Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4205
Author(s):  
Jiuyun Xu ◽  
Shuang Liu ◽  
Xiaoxuan Lu ◽  
Li Li ◽  
Hongliang Liang ◽  
...  

Data fusion in the Internet of Things (IoT) environment demands collecting and processing a wide variety of data with mixed time characteristics, both real-time and non-real-time data. Most of the previous research on data fusion was about the data processing aspect; however, successful data transmission is a prerequisite for high-performance data fusion in IoT. On the other hand, research on data transmissions in IoT mainly focuses on networking without sufficiently considering the special requirements of the upper-layer applications, such as the data fusion process, that are consuming the transmitted data. In this paper, we tackle the problem of data transmission for data fusion in an IoT environment by proposing a distributed scheduling mechanism VD-CSMA in wireless sensor networks, which considers the values for data fusion, as well as the delay constraints of packets when determining their priority levels for transmission. Simulation results have shown that VD-CSMA may enhance both throughput and delay performance of data transmission as compared to the typical scheduling schemes used for data fusion in IoT.


Author(s):  
Florin Pop

This chapter presents a fault tolerant framework for the applications scheduling in large scale distributed systems (LSDS). Due to the specific characteristics and requirements of distributed systems, a good scheduling model should be dynamic. More specifically, it should adapt the scheduling decisions to resource state changes, which are commonly captured through monitoring. The scheduler and the monitor are two important middleware pieces that correlate their actions to ensure the high performance execution of distributed applications. The chapter presents and analyses agent based architecture for scheduling in large scale distributed systems. Then the user and resources management are presented. Optimization schemes for scheduling consider the near-optimal algorithm for distributed scheduling. The chapter presents the solution for scheduling optimization. The chapter covers and explains the fault tolerance cases for Grid environments and describes two possible scenarios for scheduling system.


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