Showing papers by "Helen C. Leligou published in 2022"

Blockchain adoption from the Shipping industry: An empirical study

Abstract: Blockchain technology can have numerous applications in the shipping industry (although it is a newcomer in blockchain technology) as it provides transparency, security, and cost reduction in financial transactions. Although at its early steps, this technology has been applied in the shipping industry. The majority of the related literature deals with the applicability of blockchain technology in the various processes of a shipping company and the related supply chains. Instead, this paper focuses on the willingness of shipping companies to adopt blockchain technology through an exploratory survey. It develops a framework based on a slightly altered version of the classic unified theory of acceptance using the technology (UTAUT) model and linear regression analysis. The outcome is a set of linear equations of behavioral intention and expectation of the Greek shipping industry adopting blockchain technology, highlighting the most influential factors. This paper contributes to the related discussion by unveiling the intention of Greek bulk shipping companies to adopt blockchain technology in their daily operations.

Design Space Exploration of a Multi-Model AI-Based Indoor Localization System

Abstract: In this paper, we present the results of a performance evaluation and optimization process of an indoor positioning system (IPS) designed to operate on portable as well as miniaturized embedded systems. The proposed method uses the Received Signal Strength Indicator (RSSI) values from multiple Bluetooth Low-Energy (BLE) beacons scattered around interior spaces. The beacon signals were received from the user devices and processed through an RSSI filter and a group of machine learning (ML) models, in an arrangement of one model per detected node. Finally, a multilateration problem was solved using as an input the inferred distances from the advertising nodes and returning the final position approximation. In this work, we first presented the evaluation of different ML models for inferring the distance between the devices and the installed beacons by applying different optimization algorithms. Then, we presented model reduction methods to implement the optimized algorithm on the embedded system by appropriately adapting it to its constraint resources and compared the results, demonstrating the efficiency of the proposed method.

Towards Data-Driven Models in the Prediction of Ship Performance (Speed—Power) in Actual Seas: A Comparative Study between Modern Approaches

Abstract: In the extremely competitive environment of shipping, minimizing shipping cost is the key factor for the survival and growth of shipping companies. However, stricter rules and regulations that aim at the reduction of greenhouse gas emissions published by the International Maritime Organization, force shipping companies to increase the operational efficiency of their fleet. The prediction of a ship speed in actual seas with a given power by its engine is the most important performance indicator and thus makes it the “holy grail” in pursuing better efficiency. Traditionally, tank model tests and semi-empirical formulas were the preferred solution for the aforementioned prediction and are still widely applied. However, currently, with the increased computational power that is widely available, novel and more sophisticated methods taking into consideration computational fluid dynamics (CFD) and machine learning (ML) algorithms are emerging. In this paper, we briefly present the different approaches in the prediction of a ship’s speed but focus on ML methods comparing a representative number of the latest data-driven models used in papers, to provide guidelines, discover trends and identify the challenges to be faced by researchers. From this comparison, we can distinguish that artificial neural networks (ANN), being used in 73.3% of the reviewed papers, dominate as the algorithm of choice. Researchers mostly rely on physical laws governing the phenomena in the crucial part of data preprocessing tasks. Lastly, most researchers rely on data acquisition systems installed at ships in order to achieve usable results.

Intelligent Network Service Optimization in the Context of 5G/NFV

Abstract: Our contemporary society has never been more connected and aware of vital information in real time, through the use of innovative technologies. A considerable number of applications have transitioned into the cyber-physical domain, automating and optimizing their routines and processes via the dense network of sensing devices and the immense volumes of data they collect and instantly share. In this paper, we propose an innovative architecture based on the monitoring, analysis, planning, and execution (MAPE) paradigm for network and service performance optimization. Our study confirms distinct evidence that the utilization of learning algorithms, consuming datasets enriched with the users’ empirical opinions as input during the analysis and planning phases, contributes greatly to the optimization of video streaming quality, especially by handling different packet loss rates, paving the way for the achievable provision of a resilient communications platform for calamity assessment and management.

Machine Learning Resource Optimization Enabled by Cross Layer Monitoring

Abstract: In this paper, we introduce a novel architecture and its open-source implementation that exploits the monitoring data from heterogeneous resources and uses them to train machine learning models, which can be used for dynamic resource management optimization. The existence of such a solution is extremely important for Service Providers (SP) as it can lead to the optimal use of their physical and virtual infrastructures avoiding potential waste of resources due to overdesign while at the same time it can ensure that the required Quality of Service (QoS) levels are met. The proposed solution is validated in two real-life services showing very good accuracy in predicting the required resources in both cases for a large number of operational scenarios.

Innovation for People with Disabilities in Hospitality Industry: A Theoretical Approach

Abstract: Hotels are forms of businesses connected to the entire system of production and distribution of tourist products. Essentially, they provide hospitality goods and services to travelers, individuals with different profiles and interests, and thus play a very important role in the tourism sector. Therefore, the purpose of the article is to point out the theoretical approach of innovation in the context of its utilization in hotels for individuals with disabilities. We have reviewed the existing theoretical approaches to innovation and then analysed their applicability in the tourism sector. Based on the findings, we developed theoretical approaches, such as the coupling theory and the innovation diffusion theory, that can be applied to the target sectors and provide valuable insights to the relevant actors. Research shows that innovation, whether it concerns technological applications or processes, affects the enrichment of hotel services provided for people with disabilities and influences the technical-functional and organizational processes of hospitality. On this basis, innovation is a key factor of growth for any hotel, as it increases its competitiveness and sustainability through the utilization of the potential provided by the use of new innovative technological applications or processes for people with disabilities.JEL Classification: L83, O30. Doi: 10.28991/HIJ-2022-03-01-010 Full Text: PDF

Design Challenges on Machine-Learning Enabled Resource Optimization

Abstract: Nowadays, service providers’ (SPs) need for efficient resource utilization solutions is more demanding than ever. The optimal use of the physical and virtual infrastructures guarantees that the waste of resources due to overdesign is minimized while the provided services enjoy the required quality of service levels. However, the prediction of the exact amount of the required resources per service at any time of its lifecycle is not an easy process. For this purpose, we propose a solution that handles the infrastructure in a holistic manner introducing a novel architecture that exploits the monitoring data from three layers (hardware, virtualization, and application) and uses them to train machine learning models, which can accurately predict the exact amount of the required resources per service. Its implementation using open-source tools and its performance are also presented.

Fiber optics and broadband over power lines in smart grid: a communications system architecture for overhead high-voltage, medium-voltage and low-voltage power grids

Abstract: |This paper proposes a network system architecture that integrates the operation of two communications technologies of the smart grid, i.e., (cid:12)ber optics and broadband over power lines, across the same overhead transmission and distribution power grid. This integration brings bene(cid:12)ts for the power utilities, telecommunications providers and customers alike. The proposed system architecture is expandable by allowing more communications technologies of the smart grid, such as DSL, (cid:12)ber, WPAN, WiFi, WiMAX, GSM (4G, 5G), and satellite, to connect. Issues concerning wireless sensor networks, towersharing, and terabit-class backbone networks are discussed.