Abstract
Due to the wider application of wireless sensor networks in real life, 3D coverage closer to the actual application environment has become a research hotspot of current sensor networks. To this end, this paper proposes a three-dimensional coverage deployment method based on RSS (Received Signal Strength) under a probabilistic model. According to the path loss of the wireless signal in the propagation process, the distance between the nodes can be roughly calculated, and the maximum distance between the nodes is defined by setting a threshold of the path loss, thereby further ensuring network connectivity and network quality. The probability coverage model is used and the cube-based network coverage is constructed. Based on this, the optimal coverage deployment problem in 3D-WSN is explored. Combining and improving the traditional particle swarm optimization algorithm can converge faster and avoid falling into local optimum. The simulation results show that the proposed method can converge quickly to improve network coverage and effectively reduce network energy consumption. In addition, we built a real experimental environment to verify the network quality by observing the RSSI (Received Signal Strength Indicator) changes. The experimental results verify the effectiveness of the proposed method.
Highlights
WSNs (Wireless Sensor Networks) is a wireless network composed of a large number of micro sensor nodes through multi-hop and self-organizing, which can be applied to military, industrial and agricultural control, biomedical, environmental testing, disaster relief and other fields [1]
By comparing the range of RSSI values to determine whether the path loss is uniform, the network connectivity and network quality are further verified
Compared with figure 11 (a) and figure 11 (b), it can be concluded that compared with the square scene, the average RSSI of each node before and after optimal deployment in the circular scene is smaller as a whole, so the path loss is larger and the network quality is poor
Summary
Ns (Wireless Sensor Networks) is a wireless network composed of a large number of micro sensor nodes through multi-hop and self-organizing, which can be applied to military, industrial and agricultural control, biomedical, environmental testing, disaster relief and other fields [1]. The main challenge is that the designed algorithms rarely meet multiple performance indicators, and most algorithms only consider the network coverage and ignore the problem of network energy consumption, or only consider the network life cycle and can not cover the whole monitoring area These performance indicators are all extremely important in wireless sensor networks. We study the coverage monitoring problem of balanced energy consumption, and the goal is to improve the network quality and prolong the network life cycle under the condition of satisfying the coverage rate To this end, the main contributions made in this paper are as follows: 1) Considering the influence of the actual signal propagation attenuation and measurement error, the probability model is closer to the actual sensor perception behavior, and the 3D wireless sensor network is closer to the real application environment.
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