关键词：
Sensor scheduling;wireless sensor networks;multi-modal confident information coverage;set cover
摘要：
Network lifetime maximization with guaranteed coverage is an important issue in wireless sensor networks. Based on our recently proposed confident information coverage (CIC) model, this paper studies the multi-modal confident information coverage (M2CIC) problem. Assuming that each node is equipped with different types of sensors, the objective is to schedule the multi-modal sensors' activity, such that the confident information coverage for each sensing modality can be guaranteed while the network lifetime can be maximized. We model the M2CIC problem as a multi-modal set cover problem (M2SC) and prove its NP-completeness. For solving the M2SC problem, we design two energy-efficient heuristics including a centralized one and a distributed one. In the proposed algorithms, different modal sensors are organized into a family of set covers, each of which can provide confident information coverage for all the monitored physical phenomena. Simulation results show that both the proposed algorithms can efficiently prolong the network lifetime and outperform two classical peer algorithms in terms of the extended network lifetime.
摘要：
Coverage is an important performance metric in sensor networks. The traditional disk coverage model uses a very simple geometric relation between a sensor and its surrounding space points to capture the sensor's sensing capability and quality, which are not enough for many practical applications. In this article, motivated from the application of precision agriculture, we propose a new confident information coverage model for field reconstruction, where the objective is to obtain reconstruction maps of some physical phenomena's attribute with a given reconstruction quality for the whole sensor field, including points been sampled and not sampled. The proposed model is downward compatible with the disk coverage model, while it can greatly reduce sensor density for area coverage. Simulation results show that for the same reconstruction quality, the required sensor density based on the proposed new model is much less than that based on the disk model in both the deterministic and random sensor deployment. In practice, the proposed model helps to determine the number of sensors to be deployed for a given farmland and their locations in the deterministic deployment. The proposed model can also help to guide network operations for energy efficient data collection with guaranteed reconstruction quality.
