DCCC6: Duty Cycle-aware congestion control for 6LoWPAN networks

Michopoulos, Vasilis; Guan, Lin; Oikonomou, George; Phillips, Iain

doi:10.1109/percomw.2012.6197495

Cited by 29 publications

(21 citation statements)

References 4 publications

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“…In related work, four proposed algorithms exist that use traffic control strategies. These algorithms are: DCCC6 [16], Griping [17], Deaf [17] and Fuse [17]. The working principle of Deaf and Fuse algorithms is based on ACK packet loss as the congestion indicator.…”

Section: Performance Evaluationmentioning

confidence: 99%

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Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

Al-Kashoash

Hafeez

Kemp

2017

IEEE Internet Things J.

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Abstract-The Internet of Things (IoT) has been considered as an emerging research area where the 6LoWPAN (IPv6 over Low-Power Wireless Personal Area Network) protocol stack is considered as one of the most important protocol suite for the IoT. Recently, the Internet Engineering Task Force has developed a set of IPv6 based protocols to alleviate the challenges of connecting resource limited (i.e. power supply, processing and memory) sensor nodes to the Internet. In 6LoWPAN networks, heavy network traffic causes congestion which significantly degrades network performance and effects the quality of service (QoS) aspects e.g. throughput, end-to-end delay and energy consumption. In this paper, we formulate the congestion problem as a noncooperative game framework where the nodes (players) behave uncooperatively and demand high data rate in a selfish way. Then, the existence and uniqueness of Nash equilibrium is proved and the optimal game solution is computed by using Lagrange multipliers and KKT conditions. Based on this framework, we propose a novel and simple congestion control mechanism called game theory based congestion control framework (GTCCF) specially tailored for IEEE 802.15.4, 6LoWPAN networks. GTCCF is aware of node priorities and application priorities to support the IoT application requirements. The proposed framework has been tested and evaluated through two different scenarios by using Contiki OS and compared with comparative algorithms. Simulation results show that GTCCF improves performance in the presence of congestion by an overall average of 30.45%, 39.77%, 26.37%, 91.37% and 13.42% in terms of throughput, end-to-end delay, energy consumption, number of lost packets and weighted fairness index respectively as compared to DCCC6 algorithm.

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Section: Performance Evaluationmentioning

confidence: 99%

“…In [16], Michopoulos et al proposed a new congestion control algorithm called Duty Cycle-Aware Congestion Control (DCCC6) for 6LoWPAN networks. The proposed algorithm detects the presence of radio duty cycle and adjusts its operation accordingly.…”

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confidence: 99%

Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

Al-Kashoash

Hafeez

Kemp

2017

IEEE Internet Things J.

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“…In [14], Michopoulos et al proposed a new congestion control algorithm called Duty Cycle-Aware Congestion Control for 6LoWPAN networks (DCCC6). The proposed algorithm detects the presence of radio duty cycle and adjusts its operation accordingly.…”

Section: Related Workmentioning

confidence: 99%

“…In the second scenario, we use a network of one sink node, 18 intermediate nodes and 6 source nodes with node ID of N 20 , N 21 , N 22 , N 23 , N 24 and N 25 . Also, our proposal is compared with a traffic control based algorithm (DCCC6 [14]) and a resource control based algorithm (QU-RPL [17], [18]). In the simulation, the source nodes start sending packets at high data rate (6 packets/s) to create a congested situation.…”

Section: Performance Evaluationmentioning

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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“…For instance, to adapt to these changing requirements, traditional duty cycling algorithms which relied on the assumption of low data rates are now evolving in order to perform efficiently under high and bursty traffic patterns [13]. It is thus becoming increasingly common to conduct WSN protocol evaluation under conditions of heavy network load [13,30].…”

Section: Simulation Topologies and Configurationmentioning

confidence: 99%

IPv6 Multicast Forwarding in RPL-Based Wireless Sensor Networks

Oikonomou

Phillips

Tryfonas

2013

Wireless Pers Commun

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In wireless sensor deployments, network layer multicast can be used to improve the bandwidth and energy efficiency for a variety of applications, such as service discovery or network management. However, despite efforts to adopt IPv6 in networks of constrained devices, multicast has been somewhat overlooked. The Multicast Forwarding Using Trickle (Trickle Multicast) internet draft is one of the most noteworthy efforts. The specification of the IPv6 Routing Protocol for Low power and Lossy Networks (RPL) also attempts to address the area but leaves many questions unanswered. In this paper we highlight our concerns about both these approaches. Subsequently, we present our alternative mechanism, called Stateless Multicast RPL Forwarding algorithm (SMRF), which addresses the aforementioned drawbacks. Having extended the TCP/IP engine of the Contiki embedded operating system to support both Trickle Multicast (TM) and SMRF, we present an in-depth comparison, backed by simulated evaluation as well as by experiments conducted on a multi-hop hardware testbed. Results demonstrate that SMRF achieves significant delay and energy efficiency improvements at the cost of a small increase in packet loss. The outcome of our hardware experiments show that simulation results were realistic. Lastly, we evaluate both algorithms in terms of code size and memory requirements, highlighting SMRF's low implementation complexity. Both implementations have been made available to the community for adoption.

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DCCC6: Duty Cycle-aware congestion control for 6LoWPAN networks

Cited by 29 publications

References 4 publications

Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

Congestion Control for 6LoWPAN Networks: A Game Theoretic Framework

Optimization Based Hybrid Congestion Alleviation for 6LoWPAN Networks

IPv6 Multicast Forwarding in RPL-Based Wireless Sensor Networks

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