Fuzzy Aggregator Based Energy Aware RPL Routing for IoT Enabled Forest Environment

Abstract

Forested areas are extremely vulnerable to disasters leading to environmental destruction. Forest Fire is one among them which requires immediate attention. There are lot of works done by authors where Wireless Sensors and IoT have been used for forest fire monitoring. So, towards monitoring the forest fire and managing the energy efficiently in IoT, Energy Efficient Routing Protocol for Low power lossy networks (E-RPL) was developed. There were challenges about the scalability of the network resulting in a large end-to-end delay and less packet delivery which led to the development of Aggregator-based Energy Efficient RPL with Data Compression (CAA-ERPL). Though CAA-ERPL proved effective in terms of reduced packet delivery, less energy consumption, and increased packet delivery ratio for varying number of nodes, there is still challenge in the selection of aggregator which is based purely on probability percentage of nodes. There has been research work where fuzzy logic been employed for Mobile Ad-hoc Routing, RPL routing and cluster head selection in Wireless Sensor. There has been no work where fuzzy logic is employed for aggregator selection in Energy Efficient RPL. So accordingly, we here have proposed Fuzzy Based Aggregator selection in Energy-efficient RPL for region thereby forming DODAG for communicating to Fog/Edge. We here have developed fuzzy inference rules for selecting the aggregator based on strength which takes residual power, Node degree, and Expected Transmission Count (ETX) as input metrics. The Fuzzy Aggregator Energy Efficient RPL (FA-ERPL) based on fuzzy inference rules were analysed against E-RPL in terms of scalability (First and Half Node die), Energy Consumption, and aggregator node energy deviation. From the analysis, it was found that FA-ERPL performed better than E-RPL. These were simulated using MATLAB and results.

Fuzzy Theory in Fog Computing: Review, Taxonomy, and Open Issues

“…The recommended general architecture is supported by a three-level data management paradigm consisting of dew, fog, and cloud computing for efficient data flow in IoT-based homecare systems. In addition, another study [199] proposed fuzzy-based aggregator selection in energy-efficient RPL for a region, thereby forming DODAG for communicating to Fog/Edge. Fuzzy inference rules were developed for selecting the aggregator based on strength which takes residual power, node degree, and expected transmission count (ETX) as input metrics.…”