CoCoA++: Delay gradient based congestion control for Internet of Things

Rathod, Vishal J.; Jeppu, Yogananda; Sastry, Samanvita; Singala, Shruti; Tahiliani, Mohit P.

doi:10.1016/j.future.2019.04.054

Cited by 29 publications

(17 citation statements)

References 16 publications

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“…The concept of enhancing the precision of RTT and RTO adjustments by means of time-series forecasting has since continuously propelled developments in the field. In 2019, V. Rathod et al proposed CoCoA++ in an article on delay gradient-based congestion control for the Internet of Things [27]. In their proposal, the precision of RTT estimation was enhanced by using a statistical prediction method called the CAIA delay gradient (CDG) in predicting RTT levels during congestion.…”

Section: Related Workmentioning

confidence: 99%

“…Nevertheless, the changes in network traffic still conform to cyclical trends. According to previous studies [14], [26], [27], the initial RTO is estimated by the expressions given in Eqs.…”

Section: A Initial Rto Mismatch In Bursty Trafficmentioning

confidence: 99%

“…This results in having to increase the RTO via a backoff mechanism. To this end, existing studies [14], [24], [26], [27] propose various backoff approaches, examples of which are illustrated in Fig. 2.…”

Section: B Lack Of a Backoff Mechanism In A Constraint Networkmentioning

confidence: 99%

“…Sometimes, an unsuitable RTO, determined by providing more or less backoff than necessary, can cause retransmission overhead and hence further delay. Later, another study [27] proposed flexible backoff adjustment by using a PBF, as given in Eq. (6).…”

Section: B Lack Of a Backoff Mechanism In A Constraint Networkmentioning

confidence: 99%

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An Enhanced CoAP Scheme Using Fuzzy Logic With Adaptive Timeout for IoT Congestion Control

2021

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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.

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Section: Related Workmentioning

confidence: 99%

Section: A Initial Rto Mismatch In Bursty Trafficmentioning

confidence: 99%

Section: B Lack Of a Backoff Mechanism In A Constraint Networkmentioning

confidence: 99%

Section: B Lack Of a Backoff Mechanism In A Constraint Networkmentioning

confidence: 99%

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An Enhanced CoAP Scheme Using Fuzzy Logic With Adaptive Timeout for IoT Congestion Control

2021

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“…Their performance analysis shows that, in a network of bursty traffic, the performance of CoCoA+ can be significantly worse than the default CoAP because of the inability to adequately select a proper RTO value. Rathod et al proposed a new CC mechanism for CoAP based on CAIA delay gradient (CDG) to predict and determine the network congestion, obtain the gradient of RTT over time and using a probabilistic backoff factor (PBF) to monitor and control the network congestion and to enable the adjustment of RTO based on inferred condition. Results show that with the implementation of delay gradients, it is quite effective to determine the accurate level of congestion, reduce the RTO with minimal delays and high packet sending rates.…”

Section: Introductionmentioning

confidence: 99%

CACC: Context‐aware congestion control approach for lightweight CoAP/UDP‐based Internet of Things traffic

Akpakwu

Hancke

Abu‐Mahfouz

2019

Trans Emerging Tel Tech

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With the emerging applications of the Internet of Things (IoT), a congestion control mechanism becomes a critical phenomenon for efficient communication in networks of constrained devices. The Internet Engineering Task Force developed the constrained application protocol (CoAP) as a standard communication protocol that favors lightweight interoperability for accommodating resource‐constrained devices. However, the base CoAP specification congestion control is insensitive to various network conditions. Thus, differentiating the scenario of packet loss due to bit error rate and congestion, and identifying correct round trip time (RTT) of retransmitted message‐acknowledgement is quite essential to adapt the CoAP behavior based on the network status. In this paper, we present a context‐aware congestion control (CACC) approach for lightweight CoAP/user datagram protocol–based IoT traffic. The CACC proposes mechanisms that include retransmission timeout (RTO) estimator, retransmission count–based smoothed round‐trip‐time observation, lower bound RTO restriction approach, and aging concept. The proposed RTO estimators utilize the strong, weak, and failed RTT to identify exact network status and provide adaptive congestion control. The CACC incorporates the variable of retransmission count in request‐response interaction model to mitigate the negative variation in RTT due to the fluctuation in the IoT environment. Moreover, with lower bound RTO restriction approach, the unnecessary spurious retransmissions are avoided, and the aging mechanism limits the validity of the RTO value to improve the efficiency of the proposed scheme. The proposed model is validated against baseline CoAP and CoCoA+ using Contiki OS and the Cooja simulator. The results are impressive under different network topologies.

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