RVFL-LQP: RVFL-Based Link Quality Prediction of Wireless Sensor Networks in Smart Grid

In the application of wireless sensor networks (WSNs) to smart grid, real-time and accurate wireless link quality prediction (LQP) is important to determine which link is reliable enough to undertake the communication task. However, the existing LQP methods are neither suitable to describe the dynamic stochastic features of link quality nor to ensure the validity of prediction results. In this paper, a random-vector-functional-link-based LQP (RVFL-LQP) algorithm is proposed. The algorithm selects the signal-to-noise ratio (SNR) as the link quality metric and decomposes the raw SNR sequence into the time-varying sequence and the stochastic sequence according to the analysis of wireless link characteristics. Then, the RVFL network is used to establish the prediction model of the time-varying sequence and the variance of the stochastic sequence. Lastly, the probability-guaranteed interval boundary of SNR is predicted, and the validity and practicability of prediction results are evaluated by comparative experiments and real-world application, respectively.

[1]  Andreas Willig,et al.  The Triangle Metric: Fast Link Quality Estimation for Mobile Wireless Sensor Networks , 2010, 2010 Proceedings of 19th International Conference on Computer Communications and Networks.

[2]  Mohamed Jmaiel,et al.  Wireless Sensor Network Based Smart Grid Communications: Challenges, Protocol Optimizations, and Validation Platforms , 2017, Wirel. Pers. Commun..

[3]  Edward J. Coyle,et al.  A Kalman Filter Based Link Quality Estimation Scheme for Wireless Sensor Networks , 2007, IEEE GLOBECOM 2007 - IEEE Global Telecommunications Conference.

[4]  Qiyue Li,et al.  End-to-End Data Delivery Reliability Model for Estimating and Optimizing the Link Quality of Industrial WSNs , 2018, IEEE Transactions on Automation Science and Engineering.

[5]  Mohsen Guizani,et al.  Cyber Security Analysis and Protection of Wireless Sensor Networks for Smart Grid Monitoring , 2017, IEEE Wireless Communications.

[6]  Taskin Koçak,et al.  A Survey on Smart Grid Potential Applications and Communication Requirements , 2013, IEEE Transactions on Industrial Informatics.

[7]  Michael Small,et al.  Evolving networks—Using past structure to predict the future , 2016 .

[8]  Gul Agha,et al.  Link Quality Estimation for Data-Intensive Sensor Network Applications , 2011 .

[9]  Robert Tappan Morris,et al.  a high-throughput path metric for multi-hop wireless routing , 2005, Wirel. Networks.

[10]  Joe-Air Jiang,et al.  Cost-Efficient Placement of Communication Connections for Transmission Line Monitoring , 2017, IEEE Transactions on Industrial Electronics.

[11]  Zhi Liu,et al.  Mining Mobile Intelligence for Wireless Systems: A Deep Neural Network Approach , 2020, IEEE Computational Intelligence Magazine.

[12]  Ian F. Akyildiz,et al.  Channel-aware routing and priority-aware multi-channel scheduling for WSN-based smart grid applications , 2016, J. Netw. Comput. Appl..

[13]  Qiyue Li,et al.  A radio link reliability prediction model for wireless sensor networks , 2018, Int. J. Sens. Networks.

[14]  Daibo Liu,et al.  Frame Counter: Achieving Accurate and Real-Time Link Estimation in Low Power Wireless Sensor Networks , 2016, 2016 15th ACM/IEEE International Conference on Information Processing in Sensor Networks (IPSN).

[15]  Toshitaka Tsuda,et al.  Data Driven Cyber-Physical System for Landslide Detection , 2019, Mob. Networks Appl..

[16]  Ulf Bodin,et al.  Proportional throughput differentiationwith cognitive load-control on WSN channels , 2015, EURASIP J. Wirel. Commun. Netw..

[17]  M. Hemalatha,et al.  Link Quality Estimation for Adaptive Data Streaming in WSN , 2017, Wirel. Pers. Commun..

[18]  Gerhard P. Hancke,et al.  Opportunities and Challenges of Wireless Sensor Networks in Smart Grid , 2010, IEEE Transactions on Industrial Electronics.

[19]  Bülent Tavli,et al.  Packet Size Optimization in Wireless Sensor Networks for Smart Grid Applications , 2017, IEEE Transactions on Industrial Electronics.

[20]  David E. Culler,et al.  Taming the underlying challenges of reliable multihop routing in sensor networks , 2003, SenSys '03.

[21]  Xiao Ma,et al.  Author's Personal Copy Lips: Link Prediction as a Service for Data Aggregation Applications , 2022 .

[22]  LiuZhi,et al.  Data Driven Cyber-Physical System for Landslide Detection , 2019 .

[23]  Yan Wang,et al.  Link prediction for tree-like networks. , 2019, Chaos.

[24]  Koen Langendoen,et al.  Link layer measurements in sensor networks , 2004, 2004 IEEE International Conference on Mobile Ad-hoc and Sensor Systems (IEEE Cat. No.04EX975).

[25]  Yin Chen,et al.  On the Mechanisms and Effects of Calibrating RSSI Measurements for 802.15.4 Radios , 2010, EWSN.

[26]  Philip Levis,et al.  The β-factor: measuring wireless link burstiness , 2008, SenSys '08.

[27]  Carolina Fortuna,et al.  Analysis of Machine Learning for Link Quality Estimation , 2018, 1812.08856.

[28]  Michael Small,et al.  Link direction for link prediction , 2017 .

[29]  Shaoyong Guo,et al.  Distributed Fault Detection Based on Credibility and Cooperation for WSNs in Smart Grids , 2017, Sensors.

[30]  Juan M. Corchado,et al.  How Blockchain Could Improve Fraud Detection in Power Distribution Grid , 2018, SOCO-CISIS-ICEUTE.

[31]  Philip Levis,et al.  An empirical study of low-power wireless , 2010, TOSN.

[32]  Daibo Liu,et al.  Achieving Accurate and Real-Time Link Estimation for Low Power Wireless Sensor Networks , 2017, IEEE/ACM Transactions on Networking.

[33]  Volker Turau,et al.  Prediction Accuracy of Link-Quality Estimators , 2011, EWSN.

[34]  Wenyuan Xu,et al.  LESS: Link Estimation with Sparse Sampling in Intertidal WSNs , 2018, Sensors.

[35]  Jinliang Ding,et al.  Link Quality Estimation in Industrial Temporal Fading Channel With Augmented Kalman Filter , 2019, IEEE Transactions on Industrial Informatics.

[36]  Yong Wang,et al.  Predicting link quality using supervised learning in wireless sensor networks , 2007, MOCO.

[37]  Marco Zuniga,et al.  An analysis of unreliability and asymmetry in low-power wireless links , 2007, TOSN.

[38]  Anis Koubaa,et al.  F-LQE: A Fuzzy Link Quality Estimator for Wireless Sensor Networks , 2010, EWSN.

[39]  Tao Zhou,et al.  Link prediction in weighted networks: The role of weak ties , 2010 .

[40]  Qilian Liang,et al.  Performance Analysis of Multiuser Selection Scheme in Dynamic Home Area Networks for Smart Grid Communications , 2013, IEEE Transactions on Smart Grid.

[41]  Wei Liu,et al.  Distance Measurement Model Based on RSSI in WSN , 2010, Wirel. Sens. Netw..

[42]  Vehbi C. Gungor,et al.  Analysis of low power wireless links in smart grid environments , 2013, Comput. Networks.

[43]  Dana Marinca,et al.  On-line learning and prediction of link quality in wireless sensor networks , 2014, 2014 IEEE Global Communications Conference.

[44]  Bernhard Plattner,et al.  Link quality prediction in mesh networks , 2008, Comput. Commun..