A measurement study of TCP over RPL in low-power and lossy networks

Kim, Hyung-Sin; Im, Heesu; Lee, Myung-Sup; Paek, Jeongyeup; Bahk, Saewoong

doi:10.1109/jcn.2015.000111

Cited by 38 publications

(16 citation statements)

References 27 publications

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“…TinyRPL operates in storing mode for downward routing. The more detailed operation and bugfixes of TinyRPL are described in Kim et al [30,31]. Figure 6(a) through (f) plot relevant performance metrics of RPL with varying traffic load when nodes use transmit power of 0 dBm, which is the maximum allowed by the IEEE 802.15.4 standard.…”

Section: Rpl: Ipv6 Routing Protocol For An Llnmentioning

confidence: 99%

“…With respect to implementation, Ko et al [38] experimentally evaluated the performance of RPL in TinyOS and showed that its performance is similar to that of CTP, the de facto data collection protocol in TinyOS. Two pieces of work in Kim et al [30] and Park and Paek [55] investigated the performance of TCP when used over RPL via testbed experiments, and Ko et al [39] investigated interoperability of two modes of operations (MOPs) defined in the RPL standard. In addition, Clausen et al [9] provided a critical evaluation of RPL regarding limitations, trade-offs, and suggestions for improvements.…”

Section: Transmission Power and Rssi Thresholds Controlmentioning

confidence: 99%

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Pc-RPL

Kim

Paek

Culler

et al. 2020

ACM Trans. Sen. Netw.

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We present PC-RPL, a transmission power-controlled IPv6 routing protocol for low-power and lossy wireless networks that significantly improves the end-to-end packet delivery performance under heavy traffic compared to the standard RPL. We show through actual design, implementation, and experiments that a multihop wireless network can achieve better throughput and routing stability when transmission power and routing topology are "jointly and adaptively" controlled. Our experiments show that the predominant "fixed and uniform" transmission power strategy with "link quality and hop distance"-based routing topology construction (i.e., RPL) loses significant bandwidth due to hidden terminal and load imbalance problems. We design an adaptive and distributed control mechanism for transmission power and routing topology, named PC-RPL, on top of the standard RPL routing protocol for hidden terminal mitigation and load balancing. We implement PC-RPL on real embedded devices and evaluate its performance on a 49-node multihop testbed. PC-RPL reduces total end-to-end packet losses by approximately sevenfold without increasing hop distance compared to RPL with the highest transmission power, resulting in 17% improvement in aggregate bandwidth and 64% improvement for the worst-case node by successfully alleviating both hidden terminal and load imbalance problems.

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Section: Rpl: Ipv6 Routing Protocol For An Llnmentioning

confidence: 99%

Section: Transmission Power and Rssi Thresholds Controlmentioning

confidence: 99%

Pc-RPL

Kim

Paek

Culler

et al. 2020

ACM Trans. Sen. Netw.

Self Cite

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“…Also, the proposed model studies the effect of the segment size, the FEC redundancy ratio, and the maximum MAC retransmissions on the total energy consumption. In [105], Kim et al presented a comprehensive experimental study on the performance of TCP over RPL in 6LoWPAN network by using TinyOS and a multihop testbed of 30 node network. The experimental results show that TCP sacrifices significant throughput to maintain its reliability.…”

Section: B Resource Control Algorithmsmentioning

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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“…The standard RPL, the IPv6 routing protocol for low-power and lossy networks [42], offers the capacity of downward routing, but it has been found that RPL has several significant flaws in its downward point-to-multipoint communication (e.g., [4,6,13,14,[17][18][19][20][21]). RPL essentially suffers from the severe scalability problem for downward routing [4,13,14,21].…”

Section: Introductionmentioning

confidence: 99%

Scalable Downward Routing for Wireless Sensor Networks Actuation

Zhong

Liang

2019

IEEE Sensors J.

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In this paper, we study the downward routing for network control/actuation in large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things (IoT). We propose the Opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for WSNs/IoT. OSR introduces opportunistic routing into traditional source routing based on the parent set of a node's upward routing in data collection, significantly addressing the drastic link dynamics in low-power and lossy WSNs. We devise a novel adaptive Bloom filter mechanism to effectively and efficiently encode a downward source-route in OSR, which enables a significant reduction of the length of source-route field in packet header. OSR is scalable to very large-size WSN/IoT deployments, since each resource-constrained node in the network only stores the set of its direct children. The probabilistic nature of the Bloom filter passively explores opportunistic routing. Upon a delivery failure at any hop along the downward path, OSR actively performs opportunistic routing to bypass the obsolete/bad link. We demonstrate the desirable scalability of OSR against the standard RPL downward routing. We evaluate the performance of OSR via both simulations and real-world testbed experiments, in comparison with the standard RPL (both storing mode and non-storing mode), ORPL, and the representative dissemination protocol Drip. Our results show that OSR significantly outperforms RPL and ORPL in scalability and reliability. OSR also achieves significantly better energy efficiency compared to TinyRPL and Drip which are based on the same TinyOS platform as OSR implementation.

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A measurement study of TCP over RPL in low-power and lossy networks

Cited by 38 publications

References 27 publications

Pc-RPL

Pc-RPL

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

Scalable Downward Routing for Wireless Sensor Networks Actuation

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