MEC-based Mobility Tracking and Safety Service through IoT

L'abstract è presente nell'allegato / the abstract is in the attachmen

Neural Network Contribute to Reverse Cryptographic Processes in Bitcoin Systems: attention on SHA256

Bitcoin is a digital currency created in January 2009 following the housing market crash that promises lower transaction fees than traditional online payment mechanisms. Though each bitcoin transaction is recorded in a public log, the names of buyers and sellers are never revealed. While that keeps bitcoin users' transactions private, it also lets them buy or sell anything without easily tracing it back to them. Bitcoin is based on cryptographic evidence, which therefore does not suffer from the weakness present in a model based on trust in guarantee authorities. The use of cryptography is of crucial importance in the Bitcoin system. In addition to maintaining data secrecy, in the case of Bitcoin, cryptography is used to make it impossible for anyone to spend money from another user's wallet. In our paper, we develop the idea that it is possible to reverse the cryptography process based on hash functions (one-way) through Machine Translation with neural networks. Assuming this hypothesis is true and considering some quantistic algorithms to decrypt certain types of hash functions, we will highlight their effects on the Bitcoin system

2D Time-frequency interference modelling using stochastic geometry for performance evaluation in Low-Power Wide-Area Networks

In wireless networks, interferences between trans- missions are modelled either in time or frequency domain. In this article, we jointly analyze interferences in the time- frequency domain using a stochastic geometry model assuming the total time-frequency resources to be a two-dimensional plane and transmissions from Internet of Things (IoT) devices time- frequency patterns on this plane. To evaluate the interference, we quantify the overlap between the information packets: provided that the overlap is not too strong, the packets are not necessarily lost due to capture effect. This flexible model can be used for multiple medium access scenarios and is especially adapted to the random time-frequency access schemes used in Low-Power Wide-Area Networks (LPWANs). By characterizing the outage probability and throughput, our approach permits to evaluate the performance of two representative LPWA technologies Sigfox{\textsuperscript \textregistered} and LoRaWA{\textsuperscript \textregistered}

Vehicular Fog Computing Enabled Real-time Collision Warning via Trajectory Calibration

Vehicular fog computing (VFC) has been envisioned as a promising paradigm for enabling a variety of emerging intelligent transportation systems (ITS). However, due to inevitable as well as non-negligible issues in wireless communication, including transmission latency and packet loss, it is still challenging in implementing safety-critical applications, such as real-time collision warning in vehicular networks. In this paper, we present a vehicular fog computing architecture, aiming at supporting effective and real-time collision warning by offloading computation and communication overheads to distributed fog nodes. With the system architecture, we further propose a trajectory calibration based collision warning (TCCW) algorithm along with tailored communication protocols. Specifically, an application-layer vehicular-to-infrastructure (V2I) communication delay is fitted by the Stable distribution with real-world field testing data. Then, a packet loss detection mechanism is designed. Finally, TCCW calibrates real-time vehicle trajectories based on received vehicle status including GPS coordinates, velocity, acceleration, heading direction, as well as the estimation of communication delay and the detection of packet loss. For performance evaluation, we build the simulation model and implement conventional solutions including cloud-based warning and fog-based warning without calibration for comparison. Real-vehicle trajectories are extracted as the input, and the simulation results demonstrate that the effectiveness of TCCW in terms of the highest precision and recall in a wide range of scenarios

Ship Behaviour and Ship Bridge Allision Analysis

The demand for maritime transport has increased with the growing demand for worldwide trade. This has led to a major increase in maritime traffic and ship sizes over the last decades, which raises the probability of accidents. The methods used in maritime risk assessments today are based on old hypotheses that do not include all data available today. The main objective of this thesis is to develop numerical models and methods for the analysis of what is considered as normal navigation behaviour at sea today and improve the analysis of probability for ship-bridge allisions. The first part of the thesis describes what is considered as normal meeting distance at sea today. This information is later used while identifying failure events to ensure that the event behaviour was not caused by other ships. These few cases are excluded from the methodology since the communication and situational awareness in the situations are not known. However, while studying the probability of ship-bridge accidents, it is also important to understand how waterway restrictions may affect the probability of ship-ship collisions. Therefore, this thesis also includes a study of how the improved knowledge concerning meeting distance could be used in a near ship-ship collision identification model. One of the main findings considering normal meeting distance is that small and large ships meet each other at a similar distance at sea.In the second part of the thesis, a methodology is proposed to estimate the probability of ship-bridge allision. The presented methodology uses Automatic Identification System (AIS) data and a ship manoeuvring simulator to simulate and analyse marine traffic with regards to risks for accidents, such as ship-bridge allisions. A failure event identification method is also presented, which is needed to determine the frequency, duration and behaviour for the accident scenarios. The three events that were modelled and simulated in the simulator were: drifting ship, sharp turning ship and missing turning point. The probability of the different failure events corresponded to previous statistics confirming the AIS-based methodology. This means the methods to obtain the probability and duration of the failure events could be utilised in other areas. The simulation methodology was confirmed with the probability of grounding in the Great Belt VTS area.This thesis firstly contributes to a better understanding of the modelling of probability for ship-bridge allisions. This will support bridge-building engineers who need to take into account accidental loads from ship-bridge allision while designing bridges. Secondly, this thesis also contributes to a better representation of normal behaviour at sea, which is used both in fairway designs and in estimations of ship-ship collisions

Software for efficient file elimination in computer forensics investigations

Computer forensics investigators, much more than with any other forensic discipline, must process an ever continuing increase of data. Fortunately, computer processing speed has kept pace and new processes are continuously being automated to sort through the voluminous amount of data. There exists an unfulfilled need for a simple, streamlined, standalone public tool for automating the computer forensics analysis process for files on a hard disk drive under investigation. A software tool has been developed to dramatically reduce the number of files that an investigator must individually examine. This tool utilizes the National Institute of Standards and Technology (NIST) National Software Reference Library (NSRL) database to automatically identify files by comparing hash values of files on the hard drive under investigation to known good files (e.g., unaltered application files) and known bad files (e.g., exploits). This tool then provides a much smaller list of unknown files to be closely examined