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Internet of Things (IoT) is a novel paradigm, which not only facilitates a large number of devices to be ubiquitously connected over the Internet but also provides a mechanism to remotely control these devices. The IoT is pervasive and is almost an integral part of our daily life. These connected devices often obtain user's personal data and store...
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Internet of Things (IoT) has recently
emerged as one of the buzzwords in
the IT world. It serves as a catalyst for
rapidly growing smart networks with the
deployment of sensors, communication
and control infrastructure at a rapid
pace. IoT envisions the concept in
which all the physical objects, generally
referred to as “things” get connected
to ea...
The Cellular Internet of Things (CIoT), a new paradigm, paves the way for a large-scale deployment of IoT devices. CIoT promises enhanced coverage and massive deployment of low-cost IoT devices with an expected battery life of up to 10 years. However, such a long battery life can only be achieved provided the CIoT device is configured with energy e...
This paper presents a layered hierarchy that depicts the progressive relationship between data, information, knowledge, and wisdom. To begin with, data is gathered and organized into information. Information is gathered, filtered, refined, and put through an investigation process to create knowledge. Wisdom is attained after knowledge discovery thr...
Recent years have seen the rapid adoption of Internet of Things (IoT) technologies, where billions of physical devices are interconnected to provide data sensing, computing and actuating capabilities. IoT-based systems have been extensively deployed across various sectors, such as smart homes, smart cities, smart transport, smart logistics and so f...
Blockchain-based Internet of Things (BIoT) is an emerging paradigm of Internet of Things (IoT) which utilizes the blockchain technology to provide security services to the IoT applications. In essence, the blockchain built-in security mechanism can provide services such as availability, authentication, authorization, confidentiality, and integrity...
Citations
... Device Hijacking: IoT devices fall under attacker control for conducting diverse malicious attacks including expanded denial-of-service attacks which target critical services and data breaches for sensitive information access [22]. The interconnected features of IoT systems expose them to high risks of these attacks because a . ...
The quick development of Internet of Things technology has redefined our connected environment by creating thousands of new applications. Extended connectivity brings substantial security hazards that need solution to safeguard user privacy and safety. This research paper evaluates the primary security threats in IoT through analysis of design flaws in devices combined with network security weaknesses and data protection failure. This study analyzes upcoming methods and proven practices which help minimize security risks which focus on device security design and network partitioning and sophisticated hazard recognition. Through the Internet of Things, we now interact differently with our environment because its vast connected device network collects data which undergoes processing before exchanging information. The extensive accessibility has produced security weaknesses that criminal groups can use to harm systems.
... Despite the manifold benefits associated with the union of IIoT and MIS, significant challenges persist that hinder the successful realization of genuinely smart manufacturing environments. Chief among these obstacles are security concerns, given the extensive attack surface that arises when numerous devices, sensors, and networks interconnect [5]. As data moves through various points of the digital chain-edge devices, gateways, cloud servers, and onpremise databases-each juncture becomes a potential target for malicious exploitation, risking confidentiality, integrity, and availability. ...
The Industrial Internet of Things (IIoT) integrated with Management Information Systems (MIS) is dramatically transforming factories by enabling real-time data analysis, automation, and enhanced productivity. This research aims to develop a holistic framework for integrating IIoT with MIS to achieve smart factory automation, focusing on operational efficiency, data-driven insights, and long-term global significant competitive advantage. This study was conducted by the Department of Management Information Systems at Lamar University, Texas, USA, from January 2023 to December 2023. A mixed-method approach was employed, encompassing system modeling, simulation experiments, and real-time sensor data analyses. Statistical tools measured efficiency gains, data accuracy, and integration viability across multiple pilot manufacturing sites and rigorous stakeholder interviews to validate practical outcomes. Implementation yielded a 28% improvement in production throughput and a 22% reduction in downtime across test sites. Mean data accuracy for real-time monitoring reached 95.6% (SD ± 2.3), indicating reliable sensor integration. Analysis of variance revealed significant enhancements in predictive maintenance scheduling (p < 0.01), correlating to a 15% decrease in unplanned repairs. Furthermore, user adoption rates climbed by 36% (SD ± 4.1), underscoring the system’s usability. Inventory turnover ratios also improved by 18%, optimizing resource allocation. The resulting integrated framework demonstrated robust interoperability between IIoT devices and MIS modules, empowering decision-makers with timely insights, reduced operational latency, and scalable deployment across diverse manufacturing scenarios with minimal data loss events. Overall, integrating IIoT with MIS provides a scalable, data-centric foundation for smart factories, enabling substantial gains in efficiency, predictability, and responsiveness across evolving industrial ecosystems, thereby fostering sustainable innovation globally.
... A detailed analysis of IoT risks and solutions was provided by Deep, et al. (2022). The general IoT architecture consists of a perception layer of physical devices and sensors, a network layer of interconnection and communication protocols, a middle layer of intelligent computing, storing and analysing data and an application layer of specific services to users as a user interface. ...
... Security risks and their parameters(Deep, et al., 2022). ...
... Most papers in the literature categorised the threats to privacy according to the layers in the IoT architecture(Deep et al. 2022;Elhoseny et al. (2021). If the survey participants were able to categorise privacy threats, it might have been more useful for them. ...
When firms implement the Internet of Things (IoT), they face many privacy issues. The literature identifies many issues related to the different layers of IoT. However, empirical studies on firms implementing IoT are rare. This study aimed to address this gap. An online survey using Survey Monkey obtained 199 valid responses. All ethical aspects were fully complied with. These responses were analysed for their frequencies. The results were presented and discussed. From the results and discussions of this study, large Saudi firms face many IoT privacy issues. However, most of these firms solve these problems by implementing effective solutions. Other firms that have not implemented effective solutions can learn from the firms that have effectively implemented solutions. The best practices derivable from the results are: (1) Perform a detailed analysis of IoT privacy issues in the organisation, identifying the threat to each layer; (2) Rate the privacy threats according to their frequency, probability and impact rather than through guesswork. This can be achieved by regular monitoring of IoT risks, (3) Implementing solutions based on the type of issue and the vulnerable IoT layer using the rating results, and (4) Regularly monitoring, reviewing and improving the implemented solutions to IoT privacy issues. Some limitations of this study and the scope for future research have been mentioned at the end of this paper.
... Moreover, many IoT devices are designed with minimal security features due to cost constraints or limited computational resources, making them susceptible to various cyber threats. The consequences of such vulnerabilities can be severe, ranging from the compromise of individual devices to large-scale disruptions in critical infrastructure systems, potentially leading to catastrophic outcomes in sectors such as healthcare, energy, and transportation [4][5][6]. ...
The proliferation of the Internet of Things (IoT) has transformed the digital landscape, enabling a vast array of interconnected devices to communicate and share data seamlessly. However, the rapid expansion of IoT networks has also introduced significant cybersecurity challenges. This paper presents a comprehensive survey of cybersecurity in the IoT ecosystem, examining the current state of research, identifying critical security vulnerabilities, and exploring advanced strategies for mitigating threats. The survey covers various facets of IoT security, including device authentication, data integrity, privacy, network security, and the emerging role of artificial intelligence (AI) in bolstering cybersecurity defenses. By synthesizing existing research and highlighting ongoing challenges, this survey aims to provide a holistic understanding of IoT cybersecurity and to guide future research endeavors.
... Despite the manifold benefits associated with the union of IIoT and MIS, significant challenges persist that hinder the successful realization of genuinely smart manufacturing environments. Chief among these obstacles are security concerns, given the extensive attack surface that arises when numerous devices, sensors, and networks interconnect [5]. As data moves through various points of the digital chain-edge devices, gateways, cloud servers, and onpremise databases-each juncture becomes a potential target for malicious exploitation, risking confidentiality, integrity, and availability. ...
... Transmission Control Protocol (TCP) Synchronization (SYN) flooding and User Datagram Protocol (UDP) flooding attacks. Lastly, the application layer, which enables services for enduser applications, is a common target for various forms of malware, phishing, and web attacks [13] [14]. ...
The transition from fifth-generation (5G) to sixth-generation (6G) mobile networks necessitates network automation to meet the escalating demands for high data rates, ultra-low latency, and integrated technology. Recently, Zero-Touch Networks (ZTNs), driven by Artificial Intelligence (AI) and Machine Learning (ML), are designed to automate the entire lifecycle of network operations with minimal human intervention, presenting a promising solution for enhancing automation in 5G/6G networks. However, the implementation of ZTNs brings forth the need for autonomous and robust cybersecurity solutions, as ZTNs rely heavily on automation. AI/ML algorithms are widely used to develop cybersecurity mechanisms, but require substantial specialized expertise and encounter model drift issues, posing significant challenges in developing autonomous cybersecurity measures. Therefore, this paper proposes an automated security framework targeting Physical Layer Authentication (PLA) and Cross-Layer Intrusion Detection Systems (CLIDS) to address security concerns at multiple Internet protocol layers. The proposed framework employs drift-adaptive online learning techniques and a novel enhanced Successive Halving (SH)-based Automated ML (AutoML) method to automatically generate optimized ML models for dynamic networking environments. Experimental results illustrate that the proposed framework achieves high performance on the public Radio Frequency (RF) fingerprinting and the Canadian Institute for Cybersecurity Intrusion Detection System 2017 (CICIDS2017) datasets, showcasing its effectiveness in addressing PLA and CLIDS tasks within dynamic and complex networking environments. Furthermore, the paper explores open challenges and research directions in the 5G/6G cybersecurity domain. This framework represents a significant advancement towards fully autonomous and secure 6G networks, paving the way for future innovations in network automation and cybersecurity.
... Several theoretical frameworks have been proposed and applied to assess the benefits of integrating AI to enhance organizational outcomes, such as sustainability and performance. Among the various theoretical perspectives, including dynamic capabilities theory, cognitive theory, and market orientation perspective [24,30], two approaches have gained significant traction in linking AI's advantages to firm performance: the Resource-Based View (RBV) theory [54] and the Information System Success Model (ISSM) [19]. These theories have been pivotal in advocating for the use of Big Data Analytics (BDA), including AI, to achieve superior organizational performance and a sustainable competitive edge [8]. ...
Artificial Intelligence (AI) has emerged as a transformative force in modern business, driving significant improvements in efficiency, decision-making, and customer engagement. By harnessing the power of AI, organizations can enhance operational performance, streamline workflows, and develop data-driven strategies that improve competitiveness in rapidly changing markets. This paper explores how AI technologies such as machine learning, natural language processing, and predictive analytics can optimize business processes across various sectors, from finance to healthcare and manufacturing. By automating routine tasks, AI allows businesses to focus on high-value strategic initiatives, enabling faster responses to market demands and improving customer satisfaction through personalized experiences. Moreover, AI's capacity to analyze large volumes of data offers predictive insights that can inform better decision-making, reduce costs, and uncover new growth opportunities. Challenges such as data privacy, ethical concerns, and the need for skilled talent are also discussed, along with strategies for overcoming them. This paper highlights the pivotal role AI can play in maximizing business performance, offering a roadmap for businesses to integrate AI technologies and remain agile and competitive in the digital era.
... The network layer processes the data transmission and delivery to relevant parties in real time. A middleware layer handles data management and integration, while the application layer supports user interaction for healthcare providers and patients in monitoring, analyzing, and decision-making using IoT [6]. This architecture is widely used in many applications across different industries, especially in healthcare where IoT devices play an important role in remote monitoring, early diagnosis, and personalized treatment plans [7]. ...
The increasing adoption of healthcare devices necessitates a deeper understanding of the factors that influence user acceptance in this rapidly evolving area. Therefore, this study examined the factors influencing the technology acceptance of healthcare devices, focusing on radar sensors and wearable devices. A total of 1158 valid responses were used to test hypotheses, mediation, and moderation effects using SmartPLS 4.0. The results highlighted the significant role of performance expectancy, effort expectancy, social influence, facilitating conditions, and perceived risk in shaping user attitudes and trust, which in turn influence behavioral intention. The findings suggested that attitudes fully mediate the effects of performance expectancy and effort expectancy on behavioral intention, while social influence, facilitating conditions, and perceived risk exhibit partial mediation. Moderation analysis revealed significant effects of generation on the relationship between attitude, trust, and behavioral intention. Additionally, device type moderated the effect of trust on behavioral intention, showing a different influence between radar sensors and wearable devices. These findings provide theoretical contributions by extending the unified theory of acceptance and use of technology (UTAUT) model and offering practical implications for manufacturers and policymakers to tailor strategies that foster positive attitudes, enhance trust, and address generational and device-specific differences in healthcare technology adoption.
... Types of Attacks Impact Proposed Countermeasure Brute-force attacks [117], [79] Exploitation of devices without authorization Establishing robust authentication and key management practices Password attacks [63], [13] Unauthorized hacking and access Deploying encryption methods and efficient key management protocols Privacy breaches [43], [7] Breaches of privacy and security Leveraging AI technologies to detect and thwart these attacks Illegitimate attacks [120] Illegitimate access to systems Strengthening security measures and conducting awareness campaigns Attacks on smart devices [9] Unauthorized exploitation of devices Integrating contemporary authentication systems into smart devices and IoT devices and brute force attacks, along with their descriptions and the tools used. By understanding these threats, we can proactively implement preventive measures to ensure the integrity and resilience of IoT networks. ...
In the ever-evolving information technology landscape, the Internet of Things (IoT) is a groundbreaking concept that bridges the physical and digital worlds. It is the backbone of an increasingly sophisticated interactive environment, yet it is a subject of intricate security challenges spawned by its multifaceted manifestations. Central to securing IoT infrastructures is the crucial aspect of authentication, necessitating a comprehensive examination of its nuances, including benefits, challenges, opportunities, trends, and societal implications. In this paper, we thoroughly review the IoT authentication protocols, addressing the main challenges such as privacy protection, scalability, and human factors that may impact security. Through exacting analysis, we evaluate the strengths and weaknesses of existing authentication protocols and conduct a comparative performance analysis to evaluate their effectiveness and scalability in securing IoT environments and devices. At the end of this study, we summarize the main findings and suggest ways to improve the security of IoT devices in the future.
... First of all, the IoT involves a large amount of data processing and transmission, and security issues have become a major problem in development [32]. With the increase in the number of IoT devices, security issues such as network attacks and data leakage are also increasing. ...
The Internet of Things (IoT) is an advanced tech that transfers game data such as graphics, text, and other data
over real-time networks. Game player surroundings can be sensed, or movement or situation can be observed for
wearable devices that use sensors and actuators, this increases sales of wearables from 18 million in 2015 to
533.6 million units by 2021. The information of game players gathered by sensors and actuators is sent for
further action in the game engine. In this paper, we survey and analyze the involvement of IoT technology
sensors and wearables in serious and social gaming. Further, we reviewed IoT applications for gaming, as well as
their challenges and limitations, with open research issues for future development.
