Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks

Seng, Kah Phooi; Ang, Li-Minn; Ngharamike, Ericmoore

doi:10.1177/15501477211062835

Cited by 32 publications

(25 citation statements)

References 93 publications

(168 reference statements)

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“…By using AI in the security system, it also can narrow the probability of getting cyber threats. The communications between IoT devices will also be more effective while preventing the cyber threats [16]. This is because Ai will learn the behaviour of the network and it will adapt with the surrounding.…”

Section: Security In Iotmentioning

confidence: 99%

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Zainuddin,

Zakirudin,

Syafiq Zulkefli

et al. 2024

IJPCC

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The confluence of Artificial Intelligence (AI) with the Internet of Things (IoT) has created new opportunities for distributed sensor networks in a variety of sectors. The potential of AI as a new paradigm for distributed sensor networks in the IoT is investigated in this review article, with an emphasis on the convergence of architecture, methodologies, platforms, sensors, devices, energy approaches, communication and networking, and applications. An analysis was conducted to examine the existing research in this field using a comprehensive literature review, revealing notable advancements, and indicating the need for further investigation. Furthermore, Moreover, suggestions and ideas are presented regarding potential innovation within the sector, with a particular emphasis on the imperative for further research and development. Our findings highlight AIoT's transformational ability in allowing more efficient and intelligent sensor networks, with implications for smart homes, healthcare, environmental monitoring, and industrial automation. The potential for harnessing the power of AI holds the key to unlocking novel opportunities and addressing the challenges inherent in dispersed sensor networks within the realm of the Internet of Things (IoT). This research advances our understanding of AIoT convergence and lays the groundwork for future breakthroughs in this interesting topic.

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Section: Security In Iotmentioning

confidence: 99%

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Zainuddin,

Zakirudin,

Syafiq Zulkefli

et al. 2024

IJPCC

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“…Furthermore, AIoT as a technological framework optimizes the efficiency of IoT processes, improves the interaction between humans and machines, augments data management and analytics capabilities, and enhances decision-making processes [1]. AIoT entails augmenting IoT systems and applications with intelligence and decision-making capabilities to solve the problems and challenges facing IoT in terms of the ever-increasing deluge of data and the complexity surrounding its processing and analysis (e.g., [1,125]). Moreover, Zhu et al [126] propose an innovative approach to the human-machine interface by incorporating triboelectric display components and sensory gloves in VR devices to recognize the multi-dimensional motion of gestures by leveraging ML/DL algorithms to enhance users' quality of experience in the virtual world by manipulating virtual objects in the real-time VR/AR space.…”

Section: The Convergence Of Aiot and Xr Technologiesmentioning

confidence: 99%

“…In fact, brain science has inspired many applications in computer science. For example, many of the applications of AI and AIoT technologies are based on DL (see, e.g., [1,125,142]), a brain-inspired ML model [143,144] for pattern recognition and reinforcement learning for action and planning [145]. DL emulates the way humans gain certain types of knowledge by collecting, analyzing, and interpreting large amounts of data and make decisions accordingly in a faster and easier manner.…”

Section: Emerging Applicationsmentioning

confidence: 99%

The Metaverse as a Virtual Model of Platform Urbanism: Its Converging AIoT, XReality, Neurotech, and Nanobiotech and Their Applications, Challenges, and Risks

Bibri

2023

Smart Cities

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With their exponentially rising computational power, digital platforms are heralding a new era of hybrid intelligence. There has recently been much enthusiasm and hype that the Metaverse has the potential to unlock hybrid intelligence. This is premised on the idea that the Metaverse represents an applied convergence of Artificial Intelligence of Things (AIoT) and Extended Reality (XR) that intersects with urbanism in terms of the distinctive features of platform-mediated everyday life experiences in cities. However, social interaction and its resulting social organization in the Metaverse are mediated and governed by algorithms and thus submitted to—a dream of—complete logical ordering. This raises a plethora of concerns related to the systemic collection and algorithmic processing of users’ personal, brain, and biometric data, i.e., profound societal—and the hardest to predict ethical—implications. Therefore, this study analyzes and synthesizes a large body of scientific literature on the unfolding convergence of AIoT and XR technologies, neurotechnology, and nanobiotechnology in the realm of the Metaverse in order to derive a novel conceptual framework for the Metaverse as an envisioned virtual model of platform urbanism. Further, it examines the key challenges and risks of these converging technologies in relation to the Metaverse and beyond. This study employs thematic analysis and synthesis to cope with multidisciplinary literature. The analysis identifies seven themes: (1) Platformization, (2) platform urbanism, (3) virtual urbanism, (4) XR technologies, (5) AIoT technologies, (6) neurotechnology, and (7) nanobiotechnology. The synthesized evidence reveals that, while neurotechnology and nanobiotechnology have numerous benefits and promising prospects, they raise contentions and controversies stemming from their potential use to inflict harm to human users—if left unchecked—through the black box of the algorithmic mediation underpinning the Metaverse. The findings serve to steer the Metaverse to contribute to human flourishing and wellbeing by adhering to and upholding ethical principles as well as leveraging its underlying disruptive technologies in meaningful ways. They also aid scholars, practitioners, and policymakers in assessing the pros and cons of these technologies, especially their inevitable ramifications.

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“…Besides, the electromechanical conversion of the TEHG also requires a torque as T e . In stable operation, the driving torque will overcome the resistance torques above and form a balance, which can be expressed as Equation (8). Then the rotation speed (n) can be calculated from Equations (5−7) as Equation ( 9).…”

Section: Optimization Of Aerodynamic Performancementioning

confidence: 99%

“…[1][2][3][4][5][6] Realistically, the challenge of long-term, stable, and maintenance-free energy supply for massive sensors in the IoT definitely exerts a critical bottleneck role. [7] The IoT sensors with the features of random and disordered distribution, [8,9] could be powered by utilizing the eco-friendly and sustainable energy collection technologies especially natural wind energy harvesting, rather than conventional centralized generators. [10][11][12][13] Traditionally, natural wind energy is mainly captured by using fan blades of turbines to convert wind energy into mechanical energy, which is then generated by electromagnetic generators (EMGs).…”

Section: Introductionmentioning

confidence: 99%

Environmental Self‐Adaptive Wind Energy Harvesting Technology for Self‐Powered System by Triboelectric‐Electromagnetic Hybridized Nanogenerator with Dual‐Channel Power Management Topology

Yong

Wang

Lin

et al. 2022

Advanced Energy Materials

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Natural wind energy harvesting enables a far‐reaching and sustainable solution to supply pervasive sensors in the Internet of Things (IoT). Electromagnetic generators (EMGs) struggle to harvest energy from breezes, which causes regrettable energy wastage. Herein, a triboelectric‐electromagnetic hybridized nanogenerator (TEHG) is designed with a dual‐rotor structure to consolidate harvesting band for high efficiency of triboelectric nanogenerators (TENGs) in breeze and the EMG in high wind speeds. The TEHG performs an efficient energy collection (41.05 W m−3) and a smooth output in the wind speed of 2−16 m s−1, attributed to the environmental self‐adaptive cooperation between TENGs and EMGs. The TENG output power contribution is more than 70% at low wind speeds (<5 m s−1). Moreover, a dual‐channel power management topology (DcPMT) is established to co‐manage outputs of two modules in TEHG. By virtue of the DcPMT hierarchically combining the isolated storage with undervoltagelockout strategy, the TEHG steadily supplies a standardized 3.3 V voltage for commercial electronics. Furthermore, a TEHG‐based self‐powered system is demonstrated for driving sensors to monitor meteorological information. The TEHG with DcPMT is advantageous in broad‐band and high‐efficiency of wind energy harvesting, thus exhibiting a great potential for elevating the environmental self‐adaptability and stability margin of the IoT.

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Artificial intelligence Internet of Things: A new paradigm of distributed sensor networks

Cited by 32 publications

References 93 publications

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

Artificial Intelligence: A New Paradigm for Distributed Sensor Networks on the Internet of Things: A Review

The Metaverse as a Virtual Model of Platform Urbanism: Its Converging AIoT, XReality, Neurotech, and Nanobiotech and Their Applications, Challenges, and Risks

Environmental Self‐Adaptive Wind Energy Harvesting Technology for Self‐Powered System by Triboelectric‐Electromagnetic Hybridized Nanogenerator with Dual‐Channel Power Management Topology

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