Panagiotis Fountas

The huge growth and use of the Internet of Things (IoT) in everyday activities in combination with the limited resources present in the involved devices has induced the adoption of Edge Computing (EC) that offers a vast infrastructure where the data can be collected and processed before they are transferred to the Cloud. In the EC infrastructure, there is an increased number of nodes that enhance the pervasiveness of services and applications as EC nodes are present close to end users. From a global view, the IoT infrastructure plays the role of the data collector while the EC nodes act as intermediate processing points where many tasks can be executed. One significant problem is the presence of missing values in the collected data. In this paper, we propose an ensemble-based model for missing values imputation. We adopt a Reinforcement Learning mechanism for the proactive detection of the nodes that have similar behavior to the node that detects missing values. We aim to perform the envisioned imputation value through the aggregation of the suggested replacements based on the local dataset and the weighted replacements as delivered by the group, i.e., nodes with similar data. Moreover, we elaborate on a weighted mechanism for the assignment of proper weights in the nodes with similar datasets as they are depicted by historical records. We evaluate our model by adopting various experimental scenarios.

The ever-increasing demand for services of end-users in the Internet of Things (IoT) often causes great congestion in the nodes dedicated to serving their requests. Such nodes are usually placed at the edge of the network, becoming the intermediates between the IoT infrastructure and Cloud. Edge nodes offer many advantages when adopted to perform processing activities that are realized close to end-users, limiting the latency in the provision of responses. In this article, we attempt to solve the problem of the potential overloading of edge nodes by proposing a mechanism that always keeps free space in their queue to host high-priority processing tasks. We introduce a proactive, self-healing mechanism that utilizes the principles of Fuzzy Logic, in combination with a non-parametric statistical method that reveals the trend of nodes’ loads as depicted by the incoming tasks and their capability to serve them in the minimum possible time. Through our approach, we manage to ensure the uninterrupted service of high-priority tasks, taking into consideration the demand for tasks as well. Based on this approach, we ensure the fastest possible delivery of results to the requestors while keeping the latency for serving high-priority tasks at the lowest possible levels. A set of experimental scenarios is adopted to evaluate the performance of the suggested model by presenting the corresponding numerical results.

Data management at the edge of the network is a significant research subject. Devices being active at the Internet of Things (IoT) can collect data and transfer them to a set of edge nodes for further processing. There, various activities can be realized. Among them, of great importance it is the detection of anomalies in the incoming data and their preparation to be the subject of advanced processing tasks. In this paper, we propose an ensemble scheme for data anomalies detection and elaborate on the use of an extended sliding window approach. We differentiate from the state of the art solutions and argue on the concept of potential anomalies confirming their presence by incorporating more data into our decision mechanism. The performance of the proposed scheme is evaluated by a set of experimental scenarios being also exposed by numerical results.

Over the years, the digitization of all aspects of life in modern societies is considered an acquired advantage. However, like the terrestrial world, the digital world is not perfect and many dangers and threats are present. In the present work, we conduct a systematic review on the methods of network detection and cyber attacks that can take place in a critical infrastructure. As is shown, the implementation of a system that learns from the system behavior (machine learning), on multiple levels and spots any diversity, is one of the most effective solutions.