Critical Sensor Density for Partial Coverage under Border Effects in Wireless Sensor Networks

Coverage is an important metric to measure the quality of service of a wireless sensor network monitoring a field of interest (FoI). From an energy perspective, it is often very important to maintain the desired coverage ratio with a minimum number of sensors. The literature on determining the critical sensor density (CSD) for the desired coverage ratio assumes that the FoI is unbounded or toroidal in shape. Although it is not a realistic assumption, it eliminates the border effects in analysis. Since the entire sensing area of the sensors near the boundary may not be useful for the coverage, the CSD estimated without the border effects is lower than the actual value. In this paper, we assume that the sensors are deployed uniformly at random in a convex polygon-shaped FoI and consider the border effects to derive the expected sensing area of a sensor used in the coverage. Next, we estimate the CSD required for the desired coverage ratio. We validate the analysis and demonstrate the impact of border effects on CSD using numerical results. Results show that our approach estimates the CSD better than another one that does not consider the exact geometry of the FoI.

[1]  B. Ripley,et al.  Introduction to the Theory of Coverage Processes. , 1989 .

[2]  Wei Wang,et al.  Information Coverage in Randomly Deployed Wireless Sensor Networks , 2007, IEEE Transactions on Wireless Communications.

[3]  Vlady Ravelomanana,et al.  Limit Theorems for Degree of Coverage and Lifetime in Large Sensor Networks , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[4]  Deborah Estrin,et al.  Habitat monitoring with sensor networks , 2004, CACM.

[5]  Pierre Alliez,et al.  An Optimal Transport Approach to Robust Reconstruction and Simplification of 2d Shapes , 2022 .

[6]  Bang Wang,et al.  Coverage problems in sensor networks: A survey , 2011, CSUR.

[7]  Cecilia Mascolo,et al.  WILDSENSING , 2012, ACM Trans. Sens. Networks.

[8]  Jenn-Wei Lin,et al.  Improving the coverage of randomized scheduling in wireless sensor networks , 2008, IEEE Transactions on Wireless Communications.

[9]  Yan Jin,et al.  EECCR: An Energy-Efficient $m$-Coverage and $n$-Connectivity Routing Algorithm Under Border Effects in Heterogeneous Sensor Networks , 2009, IEEE Transactions on Vehicular Technology.

[10]  Sajal K. Das,et al.  Centralized and Clustered k-Coverage Protocols for Wireless Sensor Networks , 2012, IEEE Transactions on Computers.

[11]  Radha Poovendran,et al.  Stochastic coverage in heterogeneous sensor networks , 2006, TOSN.

[12]  Yingtao Jiang,et al.  EECCR: An Energy-Efficient -Coverage and -Connectivity Routing Algorithm Under Border Effects in Heterogeneous Sensor Networks , 2009, IEEE Trans. Veh. Technol..

[13]  Peter Gritzmann,et al.  Inner and outerj-radii of convex bodies in finite-dimensional normed spaces , 1992, Discret. Comput. Geom..

[14]  WanPeng-Jun,et al.  Coverage by randomly deployed wireless sensor networks , 2006 .

[15]  Özgür Ulusoy,et al.  A framework for use of wireless sensor networks in forest fire detection and monitoring , 2012, Comput. Environ. Urban Syst..

[16]  Li-Hsing Yen,et al.  Expected k-coverage in wireless sensor networks , 2006, Ad Hoc Networks.

[17]  J. Seaman Introduction to the theory of coverage processes , 1990 .

[18]  Hari Prabhat Gupta,et al.  Analysis of the redundancy in coverage of a heterogeneous wireless sensor network , 2013, 2013 IEEE International Conference on Communications (ICC).

[19]  King-Shan Lui,et al.  On Perimeter Coverage in Wireless Sensor Networks , 2010, IEEE Transactions on Wireless Communications.

[20]  Jennifer C. Hou,et al.  On deriving the upper bound of α-lifetime for large sensor networks , 2004, MobiHoc '04.

[21]  Shaojie Tang,et al.  Canopy closure estimates with GreenOrbs: sustainable sensing in the forest , 2009, SenSys '09.

[22]  Jennifer C. Hou,et al.  Is Deterministic Deployment Worse than Random Deployment for Wireless Sensor Networks? , 2006, Proceedings IEEE INFOCOM 2006. 25TH IEEE International Conference on Computer Communications.