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Researcher profile

Kostas Kolomvatsos

Kostas Kolomvatsos contributes to research discovery and scholarly infrastructure.

ResearcherAffiliation not importedOpen to collaborate
Distributed, Parallel, and Cluster ComputingMachine Learningeess.SPNetworking and Internet ArchitectureArtificial IntelligenceDatabases

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Published work

10 published item(s)

preprint2026arXiv

A Deep Learning Model for Battery State Prediction towards Intelligent Energy Management

Accurate forecasting of battery health indicators, including remaining capacity and lifetime, is of paramount importance for ensuring the reliability, safety, and operational efficiency of applications such as electric vehicles and large scale energy storage infrastructures. The result of the forecasting can be adopted to build an advanced monitoring mechanism for continuous checking batteries' health status to assist in the efficient real-time management of numerous applications. This research investigates the development and implementation of a Deep Learning (DL) model for the prediction of the future state and performance of industrial electrochemical energy storage systems. To address this challenge, we propose a dedicated computational framework that integrates advanced neural network architectures with large-scale training datasets, enabling precise modeling of batteries degradation dynamics and operational trends. The proposed approach provides a decision support mechanism for the optimal management of batteries facilitating both predictive maintenance and the efficient allocation of energy resources. Our findings highlight the potential of DL-based predictive modeling to significantly contribute to the advancement of sustainable and intelligent energy management systems.

0 citations0 reviewsNo code linkedOpen access
preprint2021arXiv

A Soft Method for Outliers Detection at the Edge of the Network

The combination of the Internet of Things and the Edge Computing gives many opportunities to support innovative applications close to end users. Numerous devices present in both infrastructures can collect data upon which various processing activities can be performed. However, the quality of the outcomes may be jeopardized by the presence of outliers. In this paper, we argue on a novel model for outliers detection by elaborating on a `soft' approach. Our mechanism is built upon the concepts of candidate and confirmed outliers. Any data object that deviates from the population is confirmed as an outlier only after the study of its sequence of magnitude values as new data are incorporated into our decision making model. We adopt the combination of a sliding with a landmark window model when a candidate outlier is detected to expand the sequence of data objects taken into consideration. The proposed model is fast and efficient as exposed by our experimental evaluation while a comparative assessment reveals its pros and cons.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Data Imputation Model based on an Ensemble Scheme

Edge Computing (EC) offers an infrastructure that acts as the mediator between the Cloud and the Internet of Things (IoT). The goal is to reduce the latency that we enjoy when relying on Cloud. IoT devices interact with their environment to collect data relaying them towards the Cloud through the EC. Various services can be provided at the EC for the immediate management of the collected data. One significant task is the management of missing values. In this paper, we propose an ensemble based approach for data imputation that takes into consideration the spatio-temporal aspect of the collected data and the reporting devices. We propose to rely on the group of IoT devices that resemble to the device reporting missing data and enhance its data imputation process. We continuously reason on the correlation of the reported streams and efficiently combine the available data. Our aim is to `aggregate' the local view on the appropriate replacement with the `opinion' of the group. We adopt widely known similarity techniques and a statistical modelling methodology to deliver the final outcome. We provide the description of our model and evaluate it through a high number of simulations adopting various experimental scenarios.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Fuzzy Logic Controller for Tasks Scheduling Using Unreliable Cloud Resources

The Cloud infrastructure offers to end users a broad set of heterogenous computational resources using the pay-as-you-go model. These virtualized resources can be provisioned using different pricing models like the unreliable model where resources are provided at a fraction of the cost but with no guarantee for an uninterrupted processing. However, the enormous gamut of opportunities comes with a great caveat as resource management and scheduling decisions are increasingly complicated. Moreover, the presented uncertainty in optimally selecting resources has also a negatively impact on the quality of solutions delivered by scheduling algorithms. In this paper, we present a dynamic scheduling algorithm (i.e., the Uncertainty-Driven Scheduling - UDS algorithm) for the management of scientific workflows in Cloud. Our model minimizes both the makespan and the monetary cost by dynamically selecting reliable or unreliable virtualized resources. For covering the uncertainty in decision making, we adopt a Fuzzy Logic Controller (FLC) to derive the pricing model of the resources that will host every task. We evaluate the performance of the proposed algorithm using real workflow applications being tested under the assumption of different probabilities regarding the revocation of unreliable resources. Numerical results depict the performance of the proposed approach and a comparative assessment reveals the position of the paper in the relevant literature.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

A Probabilistic Approach for Data Management in Pervasive Computing Applications

Current advances in Pervasive Computing (PC) involve the adoption of the huge infrastructures of the Internet of Things (IoT) and the Edge Computing (EC). Both, IoT and EC, can support innovative applications around end users to facilitate their activities. Such applications are built upon the collected data and the appropriate processing demanded in the form of requests. To limit the latency, instead of relying on Cloud for data storage and processing, the research community provides a number of models for data management at the EC. Requests, usually defined in the form of tasks or queries, demand the processing of specific data. A model for pre-processing the data preparing them and detecting their statistics before requests arrive is necessary. In this paper, we propose a promising and easy to implement scheme for selecting the appropriate host of the incoming data based on a probabilistic approach. Our aim is to store similar data in the same distributed datasets to have, beforehand, knowledge on their statistics while keeping their solidity at high levels. As solidity, we consider the limited statistical deviation of data, thus, we can support the storage of highly correlated data in the same dataset. Additionally, we propose an aggregation mechanism for outliers detection applied just after the arrival of data. Outliers are transferred to Cloud for further processing. When data are accepted to be locally stored, we propose a model for selecting the appropriate datasets where they will be replicated for building a fault tolerant system. We analytically describe our model and evaluate it through extensive simulations presenting its pros and cons.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Intelligent Edge-Centric Queries Allocation Scheme based on Ensemble Models

The combination of Internet of Things (IoT) and Edge Computing (EC) can assist in the delivery of novel applications that will facilitate end users activities. Data collected by numerous devices present in the IoT infrastructure can be hosted into a set of EC nodes becoming the subject of processing tasks for the provision of analytics. Analytics are derived as the result of various queries defined by end users or applications. Such queries can be executed in the available EC nodes to limit the latency in the provision of responses. In this paper, we propose a meta-ensemble learning scheme that supports the decision making for the allocation of queries to the appropriate EC nodes. Our learning model decides over queries' and nodes' characteristics. We provide the description of a matching process between queries and nodes after concluding the contextual information for each envisioned characteristic adopted in our meta-ensemble scheme. We rely on widely known ensemble models, combine them and offer an additional processing layer to increase the performance. The aim is to result a subset of EC nodes that will host each incoming query. Apart from the description of the proposed model, we report on its evaluation and the corresponding results. Through a large set of experiments and a numerical analysis, we aim at revealing the pros and cons of the proposed scheme.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

An Intelligent Scheme for Uncertainty Management of Data Synopses Management in Pervasive Computing Applications

Pervasive computing applications deal with the incorporation of intelligent components around end users to facilitate their activities. Such applications can be provided upon the vast infrastructures of Internet of Things (IoT) and Edge Computing (EC). IoT devices collect ambient data transferring them towards the EC and Cloud for further processing. EC nodes could become the hosts of distributed datasets where various processing activities take place. The future of EC involves numerous nodes interacting with the IoT devices and themselves in a cooperative manner to realize the desired processing. A critical issue for concluding this cooperative approach is the exchange of data synopses to have EC nodes informed about the data present in their peers. Such knowledge will be useful for decision making related to the execution of processing activities. In this paper, we propose n uncertainty driven model for the exchange of data synopses. We argue that EC nodes should delay the exchange of synopses especially when no significant differences with historical values are present. Our mechanism adopts a Fuzzy Logic (FL) system to decide when there is a significant difference with the previous reported synopses to decide the exchange of the new one. Our scheme is capable of alleviating the network from numerous messages retrieved even for low fluctuations in synopses. We analytically describe our model and evaluate it through a large set of experiments. Our experimental evaluation targets to detect the efficiency of the approach based on the elimination of unnecessary messages while keeping immediately informed peer nodes for significant statistical changes in the distributed datasets.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Data Synopses Management based on a Deep Learning Model

Pervasive computing involves the placement of processing services close to end users to support intelligent applications. With the advent of the Internet of Things (IoT) and the Edge Computing (EC), one can find room for placing services at various points in the interconnection of the aforementioned infrastructures. Of significant importance is the processing of the collected data. Such a processing can be realized upon the EC nodes that exhibit increased computational capabilities compared to IoT devices. An ecosystem of intelligent nodes is created at the EC giving the opportunity to support cooperative models. Nodes become the hosts of geo-distributed datasets formulated by the IoT devices reports. Upon the datasets, a number of queries/tasks can be executed. Queries/tasks can be offloaded for performance reasons. However, an offloading action should be carefully designed being always aligned with the data present to the hosting node. In this paper, we present a model to support the cooperative aspect in the EC infrastructure. We argue on the delivery of data synopses to EC nodes making them capable to take offloading decisions fully aligned with data present at peers. Nodes exchange data synopses to inform their peers. We propose a scheme that detects the appropriate time to distribute synopses trying to avoid the network overloading especially when synopses are frequently extracted due to the high rates at which IoT devices report data to EC nodes. Our approach involves a Deep Learning model for learning the distribution of calculated synopses and estimate future trends. Upon these trends, we are able to find the appropriate time to deliver synopses to peer nodes. We provide the description of the proposed mechanism and evaluate it based on real datasets. An extensive experimentation upon various scenarios reveals the pros and cons of the approach by giving numerical results.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

On the Use of Interpretable Machine Learning for the Management of Data Quality

Data quality is a significant issue for any application that requests for analytics to support decision making. It becomes very important when we focus on Internet of Things (IoT) where numerous devices can interact to exchange and process data. IoT devices are connected to Edge Computing (EC) nodes to report the collected data, thus, we have to secure data quality not only at the IoT but also at the edge of the network. In this paper, we focus on the specific problem and propose the use of interpretable machine learning to deliver the features that are important to be based for any data processing activity. Our aim is to secure data quality, at least, for those features that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining in every dataset, thus, they can be adopted for dimensionality reduction. We focus on multiple methodologies for having interpretability in our learning models and adopt an ensemble scheme for the final decision. Our scheme is capable of timely retrieving the final result and efficiently select the appropriate features. We evaluate our model through extensive simulations and present numerical results. Our aim is to reveal its performance under various experimental scenarios that we create varying a set of parameters adopted in our mechanism.

0 citations0 reviewsNo code linkedOpen access
preprint2020arXiv

Proactive Tasks Management based on a Deep Learning Model

Pervasive computing applications deal with intelligence surrounding users that can facilitate their activities. This intelligence is provided in the form of software components incorporated in embedded systems or devices in close distance with end users.One example infrastructure that can host intelligent pervasive services is the Edge Computing (EC) infrastructure. EC nodes can execute a number of tasks for data collected by devices present in the Internet of Things (IoT) infrastructure. In this paper, we propose an intelligent, proactive tasks management model based on the demand. Demand depicts the number of users or applications interested in using the available tasks in EC nodes, thus, characterizing their popularity. We rely on a Deep Machine Learning (DML) model and more specifically on a Long Short Term Memory (LSTM) network to learn the distribution of demand indicators for each task and estimate the future interest. This information is combined with historical observations and support a decision making scheme to conclude which tasks will be offloaded due to limited interest on them. We have to notice that in our decision making, we also take into consideration the load that every task may add to the processing node where it will be allocated. The description of our model is accompanied by a large set of experimental simulations for evaluating the proposed mechanism. We provide numerical results and reveal that the proposed scheme is capable of deciding on the fly while concluding the most efficient allocation.

0 citations0 reviewsNo code linkedOpen access