Table of Content

Open Access

ARTICLE

Alla Abbas Khadir1, Seyed Amin Hosseini Seno1,2,*, Baydaa Fadhil Dhahir2,3, Rahmat Budiarto4

1 Department of Electrical Power Techniques Engineering, Al-Hussain University College, Karbala, Iraq
3 Department of Computer Science, Thi-Qar University, Thi-Qar, Iraq
4 Department of Computer Science, Mercu Buana University, Jakarta, Indonesia

* Corresponding Author: Seyed Amin Hosseini Seno. Email:

Computer Systems Science and Engineering 2023, 45(2), 2223-2234. https://doi.org/10.32604/csse.2023.032316

Keywords

1  Introduction

(1)   We propose a RSPV offloading scheme in the Fog-IoV network, such that the idle computing resources of RSPV can be utilized effectively for processing the offloaded tasks.

(3)   We solve the mathematical model by CPLEX optimization software, in which the offloaded tasks can be assigned to the optimal RSPVs. Simulation results validate the effectiveness of our offloading scheme.

The rest of this paper is organized as follows: Section 2 compares our proposed scheme with some related works. Section 3 describes the system model and problem formulation. The proposed solution and example are presented in Section 4. Simulation results and discussions are presented in Section 5 and Section 6. Finally, Section 7 concludes the work and presents suggestions for the future scope of research. Tab. 1 explains the abbreviations that are used in the paper.

2  Related Works

3  System Model and Problem Formulation

Figure 1: Proposed RSPV network architecture

Dij=lirj+liwiμj (1)

where rj and μj are the data transmission rate and computation capacity of the RSPV j, respectively. We assume the data transmission rate rj remains the same for bidirectional communication. To ensure the success of the offloading process, the task offloading delay should not exceed the deadline of the task and total contact time of the moving vehicle with the RSPV, that is, DijtimaxΔtC , where the contact time ΔtC can be calculated as [33]. Where τ,v are the communication range and speed of the moving vehicle respectively; λ and M are the intensity and the number of the RSPVs on the roadside. From Eq. (2), we can see the contact time is affected by the speed of the moving vehicle and the intensity of the RSPVs. For the sake of simplicity, we assume both of them (i.e., τ and v ) are set to the suitable value, such that the contact time will be high.

ΔtC=2τ(1v+M1λ) (2)

Generally, the offloading cost consists of the data transmission cost and task processing cost [34]. To obtain the data transmission cost, we define σj as the cost per unit time for using network channel bandwidth of the RSPV j. Hence, the data transmission cost to transfer the data of task i to the RSPV j can be given by (3).

Cijtrans=lirjσj (3)

Similarly, to calculate the task processing cost, we define δj as the cost per unit time for using the computing resource of the RSPV j. Thus, the processing cost for the task i can be obtained by (4).

Cijproc=liwiμjδj (4)

Cij=Cijtrans+Cijproc (5)

3.3 Problem Formulation

Now, we formulate the assignment task offloading as an optimization problem. The goal is to minimize the total offloading cost under budget and deadline constraints. Therefore, we define an assignment optimization variable xij . Here, xij=1 means that the fog node assigns the task i to the RSPV j , and otherwise, xij=0 . The optimization problem can be written as (6).

P1=miniN,jMxijCij, (6)

Subject to:

j=1Mxij=1,iN, (C1)

i=1Nxij=1,jM, (C2)

Cijbi,iN,jM, (C3)

DijtimaxΔtC,iN,jM, (C4)

xij{0,1},iN,j M. (C5)

4  Proposed Solution and Example

Minimization of the total offloading cost (P2) is considered a MILP problem which is solved by using CPLEX mathematical optimization software. The following example is performed to explain the effectiveness of the proposed model. Let the number of tasks ( N ), the number of the RSPVs (M) , and the total contact time ΔtC are 3, 6 and 60 s respectively. The data of the related tasks and RSPVs are organized in Tabs. 4 and 5, respectively.

The optimal solution of the (P1) in this example is 0.01139 $. The suitable assignment of the tasks in terms of the offloading cost associated with the offloading delay is shown in Tab. 6. 5 Simulation Results In this section, the proposed RSPV offloading scheme is evaluated to investigate its performance and effectiveness in comparison with three benchmark offloading schemes which are: • Fog-Cloud offloading scheme [35]: The fog node will re-offload the tasks to the cloud for execution with a high cost of transmission and processing. In the simulation scenario, we assume the cost unit of data transmission and task processing for the cloud in the range (0.01–0.09)$/s. Also, the computation capacity and the data transmission rate are assumed 100 cycle/s and 50 Mb/s respectively.

•   Fog-Fog offloading scheme [36]: The fog node will re-offload the tasks to the neighboring fog nodes with additional transmission costs. In the simulation scenario, we assume the cost unit of data transmission and task processing of the fog in the range (0.001–0.009) $/s and the cost unit of data transmission between the fog nodes has a fixed value of 0.002$/s. Also, the computation capacity and the data transmission rate are assumed 50 cycles/s and 25 Mb/s respectively.

•   Cost-aware offloading scheme [14]: The vehicle makes MEC server selection based on the predicted cost and load distribution of MEC servers. In this scheme, the transmission cost represents the cost of the V2I transmission or multi-hop V2V relay transmission. Similarly, the cost of result feedback takes a multi-hop wireless backhaul relay between several MECs. In the simulation scenario, we assume the cost unit of data transmission and task processing of the MEC in the range (0.001–0.009) $/s and the cost unit of data transmission through the multi-hop wireless backhaul and V2V relay are 0.002$/s and 0.001\$/s respectively. Also, the computation capacity and the data transmission rate are assumed 50 cycles/s and 30 Mb/s respectively. We consider a simulation scenario with the following setting and the detail is shown in Tab. 7.

6  Results and Discussion

Fig. 4 illustrates the revenue of the three schemes. Here the revenue represents the difference cost between the total budget of the tasks and the total offloading cost. From the Figure, we observe that our offloading scheme has the best revenue in all budgets because of the following reasons: First, the processing cost of the RSPVs will be relatively less because of the low cost of infrastructure and application deployment. Whereas the fog and cloud have a higher processing cost because of their high deployment and maintenance costs. Second, there is no backhaul transmission cost between the RSPVs as in the Fog-Fog and Fog-Cloud schemes which are highly expensive [27]. The Cost-aware offloading scheme did not consider the budget in the characteristic of the task. Therefore, this scheme has not been compared with our proposed scheme in terms of revenue.

Figure 4: The revenue vs. the budget of tasks

7  Conclusion and Future Work

Funding Statement: The authors received no specific funding for this study.

Conflicts of Interest: The authors declare that they have no conflicts of interest to report regarding the present study.

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A. Abbas Khadir, S. A. Hosseini Seno, B. Fadhil Dhahir and R. Budiarto, "Efficient-cost task offloading scheme in fog-internet of vehicle networks," Computer Systems Science and Engineering, vol. 45, no.2, pp. 2223–2234, 2023.

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