|Author||Dias, Gabriel Martins ♦ Bellalta, Boris ♦ Oechsner, Simon|
|Source||ACM Digital Library|
|Publisher||Association for Computing Machinery (ACM)|
|Subject Domain (in DDC)||Computer science, information & general works ♦ Data processing & computer science|
|Subject Keyword||Predictions ♦ Data reduction ♦ Data science ♦ Machine learning ♦ Wireless sensor networks|
|Abstract||One of the main characteristics of Wireless Sensor Networks (WSNs) is the constrained energy resources of their wireless sensor nodes. Although this issue has been addressed in several works and received much attention over the years, the most recent advances pointed out that the energy harvesting and wireless charging techniques may offer means to overcome such a limitation. Consequently, an issue that had been put in second place now emerges: the low availability of spectrum resources. Because of it, the incorporation of the WSNs into the Internet of Things and the exponential growth of the latter may be hindered if no control over the data generation is taken. Alternatively, part of the sensed data can be predicted without triggering transmissions that could congest the wireless medium. In this work, we analyze and categorize existing prediction-based data reduction mechanisms that have been designed for WSNs. Our main contribution is a systematic procedure for selecting a scheme to make predictions in WSNs, based on WSNs’ constraints, characteristics of prediction methods, and monitored data. Finally, we conclude the article with a discussion about future challenges and open research directions in the use of prediction methods to support the WSNs’ growth.|
|Description||Author Affiliation: Pompeu Fabra University (Dias, Gabriel Martins; Bellalta, Boris; Oechsner, Simon)|
|Age Range||18 to 22 years ♦ above 22 year|
|Education Level||UG and PG|
|Learning Resource Type||Article|
|Publisher Place||New York|
|Journal||ACM Computing Surveys (CSUR)|
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