Publications (3)0 Total impact
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ABSTRACT: Recent developments in wireless communications have enabled the development of low-cost, low-power wireless sensor networks (WSN) with wide applicability, including environment and vehicle-health monitoring. Minimizing energy consumption and hence maximizing the life time of the network are key requirements in the design of optimum sensor networking protocols and algorithms. Several routing protocols with different objectives have already been proposed for energy-efficient WSN applications. This paper surveys a sample of existing energy efficient cluster-based and QoS-aware routing protocols and highlights their key features, including strengths and weaknesses.
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ABSTRACT: The availability of low-powered and cheap microprocessors, radio frequency integrated circuits and the development of new wireless communication techniques, make the wireless sensor networks (WSN) one of todays most promising technologies. Minimizing energy consumption and maximizing the lifetime of the networks are key requirements in the design of sensor network applications. Optimally designed medium access control (MAC) and routing protocols minimize energy consumption and prolong the network life. In this study, we have investigated an energy-efficient adaptive TDMA (EA-TDMA) protocol for railway applications that used in communication between sensor nodes and the cluster-head (CH) placed in a railway wagon. This protocol is suitable for medium traffic applications and reduces energy consumption by shortening the idle period when devices have no data to transmit. We have developed an analytical model for EA-TDMA and compared its performance with conventional TDMA and bit-map-assisted (BMA) protocols.
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ABSTRACT: Wireless sensor networking (WSN) and modern machine learning techniques have encouraged interest in the development of vehicle monitoring systems that ensure safe and secure operations of the rail vehicle. To make an energy efficient WSN application, power consumption due to raw data collection and pre-processing needs to be kept to a minimum level. In this paper, an energy-efficient data acquisition method has investigated for WSN applications using modern machine learning techniques. In an existing system, four sensor nodes were placed in each railway wagon to collect data to develop a monitoring system for railways. In this system, three sensor nodes were placed in each wagon to collect the same data using popular regression algorithms, which reduces power consumption of the system. This study was conducted using six different regression algorithms with five different datasets. Finally the best suitable algorithm have suggested based on the performance metrics of the algorithms that include: correlation coefficient, root mean square error (RMSE), mean absolute error (MAE), root relative squared error (RRSE), relative absolute error (RAE)and computation complexity.
