Modeling packet loss rate of IEEE 802.15.4 links in diverse environmental conditions

Modeling and prediction of Packet Loss Rate (PLR) of wireless links using hardware information is essential for the design of higher-layer protocols in Wireless Sensor Networks. While many previous studies revealed the spatio-temporal variation of various link quality metrics, how environment impacts on the mapping between PLR and hardware indicators still remains unclear. Without a comprehensive understanding of such environmental impact, the acquired empirical PLR models are severely limited to specific scenarios. In this paper, we present the results of indoor and outdoor experimental campaigns focusing on the impact of various environmental factors (e.g., obstacles, human activities, climate conditions) on the dependency between the link PLR, signal to noise ratio (SNR) and packet length. Rich observations are made on the spatio-temporal characteristics of the PLR-SNR relationship and our analysis shows that link PLR can be modeled, in all experimented scenarios, as an exponential function of SNR and packet length with two model parameters that may vary over space and time. Besides, implications of the observations are summarized, providing guidelines to construct and adapt PLR models in different environments.

[1]  Bhaskar Krishnamachari,et al.  Experimental study of concurrent transmission in wireless sensor networks , 2006, SenSys '06.

[2]  Anis Koubaa,et al.  Radio link quality estimation in wireless sensor networks , 2012, ACM Trans. Sens. Networks.

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

[4]  Kamin Whitehouse,et al.  Toward Stable Network Performance in Wireless Sensor Networks: A Multilevel Perspective , 2015, ACM Trans. Sens. Networks.

[5]  Sinem Coleri Ergen,et al.  Spatio-temporal characteristics of link quality in wireless sensor networks , 2012, 2012 IEEE Wireless Communications and Networking Conference (WCNC).

[6]  Yan Zhang,et al.  Experimental Study for Multi-layer Parameter Configuration of WSN Links , 2015, 2015 IEEE 35th International Conference on Distributed Computing Systems.

[7]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[8]  P. Levis,et al.  RSSI is Under Appreciated , 2006 .

[9]  Amy L. Murphy,et al.  How Environmental Factors Impact Outdoor Wireless Sensor Networks: A Case Study , 2013, 2013 IEEE 10th International Conference on Mobile Ad-Hoc and Sensor Systems.

[10]  Andrzej Duda,et al.  Link quality metrics in large scale indoor wireless sensor networks , 2013, 2013 IEEE 24th Annual International Symposium on Personal, Indoor, and Mobile Radio Communications (PIMRC).

[11]  Yuming Jiang,et al.  A Statistical Property of Wireless Channel Capacity: Theory and Application , 2018, PERV.

[12]  James Brown,et al.  TempLab: A testbed infrastructure to study the impact of temperature on wireless sensor networks , 2014, IPSN-14 Proceedings of the 13th International Symposium on Information Processing in Sensor Networks.

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

[14]  Marco Zuniga,et al.  JamLab: Augmenting sensornet testbeds with realistic and controlled interference generation , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[15]  James Brown,et al.  Hot packets:a systematic evaluation of the effect of temperature on low power wireless transceivers , 2013 .