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Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation

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Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation Scalable Downward Routing for Wireless Sensor Networks and Internet of Things Actuation Xiaoyang Zhong Yao Liang Indiana University Purdue University Indiana University Purdue University Indianapolis, IN Indianapolis, IN [email protected] [email protected] ABSTRACT Opportunistic Flooding [12], are flooding based and disseminate In this paper, we study the downward routing for network con- control packets to the entire network. The lack of ability in address- trol/actuation in large-scale and heterogeneous wireless sensor ing individual node(s) in those dissemination protocols makes them networks (WSNs) and Internet of Things (IoT). We propose the inefficient and impractical for network actuation in low-power and Opportunistic Source Routing (OSR), a scalable and reliable down- large-scale WSN/IoT deployments. ward routing protocol for WSNs/IoT. OSR introduces opportunistic The standard RPL, the IPv6 routing protocol for low-power and routing into traditional source routing based on the parent set of lossy networks [42], offers the capacity of downward routing, but a node’s upward routing in data collection, significantly address- it has been found that RPL has several significant flaws in its down- ing the drastic link dynamics in low-power and lossy WSNs. We ward point-to-multipoint communication (e.g.,[4, 6, 13, 14, 17–21]). devise a novel adaptive Bloom filter mechanism to effectively and RPL essentially suffers from the severe scalability problem for down- efficiently encode a downward source-route in OSR, which enables ward routing [4, 13, 14, 21]. In RPL storing mode, a node stores a significant reduction of the length of source-route field in packet routing entries for all destinations in its subgraph/subtree, poten- header. OSR is scalable to very large-size WSN/IoT deployments, tially suffering from severe scalability and reliability problem in since each resource-constrained node in the network only stores large WSNs. On the other hand, RPL non-storing mode uses source the set of its direct children. The probabilistic nature of the Bloom routing [16] through the sink/root, which suffers from not only filter passively explores opportunistic routing. Upon a delivery fail- increased risk of packet fragmentation and thus increased battery ure at any hop along the downward path, OSR actively performs power and network capacity consumption, but also the scalability opportunistic routing to bypass the obsolete/bad link. We demon- issue of the possible length of route in a network, given constrained strate the desirable scalability of OSR against the standard RPL wireless layers, such as IEEE 802.15.4 with a maximum frame size downward routing. We evaluate the performance of OSR via both of 127 bytes (including header) [34]. Moreover, it seems that RPL simulations and real-world testbed experiments, in comparison (non-storing mode) might not effectively fix any unreachable fail- with the standard RPL (both storing mode and non-storing mode), ure in downward routing due to wireless link dynamics. Although ORPL, and the representative dissemination protocol Drip. Our recent approaches such as ORPL [7] and CBFR [30] attempted to results show that OSR significantly outperforms RPL and ORPL address the scalability issue of downward routing, these improve- in scalability and reliability. OSR also achieves significantly better ments are limited for highly resource-constrained wireless devices energy efficiency compared to TinyRPL and Drip which are based (see [14], for example). Indeed, it is increasingly urgent to systemat- on the same TinyOS platform as OSR implementation. ically study scalable, reliable and resource-efficient WSN/IoT down- ward routing for emerging large-scale and resource-constrained KEYWORDS WSN/IoT system. Source routing includes the source-route information in the Wireless Sensor Networks, Internet of Things, downward routing, packet header to route packets from the source node to destination scalability, adaptive Bloom filter without building and maintaining routing tables at intermediate nodes. However, a direct application of source routing (e.g., RPL arXiv:1802.03898v1 [cs.NI] 12 Feb 2018 1 INTRODUCTION non-storing mode) to WSN/IoT downward routing is problematic. Wireless sensor networks (WSNs) and Internet of Things (IoT) have First, the dynamic nature of WSNs significantly affects the reliabil- been increasingly applied to various areas such as environmental ity of the traditional source routing. The specified source-route of a monitoring, structure monitoring, smart buildings, smart cities, pre- control packet may be obsolete and therefore unavailable when the cision agriculture, and e-health systems. Data collection is the basic packet arrives at an intermediate node due to wireless link dynamics. application scenario in WSNs, where sensor nodes periodically sam- Second, the traditional source routing does not scale well in WSNs, ple and transmit data packets upward to one or multiple network because physical layer protocols of WSNs are designed to have a sink(s). On the other hand, delivering control packets downward small frame size (e.g., IEEE 802.15.4 [34]) for energy efficiency. As from the sink to individual sensor/actuator nodes is also essential the network diameter and hence the path length increases, contain- in many WSN/IoT application scenarios, including actuating target ing the full source-route in a packet is inefficient and may even be actuator(s), reconfiguring node parameters (e.g., sampling rate), and infeasible. Thus, a desirable and practical source routing protocol in querying data from specific node(s). However, the WSN downward WSNs/IoT must simultaneously satisfy the requirements of reliabil- routing is significantly less studied than WSN upward routing. The ity under a highly dynamic wireless communication environment major downstream protocols, such as Drip [39], Glossy [8], and and scalability for very large WSN/IoT deployments. overhead. OSR enables each node to only store its direct child set as the node’s downward routing table, and therefore scales well to large-size WSN/IoT deployments. • We develop our OSR protocol, working with the popular Collection Tree Protocol (CTP) [11]. OSR inspects CTP data packets to build the direct child set of each node. Thus, the energy overhead is negligible for building and maintaining the downward routing table. Downward routing paths are obtained based on network upward routing topology tomog- raphy. • We evaluate our OSR protocol through both simulations and real-world testbed experiments. We show that OSR sig- Figure 1: A conceptual illustration of downward packet de- nificantly outperforms RPL (both storing and non-storing livery with OSR versus RPL/ORPL/CBFR. modes) and ORPL on scalability and reliability. OSR is also much more energy efficient than TinyRPL and Drip which are based on the same TinyOS platform as OSR implementa- In this work, we present an Opportunistic Source Routing proto- tion. col, referred to as OSR, to achieve desirable scalability and reliability The remainder of the paper is organized as follows. Section 2 for heterogeneous WSN/IoT actuation. Our approach is leveraged presents the design of OSR. Section 3 evaluates OSR via both simu- on the recent new WSN capability of the reconstruction of upward lations and testbed experiments. Section 4 discusses our insights routing paths [10, 24, 25], where individual upstream data packet and the limitations of the current OSR implementation. Section 5 paths from WSN nodes to the sink can be reconstructed at the describes the related work in detail. Finally, Section 6 concludes sink with a minimal overhead of path encoding piggybacking to our work and discusses the future work. each data packet and updated in every data collection cycle. Our designed OSR protocol introduces opportunistic routing into the source routing, which is based on the parent set [27] of a node’s 2 OSR DESIGN upward routing, to exploit alternative downward paths to address OSR introduces opportunistic routing into source routing, and cre- wireless link dynamics. We devise a novel adaptive Bloom filter ates an adaptive Bloom filter mechanism to encode the downward mechanism to efficiently encode and compress the source-route source-route path. This section presents the core mechanisms of path. The probabilistic nature of the Bloom filter passively enables OSR including path representation, direct child set maintenance, opportunistic routing for downward packet forwarding. In addition, and opportunistic routing. when a downward link between a parent node and its child node is broken, active opportunistic routing is activated to find one or more 2.1 Adaptive Bloom Filter for Path Encoding other parent(s) in the child’s parent set to continue the downward In traditional source routing, the entire raw routing path is included forwarding. OSR only requires that each node store its direct child in the packet header. As network grows, this approach consumes too set rather than its entire subgraph of descendants as in RPL (storing much overhead or may even be infeasible for large-scale WSNs. For mode) or in ORPL (compressed entire subgraph) for making down- instance, containing a source-route of 20 hops using two-byte short ward routing decision, and therefore, OSR is extremely scalable for address in IEEE 802.15.4 takes nearly one third of the maximum link constrained WSN/IoT sensor/actuator nodes. The proposed OSR is layer frame size (i.e., 127 bytes). Thus, path encoding becomes a ne- general and independent of the underlying link layer. cessity for source routing to scale in resource-restricted WSNs/IoT. To illustrate, Fig. 1 shows an example of WSN/IoT downward OSR exploits the Bloom filter2 [ , 36] to encode the source-route actuation. To deliver a packet to node G, OSR includes the source- path, that is, a Bloom filter representing the source-route is included route encoded using Bloom filter and the destination in the packet in the packet header instead of the raw path. header; each node only stores its direct children.
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