An Opportunistic Data Dissemination Strategy of Crowd Sensing based on Local Data

An opportunistic data dissemination strategy based on map division is designed in this paper. The data dissemination strategy is combined with the temporal and spatial sensitivity features of effective local data and suitable for the scene of regional data acquisition. The strategy is divided into four parts. Firstly, nodes with different regional attributes collect data periodically, then, the judgment of edge nodes and position update of nodes are executed between nodes with different location attributes when nodes encounter each other, also, transmission of regional data with space-time sensitivity is completed, finally, cache updates and discarding are implemented when the buffer memory is full or the data stored in the cache is invalid. The data dissemination strategy can achieve local data collection and has a great edge detection function. The simulation results show that the data dissemination algorithm based on map partition has good data acquisition rate and low network overhead. It can accomplish reliable regional data collection and data sharing under the premise that the performance of our proposed algorithm is close to that of Epidemic algorithm. In some conditions, the performance of proposed algorithm even can exceed that of Epidemic algorithm.