Comparative Double Auction Approach for Peer-to-Peer Energy Trading on Multiple microgrids.

Peer-to-peer (P2P) energy trading is one of the most effective methods to increase the usage of Renewable Energy (RE) resources in the distribution network and reduce losses by eliminating long transmission and distribution lines. This research aims to enhance the efficiency of P2P energy trading by examining the suitability of four distinct double auction mechanisms: Average, McAfee, Trade Reduction and Vickrey-Clarke-Groves (VCG). We conducted a systematic evaluation of these mechanisms across various microgrid (MG) types.

FLDID: Federated Learning Enabled Deep Intrusion Detection in Smart Manufacturing Industries.

The rapid development in manufacturing industries due to the introduction of IIoT devices has led to the emergence of Industry 4.0 which results in an industry with intelligence, increased efficiency and reduction in the cost of manufacturing. However, the introduction of IIoT devices opens up the door for a variety of cyber threats in smart industries.

Rumour prevention in social networks with layer 2 blockchains.

Social bots can cause social, political, and economical disruptions by spreading rumours. The state-of-the-art methods to prevent social bots from spreading rumours are centralised and such solutions may not be accepted by users who may not trust a centralised solution being biased. In this paper, we developed a decentralised method to prevent social bots.

Industry 4.0 towards Forestry 4.0: Fire Detection Use Case.

Forestry 4.0 is inspired by the Industry 4.0 concept, which plays a vital role in the next industrial generation revolution.

EAGLE-A Scalable Query Processing Engine for Linked Sensor Data.

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio-temporal correlations. Most semantic approaches do not have spatio-temporal support.

Online Influence and Sentiment of Fitness Tweets: Analysis of Two Million Fitness Tweets.

Background: Publicly available fitness tweets may provide useful and in-depth insights into the real-time sentiment of a person's physical activity and provide motivation to others through online influence.

Objective: The goal of this experimental approach using the fitness Twitter dataset is two-fold: (1) to determine if there is a correlation between the type of activity tweet (either workout or workout+, which contains the same information as a workout tweet but has additional user-generated information), gender, and one's online influence as measured by Klout Score and (2) to examine the sentiment of the activity-coded fitness tweets by looking at real-time shared thoughts via Twitter regarding their experiences with physical activity and the associated mobile fitness app.

Methods: The fitness tweet dataset includes demographic and activity data points, including minutes of activity, Klout Score, classification of each fitness tweet, the first name of each fitness tweet user, and the tweet itself.

Do as I tweet, not as I do: comparing physical activity data between fitness tweets and Healthy People 2020.

Background: The goal of this research was to compare the self-reported estimates of daily physical-activity data provided to the Healthy People 2020 research team via a telephone survey to the mobile fitness app real-time reporting of physical activity using Twitter.

Methods: The fitness tweet classification data set was collected from mobile fitness app users who shared their physical activity over Twitter. Over 184 days, 2,856,534 tweets were collected in 23 different languages.

Twitter classification model: the ABC of two million fitness tweets.

The purpose of this project was to design and test data collection and management tools that can be used to study the use of mobile fitness applications and social networking within the context of physical activity. This project was conducted over a 6-month period and involved collecting publically shared Twitter data from five mobile fitness apps (Nike+, RunKeeper, MyFitnessPal, Endomondo, and dailymile). During that time, over 2.

Crowdsourcing, citizen sensing and sensor web technologies for public and environmental health surveillance and crisis management: trends, OGC standards and application examples.

'Wikification of GIS by the masses' is a phrase-term first coined by Kamel Boulos in 2005, two years earlier than Goodchild's term 'Volunteered Geographic Information'. Six years later (2005-2011), OpenStreetMap and Google Earth (GE) are now full-fledged, crowdsourced 'Wikipedias of the Earth' par excellence, with millions of users contributing their own layers to GE, attaching photos, videos, notes and even 3-D (three dimensional) models to locations in GE. From using Twitter in participatory sensing and bicycle-mounted sensors in pervasive environmental sensing, to creating a 100,000-sensor geo-mashup using Semantic Web technology, to the 3-D visualisation of indoor and outdoor surveillance data in real-time and the development of next-generation, collaborative natural user interfaces that will power the spatially-enabled public health and emergency situation rooms of the future, where sensor data and citizen reports can be triaged and acted upon in real-time by distributed teams of professionals, this paper offers a comprehensive state-of-the-art review of the overlapping domains of the Sensor Web, citizen sensing and 'human-in-the-loop sensing' in the era of the Mobile and Social Web, and the roles these domains can play in environmental and public health surveillance and crisis/disaster informatics.

Mental health literacy among Internet users.

Background: Intervening early in the course of psychotic illness may improve the long-term outcome. Early intervention requires early recognition, and one factor that influences early recognition is the level of mental health literacy (MHL) in the population.

Aim: To investigate the level of MHL regarding depression and psychosis in an Irish population.