Congestion-aware RPL for 6L0WPAN networks

Al-Kashoash, Hayder A. A.; Al-Nidawi, Yaarob; Kemp, Andrew H.

doi:10.1109/wts.2016.7482026

Cited by 43 publications

(35 citation statements)

References 14 publications

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“…Some papers call it loss probability [42]. This metric takes into account the total number of lost packets due to buffer overflow and wireless channel loss [39], [44]. Queue length (queue level): this metric shows the average number of packets stored in the nodes' buffer over time [45], [46].…”

Section: Performance Evaluation Metricsmentioning

confidence: 99%

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

et al. 2018

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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.

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Section: Performance Evaluation Metricsmentioning

confidence: 99%

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

et al. 2018

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“…The lower density gives the objective function less options making the difference in performance of mRPL and D-RPL down to 2% only. Higher node density increases the chance of collisions and leads to higher packet loss due to interference and congestion [22].…”

Section: Practical Testingmentioning

confidence: 99%

Dynamic RPL for multi-hop routing in IoT applications

Kharrufa

Al-Kashoash

Al-Nidawi

et al. 2017

2017 13th Annual Conference on Wireless on-Demand Network Systems and Services (WONS)

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Abstract-The Routing Protocol for Low Power and Lossy Networks (RPL) has become the standard routing protocol for the Internet of Things (IoT). This paper investigates the use of RPL in dynamic networks and presents an enhanced RPL for different applications with dynamic mobility and diverse network requirements. This implementation of RPL is designed with a new dynamic Objective-Function (D-OF) to improve the Packet Delivery Ratio (PDR), end-to-end delay and energy consumption while maintaining low packet overhead and loop-avoidance. We propose a controlled reverse-trickle timer based on received signal strength identification (RSSI) readings to maintain high responsiveness with minimum overhead and consult the objective function when a movement or an inconsistency is detected to help nodes make an informed decision. Simulations are done using Cooja with random waypoint mobility scenario for healthcare applications considering multi-hop routing. The results show that the proposed dynamic RPL (D-RPL) adapts to the nodes mobility and has a higher PDR, slightly lower end-to-end delay and reasonable energy consumption compared to related existing protocols.

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“…In [22], Al-Kashoash et al proposed a new RPL based objective function called congestion-aware objective function (CA-OF) that works efficiently when congestion occurs. The proposed objective function combines two metrics (buffer occupancy and ETX) and forwards packets to sink node through less congested nodes.…”

Section: Related Workmentioning

confidence: 99%

“…1. The authors in [17], [22], [28], [29] also have demonstrated the problem of "load balancing" or "parent selection" within congestion in the RPL routing protocol. However, even with congestion aware routing metrics and objective functions, sometimes a leaf node can not find a less or non-congested parent and the incoming rate to the parent is higher than its outgoing rate.…”

Section: Network Setup and Problem Formulationmentioning

confidence: 99%

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

Al-Kashoash

Amer

Mihaylova

et al. 2017

IEEE Internet Things J.

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Abstract-The IPv6 over Low-Power Wireless Personal Area Network (6LoWPAN) protocol stack is a key part of the Internet of Things (IoT) where the 6LoWPAN motes will account for the majority of the IoT 'things'. In 6LoWPAN networks, heavy network traffic causes congestion which significantly effects the network performance and the quality of service (QoS) metrics. Generally, two main strategies are used to control and alleviate congestion in 6LoWPAN networks: resource control and traffic control. All the existing work of congestion control in 6LoWPAN networks use one of these. In this paper, we propose a novel congestion control algorithm called optimization based hybrid congestion alleviation (OHCA) which combines both strategies into a hybrid solution. OHCA utilizes the positive aspects of each strategy and efficiently uses the network resources. The proposed algorithm uses a multi-attribute optimization methodology called grey relational analysis for resource control by combining three routing metrics (buffer occupancy, expected transmission count and queuing delay) and forwarding packets through noncongested parents. Also, OHCA uses optimization theory and Network Utility Maximization (NUM) framework to achieve traffic control when the non-congested parent is not available where the optimal nodes' sending rate are computed by using Lagrange multipliers and KKT conditions. The proposed algorithm is aware of node priorities and application priorities to support the IoT application requirements where the applications' sending rate allocation is modelled as a constrained optimization problem. OHCA has been tested and evaluated through simulation by using Contiki OS and compared with comparative algorithms. Simulation results show that OHCA improves performance in the presence of congestion by an overall average of 28.36%, 28.02%, 48.07%, 31.97% and 90.35% in terms of throughput, weighted fairness index, end-to-end delay, energy consumption and buffer dropped packets as compared to DCCC6 and QU-RPL.

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Congestion-aware RPL for 6L0WPAN networks

Cited by 43 publications

References 14 publications

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

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

Dynamic RPL for multi-hop routing in IoT applications

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

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