2016/07/12 by Gabriel Martins Dias, Dias, Gabriel Martins, Boris Bellalta +3 · 1 citation
Computer Science · Engineering · #A.1 #C.2.4 #Energy Efficient Wireless Sensor Networks #Energy Harvesting in Wireless Networks #FOS: Computer and information sciences #I.2 #Indoor and Outdoor Localization Technologies #Networking and Internet Architecture (cs.NI)
paper · pdf · doi:10.48550/arxiv.1607.03443
openalex publication_date 2016/07/12 · openalex created_date 2022/10/06 · openalex updated_date 2026/07/28
One of the main characteristics of Wireless Sensor Networks (WSNs) is the\nconstrained energy resources of their wireless sensor nodes. Although this\nissue has been addressed in several works and got a lot of attention within the\nyears, the most recent advances pointed out that the energy harvesting and\nwireless charging techniques may offer means to overcome such a limitation.\nConsequently, an issue that had been put in second place, now emerges: the low\navailability of spectrum resources. Because of it, the incorporation of the\nWSNs into the Internet of Things and the exponential growth of the latter may\nbe hindered if no control over the data generation is taken. Alternatively,\npart of the sensed data can be predicted without triggering transmissions and\ncongesting the wireless medium. In this work, we analyze and categorize\nexisting prediction-based data reduction mechanisms that have been designed for\nWSNs. Our main contribution is a systematic procedure for selecting a scheme to\nmake predictions in WSNs, based on WSNs' constraints, characteristics of\nprediction methods and monitored data. Finally, we conclude the paper with a\ndiscussion about future challenges and open research directions in the use of\nprediction methods to support the WSNs' growth.\n