In order to solve these problems such as the demand of geographic information service and the short life of the embedded system, as well as network collapse, and so on, the embedded mobile crowd service systems based on opportunistic geological grid and dynamical split was proposed. Firstly, based on the characteristics of geographical spatial information resources and service time series, a mobile geographic crowd service system was established for providing the sensing data with the mobile geographic crowd service model. Then, according to the embedded equipment complex data of the geographic crowd service system, and the relationship between the geography information service object and the user, the embedded system was proposed based on the opportunity geological grid. Finally, the optimization of the geographic crowd system was realized by the dynamic segmentation of the opportunity geographic grid. The experiment results of the equipment utilization, the life cycle of the crowd network, user satisfaction, and control complexity show that the proposed scheme is more suitable for the embedded network geographic information system.
WirelessHART network is an emerging wireless sensor network technology, famous for simplicity, safety, and reliability. WirelessHART network uses Time Division Media Access (TDMA) and Frequency Hopping Spread Spectrum (FHSS) access mechanism to avoid collisions and mitigate interference from other wireless networks. Many formulations of the link scheduling problem have been proven to be NP-complete, which is about how to assign slots and channels to links. In this paper, we propose a minimized buffered link scheduling algorithm for WirelessHART networks based on graph route (LBLSGR), which utilizes edge coloring to scheduling time slots according to the resource demand of nodes. We supply more communication resource to the nodes located nearer to the gateway, because of frequent packet forwarding. Utilizing this algorithm, the WirelessHART networks can make the average network delay least, the average network power assumption lower, and the packet buffers minimized. At last, the validity of our algorithm is proved and the test result is provided.
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