Impacts of Radio Irregularity on Duty-Cycled Industrial Wireless Sensor Networks

With rapid adoption of advanced wireless sensors in last decades, industrial wireless sensor networks (IWSNs) are increasingly deployed in the industries for various applications. Sleep scheduling is a common approach in IWSNs to overcome network lifetime problem due to energy-constrained sensor nodes. However, in real-environment, node’s transmit power varies in different directions due to non-isotropic nature of electromagnetic transmission, path-loss, noise, and temperature. Thus, radio irregularity results in link asymmetry, thereafter, affects the performance of sleep scheduling in IWSNs. In this paper, we evaluate the impacts of radio irregularity on sleeping probability and lifetime performances of well-known connected k-neighborhood (CKN)-based sleep scheduling algorithms in duty-cycled IWSNs. We derive the upper-limit of sleep probability with radio irregularity variables. From the extensive simulations, we show that radio irregularity increases the number of awake nodes in duty-cycled sensor networks, therefore, network lifetime decreases with increasing values of link asymmetry parameters. Finally, an adverse impact of radio irregularity is observed in higher k-value in CKN-based algorithm due to more awake nodes to satisfy the k-connectivity in presence of link asymmetry.

[1]  Victor C. M. Leung,et al.  Toward Offering More Useful Data Reliably to Mobile Cloud From Wireless Sensor Network , 2015, IEEE Transactions on Emerging Topics in Computing.

[2]  Gang Zhou,et al.  Models and solutions for radio irregularity in wireless sensor networks , 2006, TOSN.

[3]  Lei Shu,et al.  An Energy-Efficient CKN Algorithm for Duty-Cycled Wireless Sensor Networks , 2012, Int. J. Distributed Sens. Networks.

[4]  Ramesh Govindan,et al.  Understanding packet delivery performance in dense wireless sensor networks , 2003, SenSys '03.

[5]  Deborah Estrin,et al.  Statistical model of lossy links in wireless sensor networks , 2005, IPSN 2005. Fourth International Symposium on Information Processing in Sensor Networks, 2005..

[6]  Chuang Liu,et al.  Impact of Radio Irregularities on Connectivity of Wireless Networks with Log-Normal Shadowing , 2015, 2015 11th International Conference on Mobile Ad-hoc and Sensor Networks (MSN).

[7]  Piotr Berman,et al.  Power efficient monitoring management in sensor networks , 2004, 2004 IEEE Wireless Communications and Networking Conference (IEEE Cat. No.04TH8733).

[8]  Marco Zuniga,et al.  Analyzing the transitional region in low power wireless links , 2004, 2004 First Annual IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks, 2004. IEEE SECON 2004..

[9]  S. Nath,et al.  Communicating via Fireflies: Geographic Routing on Duty-Cycled Sensors , 2007, 2007 6th International Symposium on Information Processing in Sensor Networks.

[10]  Victor C. M. Leung,et al.  Collaborative Location-Based Sleep Scheduling for Wireless Sensor Networks Integratedwith Mobile Cloud Computing , 2015, IEEE Transactions on Computers.

[11]  Peter C. Evans,et al.  Industrial Internet: Pushing the Boundaries of Minds and Machines , 2012 .

[12]  Victor C. M. Leung,et al.  Sleep Scheduling for Geographic Routing in Duty-Cycled Mobile Sensor Networks , 2014, IEEE Transactions on Industrial Electronics.

[13]  Di Tian,et al.  A coverage-preserving node scheduling scheme for large wireless sensor networks , 2002, WSNA '02.