Efficient-Cost Task Offloading Scheme in Fog-Internet of Vehicle Networks

Fog computing became a traditional OffLad Destination(OLD)to compute the offloaded tasks of the Internet of Vehicles(IoV).Nevertheless,the limited computing resources of the fog node leads to re-offload these tasks to the neighboring fog nodes or the cloud.Thus,the IoV will incur additional offloading costs.In this paper,we propose a new offloading scheme by utilizing RoadSide Parked Vehicles(RSPV)as an alternative OLD for IoV.The idle computing resources of the RSPVs can compute large tasks with low offloading costs compared with fog nodes and the cloud.Finally,a performance evaluation of the proposed scheme has been presented and discussed with other benchmark offloading schemes.

Securing Parked Vehicle Assisted Fog Computing With Blockchain and Optimal Smart Contract Design

Vehicular fog computing(VFC)has been envisioned as an important application of fog computing in vehicular networks.Parked vehicles with embedded computation resources could be exploited as a supplement for VFC.They cooperate with fog servers to process offloading requests at the vehicular network edge,leading to a new paradigm called parked vehicle assisted fog computing(PVFC).However,each coin has two sides.There is a follow-up challenging issue in the distributed and trustless computing environment.The centralized computation offloading without tamper-proof audit causes security threats.It could not guard against false-reporting,free-riding behaviors,spoofing attacks and repudiation attacks.Thus,we leverage the blockchain technology to achieve decentralized PVFC.Request posting,workload undertaking,task evaluation and reward assignment are organized and validated automatically through smart contract executions.Network activities in computation offloading become transparent,verifiable and traceable to eliminate security risks.To this end,we introduce network entities and design interactive smart contract operations across them.The optimal smart contract design problem is formulated and solved within the Stackelberg game framework to minimize the total payments for users.Security analysis and extensive numerical results are provided to demonstrate that our scheme has high security and efficiency guarantee.

Proactive Load Balancing Mechanism for Fog Computing Supported by Parked Vehicles in IoV-SDN

Internet of Vehicles(IoV)is a new style of vehicular ad hoc network that is used to connect the sensors of each vehicle with each other and with other vehicles’sensors through the internet.These sensors generate different tasks that should be analyzed and processed in some given period of time.They send the tasks to the cloud servers but these sending operations increase bandwidth consumption and latency.Fog computing is a simple cloud at the network edge that is used to process the jobs in a short period of time instead of sending them to cloud computing facilities.In some situations,fog computing cannot execute some tasks due to lack of resources.Thus,in these situations it transfers them to cloud computing that leads to an increase in latency and bandwidth occupation again.Moreover,several fog servers may be fuelled while other servers are empty.This implies an unfair distribution of jobs.In this research study,we shall merge the software defined network(SDN)with IoV and fog computing and use the parked vehicle as assistant fog computing node.This can improve the capabilities of the fog computing layer and help in decreasing the number of migrated tasks to the cloud servers.This increases the ratio of time sensitive tasks that meet the deadline.In addition,a new load balancing strategy is proposed.It works proactively to balance the load locally and globally by the local fog managers and SDN controller,respectively.The simulation experiments show that the proposed system is more efficient than VANET-Fog-Cloud and IoV-Fog-Cloud frameworks in terms of average response time and percentage of bandwidth consumption,meeting the deadline,and resource utilization.

A unified optimal planner for autonomous parking vehicle

In order to reduce the controlling difficulty caused by trajectory meandering and improve the adaptability to parking into regular lots,a versatile optimal planner(OP)is proposed.Taking advantage of the low speed specificity of parking vehicle,the OP algorithm was modeled the planning problem as a convex optimization problem.Collision-free constraints were formalized into the shortest distance between convex sets by describing obstacles and autonomous vehicle as affine set.Since employing Lagrange dual function and combining KKT conditions,the collision-free constraints translated into convex functions.Taking the national standard into account,5 kinds of regular parking scenario,which contain 0°,30°,45°,60°and 90°parking lots,were designed to verify the OP algorithm.The results illustrate that it is benefit from the continuous and smooth trajectory generated by the OP method to track,keep vehicle's stability and improve ride comfort,compared with A*and hybrid A*algorithms.Moreover,the OP method has strong generality since it can ensure the success rate no less than 82%when parking planning is carried out at the start node of 369 different locations.Both of evaluation criteria,as the pear error and RMSE in x direction,y axis and Euclidean distance d,and heading deviation 6,are stable and feasible in real tests,which illustrates that the OP planner can satisfy the requirements of regular parking scenarios.