Towards 6G IoT : tracing mobile sensor nodes with deep learning clustering in UAV networks

Unmanned aerial vehicles (UAVs) in the role of flying anchor nodes have been proposed to assist the localisation of terrestrial Internet of Things (IoT) sensors and provide relay services in the context of the upcoming 6G networks. This paper considered the objective of tracing a mobile IoT device of unknown location, using a group of UAVs that were equipped with received signal strength indicator (RSSI) sensors. The UAVs employed measurements of the target’s radio frequency (RF) signal power to approach the target as quickly as possible. A deep learning model performed clustering in the UAV network at regular intervals, based on a graph convolutional network (GCN) architecture, which utilised information about the RSSI and the UAV positions. The number of clusters was determined dynamically at each instant using a heuristic method, and the partitions were determined by optimising an RSSI loss function. The proposed algorithm retained the clusters that approached the RF source more effectively, removing the rest of the UAVs, which returned to the base. Simulation experiments demonstrated the improvement of this method compared to a previous deterministic approach, in terms of the time required to reach the target and the total distance covered by the UAVs

Induction motors versus permanent magnet actuators for aerospace applications

This paper introduces a comparative study on the design of aerospace actuators concerning Induction Motor (IM) and Permanent Magnet Motor (PMM) technologies. In the analysis undertaken, the two candidate configurations are evaluated in terms of both their electromagnetic and thermal behavior in a combined manner. On a first step, the basic dimensioning of the actuators and their fundamental operational characteristics are determined via a time-stepping Finite Element (FE) analysis. The consideration of the thermal robustness of the proposed motor configurations is integrated in the design procedure, through the appropriate handling of their respective constraints. As a result, all comparisons are carried out on a common thermal evacuation basis. On a second step, a single objective optimization procedure is employed, considering several performance and efficiency indexes using appropriate weights. Manufacturing and construction related costs for both investigated topologies are considered employing specific penalty functions. The impact of the utilized materials is also examined. The resultant motor designs have been validated through manufactured prototypes illustrating their suitability for aerospace actuatio

NeuralPot: an industrial honeypot implementation based on convolutional neural networks

Honeypots are powerful security tools, which are developed to shield commercial and industrial networks from malicious activity. Honeypots act as passive and interactive decoys in a network by attracting malicious activity away from critical network devices. Given that the security incidents against industrial and critical infrastructure are getting sophisticated and persistent, advanced security systems are needed. In this paper, a novel industrial honeypot implementation is presented, which is based on the Modbus protocol, entitled NeuralPot. The presented NeuralPot honeypot is able to emulate industrial Modbus entities in order to actively confuse the intruders. It achieves this by introducing two distinct deep neural networks, a Generative Adversarial Network and an Autoencoder Network, which learn Modbus device behavior and generate realistic-looking traffic behavior. Based on the evaluation results, the proposed industrial honeypot performs well in terms of accuracy, similarity, and elapsed time of data generation

Bandwidth management algorithms for communication networks

The main objective of the dissertation is the proposition of new algorithms for the fair and efficient dynamic bandwidth allocation in communication networks. The proposed algorithms are applied both in wireless and in hybrid optical-wireless access networks. The proposed schemes take into account a number of crucial parameters that directly affect the network performance, such as the dynamic nature of modern access networks and the differentiated requirements regarding the quality of services (QoS) provisioning. In this context, an adaptive algorithm applied in WiMAX networks is presented. The novelty of this approach lies in the fact that it constitutes a dynamic and adaptive scheme for adjusting the downlink-to-uplink ratio based on the short-term network dynamics using learning processes. Also, novel bandwidth distribution schemes are proposed for hybrid access networks utilizing weighted fairness provisioning mechanism and optimization techniques in order to incorporate the underlying features of each traffic requests, such as the population density and the propagation delay. The proposed algorithms are extensively evaluated under realistic and dynamic scenarios compared with other competitive allocation schemes.Το αντικείμενο της παρούσας Διατριβής είναι η εισήγηση νέων αλγορίθμων για τη δίκαιη και αποδοτική διαχείριση του εύρους ζώνης σε δίκτυα επικοινωνιών. Οι προτεινόμενοι αλγόριθμοι εφαρμόζονται τόσο σε ασύρματα όσο και σε υβριδικά οπτικά-ασύρματα δίκτυα πρόσβασης. Οι προτεινόμενοι μηχανισμοί διαχείρισης του εύρους ζώνης λαμβάνουν υπόψη κρίσιμες παραμέτρους που καθορίζουν την απόδοση ενός δικτύου, όπως οι δυναμικά μεταβαλλόμενες συνθήκες ενός δικτύου πρόσβασης και οι διαφοροποιημένες απαιτήσεις για την ποιότητα των παρεχόμενων υπηρεσιών. Σε αυτό το πλαίσιο, παρουσιάζεται ένα προσαρμοστικό σχήμα δυναμικής διαχείρισης εύρους ζώνης στα ασύρματα δίκτυα και συγκεκριμένα σε ένα δίκτυο WiMAX. Ο προσαρμοστικός αλγόριθμος που προτείνεται επιχειρεί να ανταποκριθεί στις δυναμικές απαιτήσεις της καθοδικής και ανοδικής ζεύξης του δικτύου μέσω μηχανισμών μάθησης. Στη συνέχεια, παρουσιάζονται αλγόριθμοι, που εφαρμόζονται τόσο στον οπτικό όσο και στον ασύρματο τομέα ενός υβριδικού δικτύου, που υλοποιούν σταθμισμένους μηχανισμούς παροχής δικαιοσύνης και τεχνικές βελτιστοποίησης που λαμβάνουν υπόψη σημαντικές παραμέτρους όπως τον αριθμό των συνδρομητών και την καθυστέρηση. Η απόδοση των προτεινόμενων αλγορίθμων αξιολογείται μέσω εκτεταμένων συγκριτικών πειραμάτων προσομοίωσης

Extending ADR mechanism for LoRa enabled mobile end-devices

A considerable percentage of Internet of Things end-devices are characterised by mobility, a feature that adds extra complexity to protocols used in Wireless Sensor Networks. LoRa is one of the newly introduced wireless sensor protocols, capable of delivering messages in long distances and consuming low energy, features that make it proper for low cost devices. Although LoRa was introduced as a technology for stationary devices, it can also be used for mobile devices of low speed. In this paper, we introduce an enhancement to Adaptive Data Rate (ADR) mechanism to enable mobile LoRa, by improving the connection reliability of mobile end-devices, while keeping energy consumption at low levels. Firstly, we propose the Linear Regression-ADR (LR-ADR) mechanism for the Network Server side to smooth the Signal to Noise Ratio (SNR) estimates per gateway and predict the SNR of the next transmission. Secondly, we propose the Linear Regression + ADR (LR+ADR) mechanism, an adaptive method for the end-device side to regain the connectivity faster with the Network Server. We conducted simulation modelling to evaluate the performance of our implementation while we compared our results with four alternative solutions ADR, ADR+, EMA-ADR, G-ADR. The results prove that our first approach (LR-ADR) performs better than the best competitor, and our second approach (LR+ADR) brings an additional improvement in terms of Packet Delivery Ratio (PDR), while they retain the Energy Consumption per Packet Delivered (ECPD) at low levels. In particular, in a scenario that mimics real world conditions, LR+ADR presents an increase of up to 520% for PDR compared to the original ADR and an improvement of up to 38% compared to the best competitor (G-ADR). Moreover, it reduces ECPD up to 74% compared to the original ADR, while keeping it at the same level with the best competitor (G-ADR

A Survey On Honeypots, Honeynets And Their Applications On Smart Grid

Power grid is a major part of modern Critical Infrastructure (CIN). The rapid evolution of Information and Communication Technologies (ICT) enables traditional power grids to encompass advanced technologies that allow them to monitor their state, increase their reliability, save costs and provide ICT services to end customers, thus converting them into smart grids. However, smart grid is exposed to several security threats, as hackers might try to exploit vulnerabilities of the industrial infrastructure and cause disruption to national electricity system with severe consequences to citizens and commerce. This paper investigates and compares honey-x technologies that could be applied to smart grid in order to distract intruders, obtain attack strategies, protect the real infrastructure and form forensic evidence to be used in court