TY - JOUR
T1 - Teletraffic analysis of a mobile crowdsensing system
T2 - The pedestrian-to-vehicle scenario
AU - Miguel-Santiago, David
AU - Rivero-Angeles, Mario E.
AU - Garay-Jiménez, Laura I.
AU - Orea-Flores, Izlian Y.
AU - Tovar-Corona, Blanca
N1 - Publisher Copyright:
© The Author(s) 2022.
PY - 2022/11
Y1 - 2022/11
N2 - Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user’s behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles’ dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic.
AB - Crowdsensing systems are developed in order to use the computational and communication capabilities of registered users to monitor specific variables and phenomena in an opportunistic manner. As such, the Quality of Experience is not easily attained since these systems heavily rely on the user’s behavior and willingness to cooperate whenever an event with certain interest needs to be monitored. In this work, we analyze the data acquisition phase, where pedestrians opportunistically transmit to vehicles to further disseminate it in the city according to their trajectory. This highly dynamic environment (sensors and data sinks are mobile, and the number of users varies according to the region and time) poses many challenges for properly operating a crowdsensing system. We first study the statistical properties of vehicular traffic in different regions of Luxembourg City where pedestrians share their computational resources and send data to passing cars. Then we propose an Erlang distribution to model the vehicles’ dwelling times and develop a Markov chain accordingly. We model the system using two different queues: we use a single server queue to model the vehicle traffic, while we use an infinite server queue system to model the pedestrian traffic.
KW - Crowdsensing
KW - Markov chain
KW - phase-type distributions
KW - teletraffic analysis
KW - vehicular traffic analysis
UR - http://www.scopus.com/inward/record.url?scp=85141935923&partnerID=8YFLogxK
U2 - 10.1177/15501329221133291
DO - 10.1177/15501329221133291
M3 - Artículo
AN - SCOPUS:85141935923
SN - 1550-1329
VL - 18
JO - International Journal of Distributed Sensor Networks
JF - International Journal of Distributed Sensor Networks
IS - 11
ER -