Adoption Barriers of IoT in Large Scale Pilots

The pervasive connectivity of devices enabled by Internet of Things (IoT) technologies is leading the way in various innovative services and applications. This increasing connectivity comes with its own complexity. Thus, large scale pilots (LSPs) are designed to develop, test and use IoT innovations in various domains in conditions very similar to their operational scalable setting. One of the key challenges facing the diffusion of such innovations within the course of an LSP is understanding the conditions in which their respective users decide to adopt them (or not). Accordingly, in this study we explore IoT adoption barriers in four LSPs in Europe from the following domains: smart cities, autonomous driving, wearables and smart agriculture and farming. By applying Roger’s Diffusion of Innovation as a theoretical lens and using empirical data from workshops and expert interviews, we identify a set of common and domain specific adoption barriers. Our results reveal that trust, cost, perceived value, privacy and security are common concerns, yet shape differently across domains. In order to overcome various barriers, the relative advantage or value of using the innovation needs to be clearly communicated and related to the users’ situational use; while this value can be economic in some domains, it is more hedonic in others. LSPs were particularly challenged in applying established strategies to overcome some of those barriers (e.g., co-creation with end-users) due to the immaturity of the technology as well as the scale of pilots. Accordingly, we reflect on the theoretical choice in the discussion as well as the implications of this study on research and practice. We conclude with providing practical recommendations to LSPs and avenues for future research.

Download Full-text

Embodied AI-Driven Operation of Smart Cities: A Concise Review

10.36227/techrxiv.16370823 ◽

2021 ◽

Author(s):

FARZAN SHENAVARMASOULEH ◽

Farid Ghareh Mohammadi ◽

M. Hadi Amini ◽

Hamid R. Arabnia

Keyword(s):

Information And Communication Technologies ◽

Smart City ◽

Smart Cities ◽

Communication Technologies ◽

Autonomous Driving ◽

Future Research ◽

Intelligent Network ◽

Concise Review ◽

Information And Communication ◽

Cloud Technologies

<div>A smart city can be seen as a framework, comprised of Information and Communication Technologies (ICT). An intelligent network of connected devices that collect data with their sensors and transmit them using wireless and cloud technologies in order to communicate with other assets in the ecosystem plays a pivotal role in this framework. Maximizing the quality of life of citizens, making better use of available resources, cutting costs, and improving sustainability are the ultimate goals that a smart city is after. Hence, data collected from these connected devices will continuously get thoroughly analyzed to gain better insights into the services that are being offered across the city; with this goal in mind that they can be used to make the whole system more efficient.</div><div>Robots and physical machines are inseparable parts of a smart city. Embodied AI is the field of study that takes a deeper look into these and explores how they can fit into real-world environments. It focuses on learning through interaction with the surrounding environment, as opposed to Internet AI which tries to learn from static datasets. Embodied AI aims to train an agent that can See (Computer Vision), Talk (NLP), Navigate and Interact with its environment (Reinforcement Learning), and Reason (General Intelligence), all at the same time. Autonomous driving cars and personal companions are some of the examples that benefit from Embodied AI nowadays.</div><div>In this paper, we attempt to do a concise review of this field. We will go through its definitions, its characteristics, and its current achievements along with different algorithms, approaches, and solutions that are being used in different components of it (e.g. Vision, NLP, RL). We will then explore all the available simulators and 3D interactable databases that will make the research in this area feasible. Finally, we will address its challenges and identify its potentials for future research.</div>

Download Full-text

The Case of Electronic Approval System

E-Adoption and Socio-Economic Impacts ◽

10.4018/978-1-60960-597-1.ch014 ◽

2011 ◽

pp. 269-285

Author(s):

Sinawong Sang ◽

Jeong-Dong Lee ◽

Jongsu LeeSeoul

Keyword(s):

Technology Acceptance ◽

Diffusion Of Innovation ◽

User Acceptance ◽

Ease Of Use ◽

Perceived Usefulness ◽

Relative Advantage ◽

Future Research ◽

Perceived Ease Of Use ◽

User Adoption ◽

Acceptance Model

The purpose of this study is to assess and test the factors that influence user adoption of e-Government services: the Electronic Approval System (EAS). This study uses the Technology Acceptance Model (TAM), the extended TAM (TAM2), the Diffusion of Innovation (DOI), and trust to build a parsimonious yet comprehensive model of factors that influence user acceptance of the EAS. We collected data from a total of 112 public officers in 12 ministries in Cambodia. We assessed the model with regression analyses. The findings in this article show that the determinants of the model (perceived usefulness, relative advantage, and trust) explain 30.5% of the variance in user acceptance of the EAS. At the same time, image, output quality, and perceived ease of use explain 38.4% of the variance in user perception of the usefulness of the EAS. In this article, we discuss our findings, implications, and suggestions for future research.

Download Full-text

Emoji-powered Sentiment and Emotion Detection from Software Developers’ Communication Data

ACM Transactions on Software Engineering and Methodology ◽

10.1145/3424308 ◽

2021 ◽

Vol 30 (2) ◽

pp. 1-48

Author(s):

Zhenpeng Chen ◽

Yanbin Cao ◽

Huihan Yao ◽

Xuan Lu ◽

Xin Peng ◽

...

Keyword(s):

Large Scale ◽

Online Communication ◽

Future Research ◽

Technical Knowledge ◽

Affective States ◽

Emotion Detection ◽

Open Domain ◽

Software Developers ◽

Domain Specific ◽

Benchmark Datasets

Sentiment and emotion detection from textual communication records of developers have various application scenarios in software engineering (SE). However, commonly used off-the-shelf sentiment/emotion detection tools cannot obtain reliable results in SE tasks and misunderstanding of technical knowledge is demonstrated to be the main reason. Then researchers start to create labeled SE-related datasets manually and customize SE-specific methods. However, the scarce labeled data can cover only very limited lexicon and expressions. In this article, we employ emojis as an instrument to address this problem. Different from manual labels that are provided by annotators, emojis are self-reported labels provided by the authors themselves to intentionally convey affective states and thus are suitable indications of sentiment and emotion in texts. Since emojis have been widely adopted in online communication, a large amount of emoji-labeled texts can be easily accessed to help tackle the scarcity of the manually labeled data. Specifically, we leverage Tweets and GitHub posts containing emojis to learn representations of SE-related texts through emoji prediction. By predicting emojis containing in each text, texts that tend to surround the same emoji are represented with similar vectors, which transfers the sentiment knowledge contained in emoji usage to the representations of texts. Then we leverage the sentiment-aware representations as well as manually labeled data to learn the final sentiment/emotion classifier via transfer learning. Compared to existing approaches, our approach can achieve significant improvement on representative benchmark datasets, with an average increase of 0.036 and 0.049 in macro-F1 in sentiment and emotion detection, respectively. Further investigations reveal that the large-scale Tweets make a key contribution to the power of our approach. This finding informs future research not to unilaterally pursue the domain-specific resource but try to transform knowledge from the open domain through ubiquitous signals such as emojis. Finally, we present the open challenges of sentiment and emotion detection in SE through a qualitative analysis of texts misclassified by our approach.

Download Full-text

Embodied AI-Driven Operation of Smart Cities: A Concise Review

10.36227/techrxiv.16370823.v1 ◽

2021 ◽

Author(s):

FARZAN SHENAVARMASOULEH ◽

Farid Ghareh Mohammadi ◽

M. Hadi Amini ◽

Hamid R. Arabnia

Keyword(s):

Information And Communication Technologies ◽

Smart City ◽

Smart Cities ◽

Communication Technologies ◽

Autonomous Driving ◽

Future Research ◽

Intelligent Network ◽

Concise Review ◽

Information And Communication ◽

Cloud Technologies

Download Full-text

Federated Learning in Smart City Sensing: Challenges and Opportunities

Sensors ◽

10.3390/s20216230 ◽

2020 ◽

Vol 20 (21) ◽

pp. 6230 ◽

Cited By ~ 1

Author(s):

Ji Chu Jiang ◽

Burak Kantarci ◽

Sema Oktug ◽

Tolga Soyata

Keyword(s):

Data Acquisition ◽

Smart City ◽

Large Scale ◽

Smart Cities ◽

Security And Privacy ◽

Privacy And Security ◽

Security Issues ◽

Challenges And Opportunities ◽

Data Provision ◽

Novel Concept

Smart Cities sensing is an emerging paradigm to facilitate the transition into smart city services. The advent of the Internet of Things (IoT) and the widespread use of mobile devices with computing and sensing capabilities has motivated applications that require data acquisition at a societal scale. These valuable data can be leveraged to train advanced Artificial Intelligence (AI) models that serve various smart services that benefit society in all aspects. Despite their effectiveness, legacy data acquisition models backed with centralized Machine Learning models entail security and privacy concerns, and lead to less participation in large-scale sensing and data provision for smart city services. To overcome these challenges, Federated Learning is a novel concept that can serve as a solution to the privacy and security issues encountered within the process of data collection. This survey article presents an overview of smart city sensing and its current challenges followed by the potential of Federated Learning in addressing those challenges. A comprehensive discussion of the state-of-the-art methods for Federated Learning is provided along with an in-depth discussion on the applicability of Federated Learning in smart city sensing; clear insights on open issues, challenges, and opportunities in this field are provided as guidance for the researchers studying this subject matter.

Download Full-text

BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for the IoT

Computers ◽

10.3390/computers7030039 ◽

2018 ◽

Vol 7 (3) ◽

pp. 39 ◽

Cited By ~ 40

Author(s):

Ronghua Xu ◽

Yu Chen ◽

Erik Blasch ◽

Genshe Chen

Keyword(s):

Access Control ◽

Large Scale ◽

Smart Cities ◽

Single Point ◽

Raspberry Pi ◽

Role Based Access Control ◽

Privacy And Security ◽

Smart Contract ◽

Attribute Based Access Control ◽

Role Based

While Internet of Things (IoT) technology has been widely recognized as an essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today’s AC is the centralized authorization server, which can cause a performance bottleneck or be the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable effective protection for devices, services and information in large-scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registration, propagation, and revocation of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI nodes) and more powerful computing devices (i.e., laptops) and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

Download Full-text

Data Ownership: A Survey

Information ◽

10.3390/info12110465 ◽

2021 ◽

Vol 12 (11) ◽

pp. 465

Author(s):

Jad Asswad ◽

Jorge Marx Gómez

Keyword(s):

State Of The Art ◽

Smart Cities ◽

Lessons Learned ◽

Future Research ◽

Transportation Industry ◽

Privacy And Security ◽

Data Ownership ◽

The Subject ◽

Definition Of ◽

Innovation Knowledge

The importance of data is increasing along its inflation in our world today. In the big data era, data is becoming a main source for innovation, knowledge and insight, as well as a competitive and financial advantage in the race of information procurement. This interest in acquiring and exploiting data, in addition to the existing concerns regarding the privacy and security of information, raises the question of who should own the data and how the ownership of data can be preserved. This paper discusses and analyses the concept of data ownership and provides an overview on the subject from different point of views. It surveys also the state-of-the-art of data ownership in health, transportation, industry, energy and smart cities sectors and outlines lessons learned with an extended definition of data ownership that may pave the way for future research and work in this area.

Download Full-text

BlendCAC: A Smart Contract Enabled Decentralized Capability-Based Access Control Mechanism for IoT

10.20944/preprints201805.0079.v1 ◽

2018 ◽

Cited By ~ 2

Author(s):

Ronghua Xu ◽

Yu Chen ◽

Erik Blasch ◽

Genshe Chen

Keyword(s):

Access Control ◽

Large Scale ◽

Smart Cities ◽

Single Point ◽

Raspberry Pi ◽

Role Based Access Control ◽

Privacy And Security ◽

Smart Contract ◽

Attribute Based Access Control ◽

Role Based

While the Internet of Things (IoT) technology has been widely recognized as the essential part of Smart Cities, it also brings new challenges in terms of privacy and security. Access control (AC) is among the top security concerns, which is critical in resource and information protection over IoT devices. Traditional access control approaches, like Access Control Lists (ACL), Role-based Access Control (RBAC) and Attribute-based Access Control (ABAC), are not able to provide a scalable, manageable and efficient mechanism to meet the requirements of IoT systems. Another weakness in today's AC is the centralized authorization server, which can be the performance bottleneck or the single point of failure. Inspired by the smart contract on top of a blockchain protocol, this paper proposes BlendCAC, which is a decentralized, federated capability-based AC mechanism to enable an effective protection for devices, services and information in large scale IoT systems. A federated capability-based delegation model (FCDM) is introduced to support hierarchical and multi-hop delegation. The mechanism for delegate authorization and revocation is explored. A robust identity-based capability token management strategy is proposed, which takes advantage of the smart contract for registering, propagating and revocating of the access authorization. A proof-of-concept prototype has been implemented on both resources-constrained devices (i.e., Raspberry PI node) and more powerful computing devices (i.e., laptops), and tested on a local private blockchain network. The experimental results demonstrate the feasibility of the BlendCAC to offer a decentralized, scalable, lightweight and fine-grained AC solution for IoT systems.

Download Full-text

Self-Regulated Learning

10.1093/oxfordhb/9780199336746.013.23 ◽

2016 ◽

Cited By ~ 1

Author(s):

Nate Kornell ◽

Bridgid Finn

Keyword(s):

Large Scale ◽

Study Strategies ◽

Future Research ◽

Small Scale ◽

Digital Learning ◽

Self Regulated Learning ◽

Metacognitive Judgments ◽

Digital Learning Environments ◽

Practical Recommendations

Effective self-regulated studying can influence students’ learning in school and beyond. This chapter reviews research on two key decisions: when to study and how to study. It first reviews the decisions people make about when to start and stop studying—that is, when to study—and the metacognitive judgments that underlie those decisions. It distinguishes between small-scale and large-scale decisions, such as which problem to work on next and whether to study today at all, respectively. It then discusses decisions about how to study, for example, whether or not to take notes, underline, test oneself, or reread. It then discusses key areas for future research, with an emphasis on student-centric research and research in digital learning environments. It offers practical recommendations for studiers about how to avoid overconfidence and procrastination and how to choose study strategies that increase short-term difficulty and long term success.

Download Full-text

Change of Attitude, Technology and Practice: Identifying the Change for Increased Value Creation with Customer Co-creation

TRANSNATIONAL MARKETING JOURNAL ◽

10.33182/tmj.v5i1.388 ◽

2017 ◽

Vol 5 (1) ◽

pp. 70-82

Author(s):

Soumi Paul ◽

Paola Peretti ◽

Saroj Kumar Datta

Keyword(s):

Social Media ◽

New Product Development ◽

Value Creation ◽

Large Scale ◽

Customer Orientation ◽

Customer Relationships ◽

Future Research ◽

Management Competencies ◽

Business Decisions ◽

Prime Concern

Building customer relationships and customer equity is the prime concern in today’s business decisions. The emergence of internet, especially social media like Facebook and Twitter, changed traditional marketing thought to a great extent. The importance of customer orientation is reflected in the axiom, “The customer is the king”. A good number of organizations are engaging customers in their new product development activities via social media platforms. Co-creation, a new perspective in which customers are active co-creators of the products they buy and use, is currently challenging the traditional paradigm. The concept of co-creation involving the customer’s knowledge, creativity and judgment to generate value is considered not only an upcoming trend that introduces new products or services but also fitting their need and increasing value for money. Knowledge and innovation are inseparable. Knowledge management competencies and capacities are essential to any organization that aspires to be distinguished and innovative. The present work is an attempt to identify the change in value creation procedure along with one area of business, where co-creation can return significant dividends. It is on extending the brand or brand category through brand extension or line extension. This article, through an in depth literature review analysis, identifies the changes in every perspective of this paradigm shift and it presents a conceptual model of company-customer-brand-based co-creation activity via social media. The main objective is offering an agenda for future research of this emerging trend and ensuring the way to move from theory to practice. The paper acts as a proposal; it allows the organization to go for this change in a large scale and obtain early feedback on the idea presented.

Download Full-text