Intelligent Routing to Enhance Energy Consumption in Wireless Sensor Network: A Survey
Lecture Notes on Data Engineering and Communications Technologies, ISSN: 2367-4520, Vol: 68, Page: 283-300
2022
- 7Citations
- 34Captures
Metric Options: CountsSelecting the 1-year or 3-year option will change the metrics count to percentiles, illustrating how an article or review compares to other articles or reviews within the selected time period in the same journal. Selecting the 1-year option compares the metrics against other articles/reviews that were also published in the same calendar year. Selecting the 3-year option compares the metrics against other articles/reviews that were also published in the same calendar year plus the two years prior.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Book Chapter Description
Nowadays, the network and the Internet applications have gained a substantial importance in the digital world, due to the great impact which it provides for health and community services. Among the most important services that have been provided are smart devices and vital factor measurement devices for patients, whether in a hospital or outside the hospital. Furthermore, sensors collect medical data or measurements of temperature and humidity, in various critical environments. The proper types of network that may be used in such difficult environment are wireless sensor networks that used to sense and process data. Additionally, the wireless sensor networks have been used in the environment of Internet of Things and smart cities in the general services and health fields. All these reasons have made researchers focus on wireless sensor networks and addressing the challenges that face them. The most important challenge facing this type of network is energy consumption and increase battery life. This paper discusses the methodologies used in energy conservation in wireless sensor networks, such as data reduction technology, shortest path selection and artificial intelligence algorithms used in smart routing and energy saving. Besides, we have introduced comparisons between the standard algorithms which are suggested by the researchers, to make a clear picture of the energy consumption problem and propose some effective solutions in wireless sensor networks field.
Bibliographic Details
http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85111990496&origin=inward; http://dx.doi.org/10.1007/978-981-16-1866-6_21; https://link.springer.com/10.1007/978-981-16-1866-6_21; https://dx.doi.org/10.1007/978-981-16-1866-6_21; https://link.springer.com/chapter/10.1007/978-981-16-1866-6_21
Springer Science and Business Media LLC
Provide Feedback
Have ideas for a new metric? Would you like to see something else here?Let us know