Localized Confident Information Coverage Hole Detection in Internet of Things for Radioactive Pollution Monitoring

As a novel cyber-physical-social network paradigm, the Internet of Things (IoT) provides a powerful tool to monitor the hazardous fields of interest. Due to the uneven random deployment, sensor energy depletion, and external attacks, the emergence of coverage holes would remarkably degrade the network performance and quality of service. For overcoming the drawbacks resulting from the coverage holes, this paper focuses on how to locally detect coverage holes by exploiting one-hop neighboring sensors' cooperation based on the novel confident information coverage model (CIC), which is formulated as the localized confident information coverage hole detection (LCICHD) problem. For handling the CICHD problem, we devise a family of heuristic CIC holes detection schemes including the LCHD, LCHDRL, random and randomRL. Both the LCHD and LCHDRL schemes locally determine coverage status of each subregion and take the sensor communication ability into consideration. While the LCHDRL considers not only the sensor remaining energy but also the residual lifetime during the CIC hole detection. After acquiring the coverage status of each partitioned local subregion, the coverage hole boundary will be extracted by image processing techniques. For comparison, both the Random and RandomRL schemes arbitrarily select sensors within the sensing field to detect CIC holes, and the RandomRL scheme takes the sensors' residual lifetime into consideration during the hole detection process. Experimental simulations show that the proposed schemes can efficiently detect the emerged coverage holes including the locations and the number, and the LCHDRL algorithm is more practical and efficient compared with the other three peer solutions.