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Fast-response communication is crucial for Vehicular Ad Hoc Network (VANET). In practice, the conventional VANETs, suffering from the high mobility of the vehicles and the ever-growing data to percept and process, cannot meet the demand of fast response currently. In this paper, we study the Dimensioning and Layout Planning (DLP) problem under 5G-based Vehicular Edge Computing Network (VECN) architecture which integrates the 5G Micro Base Station (gNB) and Edge Computing (EC) to reduce the response time. The DLP problem aims to minimize the total placement cost under the constraint of the full coverage. This paper formulates the DLP problem as an integer linear program (ILP) and then proposes a Greedy Algorithm (GA) and a Cost-Effective Heuristic Algorithm (CEHA) toimprove the computation efficiency. The case studies have verified the feasibility and scalability of the DLP formulation and showed that the proposed CEHA is fairly effective and efficient to solve the DLP problem. INDEX TERMS Vehicular Ad Hoc network, edge computing, gNB, vehicular edge computing network, dimensioning and layout planning.

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The increasing demands for real-time marine monitoring call for the wide deployment of Marine Monitoring Networks (MMNs). The low-rate underwater communications over a long distance, long propagation delay of underwater acoustic channel, and high deployment costs of marine sensors in a large-scale three-dimensional space bring great challenges in the network deployment and management of MMN. In this paper, we first propose a multitier, hierarchical network architecture of MMN with the support of edge computing (HMMN-EC) to enable efficient monitoring services in a harsh marine environment, taking into consideration the salient features of marine communications. Specifically, HMMN-EC is composed of three subnetworks, i.e., underwater acoustic subnetwork, the sea-surface wireless subnetwork, and the air wireless subnetwork, with a diversity of network nodes with different capabilities. We then jointly investigate the deployment diverse network nodes with various constraints in different subnetworks of HMMN-EC. To this end, we formulate a Multiobjective Optimization (MO) problem to minimize the network deployment cost while achieving the maximal network lifetime, subject to the limited energy of different marine nodes and the complex deployment environment. To solve the formulated problem, we present an Ant-Colony-based Efficient Topology Optimization (AC-ETO) algorithm to find the optimal locations of nodes in different subnetworks of MMN in a large-scale deployment. The time complexity of the proposed algorithm is also analyzed. Finally, extensive simulations are carried out to validate the superior performance of the proposed algorithm compared with some existing solutions.