The rise of real-time applications, services has made Multi-access Edge Computing (MEC) essential for delivering low-latency, high-performance computing. The effectiveness of MEC, however, is largely contingent on the efficient pre-deployment of services. Despite its importance, efficient service pre-deployment is challenged by the inherent unpredictability of user mobility, the fluctuating conditions of network environments. Accurately predicting user locations, dynamically optimizing resource allocation across geographically distributed MEC servers are complex tasks that are essential to minimizing latency, maximizing data transmission efficiency. The variability in user movement patterns, network bandwidth further exacerbates these challenges, often leading to increased latency, diminished performance, which can negate the advantages offered by MEC. To address these challenges, this paper introduces a novel edge service pre-deployment scheme based on location prediction (ESPD-LP). The ESPD-LP scheme leverages historical user trajectory data to predict future locations, facilitating proactive, strategic resource allocation via a user-centric bidirectional matching algorithm across multiple MEC servers. By pre-deploying services in anticipation of user needs, this approach optimizes data transmission rates, reduces pre-deployment latency, significantly enhancing the overall performance of MEC systems. A comprehensive analysis reveals that the ESPD-LP scheme consistently outperforms similar approaches, with a 41% increase in data transmission rates, a 31% reduction in pre-deployment latency compared to the JO-CDSD, MEC-RDESN schemes, demonstrating consistently superior performance.

In recent years, edge computing, as an important pillar for future networks, has been developing rapidly. Task offloading is a key part of edge computing that can provide computing resources for resource-constrained devices to run computing-intensive applications, accelerate computing speed, and save energy. An efficient and feasible task offloading scheme can not only greatly improve the Quality of Experience (QoE) but also provide strong support and assistance for 5G/B5G networks, the Industrial Internet of Things (IIoT), computing networks, etc. To achieve these goals, this article proposes an adaptive edge task offloading scheme assisted by service deployment (SD-AETO) focusing on optimizing the energy utilization ratio (EUR) and the processing latency. In the preimplementation stage of the SD-AETO scheme, a service deployment scheme is invoked to assist with task offloading considering each service’s popularity. The optimal service deployment scheme is obtained by using the approximate deployment graph (AD-graph). Furthermore, a task scheduling and queue offloading design procedure is proposed to complete the SD-AETO scheme based on task priority. The task priority is generated by corresponding service popularity and task offloading. Finally, we analyze our SD-AETO scheme and compare it with related approaches, and the results show that our scheme has a higher edge offloading rate and lower resource consumption for massive task scenarios in the edge network.

In view of the rapid development of the Internet of Vehicles (IoV) and wireless communication technology, intelligent transportation systems play an important role in improving urban road safety, promoting the behavioral interaction between users and networks, improving the service quality, and controlling the network cost. Based on the universality and real-time nature of the IoV, data collectors can cooperate with users to sense and collect relevant data within the driving range of vehicles. To achieve this goal, this article proposes a two-tier sensing scheme around the optimization of the sensing mechanism and data processing. In the routing layer, we build a weighted graph model based on vehicle fog, and we propose a new routing strategy to maximize each vehicle’s utilization. We consider that the sensing information collected by multiple vehicles could be repetitive, which would lead to many unnecessary communication flows in the network. Therefore, in the data processing layer, we consider resource consumption in the whole fog, assign different tasks to the sensing vehicles, and filter similar information on the relay nodes of the routing paths to reduce the waste of resources in IoV. Finally, we compare and analyze our two-tier scheme with related approaches, and the results show that our scheme has higher link utilization and lower resource consumption for a high-speed mobile network environment in IoV.