Results 291 to 300 of about 3,854,170 (324)

Crowd-sensing: Why context matters

2013 IEEE International Conference on Pervasive Computing and Communications Workshops (PERCOM Workshops), 2013
Crowd-sensing is becoming a popular computing and sensing paradigm for enclosing humans in the sensing loop. The underlying idea is that people, together with their mobile device, can act as mobile and pervasive sensors, gathering information about the surrounding environment and potentially providing direct input. In this work we focus on how to embed
Carreras, Iacopo, Miorandi, Daniele, Tamilin, Andrei, Ssebaggala, Emmanuel R., Conci, Nicola   +4 more
openaire   +3 more sources

A Crowd-Sensing System for Geomatics Applications

2019
Risk prevention is recognized as one of the most critical aspects of the policies of environmental monitoring. Because of the limited resources and the large amount of structures used for erosion control and slope protection, the Civil Protection and the Italian Forestry Carabinieri are not able to supervise them directly, with enough frequency.
Lorenzo Boccia, Alessandra Capolupo, Giuseppina Esposito, Giuseppe Mansueto, Eufemia Tarantino   +4 more
openaire   +5 more sources

Mobile Crowd Sensing for Solidarity Campaigns

2015
We present an ongoing project (This work is partially supported by the InfoCrowds project-PTDC/ECM-TRA/1898/2012 This work is supported by CISUC, via national funding by the FCT - Fundacao para a Ciencia e Tecnologia.) which has two separate strands, one refers to the technological study about the applicability of high performance and high availability
Ana Alves 0001, David Silva
openaire   +2 more sources

Human factors in crowd-assisted sensing

2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops), 2017
The paradigm of crowd-assisted sensing primarily relies on volunteers, who use their personal devices to collect sensor information. To foster participants' contributions and hence ensure the viability of the underlying applications, different approaches, such as novel incentive schemes and privacy-preserving mechanisms, have been proposed.
openaire   +2 more sources

On scheduling of real-time sensing tasks in mobile crowd sensing

2015 IEEE Wireless Communications and Networking Conference (WCNC), 2015
In the area of Wireless Local Area Network (WLAN) based indoor localization, the Received Signal Strength (RSS) fingerprinting based localization technique has been studied extensively. Site survey phase in RSS fingerprinting is always considered to be time-consuming and labor intensive.
Mu Zhou, Qiao Zhang, Zengshan Tian, Kunjie Xu, Feng Qiu, Haibo Wu 0001   +5 more
openaire   +2 more sources

QoS-constrained sensing task assignment for mobile crowd sensing

2014 IEEE Global Communications Conference, 2014
The ubiquitous sensing-capable mobile devices have been fuelling the new paradigm of Mobile Crowd Sensing (MCS) to collect data about their surrounding environment. To ensure the timeliness and quality of the data samples in MCS, it is critical to select qualified participants to maintain sensing coverage ratios over important spatial areas (i.e ...
Zhijie Wang 0002, Dijiang Huang, Huijun Wu, Yuli Deng, Ailixier Aikebaier, Yuuichi Teranishi   +5 more
openaire   +1 more source

Crowd sensing with execution uncertainty

Proceedings of the 16th ACM/IEEE International Conference on Information Processing in Sensor Networks, 2017
In this study, we propose a crowd sensing framework with the existence of execution uncertainty and a given budget. Our framework consists of three stages: Task Selection, Task Allocation, and Payment. Within each stage, we define the design problems and give a preliminary solution with desirable theoretic properties.
openaire   +1 more source

Introduction to Mobile Crowd Sensing

2018
In recent years, we have been witnessing the explosive growth of mobile users with sensor-embedded smartphones. Firstly, with the development of wireless communication technology and the increase in personal income, mobile devices (e.g., smartphone, ipad, PDA, etc.) have been becoming more and more popular.
Fen Hou, Yingying Pei, Jingyi Sun
openaire   +1 more source

DLFTI: A deep learning based fast truth inference mechanism for distributed spatiotemporal data in mobile crowd sensing

Information Sciences, 2023
Jian-Heng Tang, Ke-Jia Fan, Pengzhi Yin, Zhenzhe Qu, An-Feng Liu, Nai-Xue N. Xiong, Tian Wang, Mian-Xiong Dong, Shao-Bo Zhang   +8 more
semanticscholar   +1 more source

Decentralized privacy-preserving truth discovery for crowd sensing

Information Sciences, 2023
P. Xiong, Guirong Li, Heng-Zhu Liu, Yi-Yi Hu, Dawei Jin   +4 more
semanticscholar   +1 more source