Browsing by Subject "downward routing"

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    Scalable Downward Routing for Wireless Sensor Networks Actuation
    (IEEE, 2019-10) Zhong, Xiaoyang; Liang, Yao; Computer and Information Science, School of Science
    In this paper, we study the downward routing for network control/actuation in large-scale and heterogeneous wireless sensor networks (WSNs). We propose the opportunistic source routing (OSR), a scalable and reliable downward routing protocol for WSNs. 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 the downward source-route in OSR, which enables a significant reduction of the length of source-route field in the packet header. OSR is scalable to very large-size WSN deployments, since each resource-constrained node in the network stores only the set of its direct children. We present an analytical scalability model and evaluate the performance of OSR via both the 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 the OSR significantly outperforms RPL and ORPL in scalability and reliability. OSR also achieves significantly better energy efficiency compared with TinyRPL and Drip which are based on the same TinyOS platform as OSR implementation.
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    Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation
    (IEEE, 2018-10) Zhong, Xiaoyang; Liang, Yao; Computer and Information Science, School of Science
    We present the opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things IoT. We devise a novel adaptive Bloom filter mechanism to efficiently encode the downward source route in OSR, which significantly reduces the length of the source route field in the packet header. Moreover, each node in the network stores only the set of its direct children. Thus, OSR is scalable to very large-size WSN/IoT deployments. 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 networks (LLNs). Our evaluation of OSR via both simulations and real-world testbed experiments demonstrates its merits in comparison with the state-of-the-art protocols.
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    Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation
    (IEEE, 2018) Zhong, Xiaoyang; Liang, Yao; Computer and Information Science, School of Science
    We present the opportunistic Source Routing (OSR), a scalable and reliable downward routing protocol for large-scale and heterogeneous wireless sensor networks (WSNs) and Internet of Things IoT. We devise a novel adaptive Bloom filter mechanism to efficiently encode the downward source route in OSR, which significantly reduces the length of the source route field in the packet header. Moreover, each node in the network stores only the set of its direct children. Thus, OSR is scalable to very large-size WSN/IoT deployments. 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 networks (LLNs). Our evaluation of OSR via both simulations and real-world testbed experiments demonstrates its merits in comparison with the state-of-the-art protocols.