Event-driven Architecture for Sensor Data Integration for Logistics Services

Author(s):  
Jens Leveling ◽  
Luise Weickhmann ◽  
Christian Nissen ◽  
Christopher Kirsch
Sensors ◽  
2020 ◽  
Vol 20 (10) ◽  
pp. 2779
Author(s):  
Yaoming Zhuang ◽  
Chengdong Wu ◽  
Hao Wu ◽  
Zuyuan Zhang ◽  
Yuan Gao ◽  
...  

Wireless sensor and robot networks (WSRNs) often work in complex and dangerous environments that are subject to many constraints. For obtaining a better monitoring performance, it is necessary to deploy different types of sensors for various complex environments and constraints. The traditional event-driven deployment algorithm is only applicable to a single type of monitoring scenario, so cannot effectively adapt to different types of monitoring scenarios at the same time. In this paper, a multi-constrained event-driven deployment model is proposed based on the maximum entropy function, which transforms the complex event-driven deployment problem into two continuously differentiable single-objective sub-problems. Then, a collaborative neural network (CONN) event-driven deployment algorithm is proposed based on neural network methods. The CONN event-driven deployment algorithm effectively solves the problem that it is difficult to obtain a large amount of sensor data and environmental information in a complex and dangerous monitoring environment. Unlike traditional deployment methods, the CONN algorithm can adaptively provide an optimal deployment solution for a variety of complex monitoring environments. This greatly reduces the time and cost involved in adapting to different monitoring environments. Finally, a large number of experiments verify the performance of the CONN algorithm, which can be adapted to a variety of complex application scenarios.


2012 ◽  
Vol 7 (1) ◽  
pp. 103-106
Author(s):  
Xiaoming Zhang ◽  
Wanzhen Zhou ◽  
Yongqiang Zhang

Sensors ◽  
2017 ◽  
Vol 17 (5) ◽  
pp. 1013 ◽  
Author(s):  
Diego P. Losada ◽  
Joaquín Fernández ◽  
Enrique Paz ◽  
Rafael Sanz
Keyword(s):  

10.2196/34493 ◽  
2021 ◽  
Vol 23 (11) ◽  
pp. e34493
Author(s):  
Ieuan Clay ◽  
Christian Angelopoulos ◽  
Anne Lord Bailey ◽  
Aaron Blocker ◽  
Simona Carini ◽  
...  

Data integration, the processes by which data are aggregated, combined, and made available for use, has been key to the development and growth of many technological solutions. In health care, we are experiencing a revolution in the use of sensors to collect data on patient behaviors and experiences. Yet, the potential of this data to transform health outcomes is being held back. Deficits in standards, lexicons, data rights, permissioning, and security have been well documented, less so the cultural adoption of sensor data integration as a priority for large-scale deployment and impact on patient lives. The use and reuse of trustworthy data to make better and faster decisions across drug development and care delivery will require an understanding of all stakeholder needs and best practices to ensure these needs are met. The Digital Medicine Society is launching a new multistakeholder Sensor Data Integration Tour of Duty to address these challenges and more, providing a clear direction on how sensor data can fulfill its potential to enhance patient lives.


2016 ◽  
Vol 17 (9) ◽  
pp. 2648-2657 ◽  
Author(s):  
Yanbo Han ◽  
Guiling Wang ◽  
Jian Yu ◽  
Chen Liu ◽  
Zhongmei Zhang ◽  
...  
Keyword(s):  

Sign in / Sign up

Export Citation Format

Share Document