Governing the progress of internet-of-things: Ambivalence in the quest of technology exploitation and user rights protection

This paper discusses the dynamics between the pursuit of competitiveness and governance of data security in strengthening the Internet of Things (IoT) readiness in developing economies using Malaysia as a case study. It explores the potential of the IoT regulatory framework in guarding the privacy and interests of IoT users. This paper also reveals the collaborative model of technology push-market pull for technological capabilities development as well as the measures that uphold the principles of good privacy practice. The model incorporates privacy-by-design measures that would result in higher user confidence in this emerging technology, which is vital to a healthy IoT ecosystem. Through the collaborative model of Penang as evidence, our findings indicate that Malaysia seeks to create a structure that fosters technology push-market pull forces for IoT technological capabilities development. While the model paves a co-evolutionary path for diffusion and upgrading of IoT, several issues related to the volatility of online data and databases were identified as well as the lack of responsibility and accountability of corporations in handling the sensitive personal data of customers. We see that it is essential for the regulators to play a (more) significant role in safeguarding the interests of IoT users. In this regard, the privacy-by-design, a citizen-centric regulatory framework should be considered in policy reviews in deploying IoT-based competitive promotion initiatives. This paper breaks new ground by elaborating on the common route of IoT technology capabilities development, which is typical in the developing context. While it highlights the common issues that emerge as technology advances, we propose a regulatory framework that features embedded privacy-by-design to protect the interests of the IoT users.     

Why do people want to use location-based application for emergency situations? The extension of UTAUT perspectives

This study aims to determine the factors that influence user intention to use location-based emergency applications. The Unified Theory of Acceptance and Use of Technology (UTAUT) is used as a fundamental theory by adding other relevant variables: trust, privacy concern, and fear of crime. Data were collected through questionnaires distributed to the users of the X-Igent Panic Button application. A total of 348 data were collected online using random sampling and processed using structural equation modeling. This study concludes that this application adoption's determinant factors are performance expectancy, trust, social influence, fear of crime, and collection as privacy concerns. These factors become significant predictors of behavioral intention by explaining about 60.5% of the total variation. The study also concludes that trust in services or service providers has the most significant positive impact on the behavioral intention to use location-based mobile applications in emergencies.     

Secure Multifactor Remote Access User Authentication Framework for IoT Networks

The term IoT refers to the interconnection and exchange of data among devices/sensors. IoT devices are often small, low cost, and have limited resources. The IoT issues and challenges are growing increasingly. Security and privacy issues are among the most important concerns in IoT applications, such as smart buildings. Remote cybersecurity attacks are the attacks which do not require physical access to the IoT networks, where the attacker can remotely access and communicate with the IoT devices through a wireless communication channel. Thus, remote cybersecurity attacks are a significant threat. Emerging applications in smart environments such as smart buildings require remote access for both users and resources. Since the user/building communication channel is insecure, a lightweight and secure authentication protocol is required. In this paper, we propose a new secure remote user mutual authentication protocol based on transitory identities and multi-factor authentication for IoT smart building environment. The protocol ensures that only legitimate users can authenticate with smart building controllers in an anonymous, unlinkable, and untraceable manner. The protocol also avoids clock synchronization problem and can resist quantum computing attacks. The security of the protocol is evaluated using two different methods: (1) informal analysis; (2) model check using the automated validation of internet security protocols and applications (AVISPA) toolkit. The communication overhead and computational cost of the proposed are analyzed. The security and performance analysis show that our protocol is secure and efficient.     

A multi-criteria analysis for an internet of things application recommendation system

Various internet of things applications are available that cover every aspect of daily life and users can subscribe to numerous IoT applications. Selecting the most suitable IoT applications for individual users is a critical challenge. This study aims to solve this challenge by proposing recommendation system using a hybrid multicriteria decision-making approach based on the analytical hierarchy process and simple additive weight methods. Based on the opinions and preferences of experts, the model and the hierarchy were designed to assess and compare three crucial criteria, namely smart objects, applications, and providers. The results show that applications criterion is more important for users than the other two criteria. In specific, privacy, reliability, and availability are crucial criteria for IoT applications.     

A contextualized study of the usage of the Internet of things (IoTs) in smart farming in a typical Middle Eastern country within the context of Unified Theory of Acceptance and Use of Technology model (UTAUT)

Smart Farming is the application of modern technologies, tools and gadgets for increasing the agricultural crops quality and quantity. The Internet of Things (IoT) technology has had a prominent role in the establishment of smart farming. However, the application of this technology could be hard and, in some cases, challenging for the Middle Eastern users. Therefore, the research purpose is to identify the influential factors in the adoption and then application of IoT in smart farming by farmers with a contextualized approach in Iran, a typical Middle Eastern country. Thus, the Unified Theory of Acceptance and Use of Technology (UTAUT) has contextually been used as the theoretical model of the research. The results accentuated and proved the positive impacts of performance expectancy (H1), effort expectancy (H2), social influence (H3), individual factors (H4), and facilitating conditions (H5), on the intention to use IoT technology. Ultimately, the results were indicating the significant impact of behavioral intention on the actual usage of IoT technology (H6). One of the implications of the research is for the IT policymakers in the agricultural sector in the Middle East, where water and cultivable land are two valuable but scarce economic resources. Hence, smart farming could not be promoted unless the farmers had fulfilled its prerequisite factors proposed by the research results for using the IoT technology.     

智能时代下的交互导识设计研究综述

目的 深入探讨智能时代背景下交互导识设计的重要性和未来的发展趋势,提升个体对复杂信息环境的判断能力,引导用户在复杂且无序的信息环境中探寻解决问题的途径,为用户提供直观且易操作的工具和模式,以实现更加安全、便利、高效、舒适的用户体验。方法 通过文献研究与归纳,梳理导识设计的发展来由,对比分析国内外研究学者对导识设计概念的理解与辨析,结合现状与信息交互方式和先进技术的发展趋势,提出交互导识设计的概念并对其蕴含的价值与应用范围进行全面论述。结论 交互导识设计坚持以用户为中心的设计方向,实现用户体验的升级、导识信息的有效传递。在智能时代背景下,其专业价值主要体现在理论创新与知识扩展、技术创新与实践应用、用户体验深入研究、社会文化与伦理考量,以及经济与产业影响五大维度上。这项研究不仅丰富了设计学术理论的深度和广度,也为相关行业的设计实践提供创新思路与应用策略方向。     

Applied Artificial Intelligence and user satisfaction: Smartwatch usage for healthcare in Bangladesh during COVID-19

The evolution of Artificial Intelligence (AI) has revolutionized many aspects of human life, including healthcare. Amidst the Covid-19 pandemic, AI-enabled smartwatches are being used to help users to self-monitor and self-manage their health. Using a framework based on Stimulus-Organism-Response (S–O-R) theory, this present study aimed to explore the use of AI-enabled smartwatches for health purposes, in particular the effects of product quality, service quality, perceived convenience, and perceived ease of use on user experience, trust and user satisfaction. Based on a purposive survey sample of 486 smartphone users in Bangladesh, data collected was analyzed using SPSS software for elementary analyses and PLS-SEM for hypotheses testing. The findings showed that the predictors, namely product quality, service quality, perceived convenience, and perceived ease of use, significantly affected user experience and trust. Similarly, user experience and trust were influential on user satisfaction and played partial mediating roles between predictors and user satisfaction. Besides, gender and age moderate the relationships of experience and trust with customer satisfaction. These findings support the S–O-R theoretical framework and have practical implications for brand and marketing managers of smartwatches in developing product features and understanding users' attitudes and behaviours.     

User Profile System Based on Sentiment Analysis for Mobile Edge Computing

Emotions of users do not converge in a single application but are scattered across diverse applications. Mobile devices are the closest media for handling user data and these devices have the advantage of integrating private user information and emotions spread over different applications. In this paper, we first analyze user profile on a mobile device by describing the problem of the user sentiment profile system in terms of data granularity, media diversity, and server-side solution. Fine-grained data requires additional data and structural analysis in mobile devices. Media diversity requires standard parameters to integrate user data from various applications. A server-side solution presents a potential risk when handling individual privacy information. Therefore, in order to overcome these problems, we propose a general-purposed user profile system based on sentiment analysis that extracts individual emotional preferences by comparing the difference between public and individual data based on particular features. The proposed system is built based on a sentiment hierarchy, which is created by using unstructured data on mobile devices. It can compensate for the concentration of single media, and analyze individual private data without the invasion of privacy on mobile devices.     

Computing the User Experience via Big Data Analysis: A Case of Uber Services

As of 2020, the issue of user satisfaction has generated a significant amount of interest. Therefore, we employ a big data approach for exploring user satisfaction among Uber users. We develop a research model of user satisfaction by expanding the list of user experience (UX) elements (i.e., pragmatic, expectation confirmation, hedonic, and burden) by including more elements, namely: risk, cost, promotion, anxiety, sadness, and anger. Subsequently, we collect 125,768 comments from online reviews of Uber services and perform a sentiment analysis to extract the UX elements. The results of a regression analysis reveal the following: hedonic, promotion, and pragmatic significantly and positively affect user satisfaction, while burden, cost, and risk have a substantial negative influence. However, the influence of expectation confirmation on user satisfaction is not supported. Moreover, sadness, anxiety, and anger are positively related to the perceived risk of users. Compared with sadness and anxiety, anger has a more important role in increasing the perceived burden of users. Based on these findings, we also provide some theoretical implications for future UX literature and some core suggestions related to establishing strategies for Uber and similar services. The proposed big data approach may be utilized in other UX studies in the future.     

Kill switches,remote deletion,and intelligent agents: Framing everyday household cybersecurity in the internet of things

Increasing utilizations of kill switches, remote deletion, and intelligent agents as a part of “Internet of Things” (IoT) architectures present emerging cybersecurity and privacy challenges. These issues are compounded in complexity by the frequent updates and other controls instituted by the growing assortment of purveyors of household IoT devices and systems. This paper proposes that aspects of user ownership, awareness, and voice be clarified and in some venues fostered in part to expose as quickly as possible potential technological and social dangers. It addresses rights of household participants to obtain knowledge and control over the intelligent IoT agents operating (and perhaps “quartering”) in their personal and intimate spheres, as well as to be free from inappropriately opportunistic applications associated with IoT systems.     

互联网产品设计中用户体验与交互设计优化对策

目的 探寻互联网产品设计中用户体验与交互设计优化对策,从而设计出更符合用户需求和用户体验的互联网产品。方法 采用概念分析法,对互联网产品设计中的用户体验与交互设计分别展开研究,提出要遵循用户体验设计的一致性以及用户体验设计的个性化,并提出在交互设计中应使界面与用户感知习惯契合,易于被用户感知,帮助用户形成长时记忆,唤醒用户记忆,关注用户情绪与情感,使其产生愉悦的体验感。结论 作为产品创新与开拓的基础,用户体验与交互设计从深层次发掘用户的切实需求与体验,进而有利于互联网产品的推广。     

Multi-dimensional Security Range Query for Industrial IoT

The Internet of Things (IoT) has allowed for significant advancements in applications not only in the home, business, and environment, but also in factory automation. Industrial Internet of Things (IIoT) brings all of the benefits of the IoT to industrial contexts, allowing for a wide range of applications ranging from remote sensing and actuation to decentralization and autonomy. The expansion of the IoT has been set by serious security threats and obstacles, and one of the most pressing security concerns is the secure exchange of IoT data and fine-grained access control. A privacy-preserving multi-dimensional secure query technique for fog-enhanced IIoT was proposed in light of the fact that most existing range query schemes for fog-enhanced IoT cannot provide both multi-dimensional query and privacy protection. The query matrix was then decomposed using auxiliary vectors, and the auxiliary vector was then processed using BGN homomorphic encryption to create a query trapdoor. Finally, the query trapdoor may be matched to its sensor data using the homomorphic computation used by an IoT device terminal. With the application of particular auxiliary vectors, the spatial complexity might be efficiently decreased. The homomorphic encryption property might ensure the security of sensor data and safeguard the privacy of the user's inquiry mode. The results of the experiments reveal that the computing and communication expenses are modest.     

Verifiable Identity-Based Encryption with Keyword Search for IoT from Lattice

Internet of Things (IoT), which provides the solution of connecting things and devices, has increasingly developed as vital tools to realize intelligent life. Generally, source-limited IoT sensors outsource their data to the cloud, which arises the concerns that the transmission of IoT data is happening without appropriate consideration of the profound security challenges involved. Though encryption technology can guarantee the confidentiality of private data, it hinders the usability of data. Searchable encryption (SE) has been proposed to achieve secure data sharing and searching. However, most of existing SE schemes are designed under conventional hardness assumptions and may be vulnerable to the adversary with quantum computers. Moreover, the untrusted cloud server may perform an unfaithful search execution. To address these problems, in this paper, we propose the first verifiable identity-based keyword search (VIBKS) scheme from lattice. In particular, a lattice-based delegation algorithm is adopted to help the data user to verify both the correctness and the integrity of the search results. Besides, in order to reduce the communication overhead, we refer to the identity-based mechanism. We conduct rigorous proof to demonstrate that the proposed VIBKS scheme is ciphertext indistinguishable secure against the semi-honest-but-curious adversary. In addition, we give the detailed computation and communication complexity of our VIBKS and conduct a series of experiments to validate its efficiency performance.     

基于用户体验的APP界面设计研究

目的 探讨符合用户体验的APP界面设计策略,为今后APP界面设计提供参考,更好地满足用户的需求。方法 对用户体验进行简要分析、了解之后,结合案例,并基于用户需求、用户情感体验、用户心理状态、用户使用习惯等对APP界面设计展开探究。结论 要使APP受到用户的青睐,必须要了解用户需求,把握用户心理,根据用户习惯来设计。     

Risk knowledge and concern as influences of purchase intention for internet of things devices

Consumer demand for Internet of Things (IoT) devices is projected to continue its lucrative acceleration despite growing cybersecurity concerns and incidents reported in the public press. We explore this apparent anomaly through a model, based on the relevant literature, casting consumers’ IoT Purchase Intention as driven by their pre-existing IoT Risk Knowledge, their level of IoT-related Security Concern, and their device-specific perceptions of Riskiness and Coolness. Based on this model, we collected survey data gauging Purchase Intention for four products varying in Riskiness and Coolness levels. Our analysis extends prior research by confirming the negative influence of IoT Risk Knowledge, implicating it as an emerging drag on IoT consumer demand growth. We further confirm the expected effects of Device Riskiness (negative) and Coolness (positive) as primary factors and we note the cognitive dissonance implied by the inherent IoT device trade-off: greater Coolness tends to come at the price of greater Riskiness. In this trade-off, Coolness mattered more for consumers with lower IoT Security Concern suggesting greater susceptibility to the “wow” factor as offsetting or even distracting from the associated risk. This work contributes to the literature by confirming antecedents and revealing subtleties in the interplay of general and device-specific factors affecting consumer IoT Purchase Intention, shedding light on the feature-attraction/risk-avoidance paradox and identifying implications for both research and practice as the marketplace and consumer perceptions co-evolve going forward.     

A Differential Privacy Based (<i>k</i>-Ψ)-Anonymity Method for Trajectory Data Publishing

In recent years, mobile Internet technology and location based services have wide application. Application providers and users have accumulated huge amount of trajectory data. While publishing and analyzing user trajectory data have brought great convenience for people, the disclosure risks of user privacy caused by the trajectory data publishing are also becoming more and more prominent. Traditional k-anonymous trajectory data publishing technologies cannot effectively protect user privacy against attackers with strong background knowledge. For privacy preserving trajectory data publishing, we propose a differential privacy based (k-Ψ)-anonymity method to defend against re-identification and probabilistic inference attack. The proposed method is divided into two phases: in the first phase, a dummy-based (k-Ψ)-anonymous trajectory data publishing algorithm is given, which improves (k-δ)-anonymity by considering changes of threshold δ on different road segments and constructing an adaptive threshold set Ψ that takes into account road network information. In the second phase, Laplace noise regarding distance of anonymous locations under differential privacy is used for trajectory perturbation of the anonymous trajectory dataset outputted by the first phase. Experiments on real road network dataset are performed and the results show that the proposed method improves the trajectory indistinguishability and achieves good data utility in condition of preserving user privacy.     

Privacy Computing: Concept,Computing Framework,and Future Development Trends

With the rapid development of information technology and the continuous evolution of personalized services, huge amounts of data are accumulated by large internet companies in the process of serving users. Moreover, dynamic data interactions increase the intentional/unintentional persistence of private information in different information systems. However, problems such as the cask principle of preserving private information among different information systems and the difficulty of tracing the source of privacy violations are becoming increasingly serious. Therefore, existing privacy-preserving schemes cannot provide systematic privacy preservation. In this paper, we examine the links of the information life-cycle, such as information collection, storage, processing, distribution, and destruction. We then propose a theory of privacy computing and a key technology system that includes a privacy computing framework, a formal definition of privacy computing, four principles that should be followed in privacy computing, algorithm design criteria, evaluation of the privacy-preserving effect, and a privacy computing language. Finally, we employ four application scenarios to describe the universal application of privacy computing, and discuss the prospect of future research trends. This work is expected to guide theoretical research on user privacy preservation within open environments.     

Continuous Mobile User Authentication Using a Hybrid CNN-Bi-LSTM Approach

Internet of Things (IoT) devices incorporate a large amount of data in several fields, including those of medicine, business, and engineering. User authentication is paramount in the IoT era to assure connected devices’ security. However, traditional authentication methods and conventional biometrics-based authentication approaches such as face recognition, fingerprints, and password are vulnerable to various attacks, including smudge attacks, heat attacks, and shoulder surfing attacks. Behavioral biometrics is introduced by the powerful sensing capabilities of IoT devices such as smart wearables and smartphones, enabling continuous authentication. Artificial Intelligence (AI)-based approaches introduce a bright future in refining large amounts of homogeneous biometric data to provide innovative user authentication solutions. This paper presents a new continuous passive authentication approach capable of learning the signatures of IoT users utilizing smartphone sensors such as a gyroscope, magnetometer, and accelerometer to recognize users by their physical activities. This approach integrates the convolutional neural network (CNN) and recurrent neural network (RNN) models to learn signatures of human activities from different users. A series of experiments are conducted using the MotionSense dataset to validate the effectiveness of the proposed method. Our technique offers a competitive verification accuracy equal to 98.4%. We compared the proposed method with several conventional machine learning and CNN models and found that our proposed model achieves higher identification accuracy than the recently developed verification systems. The high accuracy achieved by the proposed method proves its effectiveness in recognizing IoT users passively through their physical activity patterns.     

Access Control Policy Based on Friend Circle

Nowadays, the scale of the user’s personal social network (personal network, a network of the user and their friends, where the user we call “center user”) is becoming larger and more complex. It is difficult to find a suitable way to manage them automatically. In order to solve this problem, we propose an access control model for social network to protect the privacy of the central users, which achieves the access control accurately and automatically. Based on the hybrid friend circle detection algorithm, we consider the aspects of direct judgment, indirect trust judgment and malicious users, a set of multi-angle control method which could be adapted to the social network environment is proposed. Finally, we propose the solution to the possible conflict of rights in the right control, and assign the rights reasonably in the case of guaranteeing the privacy of the users.     

Blockchain Based Secured Load Balanced Task Scheduling Approach for Fitness Service

In recent times, the evolution of blockchain technology has got huge attention from the research community due to its versatile applications and unique security features. The IoT has shown wide adoption in various applications including smart cities, healthcare, trade, business, etc. Among these applications, fitness applications have been widely considered for smart fitness systems. The users of the fitness system are increasing at a high rate thus the gym providers are constantly extending the fitness facilities. Thus, scheduling such a huge number of requests for fitness exercise is a big challenge. Secondly, the user fitness data is critical thus securing the user fitness data from unauthorized access is also challenging. To overcome these issues, this work proposed a blockchain-based load-balanced task scheduling approach. A thorough analysis has been performed to investigate the applications of IoT in the fitness industry and various scheduling approaches. The proposed scheduling approach aims to schedule the requests of the fitness users in a load-balanced way that maximize the acceptance rate of the users’ requests and improve resource utilization. The performance of the proposed task scheduling approach is compared with the state-of-the-art approaches concerning the average resource utilization and task rejection ratio. The obtained results confirm the efficiency of the proposed scheduling approach. For investigating the performance of the blockchain, various experiments are performed using the Hyperledger Caliper concerning latency, throughput, resource utilization. The Solo approach has shown an improvement of 32% and 26% in throughput as compared to Raft and Solo-Raft approaches respectively. The obtained results assert that the proposed architecture is applicable for resource-constrained IoT applications and is extensible for different IoT applications.