Proactive Caching at Parked Vehicles for Social Networking

Elsayed, Sara A.; Abdelhamid, Sherin; Hassanein, Hossam S.

doi:10.1109/icc.2018.8422388

Cited by 13 publications

(8 citation statements)

References 11 publications

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“…4) Parked Vehicles for Caching: There are proposals to utilize parked vehicles as cache servers due to the convenience in connecting with the edge servers and moving vehicles [88], [89], [90]. In these studies, parked vehicles play a similar role as fixed edge servers.…”

Section: B Vehicle As a Servermentioning

confidence: 99%

“…Su et al [89] further developed a competition and cooperation mechanism for caching at parked vehicles to encourage parked vehicles to join in the caching system. Recently, a proactive caching method was proposed in [90], which caches social media content in parked vehicles. This is motivated by the fact that social media content is becoming one of the main contributors to the network burden of the cellular infrastructure.…”

Section: B Vehicle As a Servermentioning

confidence: 99%

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Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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The Internet of Vehicles (IoV) is an emerging paradigm, driven by recent advancements in vehicular communications and networking. Advances in research can now provide reliable communication links between vehicles, via vehicle-to-vehicle communications, and between vehicles and roadside infrastructures, via vehicle-to-infrastructure communications. Meanwhile, the capability and intelligence of vehicles are being rapidly enhanced, and this will have the potential of supporting a plethora of new exciting applications, which will integrate fully autonomous vehicles, the Internet of Things (IoT), and the environment. These trends will bring about an era of intelligent IoV, which will heavily depend upon communications, computing, and data analytics technologies. To store and process the massive amount of data generated by intelligent IoV, onboard processing and Cloud computing will not be sufficient, due to resource/power constraints and communication overhead/latency, respectively. By deploying storage and computing resources at the wireless network edge, e.g., radio access points, the edge information system (EIS), including edge caching, edge computing, and edge AI, will play a key role in the future intelligent IoV. Such system will provide not only low-latency content delivery and computation services, but also localized data acquisition, aggregation and processing. This article surveys the latest development in EIS for intelligent IoV. Key design issues, methodologies and hardware platforms are introduced. In particular, typical use cases for intelligent vehicles are illustrated, including edge-assisted perception, mapping, and localization. In addition, various open research problems are identified.

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Section: B Vehicle As a Servermentioning

confidence: 99%

Section: B Vehicle As a Servermentioning

confidence: 99%

Mobile Edge Intelligence and Computing for the Internet of Vehicles

2020

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“…It causes reducing the cache hit ratio and the content delivery ratio. To efficiently download the whole content and to enhance the cache hit ratio, some studies [ 25 , 26 , 36 , 37 , 38 , 39 ] on mobility-based precaching that exploit the mobility of the requester vehicle have been researched in CCVNs. They proactively cache the same copy of the whole content (i.e., full precaching) for the requester vehicle to the next RSU where it arrives next by its mobility.…”

Section: Related Workmentioning

confidence: 99%

“…The mechanism increases the percentage of offloaded traffic, reduces the backhaul data volume, and optimizes the content retrieval latency. Sara et al [ 36 ] proposed a scheme called Proactive Caching at Parked Vehicles (PCPV) to provide a better quality of service to vehicular users who tend to have a somewhat consistent social networking behavior. Users have a predictive behavior in terms of the type and time of access of social media platforms as a part of their daily routine during transit from one place to another.…”

Section: Related Workmentioning

confidence: 99%

Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

Nam

Choi

Shin

et al. 2021

Sensors

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Content-Centric Vehicular Networks (CCVNs) are considered as an attractive technology to efficiently distribute and share contents among vehicles in vehicular environments. Due to the large size of contents such as multimedia data, it might be difficult for a vehicle to download the whole of a content within the coverage of its current RoadSide Unit (RSU). To address this issue, many studies exploit mobility-based content precaching in the next RSU on the trajectory of the vehicle. To calculate the amount of the content precaching, they use a constant speed such as the current speed of the vehicle requesting the content or the average speed of vehicles in the next RSU. However, since they do not appropriately reflect the practical speed of the vehicle in the next RSU, they could incorrectly calculate the amount of the content precaching. Therefore, we propose an adaptive content precaching scheme (ACPS) that correctly estimates the predictive speed of a requester vehicle to reflect its practical speed and calculates the amount of the content precaching using its predictive speed. ACPS adjusts the predictive speed to the average speed starting from the current speed with the optimized adaptive value. To compensate for a subtle error between the predictive and the practical speeds, ACPS appropriately adds a guardband area to the precaching amount. Simulation results verify that ACPS achieves better performance than previous schemes with the current or the average speeds in terms of the content download delay and the backhaul traffic overhead.

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“…Generally speaking, private cars usually spend most of the time in parking lots with their resources in idle states, where the average parking duration is approximately seven hours [9]. PVs may consume energy for providing services to other vehicles, a discount in parking fees can be an incentive to a PV [10]. PVEC can take the parking lots as SPs by managing and integrating the rich resources such as storage resources and computing resources of the PVs [11].…”

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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Proactive Caching at Parked Vehicles for Social Networking

Cited by 13 publications

References 11 publications

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Mobile Edge Intelligence and Computing for the Internet of Vehicles

Adaptive Content Precaching Scheme Based on the Predictive Speed of Vehicles in Content-Centric Vehicular Networks

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

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