Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario
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 beh...
Saved in:
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-11-01
|
| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1177/15501329221133291 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1850235796954546176 |
|---|---|
| author | David Miguel-Santiago Mario E Rivero-Angeles Laura I Garay-Jiménez Izlian Y Orea-Flores Blanca Tovar-Corona |
| author_facet | David Miguel-Santiago Mario E Rivero-Angeles Laura I Garay-Jiménez Izlian Y Orea-Flores Blanca Tovar-Corona |
| author_sort | David Miguel-Santiago |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-128fad1e616a42d3bb9408dfdb440557 |
| institution | OA Journals |
| issn | 1550-1477 |
| language | English |
| publishDate | 2022-11-01 |
| publisher | Wiley |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj-art-128fad1e616a42d3bb9408dfdb4405572025-08-20T02:02:09ZengWileyInternational Journal of Distributed Sensor Networks1550-14772022-11-011810.1177/15501329221133291Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenarioDavid Miguel-Santiago0Mario E Rivero-Angeles1Laura I Garay-Jiménez2Izlian Y Orea-Flores3Blanca Tovar-Corona4UPIITA – Instituto Politécnico Nacional, Mexico City, MexicoComputer Research Center – Instituto Politécnico Nacional (CIC-IPN), Mexico City, MexicoUPIITA – Instituto Politécnico Nacional, Mexico City, MexicoUPIITA – Instituto Politécnico Nacional, Mexico City, MexicoUPIITA – Instituto Politécnico Nacional, Mexico City, MexicoCrowdsensing 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.https://doi.org/10.1177/15501329221133291 |
| spellingShingle | David Miguel-Santiago Mario E Rivero-Angeles Laura I Garay-Jiménez Izlian Y Orea-Flores Blanca Tovar-Corona Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario International Journal of Distributed Sensor Networks |
| title | Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario |
| title_full | Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario |
| title_fullStr | Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario |
| title_full_unstemmed | Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario |
| title_short | Teletraffic analysis of a mobile crowdsensing system: The pedestrian-to-vehicle scenario |
| title_sort | teletraffic analysis of a mobile crowdsensing system the pedestrian to vehicle scenario |
| url | https://doi.org/10.1177/15501329221133291 |
| work_keys_str_mv | AT davidmiguelsantiago teletrafficanalysisofamobilecrowdsensingsystemthepedestriantovehiclescenario AT marioeriveroangeles teletrafficanalysisofamobilecrowdsensingsystemthepedestriantovehiclescenario AT lauraigarayjimenez teletrafficanalysisofamobilecrowdsensingsystemthepedestriantovehiclescenario AT izlianyoreaflores teletrafficanalysisofamobilecrowdsensingsystemthepedestriantovehiclescenario AT blancatovarcorona teletrafficanalysisofamobilecrowdsensingsystemthepedestriantovehiclescenario |