Adjacent Matrix-based Hole Coverage Discovery Technique for Sensor Networks

Abstract

Wireless sensor networks are used to monitor and control areas in a variety of military and civilian areas such as battlefield surveillance, intrusion detection, disaster recovery, biological detection, and environmental monitoring. Since the sensor nodes are randomly placed in the area of interest, separation of the sensor network area may occur due to environmental obstacles or a sensor may not exist in some areas. Also, in the situation where the sensor node is placed in a non-relocatable place, some node may exhaust energy or physical hole of the sensor node may cause coverage hole. Coverage holes can affect the performance of the entire sensor network, such as reducing data reliability, changing network topologies, disconnecting data links, and degrading transmission load. It is possible to solve the problem that occurs in the coverage hole by finding a coverage hole in the sensor network and further arranging a new sensor node in the detected coverage hole. The existing coverage hole detection technique is based on the location of the sensor node, but it is inefficient to mount the GPS on the sensor node having limited resources, and performing other location information processing causes a lot of message transmission overhead. In this paper, we propose an Adjacent Matrix-based Hole Coverage Discovery(AMHCD) scheme based on connectivity of neighboring nodes. The method searches for whether the connectivity of the neighboring nodes constitutes a closed shape based on the adjacent matrix, and determines whether the node is an internal node or a boundary node. Therefore, the message overhead for the location information strokes does not occur and can be applied irrespective of the position information error.

Fig. 1. Delaunay triangulation configuration of sensor nodes

Fig. 2. The same coverage hole decision model

Fig. 3. Neighboring node sensing area model

Fig. 4. Triangular and circumscribed models composed of neighboring nodes

Fig. 5. Sensor network model

Fig. 6. The adjacency matrices of node 38

Fig. 7. Sensor network model

Fig. 8. The adjacency matrices of node 40

Fig. 9. Adjacent matrices between one hop and two hop neighbor nodes

Fig. 10. Neighboring nodes in closed shape

Fig. 11. Sensor network model

Fig. 12. AoA Location Technique

Fig. 13. Amount of calculation of the hole coverage detection methods

Fig. 14. Amount of message of the hole coverage detection methods

Table 1. Notation of elements in an algorithm