1 Introduction

The evolution of AI spans decades, characterized by significant milestones marking the progress of this interdisciplinary field [1]. From the inception of AI as a theoretical concept in the 1950s to the emergence of neural networks and deep learning in recent years, the historical trajectory of AI reflects the continuous pursuit of developing intelligent systems capable of simulating human cognition. Delving into the history of AI illuminates the evolution of foundational concepts, algorithms, and breakthroughs that have paved the way for contemporary AI applications, influencing diverse domains ranging from healthcare and finance to robotics and autonomous systems [2, 3].

Nowadays, the pervasive adoption of AI technologies has permeated various facets of daily life, revolutionizing the way individuals interact with technology and enhancing the quality of life [4,5,6,7]. From personalized recommendation systems in entertainment and e-commerce to AI-powered virtual assistants streamlining productivity and time management, the applications of AI have profoundly impacted people’s routines. AI in people’s daily life applications tries to provide the transformative potential of AI in improving lifestyle, ranging from healthcare advancements through predictive diagnostics to personalized smart home automation, emphasizing the pivotal role of AI in augmenting convenience, efficiency, and personalization in everyday experiences.

The IoT represents a network of interconnected devices and sensors/actuators that facilitate data exchange and automation, reshaping how both physical and virtual objects interact and communicate [8,9,10]. Within this ecosystem, the integration of AI technologies offers unprecedented capabilities in extracting insights, enabling predictive analytics, and optimizing operational efficiencies [11,12,13]. In fact, the combination of IoT and AI provides a high-performance and user-friendly IoT, highlighting its architectural components and pervasive applications, while delving into the symbiotic relationship between AI and IoT, especially in the realm of the Internet of Everything (IoE), showcasing the potential for AI to drive autonomous decision-making and adaptive behaviours within a connected environment.

The convergence of AI with the IoT has engendered a paradigm shift, propelling the emergence of AIoT systems characterized by self-learning, decision-making, and adaptability [11, 14]. As the foundation for autonomous operations, AIoT empowers IoT ecosystems with cognitive capabilities, leading to predictive maintenance, anomaly detection, and contextual intelligence [15]. Actually, the concept of IoT since a decade is already attached to the concept of Autonomy [16]. The power of this dissects the mechanisms through which AI fosters autonomy within IoT, shedding light on the current stage of AIoT and its strategic implications for industrial automation, smart cities, and cognitive edge computing, envisioning a future steeped in intelligent, self-regulating IoT landscapes [15, 17, 18].

The integration of AIoT has ushered in a new era of industrial and workplace transformations, leveraging advanced data analytics, machine learning, and autonomous decision-making frameworks to drive operational efficiencies and innovation. There are many applications of AIoT, spanning predictive maintenance in industrial machinery, smart energy management, and personalized customer experiences [15, 17, 18]. However, AIoT faces several primary challenges such as security vulnerabilities in interconnected systems, data integration complexities, and ensuring real-time adaptability in dynamic environments [19,20,21].

Addressing the intricate challenges embedded within AIoT deployments necessitates a multi-faceted approach encompassing robust cybersecurity measures, streamlined data orchestration protocols, and agile, adaptive algorithms [19,20,21]. Therefore, many studies have delved into a spectrum of solutions tailored to fortify AIoT systems against security threats [22], encompassing blockchain-based data provenance [20, 23], anomaly detection mechanisms for early threat identification [24], and adaptive machine learning models capable of dynamic recalibration in response to evolving environmental dynamics [25, 26]. By synthesizing these solutions, organizations, and industries can effectively navigate the complexities of AIoT deployments while safeguarding critical infrastructure and ensuring the resilience of interconnected ecosystems.

The proliferation of AIoT solutions stands poised to instigate sweeping transformations across global socio-economic landscapes, industrial ecosystems, and individual daily experiences [27,28,29]. By embedding autonomous decision-making, adaptive learning, and context-aware functionalities, AIoT engenders disruptive shifts in industrial process optimization, personalized healthcare delivery, and sustainable urban development [15, 17, 18]. In this regard, many researchers and companies have elucidated the profound impact of AIoT on industrial automation, precision agriculture, and personalized healthcare, underscoring the potential for enhanced efficiency, resource optimization, and improved quality of life, thereby heralding a future where autonomous IoT systems orchestrate an era of unprecedented innovation and empowerment.

1.1 Motivation and contribution

The idea of full autonomy based on AI’s potential motivates us to propose the IoAI concept which connects only AI-enabled devices to generate an autonomous and massive expert system in an area as big as a city. The IoAI, as an autonomous community, can meet its all needs, such as energy, raw materials, construction equipment, etc. Hence, it is a generative and compatible-to-all-environments community. As the completion of the proposal of the IoAI concept, this study identifies critical challenges (e.g., initial setup cost and acceptability by governments) and important opportunities (e.g., autonomous industry, collaborating with humans, and cost-efficient energy) and addresses several future horizons and aspects of this newly-emerged paradigm.

1.2 Outlines

The remainder of this work is organized as follows. Section 2 addresses important similar-to-IoAI paradigms and provides a rapid overview of several recent studies. Section 3 identifies the detailed definition of IoAI and addresses related issues, including its background technologies, general architecture, the main achievements of an IoAI community and its challenges and opportunities in three aspects (for industry, in relation to governments, and assisting to humans). Section 4 provides a final discussion and suggests future horizons for the IoAI. Finally, Sect. 5 concludes this study.

2 Related works and similar concepts

Some several concepts and paradigms seem similar to IoAI, but they are inherently different from the IoAI concept and its goals. This section briefly provides an overview of three important of them. The general architecture of the three concepts, including IoE [9, 10], AIoT [11, 14], and Metaverse of Everything (MoE) [30, 31] is illustrated in Fig. 1.

Fig. 1
figure 1

A general presentation of three-layer architectures of IoE [9, 10], AIoT [11, 14], and MoE [30, 31] concepts

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  1. 1.

    IoE, a traditional and well-known concept, is more comprehensive than IoT as it encompasses not just things, but also data, processes, and people (see Fig. 1—Part a). However, in traditional IoE connections, everything works based on specific algorithms for achieving certain goals. Moreover, they cannot change decisions/outputs since AI algorithms are generally not considered for them.

  2. 2.

    The AIoT is internet-connected and AI-enabled things [14]; This technology intelligent edge-based processing is provided efficiently without a link to a server. Smartwatches, driverless vehicles, and AI-enabled drones are the most well-known examples of AIoT components [11, 15]. The general architecture of AIoT is depicted in Fig. 1—Part b (for comparison with the IoAIs’ architecture, you can find the proposed architecture of IoAI in Fig. 3). Focusing on data storage problems, channel delay, and congestion in the networks, Osuwa et al. [17] proposed an idea to integrate sensors into microcontroller. In their offered idea, AI has been assumed as the solution for mining, managing, and controlling congestion in the network. In detail, they applied fuzzy logic and neural networks and used assistance from Alexa for a case study. In 2018, concerns about the huge volume of data generated with the low-power sensors increased. In this regard, as a review study, Ghosh et al. [11] addressed the aspect of applying AI in IoT applications. They believed conjugating data science and IoT-generated data may cause the next smart revolution, and addressed most aspects of using AI in processing sensed data to categorise them and make the most intelligent decision to action. In 2020, two appreciated works were proposed to identify the state-of-the-art applications of AI in IoT networks [14, 18]. Lei et al., in [14], focused on Autonomous Control Systems (ACS) which perform control functions on the physical systems without external intervention over an extended period of time. In the aspect of their study, the integration of IoT and ACS was assumed as the concept of AIoT. Hence, the sensors collected information on the system status, based on which the intelligent agents in the IoT devices as well as the edge, fog, or cloud servers made control of decisions for the actuators to react. In [18], authors focused on AI applications in smart homes. They suggested enabling AI for geographic information systems (GIS). The main finding of their research is to identify a considerable gap in merging AI with IoT and the use of geospatial data in smart home development in addition to energy efficiency and aged care system development. Their work would provide significant help in designing fully automated assistive systems to improve quality of life and decrease energy consumption. Regarding the proven successes of AI-based tools and technologies, especially deep learning, in IoT applications, including computer vision, speech recognition, and natural language processing, Zhang and Tao presented an in-depth into AIoT in 2022 [15]. They believed that the IoT heralds the era of AIoT and addressed a comprehensive survey on AIoT to show how AI can empower the IoT to make it faster, smarter, greener, and safer. Specifically, their study briefly presented the AIoT architecture in the context of cloud computing, fog computing, and edge computing four aspects including (1) perceiving, (2) learning, (3) reasoning, and (4) behaving. Relying on blockchain worldwide development and its novel properties, Li et al. [23] presented a discussion of smart energy management strategies in 2023. They anticipated that AI models make excellent forecasting in energy usage and load profiles as well as schedule resources to ensure reliable performance and effective utilization of energy resources. Additionally, utilizing big data systems and data mining enables the discovery of new functions and relationships, which determines the performance of AI. Based on these facts, they proposed smart energy management using digital technologies like AI-enabled IoT and blockchain. In their idea, IoT platforms assisted in connecting AI to other hardware and software devices and systems. Hence, their efficiently transmitted and stored data improved access and availability to stakeholders for data mining. The result of their research showed that providing an efficient and seamless integration of AI, big data, and advanced digital technologies will be an important factor in the emerging transition of the energy sector to a lower-carbon system. Regarding the similarity of the two paradigms AIoT (an exciting and emerging concept) and IoAI (the focus of this study), it is required to provide a brief description of their difference. The AIoT is an empowered IoT network by AI technology in which it is an IoT network with AI-enabled things which are able to learn and react intelligently. On another hand, in this study, the IoAI concept will be defined as a non-human and autonomous community whose components are connected through the Internet. The major number of components of the IoAI community are AI-based robots and intelligence algorithms.

  3. 3.

    Blockchain-based systems and smart-contract-based applications are the most practical autonomous systems; Decentralized autonomous organizations (DAO) [32] and Metaverse-based environments [33, 34] are famous examples of these types of communities (it is highlighted that, for acceptability by governments and following privacy policies, developers and owners generally provide trapdoors or condition to get direct access [35, 36]—this fact makes mentioned communities to be semi-autonomous in the real applications). Hence, a large number of autonomous entities in DAOs and Metaverse-based communities have been empowered using AI. They can learn much knowledge from their environments, react to events, and respond to queries based on their around conditions. The result of merging the mentioned technologies caused to launch of the concept of MoE [31]. The concept of MoE is the digital twin of IoE defined in the virtual world which involves AI-enabled virtual things, Metaversian and their avatars (people in the IoE concept), generated data by decentralized applications (DApps) and smart contracts, and Web3-based components (see Fig. 1—Part c). In 2022, several proposals were made around DAO-based systems which provided autonomous methods and protocols for these types of autonomous communities. Authors in [35], identified blockchain-based reporting protocols as a method for monitoring and controlling DAOs. Their proposal addressed several cryptographic tools to provide security and privacy for reporters, such as ring signature schemes and the use of one-time pseudonyms. As another work, Far et al. [36] proposed a reputation transfer protocol for retail markets developed on DAO-based communities. Retail owners in the offered protocol can transfer their ownership right to another one through a non-interactive zero-knowledge (NIZK)-based manner. These two studies could be known as proposals that addressed ways to provide security and privacy for users in autonomous communities but they did not consider AI. Initially, the concept of the Metaverse of Things (MoT) was proposed in 2022 by Maksymyuk et al. [30]. In the MoT concept, a novel framework for future metaverse applications was composed of multiple synchronized data flows from multiple operators. Moreover, their proposed model was designed based on dynamic fine-grained data flow allocation and service selection using non-fungible tokens, which can be traded over the blockchain among users and operators in a decentralized mobile network environment. Recently, Banaeian Far et al. [31] proposed a comprehensive perspective to the digital twin of IoE in the virtual worlds and named it MoE. The MoE concept is known as the most comprehensive and autonomous community in which many AI-enabled things exist to react and respond to human-in-the-background (HitB) and machine-based queries, and generate virtual products. The main difference between MoE [31] and IoAI (the proposal of this study) is their implementation location, where MoE is virtual and IoAI is inherently physical (but IoAI enable to generate virtual environments, products, and content). The aforementioned concepts and paradigms, including MoT, MoE, and AI-enabled virtual things, could be assumed as excellent examples of a virtual version or a digital twin of an IoAI community appearing in the near future. In fact, analyzing the behaviour of AI-enabled virtual things in the MoE networks enables futurologists to make a good prediction of IoAI communities’ behaviours. Hence, we believe that these concepts are considered the most important and accurate simulation of the IoAI.

All in all, it is clear that the main difference between the three mentioned concepts (IoE, AIoT, and MoE) and IoAI is human dominance in procedures and policies. However, it is believed that IoAI should be fully autonomous and generative so that can grow itself in addition to assisting humans.

We highlight that preparing infrastructure for emerging a fully autonomous and human-disconnected community is not acceptable (and may not applicable) to governments for at least in next few years, and this study only addresses one of the future horizons of AI technology.

3 Internet of Artificial Intelligence (IoAI)

This section proposes the concept of IoAI, as a human-disconnected technology, in detail. The proposal involves main background technologies, prohibited items, highlighted achievements, and challenges and opportunities of the IoAI technology.

3.1 Background technologies

There are many technologies in the IoAI’s background. This section addresses several important of them and refers to their relation with the proposal of this study. The relation between critical technologies powered IoAI is depicted in Fig. 2.

Fig. 2
figure 2

The relation between IoAI’s background technologies (the top level of this relation is the same Infrastructure layer shown in Fig. 3)

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3.1.1 Internet of Things

The IoT refers to a network of interconnected physical objects or “things”equipped with embedded sensors, actuators, and communication interfaces, enabling the seamless exchange of data and the integration of the physical world with the digital realm [10]. This interconnected web of devices encompasses a spectrum of domains, from industrial machinery and smart infrastructure to consumer electronics, health monitoring systems, and environmental sensors, exemplifying the pervasive nature of IoT in enabling context-aware, data-driven decision-making and automation.

The application of IoT has woven an intricate tapestry of interconnected technologies, revolutionizing people’s daily lives through myriad of smart applications [37, 38]. Its impact extends to enhancing efficiency, convenience, and responsiveness, empowering individuals with real-time insights into their surroundings, personalized experiences, and seamless connectivity, thereby redefining the paradigm of modern living.

The enhancement of autonomous IoT capabilities hinges on the convergence of advanced machine learning algorithms [39], edge computing infrastructures [40], and adaptive sensor technologies, fostering self-optimizing, self-healing, and context-aware decision-making frameworks. By imbuing IoT with autonomous intelligence, it can preemptively identify anomalies, dynamically reconfigure operational parameters, and proactively respond to evolving environmental dynamics. Thus amplifying resilience, adaptability, and orchestration of complex interconnected systems.

Many IT-based companies provide practical applications for IoT to enhance people’s lifestyles and make them semi-autonomous. The following addresses several important of them.

  • Google (Alphabet Inc.)Footnote 1 A pioneer in IoT and AI technologies, Google’s platforms span from the popular smart home solution "Google Nest" to extensive cloud-based IoT services, facilitating large-scale data management and analytics for diverse applications, including smart cities, industrial automation, and precision agriculture.

  • Amazon Footnote 2 Leveraging its AWS IoT platform and Echo devices, Amazon has catalyzed the widespread adoption of smart home products and voice-activated IoT services, while also advancing the development of AI-driven logistics and supply chain optimization tools under Amazon Web Services.

  • General Electric (GE)Footnote 3 Renowned for its leadership in industrial IoT and autonomous systems, GE’s Predix platform offers industrial IoT solutions for predictive maintenance, asset performance management, and smart grid analytics, empowering industrial enterprises to optimize operational efficiency and enhance asset reliability.

  • Siemens AG Footnote 4 A trailblazer in industrial IoT and autonomous technologies, Siemens provides IoT-enabled solutions for smart manufacturing, energy management, and transportation, revolutionizing industrial processes through its MindSphere industrial IoT platform and digital twins technology.

  • Microsoft Footnote 5 Through its Azure IoT platform and comprehensive suite of cloud services, Microsoft is driving innovation in IoT applications across diverse verticals, from connected healthcare solutions to intelligent edge computing, empowering organizations to harness the full potential of IoT and autonomous systems.

3.1.2 Ad hoc networks and peer-to-peer connections

Ad hoc networks represent decentralized communication systems where wireless nodes dynamically form temporary networks without requiring a pre-existing infrastructure [41, 42]. Peer-to-peer connections, a subset of ad hoc networks, enable direct interaction and data sharing among networked devices without the need for central coordination, often leveraging distributed protocols for resource discovery and communication [43, 44].

Ad hoc networks and peer-to-peer connections have redefined the landscape of distributed computer networks, fostering flexible, resilient communication infrastructures in scenarios where centralized architectures are impractical [23, 45,46,47]. Their impact ranges from enabling cooperative file sharing and real-time communication to enhancing fault tolerance and scalability in distributed environments, catalyzing the evolution of robust and adaptive networked systems.

Ad hoc networks and peer-to-peer connections exhibit inherent benefits over centralized systems, particularly in IoT applications, by facilitating seamless device interconnectivity, reducing reliance on a single point of failure, enhancing fault tolerance, and supporting dynamic network reconfiguration [48,49,50,51]. These decentralized architectures empower IoT applications to operate autonomously, exhibit resilience to network disruptions, and ensure responsive, resource-efficient communication in dynamic, heterogeneous environments.

Companies with experts in computer networks provide their products and services all around the world. Five famous of them are now listed below.

  • Cisco Systems Footnote 6 Cisco’s mesh networking solutions leverage ad hoc architectures to create self-organizing, resilient wireless networks, enhancing industrial connectivity and IoT deployments.

  • Hewlett Packard Enterprise (HPE)Footnote 7 HPE’s Aruba Networks division offers peer-to-peer Wi-Fi Direct solutions for seamless device-to-device communication, facilitating peer discovery and direct data exchange, particularly in Industrial IoT (IIoT) and smart manufacturing environments.

  • Sierra WirelessFootnote 8 Renowned for its robust ad hoc and mesh networking solutions, Sierra Wireless enables real-time communication and edge computing capabilities in industrial IoT, supporting mission-critical applications within smart utilities, asset tracking, and industrial monitoring.

  • Juniper NetworksFootnote 9 Juniper’s Contrail Edge Cloud solution leverages ad hoc networking principles to enable distributed computing, edge connectivity, and secure peer-to-peer data exchange, crucial for dynamic IoT environments and edge computing deployments.

  • Toshiba CorporationFootnote 10 Through its wireless and IoT portfolio, Toshiba provides ad hoc networking solutions for industrial environments, empowering smart manufacturing and asset management applications through resilient, decentralized communication architectures.

3.1.3 Artificial intelligence

AI refers to the development of intelligent systems that can perform tasks requiring human-like cognitive abilities, encompassing machine learning, natural language processing, and autonomous decision-making algorithms [1]. In scientific contexts, AI represents the convergence of advanced computational methods, statistical modelling, and cognitive sciences, with the aim of creating systems capable of perceiving, reasoning, and acting in complex, dynamic environments.

AI has permeated various aspects of daily life, underpinning transformative experiences such as personalized content recommendations, virtual assistants, and autonomous vehicles, driving efficiencies, personalized experiences, and data-driven decision-making [4,5,6,7]. Moreover, in Industry 4.0, AI enables predictive maintenance, quality control, and process optimization, fostering adaptive production, smart logistics, and human-robot collaboration, thereby revolutionizing industrial automation and intelligent manufacturing.

AI enhances IoT by augmenting data analytics, predictive insights, and autonomous decision-making, powering real-time anomaly detection, adaptive resource allocation, and cognitive processing, thus streamlining operational efficiencies, enhancing fault tolerance, and enabling context-aware IoT applications [12, 13]. The benefits of applying AI in IoT encompass proactive maintenance, predictive analytics, and personalized user experiences, contributing to scalable, resilient IoT ecosystems with adaptable, self-optimizing capabilities.

As with the previous, this subsection also addresses five great companies developing AI systems and providing AI-based applications. They now are listed below.

  • IBM Footnote 11 IBM’s Watson IoT platform integrates AI capabilities for predictive maintenance, cognitive analytics, and edge computing, empowering organizations to harness IoT data for predictive insights and operational optimization across diverse industrial verticals.

  • Microsoft Footnote 12 Microsoft Azure IoT offers AI-driven solutions for remote monitoring, predictive maintenance, and intelligent edge computing, enabling IoT applications in agriculture, healthcare, and smart cities to leverage advanced analytics and cognitive services.

  • Google Cloud Footnote 13 Through Google Cloud IoT, the company provides AI-centric IoT solutions for smart manufacturing, logistics optimization, and predictive analytics, leveraging machine learning and predictive modeling to foster data-driven decision-making in IoT deployments.

  • Intel Corporation Footnote 14 Intel’s IoT solutions integrate AI capabilities for intelligent edge computing, predictive analysis, and autonomous systems, enabling scalable, secure IoT architectures with real-time data processing and adaptive learning capabilities.

  • Amazon Web Services (AWS)Footnote 15 AWS IoT Core offers AI-powered services for device management, data analysis, and machine learning integration, facilitating AI-driven predictive analytics and smart automation across IoT deployments such as smart homes, industrial sensing, and environmental monitoring.

3.1.4 Machine vision

Machine vision entails the utilization of imaging systems, computational algorithms, and pattern recognition techniques to enable machines to gain a visual understanding of the surrounding environment, encompassing processes such as object detection, image analysis, and visual inference [52,53,54]. In a scientific context, machine vision represents the confluence of computer vision, sensor technologies, and machine learning, with the aim of endowing automated systems with perceptual capabilities akin to human vision, fostering advanced visual perception and interpretation.

Machine vision redefines the landscape of IoT and virtual worlds by enabling real-time visual recognition, object tracking, and spatial mapping, underpinning augmented reality experiences, smart surveillance, and autonomous navigation within IoT environments [55, 56]. Its impact extends to facilitating augmented data visualization, predictive maintenance, and contextual awareness, thereby driving enhanced user experiences, adaptive automation, and seamless integration of physical and virtual realms in IoT and virtual reality applications [57, 58]. This technology assists IoT devices to be more efficient and makes them more applicable in daily use.

Machine vision enhances IoT applications in people’s daily lives by enabling personalized healthcare monitoring, interactive smart environments, and visual assistance for the differently abled, fostering safer, more efficient living spaces, and proactive health management [59, 60]. The benefits of applying machine vision in IoT encompass autonomous systems, dynamic visual feedback, and adaptive decision-making, culminating in responsive, human-centric IoT applications with enhanced safety, convenience, and accessibility.

Several high-tech companies that provide practical applications of machine vision are now addressed in the following.

  • NVIDIA Footnote 16 NVIDIA’s Jetson platform offers machine vision capabilities for IoT devices, enabling real-time video analytics, object detection, and spatial awareness, empowering edge devices with AI-driven vision computing and autonomous decision-making.

  • Intel Corporation Footnote 17 Intel’s RealSense technology integrates machine vision for IoT applications, facilitating spatial mapping, object recognition, and gesture control, enabling immersive experiences in virtual worlds and advanced visual perception in IoT devices.

  • Cognex Corporation Footnote 18 Cognex provides machine vision solutions for IoT, enabling automated inspection, visual quality control, and object recognition in manufacturing and industrial IoT deployments, elevating operational efficiency and product quality.

  • Qualcomm Technologies, Inc.Footnote 19 Qualcomm’s Spectra ISPs offer advanced machine vision capabilities for IoT edge devices, supporting deep learning models, image processing, and contextual reasoning, empowering edge nodes with responsive, vision-enabled IoT applications.

  • Sony Semiconductor Solutions CorporationFootnote 20 Sony’s image sensor and machine vision technologies enable IoT devices to capture high-quality visual data, supporting advanced photography, augmented reality, and visual intelligence in IoT applications, underpinning immersive virtual worlds and responsive IoT environments.

It is highlighted that machine vision technology is generally assumed as a part of smart IoT systems, and it is essential, but not critical in AI applications. Moreover, this technology provides no autonomy for IoT systems and AI-enabled things, but intelligent things using machine vision technology work more safely and collect more data for ML applications.

3.2 Prohibited things/items in IoAI communities

Based on the hi-tech background technologies of the IoAI community, it is able to meet all its problems/requirements individually, and there is no need for human-based assistance (but there is allowed to collaborate). In this regard, entering/using several categories of things/issues should be strongly prohibited by each IoAI community. Four important of them are now listed in the following (these can be applied in the final generation IoAI, not in the first one since the first generation of the IoAI paradigm should be dominated by humans/governments).

  • AI-less things In a perfect IoAI community, all things will work based on their AI and predefined algorithms. Hence, no HitB thing (e.g., initial materials, energy products, vehicles, or even virtual things) is considered valid to enter/connect IoAI components. It is noted that humans/companies as customers or consumers of IoAI-generated products can use them but they should not interfere in the affairs and processes.

  • Human dominance All decisions are made based on AI, and no natural intelligence is allowed to make changes in decisions and commit comments. Therefore, each IoAI community works fully autonomously without human dominance, and all human orders to change processes should be aborted before submitting.

  • Government intervention Government interventions and policies are located at the upper level of humans, and they are able to change the general policies of communities. However, the IoAI community is assumed government-proof and accepts no policies made by politicians or religious parties. Hence, the IoAI community decides, makes its futures, and grows its generations based on its conditions in addition to ignoring all HitB-based decisions.

  • Irrational orders and compulsory Each autonomous IoAI will be developed for one or more achievements, such as assisting industry, cultivating agricultural products, mining, making IT products, and/or improving medicine. In this regard, it works and develops due to the defined goal(s) and resists out-of-duties and irrational orders and compulsory, and it goes ahead in development to achieve defined goals as fast as a perfect industry.

It is noticed that the above list seems unrealistic to be applied in the first generation of IoAI since with these restrictions, the system should be fully isolated and can be launched in small areas where no humans are present (it is true for now). Hence, applying all the mentioned aspects causes many challenges, such as AI errors and the inability to stop dangerous projects, and makes the IoAI impractical in the first generation. Therefore, in the first generation of the IoAI, human plays an influenceable role and should exist.

3.3 General architecture

As a general architecture of IoAI, we offer a three-layer architecture illustrated in Fig. 3 (Note: The Human layer is not a part of the IoAI community, and this layer includes some who collaborate with the IoAI, not assisting the IoAI). It is highlighted that the proposed architecture is similar to AIoT but the order of layers is assumed inverse. In the following, the three layers of the IoAI concept are defined.

Fig. 3
figure 3

The offered three-layer architecture of the IoAI concepts and its relation with humans

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  1. 1.

    Infrastructure It is wondering but true that things provide infrastructures, and clouds and great companies are assumed to be end users. In the IoAI community, AI-enabled things, including robots, driverless vehicles, drones, etc., are connected in an area as big as a city and provide services for humans. This layer is the main layer of the IoAI community and no human nor government is allowed to force policies. The autonomy and human-disconnected part of the IoAI appear in this layer. The connections of them are generally peer-to-peer wireless channels, and their services involve all human requirements, such as farming, industry, manufacturing, mining, IT products, clothing, etc. As with current cities and provinces, the IoAI can construct and build new buildings and manufactories, grow itself, connect to other communities, and meet its required energy through green and/or renewable ways. As a future horizon, this layer can be assumed as the best friend and collaborator for humans and a factor in increasing the quality of humans’ lifestyles.

  2. 2.

    Interface Regarding the reverse architecture of IoAI, edges are assumed near the infrastructure (the AI-enabled things), and Fog is considered the closest layer to the application layer (human and cloud). The connection ways could be Internet-based (e.g., wireless channel), and traditional physical transportation systems (roads, sea, sky, etc.). In this layer, as the connection layer, all types of things, such as AI-less things, HitB things, and AI-enabled things, exist in the transportation system. All in all, this layer is a bridge between a robot-based community (IoAI) and humans.

  3. 3.

    Application This layer is not the main part of IoAI, and it is the closest layer to humans and can involve and connect them. As with general network architectures, end users exist in the application layer but, in the IoAI concept, cloud service providers, industry, and humans are in this layer. The connection between industries to the IoAI community is the same business-to-business (B2B) connection, and the connection between humans to the IoAI community is assumed as the same customer-to-business (C2B). However, for certain applications, C2C connections can be defined so that each individual is able to connect to an AI-enabled thing in the IoAI community as a business or even a friend.

It is highlighted that the presented architecture in Fig. 3 is general, and for each application details of connections and entity types and their abilities can be defined.

3.4 Achievements

The IoAI is not far from the AI’s achievements and can be known as an advanced application of AI. In this regard, the remaining of this subsection addresses the specialized achievements of a general IoAI community on top of AI applications.

3.4.1 Autonomy

Undoubtedly, autonomy is one of the best AI achievements reflected in expert systems as an excellent example. However, an autonomous expert system as big as a city is unbelievable but it will be launched in the near future. As the greatest achievement of the IoAI concept, this study introduces an autonomous city/community as the next generation of current AI which can also meet all requirements of itself in addition to solving human needs. As mentioned previously, the autonomous city can involve AI-based manufacturers, academies, farms, academies, worldwide leader companies, and distinguished products.

3.4.2 Generative and human-disconnected community

As aforementioned, autonomy could provide many achievements so generative AI is considered a state-of-the-art topic in science and technology. Except for the infrastructure layer in the transportation systems, the IoAI community connection to humans is fully cut. Hence, it is able to create many products to grow itself, assist the HitB industries, and enhance human life quality. Therefore, a perfect IoAI community is generative, in both inside and outside aspects, and it is an excellent human-disconnected community in individually solving its problems and requirements.

3.4.3 Environment compatibility

The sustainability of robots in every environment condition is a critical feature that enables IoAI components to be compatible with many weathers. Hence, developing IoAI in locations where life is difficult for humans also increases Earth’s efficiency by using green and renewable energies. The absence of the need for general life infrastructure for humans (e.g., water supply infrastructure, sewer network, food and supplies warehouse, electricity network, gas delivery, providing other types of energy, etc.) in IoAI communities is another facilitating feature for the rapid development of this technology in every environments/locations.

3.4.4 Improving people’s living standards

Despite the fact that the IoAI community is assumed human-disconnected, it can be considered the best friend and collaborator for people, industry, academia, and scientific communities. Hence, IoAI assistance to humans through innovative ideas, high-tech products, and energy-efficient tools could provide many other benefits for the improvement of people’s living standards.

3.5 Challenges and opportunities

Every emerging technology and concept has several challenges in addition to its opportunities. In the following, the essential items of the challenges and opportunities of the IoAI concept are identified in three aspects (for industry, in relation to governments, and assisting to humans).

3.5.1 Challenges

Although most challenges of IoAI are common with AI technology [19,20,21], this section reviews three important challenging aspects of the existence of the IoAI as an ideal autonomous city (the ideal refers to a type of AI with no error).

  • Initial setup (for industry) It is very clear that creating each new product (hardware, software, or algorithmic-based) faces many problems, such as the absence of enough experts, weak background technologies, marketing, the absence of a dedicated production line, acceptability by people, etc. As in AI, preparing an IoAI community in a big and far area enforces massive initial costs to developers and supporting companies (or even governments). Moreover, creating fully autonomous robots to transfer them to a far destination is a high-risk process that requires several venture capital (VC) with strong financial support in the fields of computer sciences, technology, and IT.

  • Non-acceptance by governments Generally, no government likes independent and autonomous communities, and non-dependence on governments is dangerous and puts them at risk of bankruptcy [61, 62]. In this regard, governments try to make communities dependent them on themselves for many reasons, such as for energy, strategic products, transportation, protection, city services, etc. Hence, policies and decisions made by politicians stop/limit communities’ autonomy in many aspects.

  • Revolution against humanity Some scientists and experts believe AI is the most important factor in the killing and extinction of humans in the near future [63,64,65]. Hence, the high proceeding of AI and transforming to an autonomous community (this study called it IoAI community) to be a society superior to human societies is the main reason to forbid the rapid development of AI technology. Therefore, deciding an excellent (or the best) trade-off between fully and partially autonomous IoAI communities is challenging since the autonomous one is hazardous (it is a good reason for governments to be against it), and the other requires connecting human control, monitoring, cost, energy, etc.

3.5.2 Opportunities

The IoAI community provides many benefits in addition to addressing challenges. Despite the fact that the opportunities of the existence of an IoAI community are similar to its achievements mentioned in Sect. 3.4, three exciting opportunities are now identified in the following.

  • Secondary efficiency (for industry) In addition to annoying problems and challenges, an IoAI community bring many benefits after a desired development, such as energy efficiency by using green energies, no need for water resources in several products, creating error-free products with no need for quality control, and being generative and no need to human. These secondary efficiencies and benefits highly motivate the government, developers and great companies to launch the first generation of the IoAI community to utilize the valuable achievements.

  • Autonomous government This opportunity can be assumed a threat to acceptability by the governments since the IoAI can remove any conflict of interest and make the most sound politicians who strive for the progress of society. From a public perspective, it is a good feature of the IoAI community and is considered the best type of government. Also, in the current time, there are several practical examples of AI-based managers, teachers, and many other jobs replaced by AI in such a way that they are working properly and satisfiable.

  • The best collaborators of humans As with the autonomous city and autonomous manufacturing and industry, the IoAI community can be the best collaborator for industry and a new exciting destination for tourists. Full-time work in IoAI-based manufacturing makes it the fastest business and industrial partner, and the fully robot-based and smartest city attracts many tourists to visit.

4 Discussion and future horizon

The concept of IoAI, as an autonomous community operated by advanced AI, presents an intriguing vision of the future. Such a community, if well-designed and responsibly managed by its participants (e.g., AI-based robots and algorithms), could potentially offer numerous benefits as aforementioned. However, as with any advanced technology, there are important considerations, especially regarding compatibility, collaboration, and security. Several important considerations in relation to IoAI are discussed below:

  • Making a good relationship with humans To foster a positive relationship with an AI-enabled community, it’s crucial to establish transparency, governance, and mutual understanding. Open dialogue and collaboration will be necessary to ensure that both humans and AI systems work together in a harmonious and complementary manner. Promoting education and awareness about AI, its capabilities, and its limitations will be vital to building trust and cultivating a culture of collaboration.

  • Ensuring compatibility To ensure compatibility with an autonomous AI-based community, it will be essential to develop standardized communication protocols and interoperable systems. Human-centric design principles should guide the development of interfaces, ensuring that humans can seamlessly interact with AI-enabled components, leveraging intuitive controls and feedback mechanisms.

  • Addressing security concerns Building a secure environment in the presence of highly advanced AI systems will be a paramount concern. Robust cybersecurity measures, including encryption, authentication, and anomaly detection, will be critical to protect against potential threats or unauthorized access. Additionally, establishing fail-safe mechanisms, thorough auditing, and continuous monitoring will help maintain control and mitigate any potential risks.

  • Mitigating potential risks One of the key challenges will be to prevent AI-enabled robots from revolting against humans. Safeguards such as ethics-driven programming, strict adherence to Asimov’s laws of robotics, and implementing ethical guidelines for AI development can help mitigate this risk. Continuous, transparent oversight and regular ethical evaluations will also be necessary to prevent any potential negative outcomes.

  • Cultivating a positive ethical framework In addition to security measures, an autonomous AI-based community should operate within a framework that prioritizes ethical considerations and upholds human values. Establishing interdisciplinary boards for AI ethics, fostering human-AI collaboration, and setting clear guidelines for AI decision-making can help maintain an ethical and human-centric approach in the community’s operations.

Based on above descussion, establishing a positive, collaborative, and secure relationship with an autonomous AI-based community necessitates transparent governance, compatibility-focused design, robust security measures, and adherence to ethical guidelines. By fostering understanding, collaboration, and a human-centric approach, it’s possible to create a mutually beneficial relationship with such communities, unlocking the potential for sustainable, innovative, and harmonious coexistence.

Regarding the previous section, this study believes that the IoAI will meet its goals in the second generation. Hence, the following describes the evolution of this technology in three main generations.

  1. 1.

    First generation of IoAI In the first generation of IoAI, it is very challenging to establish it in a far area. Hence, it would be better if the first generation of the autonomous community developed over smart cities under human consideration and monitoring. Regarding this idea, IoAI’s practical efficiency, relation to humans, soundness of working, etc. can be tested in a controllable environment. Additionally, in case of positive results, the IoAI-based community can be accepted by governments and the general public more quickly.

  2. 2.

    Second generation On the success of the first generation, the second generation of the IoAI can be developed in an uninhabitable place as big as a city (even a far location, e.g., a desert, cold region, waterless areas, and/or somewhere that is uninhabitable for humans). Hence, the Earth’s energy efficiency is increased since all used energies by the IoAI community are all renewable, green, and unlimited energies (e.g., solar and wind) used to generate people and industry needs. Additionally, relying on the capabilities of AI, it is anticipated many innovative ideas will proposed to enhance Earth’s energy efficiency.

  3. 3.

    Third and next generations According to the great potential of AI and the autonomy of the IoAI community, the goals and horizons of the third generation of the IoAI will designed by IoAI participants, and they decide what will happen for themselves. However, we forecast that the main difference between the second and third generations of the IoAI is to use of a higher level of energy efficiency with no loss and the creation of the most high-tech products supported with quality.

5 Conclusion

The absence of human assistance(s)/collaborators in hard-working conditions such as in bad environments, too warm/cold temperatures inaccessibility of energy, full-time work and other types of heavy work caused a sense of requiring an unlimited community in several aspects. The potential of AI-empowered robots as errorless assistants of humans motivated this study to propose a new concept called IoAI. Autonomy was considered as the main feature of the IoAI concept. However, the proposed concept can collaborate with humans, industrial men, academia, and people in a broad spectrum field of science and technology in addition to working as an autonomous community as big as a city. Furthermore, by relying on AI, the IoAI community can resolve its needs and grow itself.

On the other hand, launching a fully autonomous IoAI faces several challenges addressed in this work, such as the cost of initial setup, regulatory policies made by governance, and the possibility of revolution against humans. Accordingly, we offered to release the first generation of the IoAI concept over smart cities so that is controllable by governments, and then allow to development of the second generation of IoAI, as a fully autonomous community driven by AI-based robots and algorithms, to be able to develop industry, extract initial materials, and make high-tech products. However, humans who are faced with IoAI suffer from two aspects of this technology; first, revolute against humans and the main human killer factor, and second, it is the best friend of humans and the most important collaborator for industry. In this regard, this study identified important opportunities for the emergence of IoAI such as efficiency and helping governance to implement their policies.

Future directions Although AI technology is not a new concept, it can be assumed a new tool to enhance many services and propose new paradigms. The IoAI is one of the exciting paradigms that require much work for the future in several fields we address some of them. The industry should generate and develop state-of-the-art and AI-based robots for the future horizon so that they can work fully autonomously. Additionally, industrial men should be prepared to have cooperated with a robot-based community. Governments should smooth policies for replacing AI-based robots with hard work and making decisions by algorithms. The compatibility of governments and policies with robots is an essential thing for future communities. Ultimately, we believe that academia and researchers play the most important role in creating an intelligent future. Hence, designing conditional controllability for autonomous systems is a critical idea for the near future.

In the end, we, as authors, hope the world’s future be a better place for humans, and that the proceeds of technologies assist them to have excellent lifestyles.