Adaptive collaboration for heterogeneous sensor networks in dynamic environments

摘要: Collaboration between the low-quality sensor and high-quality can achieve tradeoff accuracy energy efficiency in heterogeneous networks (HSNs). Generally, HSNs are deeply integrated with dynamic physical environments. Dynamics of monitored target most important common factors environments have great influence on system performance. If state changes, some parameters (e.g., active opportunity sampling frequency) fails to adapt which undermines collaboration's Even performance is not up requirements or a large amount consumed. To address this problem, we propose an adaptive collaboration method (EasiAC) by magnetic camera sensors. First, for dynamics target, EasiAC utilizes sensors predict target's via Bayesian filtering. Second, good above two kinds sensors, adjusts sensors' frequency dynamically according estimated results from Finally, evaluate through simulations real road environment experiments. The demonstrate that needs less consumption than traditional solutions, while maintaining at acceptable level presence target.