An Energy-efficient Data Collection Scheme by Mobile Element based on Markov Decision Process for Wireless Sensor Networks

Ullah, Ihsan; Kim, Chan-Myung; Heo, Joo-Seong; Han, Youn-Hee

doi:10.1007/s11277-021-09241-1

Cited by 4 publications

(1 citation statement)

References 26 publications

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“…e current algorithms still have a few flaws, such as incomplete edge node coverage, low node positioning precision, and excessive path energy consumption [13]. Embedded devices with specific sensing, communication, and computing capabilities are known as wireless sensor network nodes [14]. As the main component of wireless sensor networks, they self-organize into a sensor network in a wireless multihop manner in the sensor distribution area, transmit the data about the physical world obtained by monitoring to the sink node, and communicate with the outside world through the Internet or satellite communication and gateway, allowing users to finally take advantage of the corresponding services [15].…”

Section: Introductionmentioning

confidence: 99%

The Application Trend and Advantages of Mobile Technology in Higher Education

2022

Mobile Information Systems

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The emergence of mobile wireless technology has profoundly altered how people produce goods and live their lives, but even as we take advantage of its speed and convenience, we must also be aware of its drawbacks. A new technology supported by contemporary technology is mobile wireless Internet technology. It is computer-, network-, and new media-based. Education and teaching are becoming more important in society as a result of the social economy’s rapid growth and the reform and development policies’ gradual advancement. It can develop the skills required by society and make teaching more widely known. The semisupervised learning positioning algorithm used in this study lowers the training phase acquisition costs. The proposed algorithm significantly resolves the issue that the DV-Hop algorithm is not suitable for anisotropic network positioning by applying the concept of triple equidistant coverage to anisotropic wireless sensor networks and combining it with multiple mobile beacons. According to the results of the experimental analysis, the following conclusions can be made: information processing can be found to be essentially stable at a level of more than 40%, which will greatly strengthen the use of mobile wireless technology in higher education. The improvement in overall performance will reach 65.7 percent. Following the experiment, it was discovered that the average transmission efficiency for mobile wireless technology is 57.4%, which is extremely high.

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Section: Introductionmentioning

confidence: 99%