Geometric Sequence Technique for Effective RTO Estimation in CoAP

Abstract

Internet of Things (IoT) is a network where physical objects with Internet connectivity can interact and exchange information with other connected objects. IoT devices are constrained in terms of power and memory, and have limited communication capabilities. The Constrained Application Protocol (CoAP) is a lightweight messaging protocol which is widely used by various IoT applications in low power and lossy wireless networks. CoAP provides reliability and minimal congestion control via a fixed Retransmission TimeOut (RTO) and Binary Exponential Backoff (BEB). It does not maintain end-to-end connection information and therefore, cannot adapt RTO based on the network conditions. Moreover, CoAP resets the RTO to its default value after having received the ACK for the retransmitted packet. This approach of resetting the RTO degrades the performance in a network with high latency and leads to spurious retransmissions. In this paper, we propose a Geometric Sequence Technique (GST) for effective RTO estimation in CoAP. GST retains the previous RTO value after having received the ACK for the retransmitted packet and eventually returns to the default value by decreasing the RTO depending on the number of consecutive successful transmissions. The proposed technique is implemented in Contiki OS and validated against the existing mechanisms. The experiments have been conducted using the Cooja simulator and the FIT/IoT-LAB testbed to verify the effectiveness of the proposed technique. The results show that GST minimizes the Flow Completion Times (FCT), reduces the number retransmissions and improves the network throughput.