A dynamic distributed boundary node detection algorithm for management zone delineation in Precision Agriculture. (1st October 2020)

• Record Type: Journal Article
• Authors: Sapna, ; Pattanaik, K.K.; Trivedi, Aditya
• Is Part Of: Journal of network and computer applications. Volume 167(2020)
• Publication Date: 2020-10-01
• Subjects: Distributed boundary node detection -- CPS -- VRI -- WSN -- Energy consumption; Microcomputers -- Periodicals; Computer networks -- Periodicals; Application software -- Periodicals; Micro-ordinateurs -- Périodiques; Réseaux d'ordinateurs -- Périodiques; Logiciels d'application -- Périodiques; Application software; Computer networks; Microcomputers; Periodicals; 004.05; 004
• DOI: 10.1016/j.jnca.2020.102712
• Languages: English
• ISSNs: 1084-8045

Abstract

Abstract: Dividing a larger area into smaller subregions is a well addressed problem in Precision Agriculture (PA) where the existing application specific solutions (laboratory based) require human intervention and result in static region demarcation schemes. However, the boundary of a subregion is subject to change with various soil and environmental parameters. On the other hand, Wireless Sensor Networks (WSNs), a potential candidate to produce dynamic subregions due to its real-time decision making capability, are utilized as merely a data collection unit for PA. In an attempt to introduce the in-network decision making feature of WSN in PA, design of a novel three layered WSN-CPS architecture is presented in this work and at layer-I the distributed mechanism for region demarcation is proposed. The proposed scheme identifies nodes based on data values and position information, that serve as the boundary of a subregion and data transmitters to the base station for final decision making. Existing methods for boundary node detection identify network boundary nodes (not designed to demarcate the interior boundary), coverage hole boundary nodes (identifies boundary nodes of a smaller portion in network), and event boundary nodes (not able to identify outer boundary). The proposed work identifies network boundary nodes, coverage hole boundary nodes, and the subregion boundary nodes accurately. It takes various critical situations into account while labelling of nodes and shows its reliability in node failure conditions. Impact of varying transmission range and number of nodes is analyzed on proposed mechanism via simulation. In a comparative study with recent network boundary node detection scheme, decrease of 32% and 30% is seen at 200 and 300 nodes respectively in terms of energy consumption.