From the Internet of Things to the web of things-enabling by sensing as-A service

© 2016 IEEE. Sensing as a Service (SenaaS) is emerging as a prominent element in the middleware linking together the Internet of Things (IoT) and the Web of Things (WoT) layers of future ubiquitous systems. An architecture framework is discussed in this paper whereby things are abstracted into services via embedded sensors which expose a thing as a service. The architecture acts as a blueprint to guide software architects realizing WoT applications. Web-enabled things are eventually appended into Web platforms such as Social Web platforms to drive data and services that are exposed by these things to interact with both other things and people, in order to materialize further the future social Web of Things. Research directions are discussed to illustrate the integration of SenaaS into the proposed WoT architectural framework

CrowdPower: A Novel Crowdsensing-as-a-Service Platform for Real-Time Incident Reporting

Crowdsensing using mobile phones is a novel addition to the Internet of Things applications suite. However, there are many challenges related to crowdsensing, including (1) the ability to manage a large number of mobile users with varying devices’ capabilities; (2) recruiting reliable users available in the location of interest at the right time; (3) handling various sensory data collected with different requirements and at different frequencies and scales; (4) brokering the relationship between data collectors and consumers in an efficient and scalable manner; and (5) automatically generating intelligence reports after processing the collected sensory data. No comprehensive end-to-end crowdsensing platform has been proposed despite a few attempts to address these challenges. In this work, we aim at filling this gap by proposing and describing the practical implementation of an end-to-end crowdsensing-as-a-service system dubbed CrowdPower. Our platform offers a standard interface for the management and brokerage of sensory data, enabling the transformation of raw sensory data into valuable smart city intelligence. Our solution includes a model for selecting participants for sensing campaigns based on the reliability and quality of sensors on users’ devices, then subsequently analysing the quality of the data provided using a clustering approach to predict user reputation and identify outliers. The platform also has an elaborate administration web portal developed to manage and visualize sensing activities. In addition to the architecture, design, and implementation of the backend platform capabilities, we also explain the creation of CrowdPower’s sensing mobile application that enables data collectors and consumers to participate in various sensing activities

A novel sentiment analysis framework for monitoring the evolving public opinion in real-time: Case study on climate change

Smart city analytics involves tracking, interpreting, and evaluating the sentiments and emotions that are shared via online social media channels. Sentiment analysis of social media posts has become increasingly prominent in recent years as a means of gaining insights into how members of the public perceive current affairs. The ongoing research in this domain has leveraged multiple types of sentiment analysis. However, although the existing approaches enable researchers to acquire retrospective insights into public opinion, they do not enable a real-time evaluation. In addition, they are not readily scalable and necessitate the analysis of a significant amount of posts (in the millions) to facilitate a more in-depth evaluation. The study outlined in this paper was designed to address these shortcomings by presenting a framework that facilitates a real-time evaluation of the evolution of opinions shared by prominent public features and their respective followers; that is, high-impact posts. The developed solution encompasses a sophisticated Bi-directional LSTM classifier that was implemented and tested using a dataset consisting of 278,000 tweets related to the topic of climate change. The outcomes reveal that the proposed classifier achieved the following accuracies: 88.41% for discrimination; 89.66% for anger; 87.01% for inspiration; and 87.52% for joy - with negative emotions being more accurately classified than positive emotions. Similarly, the sentiment classification performance yielded accuracies of 89.32% for support and 89.80% for strong support, as well as 88.14% for opposition and 87.52% for strong opposition. In addition, the findings of the study indicated that geographic location, chosen topic, cultural sensitivities, and posting frequency all play a critical role in public reactions to posts and the ensuing perspectives they adopt. The solution stands out from existing retrospective analysis methods because it does not rely on the analysis of vast quantities of data records; rather, it can perform real-time, high-impact content analysis in a resource-efficient and sustainable manner. This framework can be used to generate insights into how public opinion is developing on a real-time basis. As such, it can have meaningful application within social media analysis efforts

A Novel Quality and Reliability-Based Approach for Participants\u27 Selection in Mobile Crowdsensing

© 2013 IEEE. With the advent of mobile crowdsensing, we now have the possibility of tapping into the sensing capabilities of smartphones carried by citizens every day for the collection of information and intelligence about cities and events. Finding the best group of crowdsensing participants that can satisfy a sensing task in terms of data types required, while satisfying the quality, time, and budget constraints is a complex problem. Indeed, the time-constrained and location-based nature of crowdsensing tasks, combined with participants\u27 mobility, render the task of participants\u27 selection, a difficult task. In this paper, we propose a comprehensive and practical mobile crowdsensing recruitment model that offers reliability and quality-based approach for selecting the most reliable group of participants able to provide the best quality possible for the required sensory data. In our model, we adopt a group-based approach for the selection, in which a group of participants (gathered into sites) collaborate to achieve the sensing task using the combined capabilities of their smartphones. Our model was implemented using MATLAB and configured using realistic inputs such as benchmarked sensors\u27 quality scores, most widely used phone brands in different countries, and sensory data types associated with various events. Extensive testing was conducted to study the impact of various parameters on participants\u27 selection and gain an understanding of the compromises involved when deploying such process in practical MCS environments. The results obtained are very promising and provide important insights into the different aspects impacting the quality and reliability of the process of mobile crowdsensing participants\u27 selection

Effects of smart city service channel- and user-characteristics on user satisfaction and continuance intention

© 2020, Emerald Publishing Limited. Purpose: Smart city services (SCS) in contrast with other technology-based services, demand significant interaction and collaboration between the users and the service providers. This study examines the SCS delivery-channel characteristics and the users\u27 personal (behavioral and demographic) characteristics that influence their satisfaction or dissatisfaction with the services, as well as their intention to adopt (i.e. continue using) the SCS-delivery channels. Design/methodology/approach: A quantitative study using a structured questionnaire was conducted for this paper. The data-collection method was administered by emailing the survey to a list of 2,350 city/urban residents who are members of the two largest universities in the greater Dubai metropolis. A total of 600 completed responses (26 percent) were received back, while 580 useable responses (25 percent) were analyzed for this paper. Findings: Our initial findings suggest that contrary to popular belief, it is not only SCS channel factors that influence user satisfaction and continuance intention. SCS users\u27 personal characteristics (such as their user innovativeness and control-seeking behavior) are also pivotal in determining their satisfaction and intention to continue or not continue using the SCS-delivery channels. Research limitations/implications: The paper argues that both SCS channel factors and SCS users\u27 personal characteristics jointly influence the users\u27 experience of the services and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users\u27 behaviors toward the emerging SCS channels in general, and it will be of great value to architects and designers of Smart City technologies around the world. Practical implications: The paper argues that both SCS channel factors and SCS users\u27 personal (behavioral and demographic) characteristics jointly influence the users\u27 trials of the services, and therefore jointly determine their satisfaction with the service as well as their SCS usage continuance intention. The result of our research gives important insights into users\u27 behavioral intentions toward the emerging SCS channels in general; and it will be of great value to architects and designers of Smart City technologies around the world. Originality/value: This paper is one of the first few studies focused on investigating the antecedents of SCS usage behaviors in the Middle Eastern region

A qualitative study exploring the difficulties influencing decision making at the end of life for people with dementia

BACKGROUND: Dementia is a progressive neurodegenerative condition characterized by declining functional and cognitive abilities. The quality of end of life care for people with dementia in the UK can be poor. Several difficult decisions may arise at the end of life, relating to the care of the person with dementia, for example management of comorbidities. OBJECTIVE: To explore difficulties in decision making for practitioners and family carers at the end of life for people with dementia. DESIGN: Qualitative methodology using focus groups and semi-structured interviews and thematic analysis methods. SETTINGS AND PARTICIPANTS: Former (n=4) and current (n=6) family carers of people with experience of end of life care for a person with dementia were recruited from an English dementia voluntary group in 2015. A further 24 health and care professionals were purposively sampled to include a broad range of expertise and experience in dementia end of life care. RESULTS: Four key themes were identified as follows: challenges of delivering coherent care in dynamic systems; uncertainty amongst decision makers; internal and external conflict amongst decision makers; and a lack of preparedness for the end of life. Overarching difficulties such as poor communication, uncertainty and conflict about the needs of the person with dementia as well as the decision maker's own role can characterize decision making at the end of life. CONCLUSIONS: This study suggests that decision making at the end of life for people with dementia has the potential to be improved. More planning earlier in the course of dementia with an on-going approach to conversation may increase preparedness and family carers' expectations of end of life

The Impact of Arabic Part of Speech Tagging on Sentiment Analysis: A New Corpus and Deep Learning Approach

Sentiment Analysis is achieved by using Natural Language Processing (NLP) techniques and finds wide applications in analyzing social media content to determine people’s opinions, attitudes, and emotions toward entities, individuals, issues, events, or topics. The accuracy of sentiment analysis depends on automatic Part-of-Speech (PoS) tagging which is required to label words according to grammatical categories. The challenge of analyzing the Arabic language has found considerable research interest, but now the challenge is amplified with the addition of social media dialects. While numerous morphological analyzers and PoS taggers were proposed for Modern Standard Arabic (MSA), we are now witnessing an increased interest in applying those techniques to the Arabic dialect that is prominent in social media. Indeed, social media texts (e.g. posts, comments, and replies) differ significantly from MSA texts in terms of vocabulary and grammatical structure. Such differences call for reviewing the PoS tagging methods to adapt social media texts. Furthermore, the lack of sufficiently large and diverse social media text corpora constitutes one of the reasons that automatic PoS tagging of social media content has been rarely studied. In this paper, we address those limitations by proposing a novel Arabic social media text corpus that is enriched with complete PoS information, including tags, lemmas, and synonyms. The proposed corpus constitutes the largest manually annotated Arabic corpus to date, with more than 5 million tokens, 238,600 MSA texts, and words from Arabic social media dialect, collected from 65,000 online users’ accounts. Furthermore, our proposed corpus was used to train a custom Long Short-Term Memory deep learning model and showed excellent performance in terms of sentiment classification accuracy and F1-score. The obtained results demonstrate that the use of a diverse corpus that is enriched with PoS information significantly enhances the performance of social media analysis techniques and opens the door for advanced features such as opinion mining and emotion intelligence

Leveraging Natural Language Processing to Analyse the Temporal Behavior of Extremists on Social Media

Aiming at achieving sustainability and quality of life for citizens, future smart cities adopt a data-centric approach to decision making in which assets, people, and events are constantly monitored to inform decisions. Public opinion monitoring is of particular importance to governments and intelligence agencies, who seek to monitor extreme views and attempts of radicalizing individuals in society. While social media platforms provide increased visibility and a platform to express public views freely, such platforms can also be used to manipulate public opinion, spread hate speech, and radicalize others. Natural language processing and data mining techniques have gained popularity for the analysis of social media content and the detection of extremists and radical views expressed online. However, existing approaches simplify the concept of radicalization to a binary problem in which individuals are classified as extremists or non-extremists. Such binary approaches do not capture the radicalization process\u27s complexity that is influenced by many aspects such as social interactions, the impact of opinion leaders, and peer pressure. Moreover, the longitudinal analysis of users\u27 interactions and profile evolution over time is lacking in the literature. Aiming at addressing those limitations, this work proposes a sophisticated framework for the analysis of the temporal behavior of extremists on social media platforms. Far-right extremism during the Trump presidency was used as a case study, and a large dataset of over 259,000 tweets was collected to train and test our models. The results obtained are very promising and encourage the use of advanced social media analytics in the support of effective and timely decision-making

Single Breath-Hold Physiotherapy Technique : Effective tool for T2* magnetic resonance imaging in young patients with thalassaemia major

Magnetic resonance imaging using T2* (MRI T2*) is a highly sensitive and non-invasive technique for the detection of tissue iron load. Although the single breath-hold multi-echo T2* technique has been available at the Sultan Qaboos University Hospital (SQUH), Muscat, Oman, since 2006, it could not be performed on younger patients due to their inability to hold their breath after expiration. This study was carried out between May 2007 and May 2015 and assessed 50 SQUH thalassaemic patients aged 7‒17 years old. Seven of these patients underwent baseline and one-year follow-up MRI T2* scans before receiving physiotherapy training. Subsequently, all patients were trained by a physiotherapist to hold their breath for approximately 15‒20 seconds at the end of expiration before undergoing baseline and one-year follow-up MRI T2* scans. Failure rates for the pre- and post-training groups were 6.0% and 42.8%, respectively. These results indicate that the training of thalassaemic patients in breath-hold techniques is beneficial and increases rates of compliance for MRI T2* scans

Illuminating Choices for Library Prep: A Comparison of Library Preparation Methods for Whole Genome Sequencing of Cryptococcus neoformans Using Illumina HiSeq.

The industry of next-generation sequencing is constantly evolving, with novel library preparation methods and new sequencing machines being released by the major sequencing technology companies annually. The Illumina TruSeq v2 library preparation method was the most widely used kit and the market leader; however, it has now been discontinued, and in 2013 was replaced by the TruSeq Nano and TruSeq PCR-free methods, leaving a gap in knowledge regarding which is the most appropriate library preparation method to use. Here, we used isolates from the pathogenic fungi Cryptococcus neoformans var. grubii and sequenced them using the existing TruSeq DNA v2 kit (Illumina), along with two new kits: the TruSeq Nano DNA kit (Illumina) and the NEBNext Ultra DNA kit (New England Biolabs) to provide a comparison. Compared to the original TruSeq DNA v2 kit, both newer kits gave equivalent or better sequencing data, with increased coverage. When comparing the two newer kits, we found little difference in cost and workflow, with the NEBNext Ultra both slightly cheaper and faster than the TruSeq Nano. However, the quality of data generated using the TruSeq Nano DNA kit was superior due to higher coverage at regions of low GC content, and more SNPs identified. Researchers should therefore evaluate their resources and the type of application (and hence data quality) being considered when ultimately deciding on which library prep method to use