The Interoperability of Things: Interoperable solutions as an enabler for IoT and Web 3.0

This paper presents an overview of the interoperability concepts along with the challenges for the IoT domain and the upcoming Web 3.0. We identify four levels of interoperability and the relevant solutions for accomplishing vertical and horizontal compatibility between the various layers of a modern IoT ecosystem, referred to as: technological, syntactic, semantic, and organizational interoperability. The goal is to achieve cross-domain interaction and facilitate the proper usage and management of the provided IoT services and applications. An interoperability framework is also proposed where the involved system components can cooperate and offer the seamless operation from the device to the backend framework. This by-design end-to-end interoperation enables the interplay of several complex service composition settings and the management of the system via patterns. The overall proposal is adopted by the EU funded project SEMIoTICS as an enabler towards the IoT and Web 3.0, even when products from different vendors are utilized

Ethics And Legal Issues In Online Counseling Services: Counseling Principles Analysis

Challenges of living and dynamics modern society has spawned complex problems experienced by individuals. Counseling process with a variety formats attempt to develop the optimization of the individual. The development of the counseling process format formed a variety of alternative media that are capable to accommodating the communication process between the counselor and the client. The Media creates an atmosphere of counseling in long-distance (distance) condition has some advantages as well as disadvantages. One of the distance counseling forms is counseling online. With one form of service with fairly new developments, online counseling has had a significant positive impact on easing the client's problems. But on the other hand, this service also has some weaknesses that must be understood and controlled, especially for a counselor. This manuscript uses a documentary study approach on ethical conditions, counseling issues in Indonesia and discusses some of the ethical issues that must be understood by counselors during the process of online counseling in keeping some of the important principles in the counseling process

Towards Self-Awareness Privacy Protection for Internet of Things Data Collection

The Internet of Things (IoT) is now an emerging global Internet-based information architecture used to facilitate the exchange of goods and services. IoT-related applications are aiming to bring technology to people anytime and anywhere, with any device. However, the use of IoT raises a privacy concern because data will be collected automatically from the network devices and objects which are embedded with IoT technologies. In the current applications, data collector is a dominant player who enforces the secure protocol that cannot be verified by the data owners. In view of this, some of the respondents might refuse to contribute their personal data or submit inaccurate data. In this paper, we study a self-awareness data collection protocol to raise the confidence of the respondents when submitting their personal data to the data collector. Our self-awareness protocol requires each respondent to help others in preserving his privacy. The communication (respondents and data collector) and collaboration (among respondents) in our solution will be performed automatically

Impact assessment of policy expressivenessof an optimised access control model forsmart sensors

In the incoming internet of things (IoT) applications, smart sensors expose services to interact with them, to be parameterised, managed and maintained. Therefore, fine-grained end-to-end access control enforcement is mandatory to tackle the derived security requirements. However, it is still not feasible in very constrained devices. There is an innovative access control model that conveys an expressive policy language and an optimised codification for tight and flexible access control enforcement in very constrained devices. Such tightness enabled by the expressiveness of the policy language leads to detailed policy instances that might impact on the performance and therefore, in the feasibility and further applicability. In this context, this study assesses how the policy length impacts the performance of the establishment of a security association through the protocol named Hidra proposed by such an adapted access control model. Consequently, the notable results of the performance evaluation prove the feasibility and adequacy of this access control model for the new smart IoT scenarios.Part of this work is funded by the Department of Economic Development and Competitiveness of the Basque Government through the SEKUrtasun TEKnologiak SEKUTEK KK-2017/00044 collaborative research project and by the Spanish Ministry of Economy, Industry and Competitiveness through the State Secretariat for Research, Development and Innovation under the 'Adaptive Management of 5G Services to Support Critical Events in Cities (5G-City)' project TEC2016-76795-C6-5-R

FROM INTELLIGENT WEB OF THINGS TO SOCIAL WEB OF THINGS

Numerous challenges, including limited resources, random mobility, and lack of standardized communication protocols, are currently preventing a myriad of heterogeneous devices to interact and provide Web services within the context of the Web of Things (WoT). We argue in this paper that these devices should be augmented with artificial intelligence techniques for an enhanced management of their resources and an easier construction of Web applications integrating Real World Things (RWT). To this end, we present a new classification of the WoT challenges and highlight the opportunities of embedding smartness into RWT. We also present our vision of Intelligent WoT by proposing a multiagent system-based architecture for intelligent Web service composition. In addition, we discuss the shift of the WoT toward a Social WoT (SWoT) and debate our ideas within two important scenarios, namely the Intelligent VANET-WoT and smart logistics

An AI approach to Collecting and Analyzing Human Interactions with Urban Environments

Thanks to advances in Internet of Things and crowd-sensing, it is possible to collect vast amounts of urban data, to better understand how citizens interact with cities and, in turn, improve human well-being in urban environments. This is a scientifically challenging proposition, as it requires new methods to fuse objective (heterogeneous) data (e.g. people location trails and sensors data) with subjective (perceptual) data (e.g. the citizens’ quality of experience collected through feedback forms). When it comes to vast urban areas, collecting statistically significant data is a daunting task; thus new data-collection methods are required too. In this work, we turn to artificial intelligence (AI) to address these challenges, introducing a method whereby the objective, sensor data is analyzed in real-time to scope down the test matrix of the subjective questionnaires. In turn, subjective responses are parsed through AI models to extract further objective information. The outcome is an interactive data analysis framework for urban environments, which we put to test in the context of a citizens’ well-being project. In our pilot study, each new entry (objective or subjective) is parsed through the AI engine to determine which action maximizes the information gain. This translates into a particular question being fired at a specific moment and place, to a specific person. With our AI data collection method, we can reach statistical significance much faster, achieving (in our city-wide pilot study) a 41% acceleration factor and a 75% reduction in intrusiveness. Our study opens new avenues in urban science, with potential applications in urban planning, citizen’s well-being projects, and sociology, to mention but a few cases

Exploring the value of big data analysis of Twitter tweets and share prices

Over the past decade, the use of social media (SM) such as Facebook, Twitter, Pinterest and Tumblr has dramatically increased. Using SM, millions of users are creating large amounts of data every day. According to some estimates ninety per cent of the content on the Internet is now user generated. Social Media (SM) can be seen as a distributed content creation and sharing platform based on Web 2.0 technologies. SM sites make it very easy for its users to publish text, pictures, links, messages or videos without the need to be able to program. Users post reviews on products and services they bought, write about their interests and intentions or give their opinions and views on political subjects. SM has also been a key factor in mass movements such as the Arab Spring and the Occupy Wall Street protests and is used for human aid and disaster relief (HADR). There is a growing interest in SM analysis from organisations for detecting new trends, getting user opinions on their products and services or finding out about their online reputation. Companies such as Amazon or eBay use SM data for their recommendation engines and to generate more business. TV stations buy data about opinions on their TV programs from Facebook to find out what the popularity of a certain TV show is. Companies such as Topsy, Gnip, DataSift and Zoomph have built their entire business models around SM analysis. The purpose of this thesis is to explore the economic value of Twitter tweets. The economic value is determined by trying to predict the share price of a company. If the share price of a company can be predicted using SM data, it should be possible to deduce a monetary value. There is limited research on determining the economic value of SM data for “nowcasting”, predicting the present, and for forecasting. This study aims to determine the monetary value of Twitter by correlating the daily frequencies of positive and negative Tweets about the Apple company and some of its most popular products with the development of the Apple Inc. share price. If the number of positive tweets about Apple increases and the share price follows this development, the tweets have predictive information about the share price. A literature review has found that there is a growing interest in analysing SM data from different industries. A lot of research is conducted studying SM from various perspectives. Many studies try to determine the impact of online marketing campaigns or try to quantify the value of social capital. Others, in the area of behavioural economics, focus on the influence of SM on decision-making. There are studies trying to predict financial indicators such as the Dow Jones Industrial Average (DJIA). However, the literature review has indicated that there is no study correlating sentiment polarity on products and companies in tweets with the share price of the company. The theoretical framework used in this study is based on Computational Social Science (CSS) and Big Data. Supporting theories of CSS are Social Media Mining (SMM) and sentiment analysis. Supporting theories of Big Data are Data Mining (DM) and Predictive Analysis (PA). Machine learning (ML) techniques have been adopted to analyse and classify the tweets. In the first stage of the study, a body of tweets was collected and pre-processed, and then analysed for their sentiment polarity towards Apple Inc., the iPad and the iPhone. Several datasets were created using different pre-processing and analysis methods. The tweet frequencies were then represented as time series. The time series were analysed against the share price time series using the Granger causality test to determine if one time series has predictive information about the share price time series over the same period of time. For this study, several Predictive Analytics (PA) techniques on tweets were evaluated to predict the Apple share price. To collect and analyse the data, a framework has been developed based on the LingPipe (LingPipe 2015) Natural Language Processing (NLP) tool kit for sentiment analysis, and using R, the functional language and environment for statistical computing, for correlation analysis. Twitter provides an API (Application Programming Interface) to access and collect its data programmatically. Whereas no clear correlation could be determined, at least one dataset was showed to have some predictive information on the development of the Apple share price. The other datasets did not show to have any predictive capabilities. There are many data analysis and PA techniques. The techniques applied in this study did not indicate a direct correlation. However, some results suggest that this is due to noise or asymmetric distributions in the datasets. The study contributes to the literature by providing a quantitative analysis of SM data, for example tweets about Apple and its most popular products, the iPad and iPhone. It shows how SM data can be used for PA. It contributes to the literature on Big Data and SMM by showing how SM data can be collected, analysed and classified and explore if the share price of a company can be determined based on sentiment time series. It may ultimately lead to better decision making, for instance for investments or share buyback