It is expected that up to 26 billion Internet of Things (IoT) equipped with sensors and wireless communication capabilities will be connected to the Internet by 2020 for various purposes. With a large scale IoT network, having each node connected to the Internet with an individual connection may face serious scalability issues. The scalability problem of the IoT network may be alleviated by grouping the nodes of the IoT network into clusters and having a representative node in each cluster connect to the Internet on behalf of the other nodes in the cluster instead of having a per-node Internet connection and communication. In this paper, we propose a multi-hop clustering mechanism for IoT networks to minimize the number of required Internet connections. Specifically, the objective of proposed mechanism is to select the minimum number of coordinators, which take the role of a representative node for the cluster, i.e., having the Internet connection on behalf of the rest of the nodes in the cluster and to map a partition of the IoT nodes onto the selected set of coordinators to minimize the total distance between the nodes and their respective coordinator under a certain constraint in terms of maximum hop count between the IoT nodes and their respective coordinator. Since this problem can be mapped into a set cover problem which is known as NP-hard, we pursue a heuristic approach to solve the problem and analyze the complexity of the proposed solution. Through a set of experiments with varying parameters, the proposed scheme shows 63–87.3% reduction of the Internet connections depending on the number of the IoT nodes while that of the optimal solution is 65.6–89.9% in a small scale network. Moreover, it is shown that the performance characteristics of the proposed mechanism coincide with expected performance characteristics of the optimal solution in a large-scale network.

Modern vehicles generate large volumes of sensor data that can exceed onboard processing capabilities. While task offloading to edge servers reduces latency and alleviates resource scarcity at vehicles, vehicular mobility and dense traffic often create localized overloads where multiple neighboring edge servers become congested simultaneously. Such regional overloads exhaust edge resources, degrade success rate, and diminish service-provider revenue. Edge-server cooperation is necessary under these conditions; however, dynamically forming cooperation groups and jointly deciding how much to offload and where to process enlarges the state–action space and complicates learning when these coupled actions are selected in a single step. To address this revenue-aware offloading problem under latency constraints and multi-tier subscription service, we formulate a joint offloading and resource-allocation problem that maximizes service-provider revenue and propose a hierarchical edge–cloud framework with three connected components: (i) a cloud-assisted dynamic edge-server clustering algorithm that groups edge servers by resource availability to form resource-balanced cooperation structures that remain effective under regional overloads; (ii) a two-stage deep reinforcement learning (DRL) policy that sequentially determines the offloading fraction and then selects the processing server to reflect their dependency and stabilize training; and (iii) a credit-based computing resource allocator that tracks cumulative service outcomes and subscription tier to enforce long-term fairness. The simulation results show that the proposed framework converges $1.75\times$ faster than the single-stage DRL baseline, achieves an approximately 10% higher success rate and increases revenue by 30% under heavy-load conditions.