Controllable Effective Threshold Based Fusion Coverage Algorithm in Mobile Sensor Networks

• Published in: Wireless communications and mobile computing 2018-01, Vol.2018 (2018), p.1-8
• Main Authors: Lu, Yong, Sun, Na
• Format: Article
• Language: English
• Subjects: Agricultural production; Algorithms; Computer simulation; Connectivity; Control algorithms; Energy consumption; Energy efficiency; Linear programming; Mathematical models; Monitoring; Nodes; Operators (mathematics); Parameters; Physical properties; Prediction models; Probability theory; Quality; Remote sensors; Sensors; Wireless networks; Wireless sensor networks
• ISSN: 1530-8669; 1530-8677
• DOI: 10.1155/2018/1529084

The coverage quality and network lifetime are two key parameters in the research of sensor networks. The coverage quality shows direct influences on the network lifetime. Meanwhile, it is influenced by many other factors such as physical parameters and environmental parameters. To reveal the connection between the coverage quality and the parameters of target node concerned, a fusion coverage algorithm with controllable effective threshold is proposed based on the sensing probability model. We give the model for the membership function of coverage intensity as well as the prediction model for the fusion operator. The range for the effective threshold is presented according to the membership function model. Meanwhile, the maximum of the effective coverage intensity for the target nodes within the monitoring area is derived. The derivation of the maximal fusion coverage intensity is elaborated utilizing a processing function on the distances from the target node to the ones in the sensor node set. Furthermore, we investigate different network properties within the monitoring area such as network coverage quality, the dynamic change of parameters, and the network lifetime, based on the probability theory and the geometric theory. Finally, we present numerical simulations to verify the performances of our algorithm. It is shown under different settings that, compared with the demand coverage quality, the proposed algorithm could improve the network coverage quality by 15.66% on average. The simulation experiment results show that our proposed algorithm has an average improvement by 10.12% and 13.23% in terms of the performances on network coverage quality and network lifetime, respectively. The research results are enlightening to the edge coverage and nonlinear coverage problems within the monitoring area.