Recent advances in Large Language Models (LLMs) have positively and efficiently transformed workflows in many domains. One such domain with significant potential for LLM integration is the Internet of Things (IoT), where this integration brings new opportunities for improved decision making and system interaction. In this paper, we explore the various roles of LLMs in IoT, with a focus on their reasoning capabilities. We show how LLM-IoT integration can facilitate advanced decision making and contextual understanding in a variety of IoT scenarios. Furthermore, we explore the integration of LLMs with edge, fog, and cloud computing paradigms, and show how this synergy can optimize resource utilization, enhance real-time processing, and provide scalable solutions for complex IoT applications. To the best of our knowledge, this is the first comprehensive study covering IoT-LLM integration between edge, fog, and cloud systems. Additionally, we propose a novel system model for industrial IoT applications that leverages LLM-based collective intelligence to enable predictive maintenance and condition monitoring. Finally, we highlight key challenges and open issues that provide insights for future research in the field of LLM-IoT integration.

Today, crop diversification in agriculture is a critical issue to meet the increasing demand for food and to improve food safety and quality. This issue is considered to be the most important challenge for the next generation of agriculture due to diminishing natural resources, limited arable land and unpredictable climatic conditions caused by climate change. In this paper, we employ emerging technologies such as the Internet of Things (IoT), machine learning (ML) and explainable artificial intelligence (XAI) to improve operational efficiency and productivity in the agricultural sector. Specifically, we propose an edge computing-based explainable crop recommendation system, AgroXAI, which suggests suitable crops for a region based on weather and soil conditions. In this system, we provide local and global explanations of ML model decisions with methods such as ELI5, LIME, SHAP, which we integrate into ML models. More importantly, we provide regional alternative crop recommendations with the Counterfactual explainability method. In this way, we envision that our proposed AgroXAI system will be a platform that provides regional crop diversity in the next generation agriculture.

Internet of Things (IoT) applications are generating tremendous amount of data which is not only extremely big, but also missing, noisy, and uncertain due to intrinsic characteristics of IoT. These phenomenons pose a number of challenges in managing the IoT network and trustworthiness of the data analytics. Specifically, transferring all IoT data to the cloud for data analytics may be costly, inefficient and infeasible in some cases. Therefore, migrating sensor data processing and analysis closer to the edge devices plays a vital role in terms of reducing the amount of data sent to the cloud, IoT service delay and network latency. In this paper, we first aim to enable deep learning models in resource constrained IoT devices. Then, we design and implement a real IoT testbed consisting of resource constrained devices. We also provide a solution to the missing sensor data problem in IoT from the perspectives of edge, fog and cloud computing. Finally, we compare all computing approaches in terms of network load, latency and delay. Experimental results show that deep learning based edge and fog computing approaches can improve network delay and bandwidth requirements greatly and efficiently.

In recent years, Internet of Things (IoT) concept has become a promising research topic in many areas including industry, commerce and education. Smart cities employ IoT based services and applications to create a sustainable urban life. By using information and communication technologies, IoT enables smart cities to make city stakeholders more aware, interactive and efficient. With the increase in number of IoT based smart city applications, the amount of data produced by these applications is increased tremendously. Governments and city stakeholders take early precautions to process these data and predict future effects to ensure sustainable development. In prediction context, deep learning techniques have been used for several forecasting problems in big data. This inspires us to use deep learning methods for prediction of IoT data. Hence, in this paper, a novel deep learning model is proposed for analyzing IoT smart city data. We propose a novel model based on Long Short Term Memory (LSTM) networks to predict future values of air quality in a smart city. The evaluation results of the proposed model are found to be promising and they show that the model can be used in other smart city prediction problems as well.