Congestion control in wireless sensor and 6LoWPAN networks: toward the Internet of Things

Abstract

The Internet of Things (IoT) is the next big challenge for the research community where the IPv6 over low power wireless personal area network (6LoWPAN) protocol stack is a key part of the IoT. Recently, the IETF ROLL and 6LoWPAN working groups have developed new IP based protocols for 6LoWPAN networks to alleviate the challenges of connecting low memory, limited processing capability, and constrained power supply sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and impacts on quality of service (QoS) aspects such as throughput, latency, energy consumption, reliability, and packet delivery. In this paper, we overview the protocol stack of 6LoWPAN networks and summarize a set of its protocols and standards. Also, we review and compare a number of popular congestion control mechanisms in wireless sensor networks (WSNs) and classify them into traffic control, resource control, and hybrid algorithms based on the congestion control strategy used. We present a comparative review of all existing congestion control approaches in 6LoWPAN networks. This paper highlights and discusses the differences between congestion control mechanisms for WSNs and 6LoWPAN networks as well as explaining the suitability and validity of WSN congestion control schemes for 6LoWPAN networks. Finally, this paper gives some potential directions for designing a novel congestion control protocol, which supports the IoT application requirements, in future work.

An Enhanced Distributed Congestion Control Method for Classical 6LowPAN Protocols Using Fuzzy Decision System

“…Nevertheless, one of the main challenges to these types of networks is increasing the quality of service and node longevity in the network, which is due to the limited resources of wireless sensor nodes [2]. Several factors cause energy loss in network nodes, which include collision frames, retransmitting from source, crosstalk, queue delay, hidden terminal, over emitting, idle-listening and control overhead of designed protocols [3].…”

“…To achieve these goals the network faces a challenge since numerous parameters within the network without the use of the decision system complicates the process. However, the MADM and MODM have been utilized as good solutions [14], [15]. In the present study, a congestion control method based on Congestion Control Fuzzy Decision Making (CCFDM) has been proposed, which prevents congestion in the network and manages the data flow by timely detecting congestion and confronting it in a distributed manner, this in turn helps in maintaining network resources.…”

RPL-Based Routing Protocols in IoT Applications: A Review

“…With multiple nodes transmitting at high rates, the risk of congestion becomes greater and both the wireless channel and the nodes' buffer become congested [84]. Congestion leads to significant deterioration in energy consumption, reliability and delay [85]. There are different approaches to solve the problem of congestion, the most common are resource control, traffic control and hybrid schemes.…”

The Future of Low-End Motes in the Internet of Things: A Prospective Paper

“…To provide connectivity and interoperability among all the existing heterogeneous wireless solutions, a truly standard and lightweight communication stack is mandatory. From sensor to cloud, a multitude of wireless technologies is already deployed, causing huge communication heterogeneity and interoperability issues when designing connected IoT devices [25][26][27][28]. The heterogeneity of the IoT infrastructure makes the standardization extremely difficult.…”

“…Meanwhile, IETF IPv6 over Low power WPAN (6LoWPAN) working group has contributed with the specification of the 6LoWPAN adaptation layer, which enables IPv6 datagrams over IEEE 802.15.4-based networks. The combination of IEEE 802.15.4-compliant radios with the 6LoWPAN specification enables the seamless integration of constrained devices with the Internet, which is also the key enabler for interoperability and connectivity between IP-enabled low-end devices [12,13,28,[31][32][33]. [31,32,34]).…”