Results 231 to 240 of about 2,442,562 (279)

Analysis of the matching of community vitality and service facility supply and demand in the pedestrian bridge areas of Beijing. [PDF]

Sci Rep
Peng H, Hu C, Lyu Y.
europepmc   +1 more source

Enhancing medical response efficiency in real-time large crowd environments via smart coverage and deep learning for stable ecological health monitoring. [PDF]

Sci Rep
Alhashmi AA, Elhessewi GMS, Ghaleb M, Ahmad N, Aljehane NO, Alkhaldi TM, Almansour H, Al Zanin S.   +7 more
europepmc   +1 more source

SOAR-RL: Safe and Open-Space Aware Reinforcement Learning for Mobile Robot Navigation in Narrow Spaces. [PDF]

Sensors (Basel)
Jun M, Park P, Jung H.
europepmc   +1 more source

CoMuRoS - An LLM-based generalizable hierarchical task planning and execution framework for heterogeneous robot teams with event-driven re-planning. [PDF]

Front Robot AI
Borate S, B BR, Pardeshi V, Vadali M.
europepmc   +1 more source

4W1H in mobile crowd sensing

IEEE Communications Magazine, 2014
With the rapid proliferation of sensor-rich smartphones, mobile crowd sensing has become a popular research field. In this article, we propose a four-stage life cycle (i.e., task creation, task assignment, individual task execution, and crowd data integration) to characterize the mobile crowd sensing process, and use 4W1H (i.e., what, when, where, who,
Bin Guo, Haoyi Xiong, Leye Wang
exaly   +3 more sources

Some of the next articles are maybe not open access.

Related searches:

Incentives for Mobile Crowd Sensing: A Survey

IEEE Communications Surveys and Tutorials, 2016
Recent years have witnessed the fast proliferation of mobile devices (e.g., smartphones and wearable devices) in people's lives. In addition, these devices possess powerful computation and communication capabilities and are equipped with various built-in functional sensors.
Kai Xing, Xinglin Zhang, Zheng Yang
exaly   +3 more sources

Opportunities in mobile crowd sensing

IEEE Communications Magazine, 2014
Mobile crowd sensing is a new paradigm that takes advantage of pervasive mobile devices to efficiently collect data, enabling numerous largescale applications. Human involvement is one of the most important features, and human mobility offers unprecedented opportunities for both sensing coverage and data transmission.
Huadong Ma, Dong Zhao, Peiyan Yuan
exaly   +3 more sources

Mobile crowd sensing based on CICN

2017 International Conference on Information and Communication Technology Convergence (ICTC), 2017
In this paper, we propose a Mobile Crowd Sensing application based on Community Information-Centric Networking to collect opportunistic sensing data in limited area where it restricts radius of Bluetooth Low Energy beacon. The application has more valuable features by comparing with common Mobile Crowd Sensing one.
Taewan You, Muhammad Umair, Yong-Gen Hong   +2 more
openaire   +2 more sources

Adaptive and Blind Regression for Mobile Crowd Sensing

IEEE Transactions on Mobile Computing, 2020
In mobile crowd sensing (MCS) applications, a public model of a system is expected to be derived from observations collected by mobile device users, through regression modeling. For example, a model describing the relationship between running speed, heart rate, height, and weight of runner can be constructed using MCS data collected from wristbands ...
Shan Chang, Chao Li, Hongzi Zhu, Hang Chen   +3 more
openaire   +2 more sources