# A Matrix-Based Proactive Data Relay Algorithm for Large Distributed Sensor Networks
## 5 Model Maintenance Algorithm
### Updating the Data Distribution Matrix D
## 7 Conclusions

Large scale multi-sensor fusion is a very important issue in future networks and internet of things, especially in the domains of disaster response and military operations. However, previous centralized data aggregation algorithms for small sensor network are no longer feasible in considering of huge network expenses as well as the energy expenses for central nodes. In this paper, we have proposed an extremely economic data relay algorithm that sensors could proactively make broadcast decisions by neat matrix computation to balance transmission and save energy. By encoding sensors' local knowledge to three matrixes: network connection, data distribution and data utility matrix, they can do the reasoning for all the pending data by only a neat matrix computation. We also built heuristic algorithms for sensors to well maintain those matrixes with only a local view to the network so that this design can be adaptive to the scalable and dynamic environment. Our experimental simulations manifested that our approach is scalable and effectively balance between promoting data fusion process and saving energy to prolong the life time of the whole network. This study was funded by National Natural Science Foundation of China 61370151 and 61202211, National Science and Technology Major Project of China 2015 ZX03003012, Central University Basic Research Funds Foundation of China ZYGX2014J055, Huawei Technology Foundation YB2013120141, YB2015070068, and the Science and Technology on Electronic Information Control Laboratory Project. [PERSON] and [PERSON] conceived and designed the experiments; [PERSON] and [PERSON] and [PERSON] performed the experiments; [PERSON] and [PERSON] and [PERSON] analyzed the data; [PERSON] and [PERSON] contributed analysis tools; [PERSON] and [PERSON] wrote the paper. The authors declare no conflict of interest. Figure 8: Energy consumption distribution(m)) for different scenarios. (**a**) introduces the comparisons of the energy distribution after relaying data with 100 events; (**b**) introduces the comparisons of the energy distribution after relaying data with 200 events. ## References

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