Open AccessIoT Based Energy-Efficient Data Aggregation Wireless Sensor Network in Agriculture: A Review
Author(s) -
Sunita Dahiya Vijay Nandal
Publication year - 2021
Publication title -
psychology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.112
H-Index - 10
ISSN - 0033-3077
DOI - 10.17762/pae.v58i1.1194
Subject(s) - wireless sensor network , data aggregator , computer science , data redundancy , redundancy (engineering) , internet of things , efficient energy use , node (physics) , context (archaeology) , computer network , energy harvesting , energy consumption , distributed computing , computer security , energy (signal processing) , engineering , database , electrical engineering , geography , structural engineering , mathematics , operating system , archaeology , statistics
Sensor nodes generate Wireless Sensor Networks (WSNs), these networks have considerable application in the areas of habitat safety, disaster management, surveillance in defense, security & many more areas. WSNs are compact in size, with short battery power & additionally their processing capabilities are low. This restriction of battery power makes them vulnerably faulty. In order to save this limited power, redundant data must be stored inside the sensor node during aggregation which will result in a reduction power dissipation associated with the sending of unnecessary data. By aggregating data, we can control energy consumption by reducing redundancy. Data aggregation is a really effective technique for WSN. In this paper we discuss the aggregation of data and their complex energy-efficient approach used for data aggregation in WSN. This paper highlights the latest innovations in WSNs vital for the research in agricultural domain, further we present their classification & did a comparative analysis of the discussed protocols, the nomenclature of energy saving & harvesting strategies used in agricultural monitoring. Further it discuss the difficulties and drawbacks of WSNs in context of agriculture, The presented comparative study will helpful in increasing number of data processing opportunities available through the Internet of Things (IoT).