Information and Knowledge in Internet of Things

This research study paper aims to provide a clear understanding of how data science and advanced analytics are being presented and studied within the academic research world and with a practical application in the commercial pharmaceutical context. This study also had key objectives to proceed with the identification of interconnection and dependencies and understand any research gaps of how both concepts are being also integrated into the commercial pharmaceutical operations like sales and marketing.

The study consisted of a hybrid approach for a deep theoretical and practical understanding of a systematic quantitative literature review of research articles and publications presented in different platforms like PubMed, Elsevier, iMedPub, Sage Journals, and Google Scholar as well as a focus group study with a group of 25 pharmaceutical professionals. The findings present in this research paper indicate an increase in new data science and advanced analytics models, techniques, and systems applying new analytical and data management techniques to large quantities of data and new decision process problems.

This study is a contribution to the discovery and understanding of how different applications of data science and advanced analytics in the pharmaceutical space are being managed, underpinning theories and key factors employed to study the past, current, and future of data science and advanced analytics adoption, utilization, and success.

The proliferation of ubiquitous computing devices, such as smartphones, biometric devices, fitness devices, entertainment gadgets, wearable, and nonwearable devices, enabled us to capture, store, process, and analyze the daily-life activities, which comes under the umbrella of lifelogging systems. These devices are equipped with various lifelogging tools and applications to capture and store user’s activities. Among these devices, smart TV is a device having all the capabilities to capture, store, process, and analyze a viewer’s activities. It supports computing capabilities, sensors, and other peripheral devices that can make a smart TV more intelligent for capturing and storing a viewer’s daily-life interactions, preferences, activities, and events of individuals and families. This captured data on a smart TV can be used for a variety of fields and domains, such as analyzing watching behavior and mental model, memory augmentation, viewer’s monitoring, user modelling, context-aware recommendations, e-learning, e-commerce, viewer’s privacy and security, and designing and development of adaptive user interface (UI). This chapter presents a critical review of current lifelogging systems and a comparison of different computing devices such as computers, smartphones, wearable devices, and smart TV for lifelogging capabilities. It also investigates the prominent applications of smart TV-based lifelogging systems, current challenges, and issues. Due to the vast applications of smart TV-based lifelogging, this area is still broad and needs proper attention to develop a secure, applicable, and robust lifelogging system.

It is an undoubted fact that we live in the age of knowledge and a moment of unpredictable events. The increasing globalization of knowledge and marketing prompts both individual and organizational knowledge. The market demands organization; further knowledge management is necessary in proportion to contemporary requirements in terms of personal skills for marketing management that altogether are expected to augment business performance. Nowadays, knowledge constitutes a significant capital, being paramount to manage it properly because it most likely provides superior productivity and competitive advantage. Only through successful knowledge management (KM in further text), companies are able to succeed in terms of their marketing strategies. Nevertheless, although KM has gained impetus in the past decade, the literature is diverse and fragmented with regard to its focus on marketing issues. In this paper, a systematic analysis of KM marketing-related literature is carried out in order to ascertain its research themes and developmental patterns. The key KM issues upon marketing debate are discussed, namely disparate contexts, KM interplay with firms’ functions, dynamic capability, conceptual maps, branding, innovation, its contribution to knowledge boundary spanning, and media and content production. These KM trends identify the research gap and provide rich avenues for further research drawn on KM and marketing literature.

Dyslexia is a learning disability characterized by difficulty in reading. Individuals suffering from this disease have normal intelligence and vision. Dyslexic patients face problems in creating links between alphabets shown to them and their phoenix. This disease affects the portion of the brain that functions to process alphabets and words. Interventions and therapies can help the patients to improve their capability to learn. A plethora of game-based interventions (GBIs) for a diverse set of learning disorders has emerged over the last decade. However, studies comprehensively reviewing GBIs for dyslexic patients, are still nonexistent. This systematic literature review (SLR) fills this research gap by providing a comprehensive overview of the use of game elements and game-based intervention methods in enhancing the learning capabilities of dyslexic patients. The methodological foundations of this SLR have been extracted from Kitchenham’s guidelines for systematic reviews. Relevant studies have been extracted from six electronic databases and the study selection process complies with the structured PRISMA guidelines. Out of an initial set of 950 studies, 42 studies were selected through a systematic and unbiased selection procedure followed by the data extraction, analysis, and reporting of findings. The results of this SLR provide evidence of the effectiveness of game elements and GBIs for patients suffering from dyslexia. The findings signify that word exercise-based games represent the predominant category of games followed by action-based video games. In contrast, other categories of the games, i.e., puzzle-based, strategy games, are rather scarce in this domain. None of the selected studies has used self-determination theory (SDT) as the basis of their game design. In addition, most of the selected studies fail to provide comprehensive coverage of the essential gaming elements. Future research efforts must be directed towards these shortcomings to improve the current state of the research.

The paper presents the base techniques for data processing, knowledge management, and integration in a smartphone-based system designed for both short-term and long-term self-monitoring of the progression of Parkinson’s disease. System is enriched with multimodal functionality and includes collecting data from internal measurement units in two modes. This enables to assess the performance of specific tasks and run specific non-obtrusive passive sensing tests based on routine activities. Information about physical activities and symptoms is processed and displayed in the form of a diary. The base components of knowledge and data acquisition are introduced. Some aspects of data analysis and data fusion are also discussed. Classification accuracy achieved with one data processing method for tremor data is 84%. Insofar as MeCo system uses different internal measurement units for tremor assessment, the enhancement of the classification accuracy may be achieved by combination of criteria from different techniques. The fusion technique in Parkinson disease symptom assessment provides up to 10% more accurate results in comparison with a single score.

Artificial intelligence (AI) is reshaping the economy, redefining the industry and the services sector. It is a disruptive technology, contributing to the development of different practices, new business models, and more efficient industrial processes. The literature highlights that AI will be included in all areas of organizations and the personal lives of citizens, and such dynamics are still significantly unstudied. The purpose of this research is to highlight the latest research about AI through a bibliometric analysis, which will include an up-to-date overview of artificial intelligence research on the goals, results, methodologies, and geographic distribution of studies during 2015–2019. AI applications to the industry and services have been growing over the last 5 years, as the research results show. The results include that most studies have concentrated on the effect of artificial intelligence on industries’ productivity potential. Robotics was the area of subject matter most researched. The findings show that the most common AI technologies are predictive analysis, machine learning, and robotics.

The objective of this work is to carry out a systematic analysis of decision support system (DSS) methodologies that could be applied to IoT systems. Decision support systems (DSS) are systems that gather information from various sources, facilitate its organization and analysis in a way that is useful for a particular decision, and provide a good interface through which users can navigate and interact easily. An important characteristic of DSS is that it facilitates the identification, analysis, and evaluation of the decision process, using decision models based on simple rules or complex mathematical models. On the other hand, the construction of this type of system implies having the support of decision-makers in all phases, as well as the allocation of the necessary resources. This chapter describes the components of a DSS and a methodological framework for the development of a decision support system in general, and later an adaptation is made for a use case applied to the Internet of things (IoT).

The Internet of Things (IoT) integrates physical objects in a networked real-world environment utilizing sensor-based software technologies in order to connect, share and exchange data with other relevant devices or platforms over the Internet. The contemporary business intelligence solutions encourage the IoT industry to reap benefits of big data dependent pervasive semantic orientation of public moods and relevant information shared in textual form using context aware integrated information management, by means of opinion mining through social media-based pervasive hybrid applications. Opinion mining (OM), also referred as pervasive sentiment analysis, is a process of extracting user orientation regarding products, services, businesses and other entities. It aims to classify an opinionative clue into positive or negative based on the sense and semantic category, which remains hidden from the human eye and non-pervasive applications.

The microblog and social media-based information is intrinsically hybrid in nature, which may comprise an ample amount of ubiquitous noise. The public opinions can be classified through supervised, semi-supervised, or unsupervised classifiers. Effective sentiment analysis is common to use in data science research, but unfortunately microblogging services like Twitter allows short text for communication, which compels online publishers to post unstructured and short form of opinions towards the target entities. Unstructured and short form of tweets makes it difficult to extract meaningful and accurate sentiment orientation. This limitation can be tackled through an effective preprocessing of text. Text normalization or preprocessing is the process of removing undesired symbols, tags and conversion of unstructured data into valuable information in order to make quality input for efficient sentiment analysis. It is observed that existing preprocessors and text normalizers ignored the informal nature of text especially slangs and subject tags due to scarcity of linguistic resources, which sometimes affects the sentiment accuracy. Therefore, this research proposes a framework for effective preprocessing of text in order to generate quality input. The proposed framework for an effective text preprocessing involves the following:

Informal text (a.k.a. slang) and tag identification are the major contributions of proposed framework. Slangs and Tags play a significant role in the semantic orientation of sentiments and emotions. The performance of proposed framework is evaluated over Twitter dataset using Python natural language toolkit. Experimental setup revealed that the proposed system achieved promising results with an average F1-Measure of 71% and accuracy of 72.4%.

This chapter presents a new framework for the use of Internet of Things technologies to improve supporters’ experience in sports events. Currently, there is a bigger distance between sports fans and their club’s stadium due to the existence of alternative ways to watch a game, for example, television broadcasts or streaming, that are more economic and with better comfort. The sports stadiums must innovate and improve the sports fans’ experience towards a more competitive use of technology. The proposed framework is based on a five-level classification that allows the improvement of the sports fans’ experience. With the purpose to assess which experiences are more important in a sports event, a questionnaire was sent to 205 sports fans. The result of the assessment was the base of the creation of a model that allows the evaluation of a certain sports stadium based on existing IoT implementations. In addition, each level contains a set of benefits to the sports fans’ experience and a set of technological recommendations to be implemented for a certain venue to level up and be called as Stadium 2.0. To assess the importance and applicability of this reference in real context, a set of interviews was carried out with two groups: spectators of sports events and sports events management staff (sports clubs, sports federations).

The technological advancements have turned smartphones into powerful lifelogging devices. Smartphone-based lifelogging system captures and stores information about peoples’ daily life activities, behaviors, interactions, and contexts into rich personal big data archives. The personal big data is of potential interest to the information sciences researchers and policy and decision makers in governments and organizations because of the availability of information, which would be impossible otherwise. Despite its potential, the smartphone-based lifelogging has been limitedly been explored from the big data perspective. This paper aims to provide a close-up view of the smartphone-based lifelogging as the source of personal big data. First, the smartphone-based lifelogging is reviewed to demonstrate its technological capabilities for personal big data generation and conformance to big data characteristics, alongside key personal big data applications. Second, a generalized architecture is presented for smartphone-based lifelog personal big data systems using big data systems design principals to advance the research in this space. Third, several challenges are highlighted regarding data capture, storage, analysis, visualization, privacy, and security. To address these concerns, several recommendations are suggested to improve personal big data generation, management, and usability.

The developmental supervision and alert device for heart problems is activated when the heart rate range presents an anomaly, that is, it is outside the established range that goes from 60 to 100 bpm in the resting state, inducing a heart problem known as cardiac arrhythmias. For real-time data, acquisition of heart rate variations is done through a MAX30100 pulse sensor (infrared sensor), also set at 660 nm to improve measurements. The device has an organic light-emitting diode (OLED) screen which indicates the values beats per minute (bpm) and oxygen saturation of blood (SpO2) of the person. Since the sensor should be located where the skin is thin, it is designed for the wrist of the hand. The communication module is the SIM808, which connects to the ThingSpeak platform using TCP/IP (Transmission Control Protocol/Internet Protocol) and then sends the data with the cardiac signals. Also, there are alerts of heart problems using Global System for Mobile Communications (GSM) network for sending the information of the acquired data by text message to the numbers registered in the device with GPS and bpm signal. Moreover, the very high performance of the power supply system employing a lithium polymer (LIPO) battery with 1800 mAh is accurate for the system.

The industrial internet of things (IIoT) is progressing owing to intervention as technology of Industry 4.0, which requires devices to be connected to the Internet in industrial systems. These are based on the efficient management and monitoring of data using a network between PLC S7-1200 and Simatic IoT2040, through an IIoT, which stores, processes, and transfers data within the Freeboard web platform. However, an important aspect is to compare the response time of processed data between a real-time mechatronic task and interface IIoT. The system used is OPC-UA for the transmission and reception of data from the devices used in the production, the IoT tool Node-Red in the gateway-connected OPC-UA client and Freeboard web platform, permitting the analysis and visualization of data in real time, guaranteeing greater assertiveness in decision-making. The average communication delay time from the production line to the Cloud ranges from 2 ms to about 10 ms.

The purpose of this research was to compare the body mass index (BMI) to the body composition and somatotype of the military personnel from the 223 Infantry Division of the Ecuadorian Air Force. It was applied a quantitative approach to collect all the information related to the state of the art and the anthropometric measurements; as for the data gathering instruments, all the measures were stored on a Google Form and then processed using a Microsoft Excel worksheet to determine body composition in order to locate them in a somatocard using the methodology proposed by Heath and Carter (J. Carter, The Heath-Carter anthropometric somatotype. J.E.L. Carter, San Diego, 2002). In addition, it is a non-experimental research with a cross-sectional design since the samples were collected once. The objective of study was to determine the anthropometric variables which involved BMI versus the body composition which included values of fat, bone, residual, and muscle tissues, as well as the somatotype to establish the relationship among them. Based on the results, it was possible to determine that the BMI is not a decisive factor to identify the weight categories that can lead to health problems in the military personnel; however, the classification of categories obtained were low weight 0%, healthy weight 36%, overweight 38%, obese I 17%, obese II 2%, and obese III 7%, in relation to the body composition and somatotype with an average in the (1) fat tissue of 15.19 ± 3.26%, (2) muscle tissue of 45. 0 ± 3.8%, (3) lean body mass of 67.0 ± 25 kg, and mesomorph-endomorph somatotype (5-6-1); these values were confronted to set morphological standards required by Air Force Infantry personnel to fulfill their duties, minimize well-being risks, and monitor their health permanently through the use of practical applications available on mobile devices; furthermore, this research has proved that the Internet of Things (IoT) represents to be a useful tool not only for civilians but also in the military field.

Land use planning is a tool to ensure the use of resources in the quality of life of people through the identification of potentialities and ecological limitations of a territory. The water resource in Ecuador is abundant, but the contamination of the water resources is a reality. The objective of this research was to identify the current situation of territorial management in relation to water resources in the provinces of Bolívar and Cañar, through a qualitative methodology, applying an analytical method on the situation of the rivers, the uses of the water resource, and the environmental management of the land use plans obtained from the National Information System. It was determined that the Territorial Development Organization Plans (PDOTs) propose projects to face the problem of water resources; however the efficient use of agrochemicals and the treatment and reuse of sewage in the provinces of Cañar and Bolívar are pending.

Latin America is seen as a pivotal supplier of agricultural and poultry commodities to an ever-growing world population. However, serious pressure has been put on the rural agriculture communities as a consequence of the urbanization. In addition, the lack of using efficient processes has hindered the development of both the agriculture and poultry sectors. One possible solution to address this issue is the Internet-of-Things (IoT)-based smart management system in order to introduce modernization in traditional methods. This chapter will begin with the examination of the challenges faced by the conventional agriculture and poultry farm followed by possible contributions of IoT-based technologies and solutions to improve quality, quantity, sustainability, and cost-effectiveness of agricultural and poultry sectors. Then, the enablement of intelligent control and real-time decision-making by applying state-of-the-art methodologies comprising sensors for monitoring, and drones for maintaining surveillance and detecting irregularities through image analysis is discussed. The major contribution of this chapter is to elucidate the system design for monitoring various environmental parameters regarding smart agriculture and poultry farms by using wireless sensor networks and artificial intelligence.

Through the development of an Internet of Things system, it will be possible to recognize how technology has the ability to expand objects, moments, knowledge, stories, and places. All these components are part of cultural heritage, and its amplification is what it is intended with the development of an IoT system, also using georeferencing and augmented reality technologies. The article establishes the relationship that it is possible to create between the cultural heritage of a village located in the interior center of Portugal, named Amiais, and the technological development of an IoT system, which then promotes cultural heritage and contributes to the evolution of the territory as smart. For this purpose, ethnographic research and the survey of the local cultural heritage are used and, subsequently, the way in which technology-based systems impact the territory and people. It was of great relevance the contact of the project team with Amiais’ inhabitants, as well as with tourists and stakeholders. But it was also determinant the contact with the territory and with the elements of cultural heritage.

Mobile technologies have undergone a great evolution in a short space of time. This rapid evolution was not limited to smartphones but also extended to a variety of smart devices that allow to easily receive and transmit data, as well as to connect to the IoT. The mobile devices together with the growing worldwide adoption of social media sites by its users, allowing them to be connected, and share data, anytime and anywhere, lead us to the concept of participatory sensing. In this context users participate as voluntarily sensors, capturing and providing data of their day-to-day life. The large amount of data makes it easier to obtain information that is not readily available with the same practically global scope. This chapter aims to discuss the research challenges and opportunities in the field of participative sensing, presenting an overview of different applications in the context of urban environment that allow a more sustainable environmental management.

Safety is the main concern and also it acts as a major hurdle in maintaining cloud-based services. To overcome the difficulties, there exists a wide range of activities to be performed and a set of techniques that are carried out in cloud-based services. Apart from these safety issues, the cloud holds a collection of different features and frameworks. The cloud analyzes the security measures that are to be carried out to ensure its safety in providing services by developing diversified cloud services frequently. Such types of cloud frameworks are set off, and their safety, confidentiality, and probabilities are discussed in this chapter. To present twofold encryption and decryption to the users, the multi-cloud framework was built using two-stage encryption and decryption. The users of the cloud services should enjoy the salient features of the cloud through feature-based encryption standards. The main focus on cloud errors which in turn ends up in the loss of information from the cloud is taken into consideration while providing security in servers. The proposed technique offers a reduction of 0.3% and 5.62% system time for creating a directory for 10k and 20k files, respectively, when compared to the conventional B-Tree technique. The proposed technique of encryption provides twofold security and confidentiality of data to the end-users through feature-based encryption.

This investigation compares two policing models on a systematic and isolated way. It presents, as a general objective, the analysis of the employment of intelligence-led policing in the Republican National Guard. The method that supports the investigation is the deductive method, the theoretical and conceptual framework that supports the present study results from the bibliographic research and analysis of institutional documents, and the empirical investigation is based on conducting interviews, following the study, a qualitative approach. The results show that this work typology is currently under development by the institution. In this path, technological investment is outlined, namely, in surveillance technologies and in the interoperability of information systems, comprehensive training on the subject for the different classes, an information structure present at all functional levels, and the institutional commitment to the model, as necessary requirements for its implementation by the entire structure.