Access Point Recruitment in a Vehicular Cognitive Capability Harvesting Network: How Much Data Can Be Uploaded?
Haichuan Ding, Chi Zhang, Beatriz Lorenzo, Yuguang Michael Fang · IEEE Transactions on Vehicular Technology · 2018
To effectively deal with exploding traffic from emerging Internet of things (IoT) and smart cities applications, we have recently designed a vehicular cognitive capability harvesting network architecture where a virtual service provider (VSP) coordinates vehicles equipped with powerful communication devices, namely cognitive radio routers, to help various end devices upload their data to data networks via deployed or recruited roadside access points (APs). To make the AP recruitment cost-effective, it is necessary for the VSP to learn what each AP can offer. Thus, in this paper, by modeling the vehicle arrival process as a Poisson process, we analyze the maximum long-term upload throughput achieved with an AP. Due to the contention inside the coverage of the AP, the amount of data uploaded by each vehicle is correlated, which makes our analysis difficult. To address this challenge, we reformulate the considered problem as a renewal reward process, which allows us to derive the closed-form expression for the maximum long-term upload throughput. We validate our analytical results via extensive simulations, which can offer us useful insights on AP recruitment.