The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

The role of big data analytics in industrial internet of things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT. We identify and discuss the indispensable challenges that remain to be addressed, serving as future research directions. © 2019 Elsevier B.V

The role of big data analytics in industrial Internet of Things

Big data production in industrial Internet of Things (IIoT) is evident due to the massive deployment of sensors and Internet of Things (IoT) devices. However, big data processing is challenging due to limited computational, networking and storage resources at IoT device-end. Big data analytics (BDA) is expected to provide operational- and customer-level intelligence in IIoT systems. Although numerous studies on IIoT and BDA exist, only a few studies have explored the convergence of the two paradigms. In this study, we investigate the recent BDA technologies, algorithms and techniques that can lead to the development of intelligent IIoT systems. We devise a taxonomy by classifying and categorising the literature on the basis of important parameters (e.g. data sources, analytics tools, analytics techniques, requirements, industrial analytics applications and analytics types). We present the frameworks and case studies of the various enterprises that have benefited from BDA. We also enumerate the considerable opportunities introduced by BDA in IIoT.We identify and discuss the indispensable challenges that remain to be addressed as future research directions as well

Framework for cost-effective analytical modelling for sensory data over cloud environment

In order to offer sensory data as a service over the cloud, it is necessary to execute a cost-effective and yet precise data analytical logic within the sensing units. However, it is quite questionable as such forms of analytical operation are quite resource dependent which cannot be offered by the resource constraint sensory units. Therefore, the proposed paper introduces a novel approach of performing cost-effective data analytical method in order to extract knowledge from big data over the cloud. The proposed study uses a novel concept of the frequent pattern along with a tree-based approach in order to develop an analytical model for carrying out the mining operation in the large-scale sensor deployment over the cloud environment. Using a simulation-based approach over the mathematical model, the proposed model exhibit reduced mining duration, controlled energy dissipation, and highly optimized memory demands for all the resource constraint nodes

A cloud-based Analytics-Platform for user-centric Internet of Things domains – Prototype and Performance Evaluation

Data analytics have the potential to increase the value of data emitted from smart devices in user-centric Internet of Things environments, such as smart home, drastically. In order to allow businesses and end-consumers alike to tap into this potential, appropriate analytics architectures must be present. Current solutions in this field do not tackle all of the diverse challenges and requirements, which were identified in previous research. Specifically, personalized, extensible analytics solutions, which still offer the means to address big data problems are scarce. In this paper, we therefore present an architectural solution, which was specifically designed to address the named challenges. Furthermore, we offer insights into the prototypical implementation of the proposed concept as well as an evaluation of its performance against traditional big data architectures

Sustainability in the digitally optimised maritime industry

The increasing volumes in global maritime trade are associated with accumulating stresses and adverse effects on the environment. Measures such as stricter regulations and renewed legislation are implemented to pivot the industry towards global sustainable development goals. While the end goal of achieving carbon neutrality remains problematic and slow-going, several short-term solutions could be found. This study aims to explore how digitalisation can support the sustainable development of the maritime industry, focusing primarily on enhancing port efficiency. The purpose of the study is divided into two sub-questions: what are the sustainability impacts of the maritime industry, and how could digitalising port calls impact shipping emissions? The study's theoretical framework consists of three large concepts; sustainability, digital transformation, and the maritime industry's complexity, particularly the port operations, are discussed. The research method used in this study is quantitative, as the research problem is best addressed by processing numerical data. The results of the study are in line with previous research, indicating that optimising the port operations and reducing waiting time could have significant impacts on CO2 emissions. Lowering the CO2 emissions leads to the sustainable development of the environment and economic and social sustainability as the cost savings have the potential to reach billions of USD and have positive effects on social well-being. While change on a global scale may not be viable to implement due to development maturity, more realistic scenarios, such as a change in top 30 GDP countries, depict the ability to implement digitalisation and JIT shipping effectively and at scale. By leveraging their current infrastructure and economic capabilities, these countries alone could produce significant sustainability impacts while remaining competitive. To tackle the global issue of climate change, the decision-makers should thus invest and incentivise the maritime actors to optimise their operations that directly lead to the industry's sustainable development.Maailman meriteollisuuden kasvuun liittyy lisääntyviä stressitekijötä ja haitallisia ympäristövaikutuksia. Toimenpiteitä, kuten tiukempia määräyksiä ja uudistettua lainsäädäntöä, toteutetaan, jotta ala saataisiin kohti globaaleja kestävän kehityksen tavoitteita. Vaikka lopullinen tavoite hiilineutraaliuden saavuttamiseksi on edelleen ongelmallinen ja hidas saavuttaa, voidaan useita lyhyen aikavälin ratkaisuja ottaa käyt-töön. Tämän tutkimuksen tarkoituksena on selvittää, miten digitalisaatio voi tukea mer-iteollisuuden kestävää kehitystä keskittymällä ensisijaisesti satamatehokkuuden paran-tamiseen. Tutkimuksen tarkoitus on jaettu kahteen alakysymykseen: mitkä ovat meren-kulkualan kestävän kehityksen vaikutukset ja miten satamatoimintojen digitalisointi voi-si vaikuttaa merenkulun päästöihin? Tutkimuksen teoreettinen kehys koostuu kolmesta suuresta käsitteestä; kestävästä kehityksestä, digitaalisesta muutoksesta ja merenkulkualan, erityisesti satamatoimintojen monimutkaisuudesta. Tutkimusmenetelmänä käytetään kvantitatiivista menetelmää, sillä tutkimusongelmaan voidaan parhaiten pureutua analysoimalla numeerista dataa. Tutkimuksen tulokset ovat aiempien tutkimusten mukaisia, ja ne osoittavat, että sa-tamatoimintojen optimoinnilla ja odotusajan lyhentämisellä voi olla merkittäviä vaikutuksia hiilidioksidipäästöihin. Hiilidioksidipäästöjen vähentäminen johtaa ympäristön kestävään kehitykseen sekä taloudelliseen ja sosiaaliseen kestävyyteen, sillä kustannussäästöt voivat saavuttaa miljardeja dollareita ja vaikuttaa myönteisesti so-siaaliseen hyvinvointiin. Vaikka globaalin mittakaavan muutos ei välttämättä ole toteut-tamiskelpoinen sen kehityksen kypsyyden vuoksi, realistisemmat skenaariot, kuten muutos 30 parhaan BKT-maan joukossa, kuvaavat kykyä toteuttaa digitalisaatio ja just-in-time (juuri ajoissa) tehokkaasti ja laajamittaisesti. Hyödyntämällä nykyistä infra-struktuuriaan ja taloudellisia valmiuksiaan nämä maat yksin voivat tuottaa merkittäviä kestävyysvaikutuksia pysyen kilpailukykyisinä. Ilmastonmuutoksen maailmanlaajuisen ongelman ratkaisemiseksi päätöksentekijöiden olisi siten investoitava ja kannustettava merenkulun toimijoita optimoimaan toimintaansa, mikä johtaa suoraan alan kestävään kehitykseen

Evolution of artificial intelligence research in Technological Forecasting and Social Change: Research topics, trends, and future directions

Artificial intelligence (AI) is a set of rapidly expanding disruptive technologies that are radically transforming various aspects related to people, business, society, and the environment. With the proliferation of digital computing devices and the emergence of big data, AI is increasingly offering significant opportunities for society and business organizations. The growing interest of scholars and practitioners in AI has resulted in the diversity of research topics explored in bulks of scholarly literature published in leading research outlets. This study aims to map the intellectual structure and evolution of the conceptual structure of overall AI research published in Technological Forecasting and Social Change (TF&SC). This study uses machine learning-based structural topic modeling (STM) to extract, report, and visualize the latent topics from the AI research literature. Further, the disciplinary patterns in the intellectual structure of AI research are examined with the additional objective of assessing the disciplinary impact of AI. The results of the topic modeling reveal eight key topics, out of which the topics concerning healthcare, circular economy and sustainable supply chain, adoption of AI by consumers, and AI for decision-making are showing a rising trend over the years. AI research has a significant influence on disciplines such as business, management, and accounting, social science, engineering, computer science, and mathematics. The study provides an insightful agenda for the future based on evidence-based research directions that would benefit future AI scholars to identify contemporary research issues and develop impactful research to solve complex societal problems