- مبلغ: ۸۶,۰۰۰ تومان
- مبلغ: ۹۱,۰۰۰ تومان
Effective energy control while maintaining reliable monitoring performance becomes a key issue in wireless sensor networks (WSNs) based surveillance applications. While importance difference of surveillance zone, limited energy and dynamic network topology pose great challenges to surveillance performance. It is necessary to adjust sensor nodes awakening frequency dynamically for information fusion. Thus an energy-aware scheduling with quality guarantee method named ESQG is proposed in this paper which considers sensor nodes residual energy, different importance degrees of the surveillance zone and network topology comprehensively. It first uses a Voronoi diagram to determine the effective scope of each sensor node and then calculates node importance according to its residual energy and the importance degree of the effective scope. Then ESQG utilizes the importance of individual sensing scope and current forwarding costs to further compute node importance and awakening frequency for information fusion. In this way, ESQG can dynamically adapts each nodes awakening frequency to its dynamic network topology and importance degree of each individual sensing scope. The nodes are then turned on stochasticlly via the node awakening probability and node importance based information fusion is conducted for target detection. Besides, an adaptive process of perception factor C is proposed to match actual situation, and automatically change according to the detected data. Experiments results demonstrate that the proposed method ESQG can reduce the number of awakening nodes to a large extent while maintaining high reliability via information fusion.
In this paper, we propose the ESQG scheme for information fusion to save 615 network resources through considering dynamic network topology and different importance degrees of blue-green algae surveillance locations. In the scheme, importance degree is introduced to reflect the different importance of the grids in the surveillance zone. Combined with the difference of surveillance zones importance degree and dynamic network topology, it is used to calculate the 620 awakening probability which determines the modes of the sensor nodes. Then node importance based Information fusion is conducted to improve target detection performance. Besides, an adaptive process of perception factor C is proposed to match actual situation, and automatically change according to the detected data. Thus a compromise is achieved between energy cost and system 625 agility. Our conducted green algae surveillance simulation results show that our approach is robust and energy-efficient. Simulation results also suggest that this scheme can reduce the number of awakening nodes to a large extent while maintaining high reliability in surveillance.