TY - JOUR
T1 - Access point recruitment in a vehicular cognitive capability harvesting network
T2 - How much data can be uploaded?
AU - Ding, Haichuan
AU - Zhang, Chi
AU - Lorenzo, Beatriz
AU - Fang, Yuguang
N1 - Publisher Copyright:
© 1967-2012 IEEE.
PY - 2018/7
Y1 - 2018/7
N2 - 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.
AB - 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.
KW - Internet of things (IoT)
KW - Offloading
KW - Performance analysis
KW - Smart cities
KW - Vehicular networks
UR - http://www.scopus.com/inward/record.url?scp=85041543126&partnerID=8YFLogxK
U2 - 10.1109/TVT.2018.2803762
DO - 10.1109/TVT.2018.2803762
M3 - Article
AN - SCOPUS:85041543126
SN - 0018-9545
VL - 67
SP - 6438
EP - 6445
JO - IEEE Transactions on Vehicular Technology
JF - IEEE Transactions on Vehicular Technology
IS - 7
ER -