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Nowadays, for improving the increasingly crowded traffic conditions, internet of vehicles (IoV) emerges. In IoV, the increase of smart vehicle applications produces computation-intensive tasks for vehicles. However, it is tough for vehicles to meet the demands required by tasks thoroughly due to the limited computing capacity deployed in vehicles. To address this challenge, the vehicle-to-everything (V2X) communication is a promising technology to support edge computing transmitting tasks across vehicles. By employing vehicle-to-infrastructure communication (V2I) and vehicle-to-vehicle communication (V2V), the origin vehicle seeks the feasible routes of offloading the computing tasks to the edge node (EN). In this paper, a computation offloading method which employs V2X technology for data transmission in edge computing, named V2X-COM, is proposed. Technically, the routing of the computing tasks is determined first. Then, non-dominated sorting genetic algorithm III (NSGA-III) is adopted to generate balanced offloading strategies. Furthermore, simple additive weighting (SAW) and multiple criteria decision making (MCDM) are employed to seek out the optimal offloading strategy. Finally, experimental evaluations are conducted to prove the validity of V2X-COM.

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Currently, smart farming has been established to realize agriculture automation by leveraging sensors to gather the growth and environmental data for crops, and realizing multiple intelligent controls, such as irrigation, fertilization, and so on, to increase the crop yields. To support real-time intelligent controls, edge computing is introduced to smart farming by endowing computing and storage capacities to edge devices nearby the geographically distributed sensors. However, the farmers are relatively willing to purchase and deploy a small quantity of edge servers (ESs) in the farm from the perspective of expenditure saving, thereby leading to a key challenge to guarantee the performance of the real-time controls and the overall edge services. In view of this challenge, a service offloading-oriented ES placement method for supporting smart farming, called SOP, is proposed to optimize the data transmission delay from sensors to ESs and the load balance among ESs. More precisely, the corresponding service range of a certain ES is ascertained according to the specific analysis of the farming service requirements. Subsequently, the layout policies for the trade-offs of the ES performance and service efficiency are acquired. Then the most balanced policy is determined as the final ES placement strategy. Eventually, we evaluate the performance of the whole ES system and the service execution efficiency with SOP.

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