부산대학교 도서관

Congestion management in the Internet of Things (IoT) is one of the most challenging tasks in improving the quality of service (QoS) of a network. This is largely because modern wireless networks can consist of an immense number of connections. Consequently, limited network resources can be consumed simultaneously. This eventually causes congestion that has adverse impacts on both throughput and transmission delay. This is particularly true in a network whose transmissions are regulated by the Constrained Application Protocol (CoAP), which has been widely adopted in the IoT network. CoAP has a mechanism that allows connection-oriented communication by means of acknowledgment messages (ACKs) and retransmission timeouts (RTOs). However, during congestion, a client node is unable to efficiently specify the RTO, resulting in unnecessary retransmission. This overhead in turn causes even more extensive congestion in the network. Therefore, this research proposes a novel scheme for optimally setting the initial RTO and adjusting the RTO backoff that considers current network utilization. The scheme consists of three main components: 1) a multidimensional congestion estimator that determines congestion conditions in various aspects, 2) precise initial RTO estimation by means of a relative strength indicator and trend analysis, and 3) a flexible and congestion-aware backoff strategy based on an adaptive-boundary backoff factor evaluated by using a fuzzy logic system (FLS). The simulation results presented here reveal that the proposed scheme outperforms state-of-the-art methods in terms of the carried load, delay and percentage of retransmission.