scholarly journals Urban IoT ontologies for sharing and electric mobility

Semantic Web ◽  
2021 ◽  
pp. 1-22
Author(s):  
Mario Scrocca ◽  
Ilaria Baroni ◽  
Irene Celino
Keyword(s):  
Service Providers ◽  
Electric Mobility ◽  
Added Value ◽  
Data Driven ◽  
Digital Information ◽  
Distributed Information ◽  
City Councils ◽  
Information Governance ◽  
Iot Devices ◽  
Data Driven Decisions

Cities worldwide are facing the challenge of digital information governance: different and competing service providers operating Internet of Things (IoT) devices often produce and maintain large amounts of data related to the urban environment. As a consequence, the need for interoperability arises between heterogeneous and distributed information, to enable city councils to make data-driven decisions and to provide new and effective added value services to their citizens. In this paper, we present the Urban IoT suite of ontologies, a common conceptual model to harmonise the data exchanges between municipalities and service providers, with specific focus on the sharing mobility and electric mobility domains.

scholarly journals COVID-19 Brings Data Equity Challenges to the Fore

2021 ◽  
Author(s):  
H.V. Jagadish ◽  
Julia Stoyanovich ◽  
Bill Howe
Keyword(s):  
Quality Control ◽  
Government Policy ◽  
Data Driven ◽  
Contact Tracing ◽  
Control Mechanisms ◽  
Sources Of Information ◽  
Decision Systems ◽  
Physical Resources ◽  
Multiple Levels ◽  
Data Driven Decisions

The COVID-19 pandemic is compelling us to make crucial data-driven decisions quickly, bringing together diverse and unreliable sources of information without the usual quality control mechanisms we may employ. These decisions are consequential at multiple levels: they can inform local, state and national government policy, be used to schedule access to physical resources such as elevators and workspaces within an organization, and inform contact tracing and quarantine actions for individuals. In all these cases, significant inequities are likely to arise, and to be propagated and reinforced by data-driven decision systems. In this article, we propose a framework, called FIDES, for surfacing and reasoning about data equity in these systems.

Against generalisation: Data-driven decisions need context to be human-compatible

2021 ◽  
pp. 026638212110619
Author(s):  
Sharon Richardson
Keyword(s):  
Artificial Intelligence ◽  
Real World ◽  
Data Science ◽  
Machine Learning Algorithms ◽  
Data Driven ◽  
Theoretical Frameworks ◽  
The Past ◽  
Real World Applications ◽  
Contextual Framework ◽  
Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

scholarly journals Towards Secure Network Computing Services for Lightweight Clients Using Blockchain

2018 ◽  
Vol 2018 ◽  
pp. 1-12 ◽  
Author(s):  
Yang Xu ◽  
Guojun Wang ◽  
Jidian Yang ◽  
Ju Ren ◽  
Yaoxue Zhang ◽  
...  
Keyword(s):  
Service Providers ◽  
Security Analysis ◽  
Service Provisioning ◽  
Network Computing ◽  
Security Risks ◽  
Blockchain Technology ◽  
Computing Services ◽  
Viable Solution ◽  
Secure Network ◽  
Iot Devices

The emerging network computing technologies have significantly extended the abilities of the resource-constrained IoT devices through the network-based service sharing techniques. However, such a flexible and scalable service provisioning paradigm brings increased security risks to terminals due to the untrustworthy exogenous service codes loading from the open network. Many existing security approaches are unsuitable for IoT environments due to the high difficulty of maintenance or the dependencies upon extra resources like specific hardware. Fortunately, the rise of blockchain technology has facilitated the development of service sharing methods and, at the same time, it appears a viable solution to numerous security problems. In this paper, we propose a novel blockchain-based secure service provisioning mechanism for protecting lightweight clients from insecure services in network computing scenarios. We introduce the blockchain to maintain all the validity states of the off-chain services and edge service providers for the IoT terminals to help them get rid of untrusted or discarded services through provider identification and service verification. In addition, we take advantage of smart contracts which can be triggered by the lightweight clients to help them check the validities of service providers and service codes according to the on-chain transactions, thereby reducing the direct overhead on the IoT devices. Moreover, the adoptions of the consortium blockchain and the proof of authority consensus mechanism also help to achieve a high throughput. The theoretical security analysis and evaluation results show that our approach helps the lightweight clients get rid of untrusted edge service providers and insecure services effectively with acceptable latency and affordable costs.

Damage Detection in Flexible Propeller Beam Structures by Exploiting Impact-Induced Coupled Acceleration Signals

2011 ◽  
Author(s):  
Ioannis T. Georgiou
Keyword(s):  
Data Driven ◽  
Distributed Information ◽  
Modal Expansion ◽  
Shape Difference ◽  
Spatio Temporal ◽  
Temporal Sample ◽  
High Level ◽  
Proper Orthogonal ◽  
Acceleration Signals

A local damage at the tip of a composite propeller is diagnosed by properly comparing its impact-induced free coupled dynamics to that of a pristine wooden propeller of the same size and shape. This is accomplished by creating indirectly via collocated measurements distributed information for the coupled acceleration field of the propellers. The powerful data-driven modal expansion analysis delivered by the Proper Orthogonal Decomposition (POD) Transform reveals that ensembles of impact-induced collocated coupled experimental acceleration signals are underlined by a high level of spatio-temporal coherence. Thus they furnish a valuable spatio-temporal sample of coupled response induced by a point impulse. In view of this fact, a tri-axial sensor was placed on the propeller hub to collect collocated coupled acceleration signals induced via modal hammer nondestructive impacts and thus obtained a reduced order characterization of the coupled free dynamics. This experimental data-driven analysis reveals that the in-plane unit components of the POD modes for both propellers have similar shapes-nearly identical. For the damaged propeller this POD shape-difference is quite pronounced. The shapes of the POD modes are used to compute indices of difference reflecting directly damage. At the first POD energy level, the shape-difference indices of the damaged composite propeller are quite larger than those of the pristine wooden propeller.

Salaries, Dollars, and Sense: Making Data Driven Decisions

2014 ◽  
Vol 3 (1) ◽  
pp. 29-32
Author(s):  
Stacy Warner ◽  
Emily S. Sparvero
Keyword(s):  
Data Driven ◽  
Sense Making ◽  
Data Driven Decisions

scholarly journals Evaluation of the COVID-19 Era by Using Machine Learning and Interpretation of Confidential Dataset

Electronics ◽  
2021 ◽  
Vol 10 (23) ◽  
pp. 2910
Author(s):  
Andreas Andreou ◽  
Constandinos X. Mavromoustakis ◽  
George Mastorakis ◽  
Jordi Mongay Batalla ◽  
Evangelos Pallis
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Research Study ◽  
Nonlinear Least Squares ◽  
Data Driven ◽  
Machine Learning Technique ◽  
Marquardt Algorithm ◽  
Learning Technique ◽  
Iot Devices ◽  
Algorithmic Techniques

Various research approaches to COVID-19 are currently being developed by machine learning (ML) techniques and edge computing, either in the sense of identifying virus molecules or in anticipating the risk analysis of the spread of COVID-19. Consequently, these orientations are elaborating datasets that derive either from WHO, through the respective website and research portals, or from data generated in real-time from the healthcare system. The implementation of data analysis, modelling and prediction processing is performed through multiple algorithmic techniques. The lack of these techniques to generate predictions with accuracy motivates us to proceed with this research study, which elaborates an existing machine learning technique and achieves valuable forecasts by modification. More specifically, this study modifies the Levenberg–Marquardt algorithm, which is commonly beneficial for approaching solutions to nonlinear least squares problems, endorses the acquisition of data driven from IoT devices and analyses these data via cloud computing to generate foresight about the progress of the outbreak in real-time environments. Hence, we enhance the optimization of the trend line that interprets these data. Therefore, we introduce this framework in conjunction with a novel encryption process that we are proposing for the datasets and the implementation of mortality predictions.

Grid Workflows with Encompassed Business Relationships

2012 ◽  
pp. 1332-1348
Author(s):  
Dimosthenis Kyriazis ◽  
Andreas Menychtas ◽  
Theodora Varvarigou
Keyword(s):  
Quality Of Service ◽  
Service Providers ◽  
Added Value ◽  
Business Relationships ◽  
Qos Provisioning ◽  
Grid Services ◽  
Qos Parameters ◽  
Mapping Process ◽  
Selection Of

This chapter focuses on presenting and describing an approach that allows the mapping of workflow processes to Grid provided services by not only taking into account the quality of service (QoS) parameters of the Grid services but also the potential business relationships of the service providers that may affect the aforementioned QoS parameters. This approach is an integral part of the QoS provisioning, since this is the only way to estimate, calculate, and conclude to the mapping of workflows and the selection of the available service types and instances in order to deliver an overall quality of service across a federation of providers. The added value of this approach lays on the fact that business relationships of the service providers are also taken into account during the mapping process.

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