## 2 Related Works  
The study presented in [26] explores data processing challenges in IoT environments, focusing on adaptability, data scheduling, and network coding. It emphasizes the importance of devices adapting to network changes to ensure reliability, transparency, and seamless access to resources. The proposed method enhances data dissemination efficiency by combining spatial and code domain scheduling with a preconfigured access mechanism aimed at reducing collision rates. Additionally, erasure coding is employed to optimize packet redundancy during data transfer. Simulations conducted using Contiki demonstrate that the Polymorphic Erasure Coding with Markov Decision Adaptability and Neural Networks (PECMAN) outperforms existing methods such as EMUSA, OSA, and EABS in terms of cost, overhead, and delay. These findings highlight PECMAN's potential to enhance efficiency and reliability in IoT systems. In [27], a smart city data platform called the City Data Hub was created in order to make data sharing and interoperability across various urban systems more efficient. The platform, created in South Korea within a national strategic project, integrates common data models, standard-based APIs (NCSI-LD), and a data marketplace in order to allow data sharing. In order to handle an array of features such as data ingestion, storage, analytics, and marketplace transactions, it follows a modular architecture. The platform's support for oneM2M-compliant IoT systems that provide interoperability with a range of smart city infrastructures are one of its most basic features. The data analytics module also makes use of machine learning models to provide predictive insights like COVID-19 contact tracing and parking space predictions. The work in [28] investigates distributed task scheduling within IoT-based serverless edge computing networks. Here, edge nodes operate as independent agents, optimizing their local scheduling performance. Task scheduling is modeled as a partially observable stochastic game (POSG), allowing nodes to autonomously manage tasks and allocate computing resources based on local observations such as task generation rates, data queue conditions, communication channel quality, and prior resource allocations. To address the challenges posed by limited observability, the study proposes a multi-agent task scheduling algorithm leveraging the dueling double deep recurrent Q-network (D3 RQN) approach. Simulations confirm the algorithm's effectiveness in approximating optimal scheduling and resource management, showcasing its practical benefits. The authors in [29] introduce a MEC-facilitated distributed cooperative microservice caching scheme, namely, DIMA, for microservice caching problem-solving. Caching is modeled in terms of a Markov Decision Process (MDP) for minimizing fetch delay and hit ratio maximization in such a scheme. To address the MDP, a distributed double dueling deep Q-network (D3 QN) algorithm is proposed, a merge of double DQN and dueling DQN, for its best performance and efficiency. With such a scheme, IoT devices can function in an autonomous manner in a decentralized environment. Experimental evaluation confirms that DIMA outperforms baseline approaches remarkably, proving its efficiency and high performance. In [30], an IoT platform with semantic data sharing and real-time processing using Kafka and Spark Streaming is proposed, with an SDN base. What is most striking about this platform is its capability of transforming streaming data into an RDF format through NLP in an automated form, with a uniform IoT ontology in terms of OWL. This ontology announces classes, objects, and data properties, with uniform data representation. SDN is utilized to dynamically discover data-producing entities such as sensors, with the flexible and efficient routing of data. Experimental evaluation with virtual sensors validates the framework, with uniform performance over a range of datasets and near zero processing times.