Optimal Sensor Fault-Tolerant Deployment for Cost Minimization in Target-based Heterogeneous Wireless Sensor Networks

Coverage and connectivity are two fundamental issues in target-based wireless sensor networks (WSNs). Considerable work has been carried out on the issues of how to maintain coverage and connectivity simultaneously in homogeneous sensor networks. In some applications, sensor nodes of multiple types with different sensing ranges, communication ranges, and prices are available for optimally organizing a network. In this paper, the problem of how to deploy such heterogeneous WSNs into predefined positions with k-coverage to all targets and c-connectivity to each sensor node under the minimum-cost constraint is addressed. First, we prove its NP-completeness. Then, this problem is formulated as an integer linear programming model. To solve this problem, we proposed an enhanced coral reef optimization algorithm (CRO). The enhanced version of CRO is named ECRO. The improvements of the ECRO lie in four aspects. First, the operator of immigration in the biogeography-based optimization algorithm is introduced into the brooding procedure of the coral larvae formation to conserve the excellent population solutions. Second, a chaotic differential mutation operator is adopted in the brooding procedure of ECRO to enhance the diversity of the descent and the optimization ability for the coral reef. Third, a random opposition-learning strategy is proposed to improve the performance of the worst individual of the population. Moreover, the reuse of excellent decision variables in the solution is proposed to improve the decision variables of other individuals. Extensive simulation experiments are performed to validate the proposed ECRO. The results show that the proposed ECRO outperforms the compared algorithms, which demonstrates the superiority of the proposed ECRO algorithm.