An Ensemble Interpretable Machine Learning Scheme for Securing Data Quality at the Edge

Data quality is a significant research subject 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 infrastructure 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 on for any data processing activity. Our aim is to secure data quality for those features, at least, that are detected as significant in the collected datasets. We have to notice that the selected features depict the highest correlation with the remaining ones 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 selecting 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

A proactive inference scheme for data-aware decision making in support of pervasive applications

The advent of the Internet of Things (IoT) offers a huge infrastructure where numerous devices can collect and process data retrieved by their environment. Due to the limited computational capabilities of IoT devices, the adoption of the Edge Computing (EC) ecosystem can provide an additional layer of processing to offer more computational resources compared to the IoT. In EC, one can find an increased number of nodes that can collaborate each other and, collectively, support advanced processing activities very close to end users enhancing the pervasiveness of services/applications. Usual collaborative activities can be met around the exchange of data or services (e.g., data/services migration) or offloading actions for tasks demanding a specific processing workflow upon the collected data. The collective intelligence of the EC ecosystem should rely on a ‘map’ of the available nodes and their resources/capabilities in order to support efficient decision making for the aforementioned activities. In this paper, we propose a model that creates this map and proactively infers the ‘matching’ between EC nodes based on their data. Our inference is based on the temporal probabilistic management of data synopses exchanged between peers in the EC ecosystem while exposing the historical correlation of the individual/distributed datasets. The adoption of a decision making scheme upon synopses can limit the circulation of data in the network and increase the speed of processing. We elaborate on an aggregation scheme applied on the outcomes of a probabilistic model and a correlation analysis scheme presenting and elaborating on the theoretical background of the proposed solution. We experiment upon real datasets and a number of evaluation scenarios to reveal the performance of the approach while placing it in the respective literature through a comparative assessment. © 2022 Elsevier B.V

Probabilistic data allocation in pervasive computing applications

Pervasive Computing (PC) deals with the placement of services and applications around end users for facilitating their everyday activities. Current advances on the Internet of Things (IoT) and the Edge Computing (EC) provide the room for adopting their infrastructures and hosting the desired services for supporting PC applications. Numerous devices present in IoT and EC infrastructures give the opportunity to record and process data through the interaction with users and their environment. Upon these data, the appropriate processing should be realized as requested by end users or applications. It is efficient to process such requests as close as possible to end users to limit the latency in the provision of responses. The research community, identifying this need, proposes the use of the EC as the appropriate place to perform the discussed processing which has the form of tasks or queries. Tasks/queries set specific conditions for data they desire imposing a number of requirements for the dataset upon which the desired processing should be executed. It is wise to pre-process the data and detect their statistics to know beforehand if it is profitable to have any dataset as part of the requested processing. This paper focuses on a model that is responsible to efficiently distribute the collected data to the appropriate datasets. We store similar data to the same datasets and keep their statistics solid (i.e., we meet a low deviation) through the use of a probabilistic approach. The second part of the proposed approach is related to an aggregation scheme upon multiple outlier detection methods. We decide to transfer outliers to Cloud avoiding to store them locally as they will jeopardize the solidity of datasets. If data are going to be locally stored, we provide a mechanism for selecting the most appropriate dataset to host them while we perform a controlled replication to support a fault tolerant system. The performance of the proposed models is evaluated by a high number of experiments for different scenarios. © 2020 IEEE

Data-Driven Type-2 Fuzzy Sets for Tasks Management at the Edge

Tasks allocation at the edge of the network is a significant research topic for the upcoming new era of the intelligent edge mesh. One can easily detect interesting attempts to define novel algorithms for distributing tasks into a number of heterogeneous edge nodes. Nodes interact in very dynamic environments, thus, their availability/capability of efficiently executing tasks in real time varies. In this paper, we propose a model for allocating tasks under the uncertainty present in an edge computing environment. The uncertainty is related to the status of edge nodes and their availability for performing the requested processing activities. To manage this uncertainty, we adopt a Type-2 Fuzzy Logic system and propose a novel approach for delivering the appropriate fuzzy sets for input and output variables. Our methodology is fully adapted to nodes' status as exposed by statistical reports exchanged at pre-defined intervals. We propose a data-driven approach that delivers the upper and lower bounds of our Type-2 fuzzy sets and present the corresponding model. We incorporate the uncertainty management mechanism into the decision making model of edge nodes being responsible to select the most appropriate peers for offloading tasks that are not possible to be executed locally. We present the performance of the proposed model through an extensive experimental evaluation and reveal its pros and cons. © 2017 IEEE

Time-optimized management of mobile IoT nodes for pervasive applications

The Internet of Things (IoT) incorporates numerous nodes adopted to support novel pervasive computing applications. Nodes are capable of interacting each other and/or collect/process huge volumes of ambient data. Any service or application executed on top of the collected data is hosted by the operating software/firmware of nodes, thus, such software should be up-to-date. Legacy techniques dealing with the update task cannot efficiently support it due to the adopted centralized approach that suffers from a number of disadvantages. In this paper, we go a step forward and propose a time-optimized and network performance-aware model for initiating and concluding the update process. Our aim is to have the nodes independently deciding the initiation of the update process by finding the appropriate time to execute it. Every node acts autonomously and monitors the network's performance to find a slot where performance parameters advocate for an efficient and uninterrupted conclusion of the update task. Hence, the proposed model can be adapted to the environment and the status of each node. The final decision is made taking into consideration multiple parameters and it is based on the solution of the widely known Secretary Problem (SP) originated in the Optimal Stopping Theory (OST). We provide the description of the problem, specific formulations and the analysis of our solution while extensive experiments reveal the advantages of the proposed scheme. © 2018 Elsevier Lt

An intelligent scheme for assigning queries

Analytics provided on top of large scale data streams are the key research subject for future decision making applications. The huge volumes of data make their partitioning imperative to efficiently support novel applications. Such applications should be based on intelligent, efficient methods for querying multiple data partitions. A processor is placed in front of each partition dedicated to manage/execute queries for the specific piece of data. Continuous queries over these data sources require intelligent mechanisms to result the final outcome (query response) in the minimum time with the maximum performance. This paper proposes a mechanism for handling the behavior of an entity that undertakes the responsibility of handling the incoming queries. Our mechanism adopts a time-optimized scheme for selecting the appropriate processor(s) for each incoming query through the use of the Odds algorithm. We try to result the optimal assignment, i.e., queries to processors, in the minimum time while maximizing the performance. We provide mathematical formulations for describing the discussed problem and present simulation results and a comparative analysis. Through a large number of experiments, we reveal the advantages of the model and give numerical results comparing it with a deterministic model as well as with other efforts in the domain. © 2017, Springer Science+Business Media, LLC, part of Springer Nature

A Proactive Uncertainty Driven Model for Data Synopses Management in Pervasive 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 the 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 an 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. © 2020 IEEE

Proactive tasks management for Pervasive Computing Applications

Current advances in the Internet of Things (IoT) and Edge Computing (EC) involve numerous devices/nodes present at both ‘layers’ being capable of performing simple processing activities close to end users. This approach targets to limit the latency that users face when consuming the provided services. The minimization of the latency requires for novel techniques that deliver efficient schemes for tasks management at the edge infrastructure and the management of the uncertainty related to the status of edge nodes during the decision making as proposed in this paper. Tasks should be executed in the minimum time especially when we aim to support real time applications. In this paper, we propose a new model for the proactive management of tasks’ allocation to provide a decision making model that results the best possible node where every task should be executed. A task can be executed either locally at the node where it is initially reported or in a peer node, if this is more efficient. We focus on the management of the uncertainty over the characteristics of peer nodes when the envisioned decisions should be realized. The proposed model aims at providing the best possible action for any incoming task. For such purposes, we adopt an unsupervised machine learning technique. We present the problem under consideration and specific formulations accompanied by the proposed solution. Our extensive experimental evaluation with synthetic and real data targets to reveal the advantages of the proposed scheme. © 2020 Elsevier Lt