Abstract
The connectivity of opportunistic sensor networks (OSNs) is changing all the time with the movement of nodes. Finding a way to monitor connectivity can help researchers response to situations faster such as the node offline. This paper uses evolving graph to describe the snapshots of OSNs, redefines the connectivity parameters, and analyses the disadvantages of traditional network monitoring model. Then the paper proposes four different ways to improve the performance of the connectivity monitoring model of OSNs with the methods such as minimum node degree and sliding time window. The paper utilizes the multiple attribute decision tree to improve the connectivity monitoring model in the end. The experimental results show that the connectivity calculated by the model is in good agreement with actual message delivery rate, and can be used to monitor the connectivity of OSNs.
This paper is supported by the National Natural Science Foundation of China (61762065, 61363015), the Natural Science Foundation of Jiang Province (20171ACB20018, 20171BBH80022).
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Shu, J., Xiong, J., Xu, L., Geng, X., Liu, L. (2019). An Improved Opportunistic Sensor Networks Connectivity Monitoring Model Based on Network Connectivity. In: Shen, S., Qian, K., Yu, S., Wang, W. (eds) Wireless Sensor Networks. CWSN 2018. Communications in Computer and Information Science, vol 984. Springer, Singapore. https://doi.org/10.1007/978-981-13-6834-9_7
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DOI: https://doi.org/10.1007/978-981-13-6834-9_7
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