Web 3.0 is an evolved version of the Web that enables the integration of applications such as the Internet of Things (IoT) with the Web. It involves the storage of large data generated by different users and efficient computation of application and web-related tasks. With the help of edge nodes installed near the users, the computation load of Web 3.0 will be efficiently managed. Thus, efficient task offloading and computation become a key concern in edge computing-enabled Web 3.0. In this paper, a novel algorithm is proposed that solves the challenges of load imbalance at the edge nodes resulting in large queue sizes and increased task delays. The proposed technique identifies the edge nodes with a large network load and pairs them with a lightly loaded edge node that can handle some of their network load. The edge node pairing is based on the Gale–Shapley stable matching algorithm. The preference profile of edge nodes is developed based on factors such as task computation delay and task transmission delay. Once the pairing is done, the number of tasks is offloaded as per the computing capacity of the lightly loaded edge nodes. A detailed simulation-based performance evaluation of the proposed technique is presented showing a reduction in task delay by 20% and task deadline miss ratio by 68%.

Intelligent transportation system (ITS) applications are dependent on secure and robust wireless data sharing among vehicles and roadside units (RSUs). Multiple types of data are shared among the ITS devices which include safety information, road services, web based information retrieval and task computation. Web 3.0 offers a decentralized, distributed and secure data sharing mechanism for ITSs. Allocation of wireless channel resources are critical to enable an efficient ITS system. In this paper, a novel data sharing technique for Web 3.0 based ITS is presented that relies on an intelligent clustering algorithm. In the first step, the proposed technique uses a K-means algorithm to find groups of vehicles with similar speeds. In the second step, each cluster is assigned an RSU which has the highest average data rate with all vehicles in the cluster. This is achieved by using a stable matching technique so that there is no contention and each cluster is assigned a separate RSU. The algorithm periodically updates the clusters and RSU allocation for web data sharing between vehicles and RSUs. Simulation results show that the proposed clustering-based data sharing technique improves sum-rate by 20% and reduces network delay by 23%.