Internet Of Things Consumers Research Articles

IoT and Machine Learning based Green Energy Generation using Hybrid Renewable Energy Sources of Solar, Wind and Hydrogen Fuel Cells

As the world seeks sustainable energy solutions, Internet of Things (IoT) applications demand consistent and efficient power sources. This paper presents an innovative hybrid renewable energy system, seamlessly integrating solar photovoltaic panels, wind turbines, and hydrogen fuel cells, tailored for IoT applications. Through machine learning algorithms, our proposed system not only optimizes energy generation in real-time but also ensures uninterrupted energy supply to IoT devices and consumers, even in fluctuating environmental conditions. This universal approach markedly diminishes the dependence on non-renewable energy sources, promoting a greener and more resilient energy infrastructure. The incorporation of hydrogen fuel cells uniquely positions our system as a reservoir for excess energy, ensuring consistent power even when solar or wind outputs diminish. Moreover, by synchronizing IoT devices with our energy system, we have procured real-time data on energy dynamics, facilitating unparalleled optimization and reduced wastage. The presented system shows the way for a sustainable future through the efficient green energy generation with the ever-evolving landscape of IoT applications and machine learning techniques.

An End-to-end Trust Management Framework for Crowdsourced IoT Services

We propose a novel end-to-end trust management framework for crowdsourced Internet of Things (IoT) services. The framework targets three main aspects:trust assessment,trust information credibility and accuracy, andtrust information storage. We harness theusage patternsof IoT consumers to offer a trust assessment thatadaptsto IoT consumers’ uses. Additionally, our framework ascertains thecredibilityandaccuracyof trust-related information before trust assessment. This is achieved by validating the data collected by IoT consumers and providers. In addition, our framework ensures thecontextual fairnessbetween IoT services and trust information. Moreover, we propose a blockchain-based trust information storage approach. Our proposed storage solution preserves theintegrityandavailabilityof trust information.

STUDY OF IoT-ENABLED SMART RETAIL: EXAMINING CONSUMER ACCEPTANCE AND ADOPTION IN INDIA

IoT-enabled smart retail refers to the integration of Internet of Things (IoT) technology into the retail environment to enhance operational efficiency, improve customer experience, and drive business outcomes. It involves connecting various devices, sensors, and systems to collect and analyze data, enabling real-time insights and actions. This descriptive research article aims to explore the consumer acceptance and adoption of IoT-enabled smart retail in the Indian context. The study utilizes secondary data from various sources to analyze the current state of IoT adoption, consumer preferences, and factors influencing the acceptance of IoT-enabled solutions in the retail sector. The findings shed light on the opportunities and challenges associated with implementing IoT technology in Indian retail stores and provide insights for retailers seeking to enhance customer experiences and drive business outcomes. This article examines the level of consumer acceptance and adoption of IoT in the Indian retail sector, highlighting its impact on customer experiences, operational efficiency, and the overall retail ecosystem.

Taming Internet of Things Application Development with the IoTvar Middleware

In the last years, Internet of Things (IoT) platforms have been designed to provide IoT applications with various services such as device discovery, context management, and data filtering. The lack of standardization has led each IoT platform to propose its own abstractions, APIs, and data models. As a consequence, programming interactions between an IoT consuming application and an IoT platform is time-consuming, is error prone, and depends on the developers’ level of knowledge about the IoT platform. To address these issues, this article introduces IoTvar , a middleware library deployed on the IoT consumer application that manages all its interactions with IoT platforms. IoTvar relies on declaring variables automatically mapped to sensors whose values are transparently updated with sensor observations through proxies on the client side. This article presents the IoTvar architecture and shows how it has been integrated into the FIWARE, OM2M, and muDEBS platforms. We also report the results of experiments performed to evaluate IoTvar, showing that it reduces the effort required to declare and manage IoT variables and has no considerable impact on CPU, memory, and energy consumption.

A blockchain-enabled framework for securing connected consumer electronics against wireless attacks

The rapid development of the emerging Internet of things (IoT) regime and of smart applications has given rise to many new wireless security vulnerabilities in consumer electronic environment networks, which compromises the integrity of the whole environment. Attacks often encompass the abuse of vulnerable wireless IoT consumer electronics devices, compromise information security, and leak sensitive and private data. This paper proposes a decentralized blockchain-enabled framework network infrastructure to improve wireless security and mitigate new wireless attacks on connected consumer electronics as a proof of concept. The system model of the proposed framework was verified mathematically and was then implemented and tested in case studies. The experimental results show that the proposed decentralized blockchain technology-featured air-cracking tool (BTFAT) provides a robust detection of vulnerabilities in the security of wireless-based IoT consumer electronics, i.e., 97% in terms of the effectiveness of packet chapters and 85% in terms of the maximum capture packet reliability, compared with the state of the art.

Edge-Assisted NDN-Based IoT Framework With Provider and Consumer Mobility Support

The Internet of Things (IoT) mainly works based on point-to-point communications where the source performs an independent data delivery procedure to acquire data from the target. This leads to considerable data delivery costs and latency. The Named Data Networking (NDN) presents an efficient communication paradigm where multiple consumers can retrieve data from the optimal provider via one data delivery process, so we are motivated to exploit NDN to deliver IoT-based data and improve efficiency of data retrieval. However, it is challenging to deploy NDN in IoT due to essentially different architectures and features. Especially, mobility of IoT providers and consumers leads to frequent IoT-based data delivery failures. In this paper, we propose an Edge-assisted NDN-based IoT framework with Provider and Consumer mobility support (ENIPC) to overcome these challenges and reduce IoT-based data delivery costs, latency and failure rates. To overcome resource limitations of IoT devices, ENIPC integrates IoT with edge computing so that edge devices can help achieve NDN in IoT and multiple consumers can exploit reverse paths to retrieve multiple data via one data delivery process with provider and consumer mobility support. The experimental results demonstrate that ENIPC effectively decreases IoT-based data delivery costs and delays.

Consumer IoT and its under‐regulation: Findings from an Australian study

AbstractThe expansive growth of consumer internet of things (IoT) has created a range of concerns around privacy, security, and their broader societal impacts. This article reports on findings from interviews with 32 key stakeholders from the fields of information security, policy and regulation, the IoT industry, consumer and privacy law, and academia in Australia. It details a broad variety of issues and concerns that go beyond the well‐recognised issue of privacy or the technical standards of information security, to encompass a wider set of issues regarding the implications for vulnerable communities, the environment and industrial standards of IoT production. Most key stakeholders expressed the view that more robust regulation is required in Australia, but no clear regulatory priority or strategy was identified by our sample. The implications of these findings for further regulation of consumer IoT and future regulatory strategies are considered.

Smart Parenting? The Internet of Things, Children’s Privacy, and Data Justice

Abstract This paper examines children’s privacy and the Internet of Things (IoT). After describing the operation of IoTs directly marketed to and for children, we outline research concerning the surveillance of children and issues associated with children’s right to privacy, including the role of parents or guardians in protecting their children’s right to privacy. We then present the findings of a survey of Australian IoT consumers and non-consumers (n = 1,052), which shows parents and guardians who purchase IoTs care about their children’s privacy and are concerned about practices of corporate surveillance. Finally, our data show that female parents or guardians have lower rates of privacy literacy than males. Analysed through the lens of data justice (Dencik et al., 2016), we argue the protection of children’s privacy rights must be understood with regard to broader structural factors, such as gender discrimination and digital housekeeping, and ultimately requires addressing corporate practices that characterise the contemporary surveillance landscape.

Deep Learning Model for Predicting Consumers’ Interests of IoT Recommendation System

This electronic the Internet of Things (IoT) technology has contributed to several domains such as health, energy, education, transportation, industry, and other domains. However, with the increased number of IoT solutions worldwide, IoT consumers find it difficult to choose the technology that suits their needs. This article describes the design and implementation of an IoT recommendation system based on consumer interests. In particular, the knowledge-based IoT recommendation system exploits a Service Oriented Architecture (SOA) where IoT device and service providers use a registry to advertise their products. Moreover, the proposed model uses a Long Short-term Memory (LSTM) deep learning technique to predict the consumer's interest based on the consumer's data. Then the recommendation system do the mapping between the consumers and the related IoT devices based on the consumer interests. The proposed Knowledge-based IoT recommendation system has been validated using a real-world IoT dataset collected from Twitter Application Programming Interface (API) that include more than 15,791 tweets. Overall the results of our experiment are promising in terms of precision and recall. Furthermore, the proposed model achieved the highest accuracy score compared with other state-of-the-art methods.

From children's ideas to prototypes for the internet of things: a case study of cross-generational end-user design

ABSTRACT A smart bracelet that reacts to a person's heartbeat. A smart bench that invites passers-by to sit close. These and others are smart things, part of the Internet of Things (IoT) and people's lives. However, people are mainly IoT consumers and rarely given the possibility of becoming IoT creators. This paper presents a case study concerning the design of smart things for outdoor environments, with end users as the main creators. Ideas of smart things were collaboratively conceptualised by child end-users with a card-based board game. Their ideas were taken up in the form of inspiration cards within a bachelor's first-year course, by students coming from different high schools. Students started from children's ideas as inspiration triggers and collaboratively evolved some of them into interactive smart-thing prototypes. The paper concludes by reflecting on its results and drawing lessons for future editions of cross-generational workshops related to IoT design with end users.

Survey of IoT for Developing Countries: Performance Analysis of LoRaWAN and Cellular NB-IoT Networks

Recently, Internet of Things (IoT) deployments have shown their potential for aiding the realisation of the Sustainable Development Goals (SDGs). Concerns regarding how the IoT can specifically drive SDGs 6, 11 and 9 in developing countries have been raised with respect to the challenges of deploying licensed and unlicensed low-power wide area network (LPWAN) IoT technologies and their opportunities for IoT consumers and service providers. With IoT infrastructure and protocols being ubiquitous and each being proposed for different SDGs, we review and compare the various performance characteristics of LoRaWAN and NB-IoT networks. From the performance analysis of our networks, NB-IoT, one of the standardised promising cellular IoT solutions for developing countries, is more expensive and less energy-efficient than LoRaWAN. Utilising the same user equipment (UE), NB-IoT consumed an excess of 2 mAh of power for joining the network and 1.7 mAh more for a 44-byte uplink message compared to LoRaWAN. However, NB-IoT has the advantage of reliably and securely delivering higher network connection capacity in IoT use cases, leveraging existing cellular infrastructure. With a maximum throughput of 264 bps at 837 ms measured latency, NB-IoT outperformed LoRaWAN and proved robust for machine-type communications. These findings will help IoT consumers and service providers understand the performance differences and deployment challenges of NB-IoT and LoRaWAN and establish new research directions to tackle IoT issues in developing countries. With Nigeria as a case study, for consumers and organisations at a crossroads in their long-term deployment decisions, the proposed LPWAN integrated architecture is an example of the deployment opportunities for consumer and industrial IoT applications in developing countries.

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.

Attributes prediction from IoT consumer reviews in the hotel sectors using conventional neural network: deep learning techniques

The Internet of Things (IoT) plays an important role in helping the hotel industry increase customer satisfaction while maintaining affordable costs. IoT consumers review and rate the hotels online. The ratings are based on the Value, Apartment, Site, Sanitation, Front Desk, Facility, Professional Provision, Internet, and Packing. Traditional systems that predict hotel ratings with minimum accuracy create complexity through their analysis of the ratings. Thus, the effective deep learning techniques are used to analyze the reviews in order to help consumers choose better hotels. In this paper, different classification algorithms, such as convolutional neural network-based deep learning (CNN-DL), support vector machine network-based deep learning are applied to predict attributes. The system utilizes the TripAdvisor site, which is a well-known America dataset for examining system efficiency. The experimental results show that the CNN-DL algorithm has better classification accuracy and a lower error rate as compared to other algorithms.

Communal Acts of IoT Consumers: A Potential Threat to Security and Privacy

Consumer Electronics Internet of Things (CEIoT) products, implemented with improper security measures and inadequate privacy-preserving mechanisms, let the hackers or intruders to access and misuse consumer's personal information. In this paper, we present a framework and perspective to study and address Internet of Things (IoT) consumer's security and privacy violation from seemingly five different angles; borrow, rent, gift, resale, and retire. In the act of borrow, an IoT consumer takes and uses another consumer's CEIoT products with the intention of return after usage. In the act of rent, a consumer offers his CEIoT products to other people for temporary usage and charges regular payment for the service. In the act of gift, a consumer willingly gives or presents his used CEIoT products to his friends or relatives without payment. In the act of resale, IoT consumers sell their previously bought or used CEIoT products. In the act of retire, IoT consumers dispose of or throw their used CEIoT products when they become out of service or after the end of product's life. This act could raise serious privacy concerns, since “IoT waste” may become an attractive gateway for cybercriminals to access private information. Moreover, we also present challenges with the above-mentioned five acts of security and privacy violation. And finally, we suggest some recommendations to preserve the security and privacy of IoT consumers.

How to footprint, report and remotely secure compromised IoT devices

The number of IoT consumer devices (eg, security cameras, projectors, refrigerators etc) connected to the Internet is constantly increasing. It has been estimated that 3.9 billion IoT consumer devices were in use in 2016 and that by 2020 up to 12.8 billion devices will be deployed.1 This trend, combined with the fact that most of the owners still lack cyber-security awareness, has established a breeding ground for new malware families targeting IoT devices. An example is the Mirai botnet that was responsible for the biggest DDoS attack ever detected in the history of the Internet.2 The size of the Internet of Things (IoT) is rapidly increasing. By 2020 up to 12.8 billion devices will be deployed. This has established a breeding ground for new malware families targeting IoT devices. Filippo Lauria of the Institute of Informatics and Telematics at the Italian National Research Council explains why mapping and classifying compromised IoT nodes is a challenging task. And he proposes a software infrastructure that could be used to remotely footprint, report and (if possible) secure compromised IoT devices.

Security and Privacy Challenges: Internet of Things

Background/Objectives: This survey reports on the current research on the Internet of Things by examining the related literature, identifying current trends, challenges that threaten IoT and future directions. Findings: Most of the people use the Internet every day but little knows how it really works. Now a Days internet of things (IoT) has been area of research as many heterogeneous devices are connected through internet. This will capacitate the devices with new abilities. In such a case, the confidentiality of data plays an important role. It includes data validation and confidentiality, secrecy and reliability amongst various users and the constraints of security and privacy policies. Traditional security methods cannot be directly applied to IoT technologies due to the different standards and communication stacks involved. Moreover, the amount of more interconnected devices will lead to less adaptability; so a more modifiable structure is needed to deal with security threats in a drastically dynamic environment. Application/Improvements: The concept of loT brings a new challenge regarding privacy, security, confidentiality, reliability and trust. The major issues are lot of devices are not protected under mechanisms such as firewalls and are not following the specific standards. They are easily attacked by wireless channels. Such devices can be stolen and can be analysed by attackers to reveal their key material. Combining data from different sources is the other major issue since there is no guarantee of security between data providers and data consumers from very beginning. Secure exchange platform is required between IoT devices and consumers of their information.

Systems Engineering of a Terabit Elliptic Orbit Satellite and Phased Array Ground Station for IoT Connectivity and Consumer Internet Access

Systems engineering of a satellite based data communication baseline concept is presented to achieve terabit per second throughput. It uses a constellation of five Molniya satellites and one dimension electronic scanning phased array ground terminals. The result is a baseline concept that meets customer needs for internet of things (IoT) data connectivity and for consumer high data rate internet access. Molniya orbit satellites provide the benefits of available bandwidth, lack of interference with other satellite links, and less crowded orbital paths. A drawback is that they are not geostationary since they have highly elliptical orbits. This requires ground station terminals with the ability track the satellites as they pass overhead during their orbit. However, since Molniya satellites with a properly selected eccentricity pass along the same path at nearly constant elevations relative to a fixed position on the Earth, simple and low-cost single axis scanning antennas can be used for the consumer ground terminal. This is a distinct advantage compared with competing low earth orbit constellations. The proposed solution leverages advances in semiconductor technology and low-cost antenna laminate substrate materials for an affordable phased array tracking ground terminal antenna. This paper presents link budget trade studies, system concept, orbital dynamics simulation results, and ground station component trade study.