Smart grids represent a modern energy distribution system that integrates traditional electricity grids with advanced digital communication and control technologies. Characterized by two-way data flow and sophisticated automation capabilities, this system facilitates seamless interaction among all components in the energy supply chain— from generation to consumption. The smart grid aims to enhance energy management by improving efficiency, reliability, and sustainability. It dynamically monitors and manages energy demand, optimizes supply-demand balance, detects and addresses faults promptly, and supports the integration of renewable energy sources. Artificial intelligence (AI) plays a transformative role in advancing the performance and efficiency of smart grids. By processing, predicting, and analyzing vast and complex datasets, AI enables precise energy demand forecasting, early fault detection and mitigation, optimized energy distribution, and robust protection against cybersecurity threats. The integration of AI in smart grids ensures greater flexibility, efficiency, and reliability in energy distribution, fostering the sustainable use of resources, reducing costs, and improving user satisfaction.

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.

The Internet of Things (IoT) is a technology that is being widely used in daily life. This technology makes it easier for devices to connect with each other. As a result of the high connectivity between devices, enormous volumes of data are being collected. Such data is called big streaming data which can be used to curate useful information by data mining techniques. One of the most used processing methods is called Frequent Itemset (Pattern) Mining (FIM) which detects recurring and common patterns over data streams. In this paper, a new algorithm based on frequently used FP-Stream algorithm is presented. The proposed algorithm enhances conventional FP-Stream algorithm to make it more adaptive to concept drifts when retaining its applicability to data streams. Conventional FP-Stream algorithms store all detected patterns. By adding weights during the pruning process based on pattern freshness, the proposed algorithm prioritizes newer patterns thereby learns new patterns and forgets older one swiftly. Performance evaluations are performed using data acquired from an IoT testbed established in KAVEM Lab of Gazi University. Evaluation results indicate that the proposed algorithm performs better than conventional FP-Stream significantly.

Recent online popular platforms such as social media, blogs, and newspapers generate a vast amount of unstructured data per second. Sentiment Analysis (SA) is an efficient technique to identify and extract subjective information in unstructured data to enable businesses to understand the emotional tendency of the interactive users towards its products or services. However, analyzing unstructured data can be more difficult than structural data. In particular, the performance of SA techniques decreases due to the structural complexity of the language. SA techniques are widely used in English since it is universal and structurally more suitable for SA. On the other hand, structural difficulties and complexities in Turkish cause performance degradation of SA studies compared to English. This study aims to overcome this difficulty by first translating Turkish texts into English texts by machine translation, and then realizing sentiment analysis on English texts. To demonstrate the success of machine translation, the experiments are conducted on two different data sets and results are given in a comparative manner for both on Turkish as the original language and English as the translated language. Data sets in both languages are classified by six different machine learning methods which are Logistic Regression, Naive Bayes, Decision Tree, Random Forest, Support Vector Machine, and Artificial Neural Network. When the success rates of machine learning methods are examined, a significant increase is observed by machine translation for most of the methods.

Emergence of Internet of Things (IoT) concept is expected to deeply change everyday life through a vast number of services and applications. Heterogeneity of IoT networks and unique characteristics of individual services, causes requirement of different quality factors for services and applications. Research on Quality-of-Service (QoS) aims to satisfy quality requirements of different services on heterogeneous IoT networks. To ensure a certain level of QoS, QoS prediction methodology is used to enhance service selection for IoT users. In this survey, QoS prediction approaches are briefly explained. The advantages and disadvantages of the existing approaches are presented and response times of QoS prediction methods are compared using two statistical metrics, namely Mean Absolute Error (MAE) and Root Mean Squared Error (RMSE).

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 this paper, we propose a generic recommender system that combines opinion mining and fuzzy quantification methods for qualitative data. The proposed system has two novel aspects. First, it employs a novel semantic orientation (SO) computation method to reduce the number of extracted features and opinion expressions. By using this new SO computation method, the proposed recommender system finds out the most related features and opinion expressions. Second, the proposed system generates short summary sentences from qualitative data using fuzzy quantification. The proposed system is evaluated using a restaurant review dataset. The results present that fuzzy quantified sentences offer brief information about the restaurant features from customers’ feedback. In addition, opinion mining extracts positive, negative, and neutral emotions from reviews.

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.