Jin Wang^1,2, Chunwei Ju², Yu Gao², Arun Kumar Sangaiah³, Gwang-jun Kim^4,*

CMC-Computers, Materials & Continua, Vol.56, No.3, pp. 433-446, 2018, DOI:10.3970/cmc.2018.04132

Abstract Wireless Sensor Networks (WSNs) are large-scale and high-density networks that typically have coverage area overlap. In addition, a random deployment of sensor nodes cannot fully guarantee coverage of the sensing area, which leads to coverage holes in WSNs. Thus, coverage control plays an important role in WSNs. To alleviate unnecessary energy wastage and improve network performance, we consider both energy efficiency and coverage rate for WSNs. In this paper, we present a novel coverage control algorithm based on Particle Swarm Optimization (PSO). Firstly, the sensor nodes are randomly deployed in a target area and remain More >

Rui Wang¹, Miaomiao Shen^1,*, Yanping Li¹, Samuel Gomes²

CMC-Computers, Materials & Continua, Vol.57, No.1, pp. 25-48, 2018, DOI:10.32604/cmc.2018.02408

Abstract Recently, sparse representation classification (SRC) and fisher discrimination dictionary learning (FDDL) methods have emerged as important methods for vehicle classification. In this paper, inspired by recent breakthroughs of discrimination dictionary learning approach and multi-task joint covariate selection, we focus on the problem of vehicle classification in real-world applications by formulating it as a multi-task joint sparse representation model based on fisher discrimination dictionary learning to merge the strength of multiple features among multiple sensors. To improve the classification accuracy in complex scenes, we develop a new method, called multi-task joint sparse representation classification based on More >

A. Rama Mohan Rao¹, S. Krishna Kumar¹, K. Lakshmi¹

Structural Durability & Health Monitoring, Vol.8, No.3, pp. 271-294, 2012, DOI:10.32604/sdhm.2012.008.271

Abstract Current advancements in structural health monitoring, sensor and sensor network technologies have encouraged using large number of sensor networks in monitoring spatially large civil structures like bridges. Large amount of spatial information obtained from these sensor networks will enhance the reliability in truly assessing the state of the health of the structure. However, if sensors go faulty during operation, the feature extraction techniques embedded into SHM scheme may lead to an erroneous conclusion and often end up with false alarms. Hence it is highly desirable to robustly detect the faulty sensors, isolate and correct the… More >

Byung-wan Jo¹, Do-keun Kim², Sung-keun Song³, Suk-un Kim⁴

The International Conference on Computational & Experimental Engineering and Sciences, Vol.1, No.4, pp. 167-172, 2007, DOI:10.3970/icces.2007.001.167

Abstract Wireless sensor networks bring new challenges to Bridge monitoring. To monitor a bridge, behavior, including vibration and displacement, must be measured to analyze the health of the structure based on measured and collected data. The collected data can be used to compute modal properties of the bridge. A bridge is moved by external forces, including wind, seismic activity, and traffic. So it is very hard reliance of safety through a preexistence method which uses Data Logger. Dynamic behavior of a bridge is difficult to measure because of costs and installation methods. In this paper, a More >