A Computation Offloading Method for Edge Computing With Vehicle-to-Everything

Abstract

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.

A survey on computation resource allocation in IoT enabled vehicular edge computing

“…Power/energy [28][29][30] QoS [33][34][35] Delay [25,26,28,[35][36][37][38][39] QoE and user satisfaction [34,37] Network/system utility [26,33,37,39,40] Reliability [20] However, at the same time, they are required to perform many intensive computations as part of the network [27]. Hence, authors in [28][29][30][31] try to optimize energy and power consumption in their resource allocation schemes.…”

“…Delay optimization or reducing the network latency is an essential goal in vehicular networks as they are of very high speed in nature. Works [25,26,28,[35][36][37][38][39] focus in reducing their delay while implementing their resource allocation scheme. Ensuring less delay makes it possible to run latencysensitive applications like assistive driving [20].…”

Performance Assessment of Context-aware Online Learning for Task Offloading in Vehicular Edge Computing Systems

“…al. [13] have formulated task offloading in VEC environments as a multi-objective optimization problem. They have solved the optimization problem using genetic algorithm with the goal of minimizing offloading latency and improving resource utilization.…”

“…Then, the offloading option with the least predicted response time is chosen for task offloading. In addition, the incremental learning-based algorithm maintains a dictionary to keep track of the selected server's context at the time of offloading (lines [12][13]. This dictionary will later be used for updating the respective model parameters when the result of offloading is disclosed.…”

Dimensioning and Layout Planning of 5G-Based Vehicular Edge Computing Networks Towards Intelligent Transportation

“…j∈ ECP e i j ≤ K 1 , ∀i ∈ BS (10) j∈ gNB e i j ≤ K 2 , ∀i ∈ ECP (11) j∈ gNB ∪ T P e i j ≤ K 3 , ∀i ∈ gNB (12) Constraints (10)-(12) limit the maximum number of the child nodes that BS, ECP, and gNB could accommodate, respectively. e i j ≤ b e , ∀i ∈ BS , ∀ j, e ∈ ECP (13) e i j ≤ b e , ∀i, e ∈ ECP , ∀ j ∈ gNB (14) e i j ≤ c n , ∀i ∈ gNB ∪ ECP , ∀ j, n ∈ gNB (15) e i j ≤ c n , ∀i, n ∈ gNB , ∀ j ∈ gNB ∪ T P (16) Constraints(13)-(16) limit the network connectivity to ensure the integrity of the network. e i j , b e , c n ∈ {0, 1} , ∀i, j ∈ , ∀e ∈ ECP , ∀n ∈ gNB (17) Constraint (17) represents that the value of each entry in e i j , b e and c n is 0 or 1.…”