A Data Collection Method Based on the Region Division in Opportunistic Networks

The popularity of wearable devices and smart phones provide a great convenience for large-scale data collection. Owing to the non-uniform distribution of mobile sensors, the data quantity collected from different regions has a wide variation. So we design the region division algorithm that divides area into different density grades and sets appropriate sampling frequency on different regions. Furthermore, we propose Circle of Time Slice (CoTS) and Cardinal Number Timing Method (CNTM) to solve the sampling error when nodes move from one area to another. On this basis, we propose the Data Collection Algorithm Based on the Sampling Frequency (DC-BSF) to reduce the data redundancy. Simulations demonstrate that the method proposed in this paper can reduce data redundancy under the condition of achieving high coverage.