关键词：
Barrier coverage;Internet of Things (IoT);barrier gap;directional sensor networks;line-based deployment
摘要：
The barrier coverage of a wireless sensor network is an important surveillance application of Internet of Things. Barrier coverage guarantees that all intruders traversing the protected region are detected by a chain of connected sensors. However, when the sensors are randomly deployed, barrier gaps may occur due to deployment randomness or insufficient sensors. How to locate the barrier gaps and mend them is an important aspect in the network. In this paper, we study the barrier gap problem in weak barrier coverage and strong barrier coverage that consist of directional sensors, and the sensors are deployed by a line-based deployment strategy. A gap-finding algorithm is proposed to find subbarriers and barrier gaps. Two gap-mending algorithms are devised to mend barrier gaps in the network: One algorithm is a simple rotation algorithm that only rotates two critical sensors in two subbarriers to fix the gap, and the other algorithm is a chain-reaction rotation algorithm that rotates sensors in the subbarrier in a chain-reaction manner to mend the gap. We conduct extensive simulations to evaluate the performance of the proposed algorithms. Simulation results show that the proposed gap-mending algorithms can effectively fix barrier gaps and improve the probability of barrier success construction.
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.
