Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the best of our knowledge, active experiments addressing the challenging issue of the assignment of MCS data collection campaigns to users, namely, MCS scheduling, in a large-scale crowdsensing real-world experiment are still missing. This paper presents the ParticipAct platform and living lab, an ongoing crowdsensing experiment at University of Bologna that involves 300 students for one year. In particular, this article focus on ParticipAct intelligent MCS scheduling of future crowdsensing campaigns based on user mobility history and powered by NoSQL technologies for fast processing of the large amount of mobility traces in the ParticipAct backend. Showed results confirm the feasibility of the proposed approach and quantify its cost.
Smartphones as smart cities sensors: MCS scheduling in the ParticipAct project / Corradi, Antonio; Curatola, Giovanni; Foschini, Luca; Ianniello, Raffaele; De Rolt, Carlos Roberto. - STAMPA. - 2016-:(2015), pp. 7405520.222-7405520.228. (Intervento presentato al convegno 20th IEEE Symposium on Computers and Communication, ISCC 2015 tenutosi a cyp nel 2015) [10.1109/ISCC.2015.7405520].
Smartphones as smart cities sensors: MCS scheduling in the ParticipAct project
CORRADI, ANTONIO;FOSCHINI, LUCA;IANNIELLO, RAFFAELE;DE ROLT, CARLOS ROBERTO
2015
Abstract
Novel sensor-equipped smartphones have enabled the possibility of harvesting large quantities of data in urban areas by opportunistically involving citizens and their portable devices, as mobile sensors widely available and distributed over Smart Cities areas, typically defined as Mobile Crowd Sensing (MCS). Although some existing efforts have already tackled some of the several MCS issues, to the best of our knowledge, active experiments addressing the challenging issue of the assignment of MCS data collection campaigns to users, namely, MCS scheduling, in a large-scale crowdsensing real-world experiment are still missing. This paper presents the ParticipAct platform and living lab, an ongoing crowdsensing experiment at University of Bologna that involves 300 students for one year. In particular, this article focus on ParticipAct intelligent MCS scheduling of future crowdsensing campaigns based on user mobility history and powered by NoSQL technologies for fast processing of the large amount of mobility traces in the ParticipAct backend. Showed results confirm the feasibility of the proposed approach and quantify its cost.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
