Congestion control in social-based sensor networks: A social network perspective
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Social-based sensor networks are prone to congestion due to the limited storage space on each node and the unpredictable end-to-end delay. In this paper, we aim to develop an efficient congestion control approach from the social network perspective. For this purpose, we first identify the role of social ties in the process of congestion and specify a list of major congestion factors. Based on these factors, we then model the congestion control as a multiple attribute decision making problem (MADM), in which the weight of congestion factors is measured by an entropy method. We present a MADM-based congestion control approach that determines a set of forwarding messages and its transmission order on each encounter event. Moreover, we design a buffer management scheme that deletes messages whose removal would incur the least impact upon the network performance when the buffer overflows. Extensive real-trace driven simulation is conducted and the experimental results finally validate the efficiency of our proposed congestion control approach.
KeywordsSocial-based Sensor Networks Congestion Control Social Network Multiple Attribute Decision Making
We first thank anonymous reviewers for their patient review and valuable comments, which significantly improve the quality of this paper. Our work was supported by National Natural Science Foundation of China (Grant No. 61421003 and 61373125 and 61402425) and the fund of the State Key Lab of Software Development Environment (Grant No. SKLSDE-2015ZX-05) and Science and Technology Planning Project of Guangdong Province, China (Grant No. 2013B010401016).
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