Artificial Intelligence in Next Generation IoT Infrastructure: Challenges and Opportunities

The Internet of Things (IoT) extend the connectivity into billions of IoT devices around the world. Artificial Intelligence (AI) is the best solution to manage huge data flow and storage in IoT network. Nowadays, IoT is becoming more popular with the invention of high internet speed and many advanced sensors that can be integrated into a microcontroller. Internet of Things relies on Artificial Intelligence technology which gives the term of Artificial Intelligence of Things (AIoT). AIoT is transformational and mutually beneficial for both technologies, as AI adds values to IoT through materials and software. This fusion impacts the revolution of industry 4.0 such as maintenance, production chains, optimization and logistics applied in industries to achieve increased productivity, profitability, efficiency, safety, and security.In this paper we will discuss the merge of Artificial Intelligence and Internet of Things, the soft-hard of AIoT and the impact of AIoT on the industry 4.0 applications.

In recent years, the combination of Artificial Intelligence (AI) and Internet of Things (IoT), known as Artificial intelligence of things (AIoT), has driven significant advancements within crowdsensing paradigms. Under crowdsensing paradigms, a large number of AIoT devices providing local data facilitates the superior completion of complex tasks, but also poses challenges in terms of bandwidth. Semantic communication has emerged as a promising solution to alleviate the bandwidth demands associated with AIoT crowdsensing. However, the substantial computational and energy costs associated with semantic communication make AIoT devices reluctant to participate in crowdsensing tasks or provide low-value semantic information. To address these challenges, we propose a novel crowdsensing incentive architecture designed to motivate smart devices to engage in crowdsensing activities. Our framework leverages contract theory and reputation models to ensure incentive compatibility within the information-asymmetry market between distributed AIoT devices and the crowdsensing platform. Furthermore, to enhance the convergence performance of market strategies in large-scale AIoT environments, we employ a diffusion model to generate contract design from complex data distributions within the market, optimizing the learning of the optimal contract. Simulation results confirm the effectiveness of our proposed architecture in improving the performance of AIoT crowdsensing, demonstrating its potential to overcome the identified challenges.

In dealing with the impacts of our changing climate, the sun, air, and water are the three main factors that will affect our ability to maintain a healthy and livable living environment. The two United Nations' Sustainable Development Goals (SDGs) of "health welfare" and "sustainable city" relate closely to how societies face climate change. Applying smart technologies such as the Artificial Intelligence of Things (AIoT), big data, blockchains, and the Internet of Things (IoT), cloud calculation, and network management allows designers to access information on relational and adaptive environmental designs. Moreover, these technologies help us learn evolutionary computation information in order to provide advanced mechanisms. Models that help promote the implementation of smart neighborhoods and cities integrate smart technologies and IoT in order to improve air quality and living convenience and achieve living environments that are livable and healthy. This article primarily addresses the impacts of climate change on our living environment and how we may use green and smart buildings to ameliorate the effects of this change on daily life, promote the efficient use of water resources, and make living spaces significantly more environmentally friendly. In addition, we hope to apply the idea of smart IoT and big data analysis to design "passive toughness adaptation" and "automatic sensing prevention" into healthy living environments, which may facilitate our ability to handle the problems of super-ageing societies and to adapt to the diminishing birthrate. An intelligent and resilient environment that is appropriate for all age groups may provide a valuable path forward for preparing effectively for the impacts of climate change.

The merger of the Internet of Things (IoT) and Artificial Intelligence (AI) promises a new era in smart business operations wherein the companies will be able to redefine their business operations mode, decision-making and value delivery mechanism.The combination of real-time data obtained on IoT-powered devices and converted into practical information due to the use of high-level AI algorithms provides an integrated approach that is undoubtedly effective.AI and IoT make intelligent automation, predictive analytics, operational efficiency, and customer experiences enabled together.Whether it is manufacturing and logistics, retail, or healthcare, companies in all industries have been taking advantage of AIoT (Artificial Intelligence of Things) to make better resource allocations, minimize downtimes, and adjust to dynamic market changes.The possibilities of AI-IoT combination are enormous.As an example, in supply chain management, AI can be used to program smart sensors and can assists in forecasting demand, management of inventory, as well as maintenance expectations thereby leading to cost savings and enhanced productivity.Chatbots powered by AI and smart devices data via Internet of Things result in very personalized user experience in customer service.Moreover, AIoT will allow monitoring the workplaces in real-time to be compliant and safe as well as energy efficient.The strategic effect is high, businesses investing in AIoT have competitive advantages thanks to data-driven processes and improved operations.Nevertheless, there are some significant challenges to integration of AI and IoT.These are matters of data security, privacy, interoperability of equipment, and cost of the infrastructure deployment which is large.The amount of data produced by IoT devices is overwhelming, and these necessitate well-developed data management systems and highly scalable cloud or edge-based computing solutions.Moreover, AI algorithms are complicated, and properly trained staff poses additional difficulties to implementation.The ethics and data defense regarding data usage and automated decision-making need to be discussed as well to foster the trust and responsibility.Strategically, companies need to pursue a comprehensive approach toward AI-IoT integration, align continuation with business plans, make an investment in cybersecurity, and develop upskilled workforces, and scalable architectures.AI and IoT integration rework business models, improve the ability to respond to the rapidly changing environment, and promote innovation when used creatively.The present paper discusses the various opportunities and challenges that AIoT represents and provides strategically viable suggestions to the organizations that attempt to explore all of its potential.Finally, AI and IoT is an empowerment process that is reinventing smart business management by challenging the current digital environment.

Artificial Intelligence of Things (AIoT), as a fusion of artificial intelligence (AI) and Internet of Things (IoT), has become a new trend to realize the intelligentization of industry 4.0 and the data privacy and security is the key to its successful implementation. To enhance data privacy protection, the federated learning has been introduced in AIoT, which allows participants to jointly train AI models without sharing private data. However, in federated learning, malicious participants might provide malicious models by launching the poisoning attack, which will jeopardize the convergence and accuracy of the global model. To solve this problem, we propose a malicious model detection mechanism based on the isolation forest (iforest), named D2MIF, for the federated learning-empowered AIoT. In D2MIF, an iforest is constructed to compute the malicious score for each model uploaded by the corresponding participant, and then, the models will be filtered if their malicious scores are higher than the threshold, which is dynamically adjusted using reinforcement learning (RL). The validation experiment is conducted on two public data sets Mnist and Fashion_Mnist. The experimental results show that the proposed D2MIF can effectively detect malicious models and significantly improve the global model accuracy in federated learning-empowered AIoT.

Internet of Things (IoT) and artificial intelligence (AI) technology are integrated under the umbrella title "Artificial Intelligence of Things" (AIoT). AIoT seeks to improve data management and analytics, increase human-machine interactions, and streamline IoT operations. The IoT is an interconnected system of computing devices, mechanical and digital machines, and items that may send data over a network without requiring human or computer-to-human interaction. Any device that can be assigned an Internet Protocol address and communicate data across a network is considered an IoT item. Examples include an implanted heart monitor or a car with built-in sensors that alert the driver when the tyre pressure is small. As AI enhances IoT through connectivity, signaling, and data exchange while IoT enhances AI through machine learning capabilities and enhanced decision-making processes, AIoT is a game-changer and mutually beneficial technology for both types of technology. The IoT may look up businesses and their services by adding value to the data they produce. Using AI, the IoT device may assess, learn from, and make judgments without the assistance of a human. The current chapter presents two new approaches towards the AIoT and IoT applications: AIoT-based water management system and IoT-based smart irrigation system which are very successful in smart agriculture system.

This book chapter explores the intersection of Artificial Intelligence (AI), Machine Learning (ML), and the Internet of Things (IoT), highlighting their transformative impact on various industries and applications. As IoT devices generate massive amounts of real-time data, AI and ML algorithms provide the analytical power necessary to derive actionable insights, automate processes, and enhance decision-making. The chapter covers key AI and ML techniques, such as supervised and unsupervised learning, reinforcement learning, and deep learning, and discusses their integration into IoT systems for improved efficiency, scalability, and real-time performance. Specific use cases, including smart agriculture, industrial IoT, and healthcare, are examined to demonstrate the practical applications of these technologies. Furthermore, the chapter addresses the challenges in data processing, security, and privacy that arise from IoT and AI integration, as well as the future potential of these systems in driving innovation across sectors. This comprehensive analysis serves as a foundational resource for understanding the synergistic relationship between AI, ML, and IoT in creating intelligent, connected systems.

Digital Mock-Up units (DMU) are a design or human factor for study and inspection of Computer Aided Designs to help designers identify key design issues and factors which can lead to the functional failure of a product in the early stages of development. Digital Mock-Ups can be used by Computer Aided Design (CAD) & Non- Computer Aided design users to review a product before sending a design to the final stage i.e. Production. Currently, Digital Mock-Ups is used in a wide variety of industries ranging from automobiles to healthcare to aerospace. Additionally, DMU also has some drawbacks such as being time consuming, leading to human errors & repetitive work etc. This paper explores what is the current review process of Digital Mock-Ups in the industry to derive technological & functional information and how latest technologies like Artificial Intelligence (AI) and internet of things (IOT) can be adopted to review Digital Mock-Ups even more effectively using technologies like computer vision and image recognition to reduce human error in the review process. Key Words: Digital Mock-up Units, Artificial Intelligence of Things, Sensors, Image Recognition, Object Detection, Machine Vision

The rapid development of contemporary artificial intelligence and Internet of Things (IoT) technologies has given rise to the emerging paradigm of the AIoT (Artificial Intelligence of Things), which is profoundly impacting human life and driving the digital transformation of industries and society. The AIoT not only enhances product functionality and convenience but also accelerates the achievement of the United Nations Sustainable Development Goals (SDGs). However, the widespread adoption of these technologies still poses challenges related to social inclusivity, particularly regarding insufficient accessibility for elderly users, which may exacerbate the digital divide and social inequality, contradicting SDG 10 (reducing inequality). This study integrates AIoT product development processes based on sustainability and universal design principles using methods such as Quality Function Deployment, the Analytic Hierarchy Process, the Scenario Method, the Entropy Weight Method, and Fuzzy Comprehensive Evaluation. The features of this process include ease of operation and high flexibility, making it suitable for cross-departmental product development while prioritizing the needs of diverse age groups throughout the development process. The research findings indicate that the AIoT product concepts proposed can meet the needs of diverse users, contributing to the realization of age-friendly products. This study provides a reference point for future AIoT product development, promoting the inclusive and sustainable development of smart technology.

With the rapid advances in information and communication technologies, the Internet of Things (IoT) has become large and complex, bearing tremendous amounts of data and running devices in various scenarios. Leveraging Artificial Intelligence (AI) technologies, IoT can achieve superior information extraction, data analytics, and decision-making, which has resulted in the revolutionized Artificial Intelligence of Things (AIoT). AIoT can alleviate the pressure of storage, computation, and communication. Despite the promising features brought by combining AI technologies into IoT infrastructure, AIoT systems still face some serious challenges including inadequate efficiency, violation of security and privacy, lack of trust, and insufficient incentive. Blockchain featured by its distributed consensus and incentive mechanisms can be a promising technology for addressing the challenges in AIoT. AIoT employing blockchain is evolving with expectations of achieving efficient, secure, and trusted network activities. In this paper, we first introduce the background of AIoT and blockchain. Then, we discuss the motivations for employing blockchain with its characteristics in AIoT. Furthermore, we comprehensively review existing solutions on blockchain for AIoT systems from aspects of efficiency, security, privacy, trust, and incentive. Finally, we discuss the challenges and future research directions on blockchain for AIoT.

Crowd-Sourced software development (CSSD) is getting a good deal of attention from the software and research community in recent times. One of the key challenges faced by CSSD platforms is the task selection mechanism which in practice, contains no intelligent scheme. Rather, rule-of-thumb or intuition strategies are employed, leading to biasness and subjectivity. Effort considerations on crowdsourced tasks can offer good foundation for task selection criteria but are not much investigated. Software development effort estimation (SDEE) is quite prevalent domain in software engineering but only investigated for in-house development. For open-sourced or crowdsourced platforms, it is rarely explored. Moreover, Machine learning (ML) techniques are overpowering SDEE with a claim to provide more accurate estimation results. This work aims to conjoin ML-based SDEE to analyze development effort measures on CSSD platform. The purpose is to discover development-oriented features for crowdsourced tasks and analyze performance of ML techniques to find best estimation model on CSSD dataset. TopCoder is selected as target CSSD platform for the study. TopCoder’s development tasks data with development-centric features are extracted, leading to statistical, regression and correlation analysis to justify features’ significance. For effort estimation, 10 ML families with 2 respective techniques are applied to get broader aspect of estimation. Five performance metrices (MSE, RMSE, MMRE, MdMRE, Pred (25) and Welch’s statistical test are incorporated to judge the worth of effort estimation model’s performance. Data analysis results show that selected features of TopCoder pertain reasonable model significance, regression, and correlation measures. Findings of ML effort estimation depicted that best results for TopCoder dataset can be acquired by linear, non-linear regression and SVM family models. To conclude, the study identified the most relevant development features for CSSD platform, confirmed by in-depth data analysis. This reflects careful selection of effort estimation features to offer good basis of accurate ML estimate.

The convergence of healthcare with the Internet of Things (IoT) and Artificial Intelligence (AI) is reshaping medical practice with promising enhanced data-driven insights, automated decision-making, and remote patient monitoring. It has the transformative potential of these technologies to revolutionize diagnosis, treatment, and patient care. This study aims to explore the integration of IoT and AI in healthcare, outlining their applications, benefits, challenges, and potential risks. By synthesizing existing literature, this study aims to provide insights into the current landscape of AI, IoT, and AIoT in healthcare, identify areas for future research and development, and establish a framework for the effective use of AI in health. A comprehensive literature review included indexed databases such as PubMed/Medline, Scopus, and Google Scholar. Key search terms related to IoT, AI, healthcare, and medicine were employed to identify relevant studies. Papers were screened based on their relevance to the specified themes, and eventually, a selected number of papers were methodically chosen for this review. The integration of IoT and AI in healthcare offers significant advancements, including remote patient monitoring, personalized medicine, and operational efficiency. Wearable sensors, cloud-based data storage, and AI-driven algorithms enable real-time data collection, disease diagnosis, and treatment planning. However, challenges such as data privacy, algorithmic bias, and regulatory compliance must be addressed to ensure responsible deployment of these technologies. Integrating IoT and AI in healthcare holds immense promise for improving patient outcomes and optimizing healthcare delivery. Despite challenges such as data privacy concerns and algorithmic biases, the transformative potential of these technologies cannot be overstated. Clear governance frameworks, transparent AI decision-making processes, and ethical considerations are essential to mitigate risks and harness the full benefits of IoT and AI in healthcare.

The accelerating convergence of artificial intelligence (AI) and Internet of Things (IoT) has sparked a recent wave of interest in Artificial Intelligence of Things (AIoT). By exploiting the novel paradigm of edge intelligence, emerging computational intensive and resource demanding AIoT applications can be efficiently supported at the network edge. However, due to the limited resource capacity and/or power budget of the edge node, AIoT applications typically deploy compressed AI models to achieve the goal of low-latency and energy-efficient model inference. However, compressed models inherently suffer from the curse of data drift, i.e., the inference data at the deployment stage diverges from the training data at the training stage, leading to reduced model inference accuracy. To handle this issue, continuous learning has been proposed to periodically retrain the AI models on new data in an incremental manner. In this article, we investigate how to coordinate the edge and the cloud resources to perform cost-efficient continuous learning, with the goal of simultaneously optimizing the model performance (in terms of accuracy and robustness) and resource cost. Leveraging the Lyapunov optimization theory, we design and analyze a cost-efficient optimization framework for making online decisions upon admission control, transmission scheduling, and resource provisioning, for the dynamically arrived new data samples of various AIoT applications. We examine the effectiveness of the proposed framework on navigating the performance–cost tradeoff theoretically and empirically through trace-driven simulations.

Toward analyzing and synthesizing previous research in early prediction of cardiac arrest using machine learning based on a multi-layered integrative framework.

Towards Sustainable Farming: Leveraging AIoT for Precision Water Management and Crop Yield Optimization