Analytical Exploration of Energy Savings for Parked Vehicles to Enhance VANET Connectivity

Sun, Gang; Yu, Mingyue; Liu, Dan; Chang, Victor

doi:10.1109/tits.2018.2834569

Cited by 35 publications

(13 citation statements)

References 36 publications

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“…Sun et al considered the parked vehicles as relay nodes in [31]. Observing that parked or slow moving vehicles have rich and under-utilized resources for task execution, some works studied utilizing vehicles to develop new computing paradigms [5,32].…”

Section: Related Workmentioning

confidence: 99%

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

Yang

et al. 2022

IEEE Internet Things J.

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With the popularity of mobile devices and development of computationally intensive applications, researchers are focusing on offloading computation to Mobile Edge Computing (MEC) server due to its high computational efficiency and low communication delay. As the computing resources of an MEC server are limited, vehicles in the urban area who have abundant idle resources should be fully utilized. However, offloading computing tasks to vehicles faces many challenging issues. In this paper, we introduce a vehicular fog-edge computing paradigm and formulate it as a multi-stage Stackelberg game to deal with these issues. Specifically, vehicles are not obligated to share resources, let alone disclose their private information (e.g., stay time and the amount of resources). Therefore, in the first stage, we design a contract-based incentive mechanism to motivate vehicles to contribute their idle resources. Next, due to the complicated interactions among vehicles, road-side unit (RSU), MEC server and mobile device users, it is challenging to coordinate the resources of all parties and design a transaction mechanism to make all entities benefit.In the second and third stages, based on Stackelberg game, we develop pricing strategies that maximize the utilities of all parties. The analytical forms of optimal strategies for each stage are given. Simulation results demonstrate the effectiveness of our proposed incentive mechanism, reveal the trends of energy consumption and offloading decisions of users with various parameters, and present the performance comparison between our framework and existing MEC offloading paradigm in vehicular networks.

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Section: Related Workmentioning

confidence: 99%

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

Yang

et al. 2022

IEEE Internet Things J.

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“…For example, in [63] a privacy preserving revoc-ation system called SmartRevoc was presented which used parked vehicles to aid in the dissemination of certificate revocation lists. This technique could potentially apply [105] to reduce SmartRevoc's energy cost which proposed a VANET routing protocol also using parked vehicles. The benefit of this scheme is that the parked vehicles selected for routing takes the limited energy of these vehicles into account, ensuring they have sufficient power for later use.…”

Section: F Cost Of Privacy Preserving Techniquesmentioning

confidence: 99%

“…These parked vehicles are also only selected when there is insufficient traffic density to relay messages. However, the use of [105] to reduce the energy cost of the scheme must be analysed to understand the impact its use will have on vehicle privacy.…”

Section: F Cost Of Privacy Preserving Techniquesmentioning

confidence: 99%

Privacy Challenges With Protecting Live Vehicular Location Context

et al. 2020

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“…It has been observed that the idle resources in parked vehicles (PVs) can be explored to efficiently provide services to other vehicles [7]. Therefore, a new computing paradigm, called parked vehicle edge computing (PVEC), has emerged to improve the vehicular IoT performance [8].…”

Section: Introductionmentioning

confidence: 99%

A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things

Cheng

et al. 2020

Sensors

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Parked vehicle edge computing (PVEC) utilizes both idle resources in parked vehicles (PVs) and roadside units (RSUs) as service providers (SPs) to improve the performance of vehicular internet of things (IoT). However, it is difficult to make optimal service migration decisions in PVEC networks due to the uncertain parking duration and resources heterogeneity of PVs. In this paper, we formulate the service migration of all the vehicles as an optimization problem with the objective of minimizing the average latency. We propose a two-stage service migration algorithm for PVEC networks, which divides the original problem into the service migration between SPs and the serving PV selection in parking lots. The service migration between SPs is transformed to an online problem based on Lyapunov optimization, where the expected parking duration of PVs is utilized. A modified Hungarian algorithm is proposed to select the PVs for migration. A series of simulation experiments based on the real-world vehicle traces are conducted to verify the superior performance of the proposed two-stage service migration (SEA) algorithm as compared with the state-of- art solutions.

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Analytical Exploration of Energy Savings for Parked Vehicles to Enhance VANET Connectivity

Cited by 35 publications

References 36 publications

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

Joint Offloading Decision and Resource Allocation for Vehicular Fog-Edge Computing Networks: A Contract-Stackelberg Approach

Privacy Challenges With Protecting Live Vehicular Location Context

A Two-Stage Service Migration Algorithm in Parked Vehicle Edge Computing for Internet of Things

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