Assuring K-coverage in the presence of mobility and wear-out failures in wireless sensor networks. (20th February 2009)

• Record Type: Journal Article
• Authors: Iyer, Jayakrishnan V.; Yu, Heeyeol; Kim, Hogil; Kim, Eun Jung; Yum, Ki; Mah, Pyeong-Soo
• Is Part Of: International journal of sensor networks. Volume 5:Number 1(2009)
• Page Start: 58
• Page End: 65
• Publication Date: 2009-02-20
• Subjects: wireless sensor networks -- WSNs -- K-coverage -- mobile sensors -- wear-out failures -- log-normal distribution -- wireless networks -- random waypoint model -- mobility -- wake-up calls -- sleeping sensors -- sensor nodes; Sensor networks -- Periodicals; 681.2
• Languages: English
• ISSNs: 1748-1279

Abstract

Along with energy conservation, it has been a critical issue to maintain a desired degree of coverage in Wireless Sensor Networks (WSNs). In this paper, we consider more realistic WSN environments where the sensor nodes are moving around, which can disappear due to wear-out failures. By enhancing a variant of random waypoint model (Li et al., 2005), we propose Mobility Resilient Coverage Control (MRCC) to assure K-coverage in the presence of mobility. Our basic goals are (1) to elaborate the probability of breaking K-coverage with moving-in and moving-out probabilities and (2) to issue wake-up calls to sleeping sensors to meet user requirement of K-coverage even in the presence of mobility. Furthermore, to show the impact of wear-out failures on the coverage achieved, we adopt a lognormal distribution to depict the conditional probability of failures and observe the influence of reduced number of active nodes on coverage. Our experiments with Network Survivability – Double Link Failure show that MRCC achieves better coverage by 1.4% with 22% fewer active sensors than that of the existing Coverage Configuration Protocol (CCP). By taking reliability of nodes into account, the performance drop with respect to coverage is 3.7% (for coverage >1) while the reduction in the number of sensor nodes is 18.19% when compared with pure MRCC. Comparing CCP and MRCC with reliability, we observe a 3.4% reduction in coverage for the average probabilistic case and 5.78% for the individual probabilistic case, while achieving a 12.82% and 28.2% reduction in number of nodes, respectively.