کیفیت پیوند برای شبکه های حسگر بی سیم
Abstract
I. Introduction
II. Related Work
III. Division of Link Quality Grade
IV. Link Quality Estimation
V. Link Quality Prediction
Authors
Figures
References
Abstract
Link quality is an important factor for nodes selecting communication links in wireless sensor networks. Effective link quality prediction helps to select high quality links for communication, so as to improve stability of communication. We propose the improved fuzzy C-means clustering algorithm (SUBXBFCM) and use it to adaptively divide the link quality grades according to the packet reception rate. The Pearson correlation coefficient is employed to analyse the correlation between the hardware parameters and packet reception rate. The averages of the received signal strength indicator, link quality indicator and the signal to noise ratio are selected as the inputs of the link quality estimation model based on the XGBoost (XGB_LQE). The XGB_LQE is constructed to estimate the current link quality grade, which takes the classification advantages of XGBoost. Based on the estimated results of the XGB_LQE, the link quality prediction model (XGB_LQP) is constructed by using the XGBoost regression algorithm, which can predict the link quality grade at the next moment with historical link quality information. Experiment results in single-hop scenarios of square, laboratory, and grove show that the SUBXBFCM algorithm is effective at dividing the link quality grades compared with the normal division methods. Compared with link quality prediction methods based on the Support Vector Regression and 4C, XGB_LQP makes better predictions in single-hop wireless sensor networks.
Introduction
Wireless sensor networks (WSNs) are multi-hop selforganizing networks that are formed by wireless communication, and they consist of large numbers of inexpensive micro sensor nodes that are deployed in a monitoring area [1]. Their purposes are to collaboratively perceive, collect and process the information of the area and then send it to the observer. Sensors, perceptual objects, and observers form the three elements of a wireless sensor network. WSNs currently have broad application prospects in the fields of smart home, urban transportation and so on. In WSNs, nodes communicate by radio frequencies, and links have characteristics such as asymmetry, irregularity, and directionality [2], [3]. The WSN links are susceptible to multipath effect, loss and adjacent channel interference, which result in unreliable links, low channel quality and frequent topological changes [4]. The quality of the communication links has a significant impact on the performance of the wireless sensor network, such as the life cycle of the network, the throughput of the network and the reliability of transmissions [5]. Selecting a link with poor link quality for communications is likely to cause a large amount of packet loss. The retransmission due to packet loss recovery is the main cause of the increase of network energy consumption [6]. Effective link quality prediction helps to select good links for data transmissions, so as to improve network throughput and to maintain normal network operations and topological stability of networks, thereby improving the performance of wireless sensor networks [7]. It can be seen that the link quality prediction for WSNs is of great significance.