Sleep Scheduling in Industrial Wireless Sensor Networks for Toxic Gas Monitoring

Toxic gas leakage that leads to equipment damage, environmental effects, and injuries to humans is the key concern in large-scale industries, particularly in petrochemical plants. Industrial wireless sensor networks (IWSNs) are specially designed for industrial applications with improved efficiency, and remote sensing for toxic gas leakage. Sleep scheduling is a common approach in IWSNs to overcome the network lifetime problem due to energy constrained nodes. In this article, we propose a sleep scheduling scheme that ensures a coverage degree requirement based on the dangerous levels of the toxic gas leakage area, while maintaining global network connectivity with minimal awake nodes. Unlike the previous sleep scheduling algorithm, for example, the connected k-neighborhood (CKN)-based approach that wakes up the sleep nodes over the entire sensing field by increasing the k-value, our proposed scheme dynamically wakes up the sleep nodes only in the particular toxic gas leakage area. Simulation results show that our proposed scheme outperforms the CKN-based sleep scheduling scheme with the same required coverage degree for the toxic gas leakage area. In addition, the proposed scheme considers multiple hazardous zones with various coverage degree requirements. We show that at the expense of a slight extra message overhead, energy consumption in terms of totally awake nodes over the entire sensing field is reduced compared to the other approaches, while maintaining network connectivity.

[1]  George Kesidis,et al.  Dynamic cluster structure for object detection and tracking in wireless ad-hoc sensor networks , 2004, 2004 IEEE International Conference on Communications (IEEE Cat. No.04CH37577).

[2]  Michele Magno,et al.  Beyond duty cycling: Wake-up radio with selective awakenings for long-lived wireless sensing systems , 2015, 2015 IEEE Conference on Computer Communications (INFOCOM).

[3]  Lei Shu,et al.  Releasing Network Isolation Problem in Group-Based Industrial Wireless Sensor Networks , 2017, IEEE Systems Journal.

[4]  Jun Zhang,et al.  A Distributed Coverage-Aware Sleep Scheduling Algorithm for Wireless Sensor Networks , 2009, 2009 Sixth International Conference on Information Technology: New Generations.

[5]  József Balogh,et al.  On k−coverage in a mostly sleeping sensor network , 2008, Wirel. Networks.

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

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

[8]  N. Kh. Arystanbekova,et al.  Application of Gaussian plume models for air pollution simulation at instantaneous emissions , 2004, Math. Comput. Simul..

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

[10]  Xiang-Yang Li,et al.  Efficient interference-aware TDMA link scheduling for static wireless networks , 2006, MobiCom '06.

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

[12]  Mingyan Liu,et al.  Network coverage using low duty-cycled sensors: random & coordinated sleep algorithms , 2004, Third International Symposium on Information Processing in Sensor Networks, 2004. IPSN 2004.

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