Novel Geodetic Fuzzy Subgraph-Based Ranking for Congestion Control in RPL-IoT Network

Abstract

Congestion control is among the most challenging tasks in enhancing QoS in the Internet of Things (IoT). Currently, wireless networks are able to have a large number of connections but with a limited amount of network resources. Consequently, congestion occurs, which adversely affects throughput, transmission delay, packet losses, power consumption management, and the lifespan of a network. This is certainly relevant in networks where transmissions are controlled by the Routing Protocol for Low-Power and Lossy Networks (RPL), which is commonly employed in the Internet of Things network. To solve this problem, a novel Geodetic fuzzy subgraph-based ranking (GFSR-RPL) for congestion control is proposed. Initially, the proposed GFSR-RPL selects the cluster head using K-means clustering. Then the rank calculation can be done via the final route setting for data transmission. A route setup scheme consists of three elements: 1) a Round Trip Time (RTT) estimator that assesses congestion conditions in a variety of ways; 2) a trend and relative strength indicator analysis; and 3) a geodetic fuzzy subgraph rank calculation method that calculates initial RTO (initial retransmission timeouts) accurately. The proposed GFSR-RPL method reduces the energy consumption of up to 43.58%, 25.8%, 14.82% and 6.85% than existing methods such as RPR, CBR-RPL, ACW and ECLRPL.

Keywords

• clustering
• geodetic fuzzy subgraph
• internet of things
• routing protocol for low-power and lossy networks
• routing