As the Internet of Things (IoT) continues to expand and diversify, addressing the inherent heterogeneity of such networks becomes crucial. Traditional solutions often rely on centric-based paradigms like cloud computing or fog networks. However, there is an increasing need to explore alternative approaches that facilitate more efficient workload sharing among devices. This thesis investigates an edge-based approach, focusing on the implemented RCS framework, which enables seamless connections between devices without requiring additional configuration by harnessing semantic information to facilitate dynamic, meaning-oriented decision-making, emphasizing the algorithms' role over the underlying technology. The Algorithms in question are the Random, Ant Colony Optimization(ACO), and the Gossips Algorithm.The experimental results demonstrated the potential of the ACO algorithm compared to the other algorithms. While there is room for improvement in metrics like service time and hit/miss ratio, the ACO algorithm showed promise in network management and resource distribution, highlighting its suitability for addressing complex IoT challenges in dynamic environments like forests.Furthermore, the Gossips algorithm revealed the benefits of a more deterministic approach to resource allocation and task scheduling. Despite generating many messaging routes and resulting in heavier communication overhead, the Gossips algorithm provided a practical and accurate solution, showcasing its potential in specific scenarios such as high-powered smart cities where the network may be more resilient. As the IoT landscape diversifies, ACO and Gossips algorithms provide viable solutions for their individual use cases and lay the groundwork for more advanced edge-based solutions. These advancements set the stage for the emergence of innovative, interconnected, and adaptable networks capable of responding to the ever-changing demands of the digital world.