematically modified the initial amount of bacteria infecting the alveolus and simulated the dynamics of infection. The underlying assumption is that the only difference between the lung alveoli at the initial point of infection is the amount of bacteria that they receive during the colonization of the alveolar tissues from the upper airways. Thus, we think our systematic simulations in which we vary bacteremia give a picture of the initial phases at the whole lung scale. However, the model cannot reflect the role played by other immune cell populations and branches of the immune response in the later phases of infection, but also cannot account for systemic processes induced by the parallel infection of a large fraction of the alveoli in the lung. These are features that require an extended version of the model with more cell types, cytokines, spatial processes and interlinked alveoli.The mouse experimental model used to build the model in the present studio can differ from community-acquired pneumonia (CAP) in humans because mice are inoculated in the experiments with a much higher amount of bacteria to ensure infection. However, this experimental model of pneumonia is considered a valid alternative to study the evolution of pneumonia in humans . Also, w


In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. One of the main goals of this review is to explore the existing methods that integrate different types of information in order to improve the identification of the biomolecular signatures of diseases and the discovery of new potential targets for treatment. These integrative approaches are expected to aid the prediction, diagnosis, and treatment of diseases, as well as to enlighten us on disease state dynamics, mechanisms of their onset and progression. The integration of various types of biological information will necessitate the development of novel techniques for integration and data analysis. Another aim of this review is to boost the bioinformatics community to develop new approaches for searching and determining significant groups/clusters of features based on one or more biological grouping functions. Biological systems are massively complex and heterologous in nature. To resolve the mysteries behind complex biological systems, large-scale studies have been conducted which yielded massive volumes of biological data, including the genetic variations associated with specific phenotypes. Currently, we are encountering an -omics revolution in which genome, epigenome, transcriptome, and other -omics can be readily characterized. With advancements in various -omics approaches, it is now possible to generate multi-omics data to answer various biological problems. Nowadays, several types of -omics data are considered as depicted in Traditional analyses attempted to untangle the molecular mechanisms of complex diseases using a single -omics dataset which contributes towards the identification of disease-specific mutations and epigenetic alterations. However, in the postgenomic era, it has been noticed that a single -omics dataset is not sufficient to explain disease hallmarks. It requires the combined analysis of various -omics datasets. As such, recent studies are shifting towards multi-omics data analysis, where each of these different -omics data types are critical for deciphering the molecular signatures of human diseases. Therefore, the integrated analysis of different data types has become a recent trend. For a holistic understanding of complex biological problems, it is becoming clear that integrations of different -omics data types are essential steps. However, it is a notorious task, as handling heterogeneous and noisy biological data is a challenging issue .In addition to the ‘omics’ realm, another major reason for phenotypic differentiation is post-translational modifications (PTM). They can be both in physiologically reasonable and pathologically anomalous forms. Methods for bioinformatically incorporating PTM effects are emerging from the gradual improvement of sequence motifs, or less directly from compensatory expression patterns that emerge when an organism seeks to correct for aberrant biochemistry arising from anomalous structural modifications.Due to the recent advancements in next-generation sequencing and microarray technologies, the cost of obtaining the gene expression profile of a sample is rapidly decreasing, and hence expression profiling has become a routine protocol in biological laboratories. The high turnaround of expression data is also coupled by the massive increase in the use of the revolutionary RNA-Seq method . It is bData analysis approaches to gene expression profiling have evolved rapidly as there are massive shifts from DNA microarray to RNA-seq-based profiling. The earlier methods involved clustering approaches and traditional ML approaches. Since a large volume of biological knowledge has become available, in the literature there are obvious shifts from the pure data-oriented approaches to biological domain knowledge-based integrative approaches. This fact has triggered bioinformatics researchers to suggest and develop advanced tools that consider the emerging biological knowledge, and hence they exploit this knowledge for deep analysis of the data. There are many resources of biological knowledge, such as textual knowledge, as more and more literature emerges, different databases and repositories such as miRTarBase  for micrMost feature selection algorithms that are applied on gene expression data are based on statistics and ML. However, most of them neglect the biological knowledge of the data that could contribute to perform better feature selection. R. Bellazzi and B. Zupan  discusseThe flood of -omics data and the need for more informative results urge the need for integrative approaches. The book of Ref.  is the fOn the other hand, multi-dimensional biological data are another challenge as these data are often derived from limited numbers of samples because of the associated costs of biological data generation. This is an example of the ‘curse of dimensionality’ problem, as initially reported by Bellman  in 1961.Gene selection approaches for gene expression datasets can be mainly categorized into two classes, such as traditional gene selection and integrative gene selection. While traditional gene selection approaches are solely based on statistical and computational analyses of the expression levels, integrative gene selection approaches incorporate domain knowledge from external biological resources during gene selection.t-test, etc.), not based on ML. Wrapping-based approaches are primarily learning techniques and these are used for the exploration of usefulness of features, whereas embedding-based approaches are combining the feature selection and the classifier construction. Wei Pan carried out a comparative study on different filtering methods in Ref. [t-test method, regression modeling approach and mixture model approach).Traditional gene selection approaches are heavily based on statistical and computational analyses of the actual expression levels. Recent reviews have summarized various methods for describing the selection process of disease-specific features from large gene expression datasets ,17. PrimAdditional comparisons of filtering techniques are available in Ref. . I. InzaThe traditional gene selection approach has several drawbacks. For example, the filtering approach evaluates the significance of each gene individually without considering the relationships and the interactions between the genes. Although the wrapping-based approaches can find the optimal set, it might be specific to the model used, such as SVM, decision trees or other models. In other words, it might be overfitting the data . The maiAlthough the traditional gene selection approaches became popular for a long time, they have several drawbacks when one needs to precisely identify the underlying biological processes. Alternatively, integrative gene selection approaches incorporate domain knowledge from external biological resources during gene selection ,18, whicOne example of the integrative gene selection approach is proposed by Qi and Tang, where they utilize the power of biological information contained in GO annotations to rank the genes . The algAnother example of the integrative gene selection approach is SoFoCles , which uYet another study by Fang et al.  combinesThe integrative gene selection approach that is proposed by Raghu et al.  makes usThe integrative approach of Quanz et al. aims to map genes into KEGG pathways and then uses these pathways as features for further pattern mining . In theiMitra et al. adopted the clustering large applications based upon randomized search (CLARANS) method to the feature (gene) selection problem via utilizing biological knowledge . Their rIn Ref. , the autThe related studies that are presented until this point are highly specific to a single knowledge-base, e.g., KEGG pathway or GO terms. On the other hand, Perscheid et al.  proposedThe above-mentioned studies proposed predictive models, but most of the time, instead of obtaining high predictive accuracies in these models, the scientists are curious about the biological meaning of the predictive model. The ‘black box’ nature of the predictive model can hamper its interpretation. The information excerpted from the model may require further processing, and careful interpretation with corresponding biological knowledge may be needed. The interpretation of the complicated cases may be quite challenging, and such an interpretation may currently be out of reach. Although the joint analysis of multiple biological data types has the potential to enlighten our understanding of complex biological phenomena, the data integration is challenging due to the heterogeneity of different data types. For example, an expression profile, as obtained from a transcriptomic study, is a vector of real values and the length of a vector is equal to the number of genes in the genome. However, the genetic variants as obtained from a genomic study are categorical, and they have different vector lengths. While different studies ,4 proposFeature selection and discovering the molecular explanation of diseases describe the same process, where the first one is a computer science term and the second one is used in the biomedical sciences. In 2007, Yousef et al. proposed a new feature selection method, support vector machines–recursive cluster elimination (SVM-RCE), to group/cluster genes for gene expression data analysis. This study invented the “recursive cluster elimination‘‘ phrase for the first time in the machine-learning domain and introduced it to the computational community. As such, this study became a pioneer study in this field. Interests in this approach have increased over time and several studies have successfully applied the SVM-RCE approach to identify the features/genes that are directly associated with a disease/condition . This grThe study of Zhao, X. et al.  has usedThe term “knowledge-driven variable selection (KDVS)” is a similar term to “integration of biological knowledge”, and both of them are used in the process of feature selection. An additional similar study that applied KDVS to SVM-RNE is presented by Ref. , in whicThe RCE algorithm  considerIn The genes that are involved in the same biological process are likely to be co-expressed . TherefobioF throughout the rest of this paper) that are integrated with the ML algorithms. The generic approach is presented in The main aim of the generic approach is to search for and determine significant groups/clusters of features based on one or more biological grouping function ) that separates the points with label + 1 from the points with label −1. The signed distance di of a point xi to the separating hyperplane  is given by di = (w·xi + w0)/||w||.For simplicity, let us define the optimal separating hyperplane to be f(x) = w0 + w1 × 1 + w2 × 2 + … + wd × d, where the  is the features and w =  is the corresponding weights. SVM is actually the solution of finding the optimal linear function, as developed by Vapnik . It is oTrain the classifier on the given data;Assign rank for each feature as its weight;Remove one feature or percentage (10%) with the smallest weight;Repeat steps 1–3 until reaching a predefined number of genes.SVM-RFE, which stands for support vector machines with recursive feature elimination, was firstly introduced by Isabelle Guyon et al.  and applDifferent studies  have emeML is becoming a very powerful computational approach in the field of bioinformatics. In this respect, the first step in utilizing ML is to apply unsupervised and supervised algorithms on biological data. However, in the era of big biological data, we come up with emerging approaches that integrate biological knowledge with ML. A recent review  on ML anbioF. Correlated genes are hypothesized to have similar biological functions. Then, the rank component is applied to assign a score for each cluster, indicating its significance in terms of the classification of the two given classes. In order to perform the rank component, each cluster is considered by representing the data based on the genes that belong to it while keeping the class labels of the original data. Now the data are transferred to cluster genes representation. The rank component performs internal cross-validation and aggregates the performance outcome as the score of the cluster. The RCE procedure is applied to remove the lowest ranked groups. The SVM-RCE results show that the classification accuracy is superior to other approaches, suggesting that the classification results are more interpretable, and this creates new hypotheses for future investigation.SVM-RCE ,36 is babioF for grouping the genes into subnetworks of genes. Then, a similar procedure of ranking is applied as described in SVM-RCE. SVM-RNE also performs the recursive elimination procedure by ranking firstly all the groups, and then removing the least significant groups. The algorithm proceeds by applying again the GXNA to suggest groups. This process is repeated until satisfying some predefined constraints on the number of groups.SVM-RNE  is an exbioF biological grouping function here is the biological association between microRNA and its set of targets. The bioF grouping function is based on the database mirTarBase [Disease development mechanisms mainly involve changes in the transcript levels and protein abundance. MicroRNAs (miRNAs) are instrumental in regulating the gene expression, and hence they affect transcript levels and protein abundance. The fact that microRNAs target more than one mRNA helps us to group the genes into groups where each group consists of the list of genes targeted by a specific microRNA. In other words, the rTarBase . mirTarBrTarBase  has beenbioF on mirTarBase. Additionally, it performs a computation procedure in order to score/rank the importance for each group for the classification tasks.j groups. The default value of j is 2. We apply to the training part of the data a t-test statistics in order to remove noisy genes. The test part of the data is used in order to estimate the performance of the tool.The inputs to the maTE tool are the gene expression data, and the list of microRNAs and its target genes. The main function of the tool is to produce a group of genes based on the miRNA target information and then rank each group by applying random forest with cross-validation, which is repeated r times. The average of the accuracy for each iteration is actually the rank for a specific group. Then the groups are ranked according to the rank values. The model will be built considering the genes on the top The CogNet tool is a classification of gene expression data based on ranked active-subnetwork-oriented KEGG pathway enrichment analysis . CogNet CogNet provides a list of significant KEGG pathways, including its genes that are able to separate the classes of the data. The list would serve the biology researcher for deep analysis and better interpretability of the role of KEGG pathways in the data, or the case that is being studied. As a future work, we would develop CogNet to explore the effectiveness of different combinations of the KEGG pathways in the data. In the current version, we treat each KEGG pathway individually.Due to the advances in technology, both mRNA and microRNA expression profiles can be generated allowing integrative analysis aiming to uncover the functional effects of RNA expression in complex diseases, such as cancer. Most of the approaches that integrate miRNA and mRNA are based on statistical methods, such as Pearson correlation, combined with enrichment analysis approaches. In this study , a novelAs we have more advanced high-throughput technologies, big transcriptomic datasets become available, and extracting insights from long lists of differentially expressed genes becomes a challenge. Since the gene expression data typically have small samples size but high dimensions and noise, the major challenge is the detection of disease-related information from vast amounts of redundant data and noise. As such, the gene (feature) selection and the removal of redundant/irrelevant genes has been a key step to address this problem. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes, and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. However, lately, the gene selection process has shifted from being purely data-centric to more incorporative analysis with additional biological knowledge. Integrative gene selection approaches incorporate domain knowledge from external biological resources during gene selection ,18, whicHigh-throughput profiling technologies currently enable us to concurrently measure gene expression levels for tens of thousands of genes in a single experiment, but they have some drawbacks. The high dimensionality of the gene expression data and relatively small sample sizes make the interpretation of the data a complicated, and often overwhelming, task. Although sample sizes have continued to grow in recent years, new and efficient feature selection algorithms are still needed to overcome the challenges in the existing methods . As suchAt present, ML is applied to specific data in order to explain and answer a specific biological query in the biological knowledge domain. One of the challenges of future integrative model-based ML is the ability to combine different biological resources to enhance our understanding of multiple biological questions. Once the full potential of the available data is achieved, they can be used in the development of gene-based diagnostic tests, drug discovery studies and in the development of therapeutic strategies for improving public health.To sum up, since biological systems are quite complex and they have an interconnected nature, a single model that is trained on a single dataset can only benefit from a small portion of the entire biomedical knowledge. For this reason, in order to get the complete picture of molecular biology and medicine, the integration of diverse biological resources and multi-omics data is crucial. In the field of gene expression data analysis, there are still many challenges that the community needs to solve, such as the integration of gene expression datasets that are generated by different research groups for the same phenotype (which will help to overcome the batch effect), and an additional obstacle is the integration of non-similar data, wherein each dataset tackles a specific disease.


The spectrum of Internet of Things (IoT) applications is exponentially growing, driving the demand for better energy performance metrics. In conjunction, Low Power Wide Area Networks (LPWAN) have evolved as long-range connectivity enabler with low management cost. The integration of LPWAN communication assists in reliable IoT operation with extended lifetime. Notable LPWAN technologies that contend for many of the IoT applications are LoRaWAN, DASH7, Sigfox, and NB-IoT. Most of the end-devices such as sensors and actuators are battery powered, therefore investigating energy consumption becomes crucial. To estimate the consumed power, it is important to analyze the energy consumption in wireless communication. This paper describes an empirical evaluation of energy consumption for LPWAN wireless technologies. We measure the current consumption of LoRaWAN, DASH7, Sigfox, and NB-IoT and derive the respective battery lifetime. These measurements help to quantify the energy performance of different protocols. We observe that LoRaWAN and DASH7 are more energy efficient when compared to Sigfox and NB-IoT. Finally, a case study on energy consumption is done on precision agriculture in the greenhouse, showing that battery lifetime in real applications can drop significantly from the ideal case. These results can be used for increasing the effectiveness of the IoT application by selecting the right technology and battery capacity. The Internet of Things (IoT), has gained a high momentum  in both LPWAN is a category of wireless communication that acts as connectivity enabler to support diverse IoT applications by complimenting short and cellular wireless communication. Cellular based solutions have long connectivity but consume more energy . LPWAN tMost of the operating end-devices such as sensors and actuators are battery-operated, thereby power depletion becomes an imperative factor and needs an energy management approach to prolong the battery life. Monitoring of energy consumption is one of the crucial considerations to optimally design the network while fulfilling the application requirements. Each task that is responsible for sensing, processing, and packet transmission consumes energy which accumulates to the power consumption. Additionally, other factors also influence energy consumption that derives total lifetime of the end-device such as configuration parameters of respective LPWAN technology, type of the hardware, battery and the firmware used on the end-device. Therefore, it is crucial to investigate the characteristics of LPWAN energy consumption. However, most of the published works only focus on energy consumption to a limited extent, providing only rough estimates through simulation or investigating only one LPWAN technology. This opens up research questions: (1) Which LPWAN wireless technology is the best connectivity enabler in the context of energy consumption for the particular set of IoT applications? (2) What can be the estimated battery lifetime and how much mAh(Milliamp-hours) capacity of battery is needed for application? (3) Also, the trade-off between energy consumption calculation for the ideal case and real-time deployment.Representation of the generic energy consumption states for LoRaWAN, DASH7, Sigfox, and NB-IoT end-device.Real-time power consumption analysis for transmitting the data and implication of different modes of LPWAN technology concerning energy performance.Investigation and analysis of the lifetime for LPWAN powered end-device with reference to the battery capacity.Real-time case study for energy consumption in an IoT application.To underpin the relevance of the research questions, the following are the contributions of this paper:The remainder of this paper is structured as follows. Energy efficiency is the key requirement for any sensor based application. LPWAN technologies are relatively new and are used in both indoor and outdoor applications ,8. SeverDriven by the challenges of energy depletion, many recent works have focused to increase the power efficiency of the network and reduce the power dissipation in communication. Mahmoud et al. , did a cMany published works focus on technology-specific energy modeling and performance evaluation mainly on coverage, capacity, and energy consumption. The papers ,17, presNon-active and Active period. Non-active period constitutes of sleep and idle mode during which most of the peripherals of the sensor node are not performing any task. In the same context, the device moves to active mode where the processing happens, message packet is formed, and eventually data is transmitted using the LPWAN radio. The total energy spent by the end sensor node is the aggregated value of energy spent during sleep and energy spent during the active period. Energy spent during sleep is calculated by the total power consumed during the sleep period. Energy spent during an active period is the total energy spent during Device wakeup ; Sensor processing ; Data processing (processing the data and preparing packet frame); Transceiver pre-processing (transmitting the packet to transceiver which involves activating the radio for transmission mode); Radio transmission ; Wait/Rx (energy spent during the receive window or wait period to receive the acknowledgement); and lastly post processing . Along with the energy consumed in the active period, the total energy spent is affected by three other major factors-battery used, firmware, and hardware. The impact of battery over the energy consumption by the end-device is discussed in The energy consumption paradigm is discussed in this section and demonstrated using In this section, we discuss the approach and assumptions used for calculating the energy consumption of the LPWAN technologies. We start with the approach, experimental setup, and then introduce the overview of different LPWAN technologies  in the context of both generic architecture and power consumption. Subsequently, the respective real-time energy consumption measurements are presented.To evaluate the energy consumption for LPWAN technology, firstly we adhere to ensure constant MCU state among all end-devices;no external hardware such as RTC were connected with the modules;same battery type and capacity are used for all end-devices;fixed packet length of 5 bytes (no sensing is performed) with the provision of only uplink messages;techniques such as fog computing, edge computing, the local database are not considered but only real-time measurement and transmission is considered;end-devices are powered at the same voltage level equal to 3.7 V.For the energy measurements we have used two different power analyzers -Keysight N6705B DC Power Analyzer  and JoulIn this section, we discuss several characteristics and overview of the communication technology and perform the power consumption analysis.frequency ; the spreading factor ; coding rate ; and the bandwidth . Total derived energy consumption depends upon the combination of these parameters.LoRaWAN is an LPWAN communication technology designed to get long range, low cost, and low power star-of-stars topology . LoRaWANConsidering different set of applications, the LoRaWAN specification  has defiClass A: This is the most deployed class of end-device where the sensor can initiate uplink transmission as per the need and have to wait for two short downlink messages. This is the baseline class of device and most power efficient as compared to other class of devices. However, it uses a pure Aloha approach which results in considerable power loss. Most of the time this class of device sleeps to reduce energy consumption.Class B: In this device class, periodic ping slots are initiated by the gateway as downlink messages. This helps in giving schedule slots to the end-device for sending the uplink messages. However, due to periodic beacon from the gateway and its data rate, there is more power loss. End-device periodically receives one of the beacons from network to align internal clock with the network. This beacon based timing reference can be used by the end-device as receive window.Class C: This category of end-device consumes maximum power with almost continuous open receive windows. They offer lowest latency as it allows downlink transmission at any time. Mostly, this class of device is best suited for over the air firmware upgrade. Class C types of devices work same as Class A type, but they do not close the receive window until the next transmission.We did the energy measurement for Class A type of devices as they are currently the most deployed device for several applications. We configured the end-device with SF 9. Different SF configuration results into varying on air time  and resuBLAST stands for Bursty, Light, Asynchronous, Stealth, and Transitive. Bursty implies the abrupt and short transfer of data in response to an event or operation, which is the most likely requirement in low power wireless and actuator networks. Light refers to the small packet size which is usually limited to 256 bytes per transaction. Asynchronous is one of the major concepts in D7A standard; the transfer of commands and responses between the gateways and end-device can happen at any time. The end-devices are bound to the gateway and hence do not require any periodic beacons to advertise its presence and this is reflected in Stealth. Finally, Transitive implies the ability of the protocol to support mobility which allows the end nodes to move seamlessly across the gateways.The D7A supports 3 classes of device: the gateway, sub-controller, and endpoint. Out of the three, gateway and the endpoints are the widely implemented device classes. The end point is the least energy demanding device and implements only a minimal version of the protocol. End-device uses intermittent channel scanning to check for any incoming packet and spends most of its time in sleep. The sub-controller in essence, is an endpoint but it is a full featured DASH7 device. Gateway connects the low power network to another network and should implement all of the D7A stack functionalities. A fundamental structure of a DASH7 network is depicted in DASH7 Alliance Protocol (D7A)  defines DASH7 physical layer specifies parameters like communication channels, modulation scheme, symbol encoding, and the packet structure. It uses Gaussian Frequency Shift Keying (GFSK) modulation scheme for modulating the data to be transmitted. Though, the specification mentions that there can be up to four modulation schemes, only two are implemented currently. The structure of the DASH7 frame is shown in The Data Link Layer (DLL) defines a background frame (carry the adhoc synchronization details) and a foreground frame . The length of the frames is variable and can have a maximum length of 256 bytes.DASH7 supports two modes of communications- PULL mode and PUSH mode as shown in We profiled the energy consumption of DASH7 in both the PUSH and PULL modes and are presented in The PULL mode of data transmission is shown in The basic elements of the Sigfox network are end-devices, base stations, and back-end core network as shown in The end-device initiates the connection in an asynchronous manner and for rest of the time it stays in sleep to save the energy consumption. The communication from end-device to the base station enables cooperative reception as the message is received by multiple base stations empowering spatial diversity. In the unidirectional communication, end-device transmits the uplink message over random frequency channel followed by its two replica messages using other channels at different time intervals as shown in Stand alone operation (utilization of GSM frequency bands); Guard band operation (utilization of LTE carrier’s guard-band that are unused); and In-band operation (utilization of resource blocks of LTE carrier) as depicted in NB-IoT is a narrowband LPWAN technology under licensed frequency bands and uses the existing LTE deployed infrastructure . It exisThe Radio Resource Control (RRC), is responsible for the mechanism of setup, modification and to release resources. It got two states: connected and idle state. All the communication happens in the RRC connected state. However, it consumes more energy, so once the communication is over the end-device moves back to RRC idle state, which triggers after network specific inactivity timer is over. RRC idle is constituted of two modes: Extended Discontinuous Reception Mode (eDRX) and Power Saving Mode (PSM) mode. From energy consumption point of view, eDRX is sleep mode which can vary from 0 to 186 min and PSM is deep sleep mode which can be up to up to 413.3 days. The eDRX mode consists of paging time window which is between 2.56 and 40.96 s. Initially, the end-device registers in the network and goes in a connected state. Thereafter, it enters the eDRX state where it can listen to the downlink control channel . This listening is termed as paging which ends when the end-device moves to PSM state. In this state, end-device keeps it registration state but turns off its radio to save energy.We have done the power consumption for the NB-IoT end-device as shown in Estimating the actual energy consumption of a low power end-device exhibits many challenges in real-life scenarios. There are many factors which determine the overall energy consumption of the end-device. A slight variation in these parameters can have a significant impact on the energy consumption of the end-device; for instance, the sleep mode current. As seen from the current consumption of different LPWAN technologies, the sleep mode current has a significant impact on the over all energy consumption of the device. As most of the time, end-device is in an in-active state so it should be optimized to achieve energy-efficiency.Battery characteristics also impact the end-device lifetime  and taskFirmware and hardware are the backbone for the end-device and directly contributes in energy consumption. With each new release of LPWAN commodity hardware implementations, they are expected to optimize the power consumption which brings the added value for the deployment. There are techniques to reduce power consumption by writing better software . EfficieApplications with multiple integrated devices need far more than battery management, such as keeping the pack of battery cells healthy. Interfacing the battery load needs a safe operation. There are multiple sensors operating at different voltage and speed involved in the operation of such devices. This generates varying workloads and heat on the battery which impacts the expected lifetime. This involves multi-directional research scope such as protecting batteries from cyber attacks using wireless networks and accurate battery management. Energy management is one of the biggest challenges in portable applications having heterogeneous components with dynamic processing. There are various intelligent energy management systems such as fuzzy-logic-approach to reduce energy consumption. Thereby, the type of application and its complexity has an impact in estimating energy efficiency. This needs to be evaluated for picking the right battery, its capacity, and wireless technology for the smooth working of the application. Moreover, applications that has an autonomous power source such as an energy harvesting system can have an extended algorithm and energy management technique to map the number of message transmissions to the available onboard energy. There is a trade-off between available energy and the application requirement that can be achieved by managing the frequency of message transmissions and optimal management of the computation.Having prior knowledge of device lifetime when operated from a battery can be beneficial during the design phases of the network. Battery selection can then be made to meet the device lifetime requirement. Based on the data produced in the energy consumption analysis, we estimated a theoretical lifetime for all the four LPWAN modules for different battery capacity. A plot of the estimated life and battery capacity is shown in Each frequency band used by the LPWAN technologies imposes limit to the number of transmissions. These limits are defined as duty cycle and polite spectrum access restrictions . Duty cyIn addition to the limitations imposed by the duty cycle, there is a limit on the transmission power which affects the total battery life. Transmission power is expressed in milliwatt (mW) or decibel-milliwatt (dBm) that ranges from 5 to 2000 mW. For example in Europe, Sigfox has a duty cycle of 1% and maximum output power of 25 mW . For eacIn The energy consumption of the end-device differs from idle case to the state in deployment due to factors such as: sensors attached to the end-device, exposure to the environment, extra time in computation, and behavior of battery. For this deployment, in addition to the idle energy consumption of LoRaWAN message there is additional task for taking the measurement from the sensors and processing the message frame. Also, it was observed that the temperature inside the sensor box increases which affects the overall energy consumption due to the generated heat. The current consumption for the sensing setup a, is shoLoRaWAN has class A type of device that is most power efficient as compared to other classes of devices. However, we observed that after each transmission there is a waiting period for two receive windows which consumes energy. This pure Aloha design scheme can be solved by using a better approach such as time synchronization or co-ordination scheme to make it efficient and qualify for multi-hop communication. DASH7 is different than LoRaWAN in the context of range and it does the polite channel access before it sends. The optimized utilization of PUSH and PULL mode helps to qualify for numerous applications while saving energy. On top of the existing solution design scheme such as waking up radio using a preamble based approach and using better channel coding scheme can help further in energy optimization. Sigfox has an advantage over other only from coverage perspective, otherwise, it has the highest energy consumption. It sends two more replica of a message in addition to the original message to support mobility and packet loss. This increases the radio transmission time and leads to spending more energy. Acknowledgement based approach or scheduling among end-devices can help the Sigfox network to extend the battery life. NB-IoT is the most robust technology among all due to its availability, coverage, and throughput. The end-device spends initial time in registration and communicating with network which is fixed from the service provider; optimization of this period can save energy. Also, the right selection of eDRX and PSM mode can help the application to have a prolonged lifetime. In our experiments, we observed that LoRaWAN and DASH7 have better energy performance as compared to NB-IoT and Sigfox. The most crucial part which impacts the overall energy consumption is the sleep or the idle time. As the end-device spends the maximum period in this state so it should consume the minimal energy consumption. Most of the research paper, claims the ideal energy consumption using the LPWAN commodity hardware but it turns upside down on the real-time deployment. As we saw, that in ideal case we got the LoRaWAN lifetime for several years but with the real time deployment it narrowed down to a few months. Thereby, its important to note this trade-off and estimate the lifetime of the LPWAN based network on the basis of environment, sensors, reading interval, and firmware.LPWAN technologies are limited by system regulation of the duty-cycle, which restricts the number of message transmission. The energy consumption analysis helps to calculate the energy requirement in different phases but its important to include other factors such as the battery, hardware, and firmware to calculate the end-device lifetime. Energy consumption depends on the type of hardware picked and the attached peripherals such as sensors. There are elements such as LEDs, resistors, and likewise part of the PCB design that consumes additional energy. For our experiments, we removed such connections to make it close to the ideal setup for better comparison. Energy consumption is one of the most crucial requirements for design and implementation of an IoT application. In this paper, we have analysed the energy consumption and quantified the energy performance of the LPWAN technologies. Also, we calculated the battery lifetime for different technologies in respect to the divergent battery capacity. Results show that message transmission and current consumption of idle period is the main contributor for the overall battery lifetime. Based on the analysis of our experiment results, it can be concluded that LoRaWAN and DASH7 are more energy-efficient as compare to NB-IoT and Sigfox. However, LoRaWAN attracts application with better coverage distance and DASH7 covers the application space demanding mid range communication. Further, NB-IoT is more efficient than other technologies where availability is of utmost importance. Sigfox has the lowest energy consumption performance as compared to other LPWAN technologies. We demonstrated the lifetime of LPWAN technologies with different battery capacity and message transmission frequency. This can give insights and help in picking the right LPWAN technology, managing the frequency of communication, and finally selecting the appropriate battery capacity.IoT requires LPWAN technology and battery powered sensors to be compatible with the lifetime demand of the application. In ideal case, the battery lifetime of the LPWAN network stretches over several years, as it is multiple years in our evaluation. However, as shown later in the paper for the real-time case study on greenhouse deployment, the battery life decreases drastically. This gives three level of energy-consumption analysis: first level with simulation, second with the real-time hardwares, and finally the real time analysis of battery life post deployment. As we proceed in the above sequential analysis, a more realistic energy consumption evaluation can be achieved that can improve the future designs. Our future work will focus on the evaluation of the impact of LPWAN configuration parameters towards energy efficiency and also to propose a feedback model for calculating the lifetime of the network prior to actual deployment.


IoT-based systems, when interacting with the physical environment through actuators, are complex systems difficult to model. Formal verification techniques carried out at design-time being often ineffective in this context, these systems have to be quantitatively evaluated for effectiveness at run-time, i.e., for the extent to which they behave as expected. This evaluation is achieved by confronting a model of the effects they should legitimately produce in different contexts to those observed in the field. However, this quantitative evaluation is not informative on the drifts in effectiveness, it does not help designers investigate their possible causes, increasing the time needed to resolve them. To address this problem, and assuming that models of legitimate behavior can be described by means of Input-Output Hidden Markov Models (IOHMMs), a novel generic unsupervised clustering-based IOHMM structure and parameters learning algorithm is developed. This algorithm is first used to learn a model of legitimate behavior. Then, a model of the observed behavior is learned from observations gathered in the field. A second algorithm builds a dissimilarity graph that makes clear structural and parametric differences between both models, thus providing guidance to designers to help them investigate possible causes of drift in effectiveness. The approach is validated on a real world dataset collected in a smart home. These challenges are difficult to meet because the physical environment is complex and difficult to model [effectiveness of these systems at run-time [Systems based on the Internet of Things (IoT-based systems) such as ambient assisted living systems (AAL)  and smarto model . Designerun-time , i.e., trun-time . This morun-time .quality  [quality of experience (QoE) [performance and reliability assessment metrics , remaining useful life (RUL), etc.), can be used to highlight any deterioration of an IoT-based system and trigger analyses leading to corrective actions. However, these assessments do not provide guidance to designers that would help them direct their research and identify the possible causes of drifts in effectiveness, increasing the time needed to resolve them. More specifically, drifts in effectiveness, from the model of the legitimate behavior perspective, can be threefold: (1) the system does not behave as expected (2), the model is incomplete, the observed behavior, although legitimate, has not been foreseen and (3), there is an incipient drift in parameters, i.e., the observed behavior, although legitimate, is slightly outside the expectations . Whether it is due to unforeseen anomalous or legitimate behaviors or to parameters drifting over time, designers must be provided with tools to support them in investigating drifts in effectiveness by correlating various events and consulting system experts to possibly determine their corresponding root causes as well as their relevant data, thereby reducing the time needed to resolve them.The quantitative effectiveness assessment, as ce (QoS) ,8, qualice (QoE) ), perforcontextual input, events); (3) it incorporates tolerances on expectations. On the basis of this assumption, the contribution of this paper is two-fold:bijection between states and observations [Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN*) [Flexible, Incremental, Scalable, Hierarchical Density-Based Clustering for Arbitrary Data and Distance (FISHDBC) [With the assumption that observations are stochastically correlated with the IoT-based system conditions , we devDBSCAN*)  clusteriFISHDBC) .(a)The learning algorithm is fed with observations corresponding to the effects expected to be produced by an IoT-based system, leading to the learning of a model of the legitimate behavior. This model can further be used to quantitatively assess the effectiveness of the system at run-time as done in .(b)directed dissimilarity graph that highlights differences between both models, thereby helping designers direct their research and identify the possible causes of drifts in effectiveness.This model is incremented with observations of the effects produced by the system in operation within its environment, leading to the learning of a model of the observed behavior. Then, on the basis of these models, an algorithm is proposed and used to build a On the basis of this algorithm, we propose a framework for investigating drifts in effectiveness of IoT-based systems. This framework takes place in two steps as depicted in In this paper, we consider IoT-based systems whose model of the legitimate behavior can be described with Input-Output Hidden Markov Models (IOHMMs) . This frfault diagnosis as well as fault detection and identification and system health monitoring, just to name a few.The problems addressed in this paper have connections with the fields of model-based In , authorsOne of the advantages of these approaches is that HMMs (and IOHMMs) are explainable models . However.BIRCH clustering algorithm [Typicality and Eccentricity Data Analytics (TEDA) clustering approach [In order to detect unknown emerging behaviors, whether legitimate or anomalous, some authors envisage models with variable state spaces on the basis of unsupervised online learning schemes. For instance, in , authorslgorithm . While tlgorithm , authorsapproach , based oapproach , a methoapproach , an adapWhile the aforementioned approaches allow to increment models with unforeseen legitimate or anomalous behaviors, they do not provide guidance to designers to help them investigate possible causes of the anomalous behaviors. For instance, in [ance, in , anomaloance, in , designePseudo Subjective Quality Assessment (PSQA) approach to automatically assess the QoE in the field of video streaming. The PSQA  approach consists of learning, by performing a set of subjective tests, how humans react to quality. The learning tool used is a specific class of neural network . The QoE assessment is then based on an estimation function Q defined during the learning phase and specific features in video streams . In [Interesting approaches have been implemented as part of the QoE. For instance, in , the aution). In , a systeRNNs can model a wide range of dynamical systems . However In [wever In , althougIn this study, a gap was found in the visualization of anomalies for the analysis. The authors recognize that new methods and approaches are needed in order to analyze anomalies. The approach proposed in this document aims at filling this gap. It is based on the IOHMM modeling framework, which makes it possible to define explainable behavioral models. Like some of the aforementioned approaches, we envisage models with variable state spaces on the basis of unsupervised online learning schemes for learning the behavior of a system from observations. However, unlike these approaches, we propose an algorithm that, beyond metrics, makes clear the differences between the model of the legitimate behavior and the one learned from observations, providing guidance to designers to help them investigate anomalous behaviors.Specific to IoT, in , a systek,i to be the first state of a state sequence. In this paper, we assume that the elements A is the n-dimensional contextual input distribution The problem of inferring the likelihood of an observation sequence The problem of inferring the distribution over hidden states anywhere in the observation sequence The problem of inferring the most likely sequence of hidden states that led to the generation of the observation sequence can be solved by the Viterbi algorithm.This model serves as a basis for efficient solutions to several inference problems :(1)The pThis modeling framework brings numerous advantages ; (1) it no solution has been proposed to date within the IOHMM framework apart from learning the parameters ,37,38. T. As part of the solutions proposed within the HMM framework, local search algorithms [State merging algorithms [agglomerative clustering algorithms) first build a maximum likelihood structure Instantaneous Topological Map (ITM) algorithm [discretizable into a finite number of observable regions, every region being represented by a discrete state in the HMM.The approach proposed in this paper considers the modeling of the legitimate behavior of an IoT-based system as an IOHMM and compares this model with that learned from field observations. The objective was to highlight the differences in behavior that may appear during the operation of the system. To achieve this, the proposed approach thus requires an IOHMM model to be learned from observations prior considering comparing both models and highlighting their differences, i.e., it requires to learn the IOHMM structure and parameters from field observations. While structure and parameters learning problem has been intensively studied in the case of HMMs, gorithms  start frgorithms   clustering algorithm  [Structure learning, whether through local search or state merging, consists in empirically estimating the best state space segmentation from data. In all the aforementioned approaches, parameters um-Welch ). Underls (GMMs)  where pasolution .In the sequel, we propose a novel unsupervised generic algorithm for learning IOHMM structure identifying the model) is first and foremost about segmenting the observation space into a finite number of relevant regions such that each region represents a discrete state, i.e., it is assumed that observations are stochastically correlated with the system conditions, thereby taking advantage of understanding its structure ["a state of nature that governs the observation generation process. It can be viewed as a source of observations whose distribution is governed by a density function specific to the region" [What characterizes discrete state space IOHMMs and derivatives is that they model stochastic processes whose states are hidden, i.e., they can only be inferred from continuous observation sequences. Learning the structure tructure . A regio region" .clustering algorithms. “These algorithms try to group observations so that the regions thereby obtained reflect the different generation processes they are governed by” [Algorithm 1: Learning of IOHMM structure Observation space segmentation into regions can be achieved using unsupervised rned by” . On thisQ (line 4) and the elements i into the most appropriate (multivariate) distribution (line 7)  are known.Observations could be noisy and, considering incremental learning, incomplete.As such, the proposed algorithm accommodates any unsupervised  clustering algorithm where incremental has to be understood here as the ability to process continuous observation sequences as they arrive . In thisSo as to address these hypotheses, we consider the HDBSCAN* hierarchical density-based clustering algorithm .Density-Based Spatial Clustering of Applications with Noise (DBSCAN) algorithm [https://hdbscan.readthedocs.io/en/latest/faq.html).The lgorithm , and itslgorithm ), is a clgorithm . It can It does not require to specify upfront the number of clusters in the data.It can find non-linearly separable clusters of varying shapes and densities.The ordering of the data does not matter.outlier (or noise) assignments as being observations isolated in sparse regions.It supports In the context of this paper, the main advantages of the algorithm lie in the fact that:Intrinsic outliers depend on how conservative one wants to be in learning the model, governed by the HDBSCAN* min_samples parameter. The larger the value of min_samples, the more conservative the clustering, i.e., clusters will be restricted to progressively more dense regions with, as a consequence, a higher number of outliers (https://hdbscan.readthedocs.io/en/latest/parameter_selection.html)  . The notrvations . On the inct see ,Extrinsic outliers depend on the observations (noise) and are particularly interesting in the context of incremental learning. Indeed, while observation sequences arrive progressively, some might reinforce the density of an existing region while some others might form a sparse region not yet dense enough to be considered as a cluster .Among the aforementioned advantages, the notion of outliers is particularly relevant when considering model learning. Let us consider two types of outliers:Algorithm 2: Outliers removal (extension of Algorithm 1).Whether intrinsic or extrinsic, outliers have to be discounted from the clustering process. Assuming that they are associated to a dummy cluster The experimentation is carried out on a real world dataset gathered in a smart-home equipped with 54 IoT devices offering up to 228 sensors and actuators . The assOn the basis of this setup, the objective is to learn a model of the legitimate behavior of a set of devices in different contexts on the basis of sound features. The devices considered for this experiment are the TV and the Amazon Echo smart speaker.From a modeling perspective, output observations (legitimate effects) are characterized by Mel-Frequency Cepstral Coeffs (MFCCs) and Zero Crossing Rate (ZCR) sound features, extracted from a microphone signal and processed at the edge of the IoT infrastructure by a Raspberry Pi. Input observations (contexts) are characterized by the operating status of the TV and the Amazon Echo smart speaker. MFCC and ZCR features have been selected for their capacity at producing a good segmentation of the observation space.While both devices can produce sound into the environment, users expect that they do not produce it simultaneously. The dataset used for the experiment reproduces this legitimate behavior as depicted in min_samples = 40. Input observations  are segmented into three clusters. Observations are colored in cyan when both devices are switched OFF. Observations are colored in yellow when the TV is ON and the Amazon Echo smart speaker is OFF. Finally, observations are colored in magenta when the TV is OFF and the Amazon Echo smart speaker is OFF. Output observations  are also segmented into three clusters. The blue one corresponds to the silence; the orange one characterizes the sound emitted by the TV and the green one characterizes the sound emitted by the Amazon Echo smart player . As expected, none of the devices operate simultaneously.Observations are colored according to the contexts identified by the HDBSCAN* algorithm with min_samples parameter set to 40. The IOHMM model of the legitimate behavior learned from the dataset is depicted in The dataset is applied to the Algorithm 1 implemented with HDBSCAN* with The completeness of the IOHMM model learned here depends, however, on the input-output observations the algorithm has been fed with during the learning process. There is no certainty that the model is complete, a legitimate behavior may occur in the long run that was not part of the initial set of observations. For instance, what if a new device producing sound is added into the environment? A new state should appear, whether legitimate or not, characterizing the sound emitted by this new device. Generally speaking, the behavior observed in the field in the long run may no longer correspond to that which is supposed to be legitimate, defined at a certain point in time. Whether this is due to the fact that the model of the legitimate behavior is incomplete , that its parameter values do not fit the concrete legitimate behavior anymore  or that new legitimate or illegitimate states and/or state transitions have appeared, it is necessary to provide designers with tools that allow them to investigate and understand changes that have potentially occurred.structural and parametric dissimilarities between the model of the legitimate behavior of an IoT-based system and a model learned from field observations. In the literature, dissimilarities between two HMMs are mainly characterized by a measure. In [In this paper, the study of drifts in effectiveness is carried out by identifying the sure. In , authorssure. In , authorsIn the sequel, we propose an algorithm that builds a dissimilarity graph between legitimate and observed behaviors, making clear both structural and parametric differences and providing guidance to designers to help them investigate possible causes of drift in effectiveness, reducing the time needed to resolve them.Structure related) Output observations gathered from the field lead new clusters to occur. These new clusters define new states  not anticipated/foreseen in the model of the legitimate behavior.(Structure related) Input observations gathered from the field lead new state transitions to occur  not anticipated/foreseen in the model of the legitimate behavior.(Parameters related) Input and output observations gathered from the field lead distribution parameters  to be slightly modified, denoting a drift in the initial parameter values.(Structure and Parameters related) Input-output observations gathered from the field do not cover the states and state transitions defined in the model of the legitimate behavior. This situation implies that either not enough observations have been gathered from the field  or that the model is somehow wrong, i.e., it expects a different behavior than the one concretely implemented. First, using the Algorithm 1 implemented with FISHDBC clustering algorithm, a model of the legitimate behavior is learned from observations (line 5) On the basis of the sets of clusters CI and CO and the sequences of clusters SI and SO obtained from the previous step, one can compute, using the Algorithm A1, the vector V elements of SO correspond to the expected states computed from (lines 6 to 16) The first V elements of SI and SO corresponding to the legitimate behavior.(lines 17 to 21) This part of the algorithm is devoted to compute the transition matrix A and (lines 22 to 30) Transition matrices IOHMMs being graphical models one can highlight the aforementioned dissimilarities, thereby helping designers to identify changes that may have occurred. To this end, the algorithm described in The dataset used in the This dataset highlights two unforeseen behaviors  in IoT-based systems whose purpose is achieved through interactions with the physical environment are complex and difficult to model. State-of-the-art formal verification techniques carried out at design-time are often ineffective even though the trustworthiness of these systems remains a first class concern. One way to address this problem is to quantitatively evaluate their effectiveness at run-time on the basis of a model of their legitimate behavior and field observations. However, while this quantitative evaluation can be leveraged as part of a monitoring process, thereby triggering actions in the field and/or conceptual investigations on the system in response to drifts in effectiveness, it does not provide guidance to designers to help them investigate their possible causes, increasing the time needed to resolve them. To address this problem, a novel unsupervised generic clustering-based Input-Output Hidden Markov Model (IOHMM) structure and parameters learning algorithm, implemented with HDBSCAN*/FISHDBC clustering algorithms was presented. These algorithms were first leveraged to learn the model of the legitimate behavior of an IoT-based system from continuous observations either generated from simulations or gathered from the field. This model was then complemented with observations from the field, characterizing the IoT-based system in operation. Then, a second algorithm was proposed to generate a dissimilarity graph making it clear to designers the structural and parametric differences between both models, helping them to investigate their possible causes. In , a gap wThe approach proposed in this paper is generic; assuming that observations are stochastically correlated with the IoT-based system conditions, (1) the unsupervised learning algorithm can accommodate any continuous observation space clustering algorithm, (2) observations can be fitted into any distribution type beyond Gaussian Mixture Models (GMM) usually assumed, for instance, in the Expectation-Maximization-based (EM) algorithms .min_samples parameter. A possible approach would be to handle model learning as an optimization problem where the min_samples parameter value chosen is the one maximizing a particular criteria ). The IOHMM unsupervised learning algorithm presented in this paper is based on observation space clustering and we also plan to leverage clustering evaluation indexes  as crit


This work is inspired by motion control of cleaning robots, operating in certain endogenous environments, and performing various tasks like door cleaning, wall sanitizing, etc. The base platform’s motion for these robots is generally similar to the motion of four-wheel cars. Most of the cleaning and maintenance tasks require detection, path planning, and control. The motion controller’s job is to ensure the robot follows the desired path or a set of points, pre-decided by the path planner. This control loop generally requires some feedback from the on-board sensors, and odometry modules, to compute the necessary velocity inputs for the wheels. As the sensors and odometry modules are prone to environmental noise, dead-reckoning errors, and calibration errors, the control input may not provide satisfactory performance in a closed-loop. This paper develops a robust-observer based sliding mode controller to fulfill the motion control task in the presence of incomplete state measurements and sensor inaccuracies. A robust intrinsic observer design is proposed to estimate the input matrix, which is used for dynamic feedback linearization. The resulting uncertain dynamics are then stabilized through a sliding mode controller. The proposed robust-observer based sliding mode technique assures asymptotic trajectory tracking in the presence of measurement uncertainties. Lyapunov based stability analysis is used to guarantee the convergence of the closed-loop system, and the proposed strategy is successfully validated through numerical simulations. In the recent past, wheeled mobile robots have played a critical role in automating various cleaning and sanitation tasks ,2. The iThe last two decades have given rise to the development of many types of cleaning robots, depending on the required functionality. The authors of  presenteEven though the cleaning robots discussed above vary in their shape, structure, and functionality, they have one common thing, which is a wheeled base platform. The number of wheels and their types are decided by the range of motion required for the task. An outdoor cleaning robot may require three or four wheels for its movement ,7, whereGiven a particular type of platform, the overall task is divided into many sub-steps. For example, a door cleaning robot should first detect the doors in an indoor environment, followed by reaching that particular position avoiding any static or dynamic obstacles ,13. GiveThe motion control for wheeled mobile robots (WMRs) has received considerable attention from the research community due to its importance in the efficient completion of the required task. Numerous linear and nonlinear control laws are proposed for trajectory following and posture stabilization of WMR ,15. SomeAll the control techniques require the measurements of the state variables, that define the mathematical model of the robot. As the measurements are carried out through sensors and odometry module, full state measurements always add to the cost and complexity of the electronics module of a robot. If some measurements are not available, then output feedback control laws  or estimIt is comparatively easy to get the position measurements, compared to the heading angle, which may require additional cost for gyroscope and IMU. Moreover, a relative coordinate  can be easily obtained in most of the practical applications by using vision based data or LIDAR/RADAR. However, the outputs of sensor modules and odometry modules are not very accurate inside the indoor environment. The sensor unpredictability may result from various noise and environmental factors. The odometry section may be affected by quantization errors and the accumulation of errors due to dead-reckoning. If the data used for the control module may not point to the exact posture/position of the robot, then they may lead to an inaccurate following of the desired trajectory. Hence, it becomes important to consider these issues while designing control laws for robots.Many designs of robot tracking controllers for dealing with the absence of heading angle measurement could be found in literature . HoweverThe mathematical model of the robot is described in This paper considers a four-wheel driven car-like robot for deriving trajectory tracking control law. The mathematical model of the robot is derived using two assumptions, similar to .(i)There is no lateral slip while in motion.(ii)The robot motion platform does not have any flexible parts.L from the robot COG. The parameter The geometric relation between the robot center of gravity (COG), and the wheel centers can be observed from The lateral slip of the vehicle is neglected. Hence, the resulting constraintv and A car-like robot with independent steering at each wheel can be driven in various ways. As the focus of the paper is to study the effect of measurement and odometry errors on trajectory tracking, we have chosen the Ackermann-Jeantand methodology. In this technique, the outer wheels (The forward kinematics of the simplified robot model can be described as:x and y.The trajectory tracking (path following) problem can be defined as : Given aFrom , it can f will be decided later. This integrator acts as a dynamic compensator whose output v. If the positions . To ensure perfect trajectory tracking the initial condition for the dynamic integrator was chosen as Initially the simulation was carried out in the absence of any measurement errors The same tracking task was given again with corrupting the x and y measurements by a set of slow varying signals It can be observed from To test the robustness of the technique against fast varying measurement errors, the position in x and y coordinates were corrupted with a set of fast varying signals The estimation errors shown in To test the efficacy of the proposed controller, it was compared with a conventional PID and a dynamic feedback linearization strategy without the sliding mode controller (SMC). The tracking errors for all three controllers are shown in A new robust observer-based control strategy is developed for car-like robots in endogenous environments, where accurate measurements for robot pose is difficult to obtain. The proposed approach can simultaneously deal with uncertainties in position measurement and absence of heading angle information. A robust observer with time varying gain was used to directly estimate the trigonometric functions involving heading angles. The estimates were exploited to formulate a dynamic feedback linearization based sliding mode design. The Lyapunov stability analysis assures closed-loop stability, and simulation results show the efficacy of the proposed methodology. The controller does not account for any modeling uncertainties and friction effects, which are the subjects of the future works.