Adaptive Congestion Window Algorithm for the Internet of Things Enabled Networks

Abstract

Heterogeneous constrained computing resources in the Internet of Things (IoT) are communicated, collected, and share information from the environment using sensors and other high-speed technologies which generate tremendous traffic and lead to congestion in the Internet of Things (IoT) networks. This paper proposes an Adaptive Congestion Window (ACW) algorithm for the Internet of Things. This algorithm is adapted to the traffic changes in the network. The main objective of this paper is to increase the packet delivery ratio and reduce delay while enhancing the throughput which can be attained by avoiding congestion. Therefore, in the proposed algorithm, the congestion window size is depending on the transmission rate of the source node, the available bandwidth of the path, and the receiving rate of the destination node. The congestion window size is altered when the link on the path needs to be shared/released with/by other paths of different transmission in the network. The proposed algorithm, ACW is simulated, evaluated in terms of packet delivery ratio, throughput, and delay. The performance of the proposed algorithm, ACW is compared with IoT Congestion Congrol Algorithm (IoT-CCA) and Improved Stream Control Transmission Protocol (IMP-SCTP) and proved to be better by 27.4%, 11.8%, and 33.7% than IoT-CCA and 44.1%, 22.6%, and 50% than IMP-SCTP concerning packet delivery ratio, throughput, and delay respectively. The variation in congestion window size with time is also projected.