CBFR: Bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes

Reinhardt, Andreas; Morar, Olivia; Santini, Silvia; Zöller, Sebastian; Steinmetz, Ralf

doi:10.1109/wowmom.2012.6263685

Cited by 21 publications

(11 citation statements)

References 21 publications

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“…A hierarchy of BFs were used by the sink, cluster head ports, and each cluster head itself to filter out unpromising transmissions [50]. Particularly, in tree-structured data collection WSNs, packets are routed towards a sink node [51]. Each node in the collection tree stores the addresses of its direct and indirect child nodes in its local BF.…”

Section: B Packet Routing and Forwardingmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

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Bloom filter (BF) has been widely used to support membership query, i.e., to judge whether a given element x is a member of a given set S or not. Recent years have seen a flourish design explosion of BF due to its characteristic of space-efficiency and the functionality of constant-time membership query. The existing reviews or surveys mainly focus on the applications of BF, but fall short in covering the current trends, thereby lacking intrinsic understanding of their design philosophy. To this end, this survey provides an overview of BF and its variants, with an emphasis on the optimization techniques. Basically, we survey the existing variants from two dimensions, i.e., performance and generalization. To improve the performance, dozens of variants devote themselves to reducing the false positives and implementation costs. Besides, tens of variants generalize the BF framework in more scenarios by diversifying the input sets and enriching the output functionalities. To summarize the existing efforts, we conduct an in-depth study of the existing literature on BF optimization, covering more than 60 variants. We unearth the design philosophy of these variants and elaborate how the employed optimization techniques improve BF. Furthermore, comprehensive analysis and qualitative comparison are conducted from the perspectives of BF components. Lastly, we highlight the future trends of designing BFs. This is, to the best of our knowledge, the first survey that accomplishes such goals.

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Section: B Packet Routing and Forwardingmentioning

confidence: 99%

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Luo

Guo

Richard

et al. 2019

IEEE Commun. Surv. Tutorials

178

View full text Add to dashboard Cite

show abstract

“…In case multiple hash functions are being used, the input data is combined with index of the hash function (similar to the notion of a cryptographic salt) prior to hashing. Bob Jenkins' hash has particularly been chosen because of its speed and its minimal resource demand on motes [21]. In order to add a destination address to the BF, we take the output of each hash function modulo the size of the Bloom filter and set the resulting bit offsets in the BF.…”

Section: Bloom Filter Dimensioning and Beaconing Energy Consumptionmentioning

confidence: 99%

RoCoCo: Receiver-Initiated Opportunistic Data Collection and Command Multicasting for WSNs

Reinhardt

Renner

2015

Lecture Notes in Computer Science

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Abstract. Many data collection protocols have been proposed to cater for the energy-efficient flow of sensor data from distributed sources to a sink node. However, the transmission of control commands from the sink to one or only a small set of nodes in the network is generally unsupported by these protocols. Supplementary protocols for packet routing and data dissemination have been developed to this end, although their energy requirements commonly thwart the low-power nature of data collection protocols. We tackle this challenge by presenting RoCoCo in this paper. It combines data collection and dissemination by extending the low-energy ORiNoCo collection protocol by means to reconfigure subsets of nodes during runtime. Synergistically leveraging existing message types, RoCoCo allows for the definition of multicast recipient groups and forwards commands to these groups in an opportunistic fashion. Relying on Bloom filters to define the recipient addresses, RoCoCo only incurs small memory and energy overheads. We confirm its feasibility by evaluating the introduced delays, command success rates, and its energy overhead in comparison to existing collection/dissemination protocols.

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“…Some works have adressed this problem from different perspectives [14,15,16,17]. [14] presents CBFR, a routing scheme that builds upon collection protocols to enable point-to-point communication. Each node in the collection tree stores the addresses of its direct and indirect child nodes using Bloom filters to save memory space.…”

Section: Related Workmentioning

confidence: 99%

“…To solve this problem, they propose a new address assignment and routing scheme for LT WSN. [14] presents CBFR, a routing scheme that builds upon collection protocols to enable point-to-point communication. To do that, each node in the collection tree stores the addresses of its There are several studies evaluating the specification and operation of the RPL protocol.…”

Section: Related Workmentioning

confidence: 99%

IPv6 Multihop Host Configuration for Low-Power Wireless Networks

Peres¹,

Goussevskaia²

2015

2015 XXXIII Brazilian Symposium on Computer Networks and Distributed Systems

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Standard routing protocols for Low power and Lossy Networks are typically designed to optimize bottom-up data flows, by maintaining a cycle-free network topology. The advantage of such topologies is low memory footprint to store routing information (only the parent's address needs to me known by each node). The disadvantage is that other communication patterns, like top-down and bidirectional data flows, are not easily implemented. In this work we propose MHCL: IPv6Multihop Host Configuration for Low-Power Wireless Networks. MHCL employs hierarchical address allocation that explores cycle-free network topologies and aims to enable top-down data communication with low message overhead and memory footprint. We evaluated the performance of MHCL both analytically and through simulations. We implemented MHCL as a subroutine of RPL protocol on Contiki OS and showed that it significantly improves top-down message delivery in RPL, while using a constant amount of memory (i.e., independent of network size) and being efficient in terms of setup time and number of control messages. homes, one can imagine a low-power wireless network connecting the appliances and other electric devices of a home to a gateway, or a border router. While away from home, one might wish to connect to the refrigerator at home to check whether one is out of milk and should buy some on the way home. In order to deliver this request, a message would need to be sent to the IPv6 address of the refrigerator. This message would first be delivered to the home gateway and then be routed downwards through the multihop wireless network connecting the appliances. Each node in this network would act as a router and decide which way to forward the message, so that it reaches the refrigerator.Even though top-down data traffic is typically not the main target of low-power multihop wireless networks, it is enabled by some popular routing protocols in an alternative operating mode, so that routes are optimized for bottom-up traffic, but top-down traffic is still possible. In the RPL protocol, for example, nodes maintain a DAG (Directed Acyclic Graph) topology, in which each node keeps in memory a small list of parent nodes, to which it forwards data upwards to the root. If a node wants to act not just as a source but a destination, it sends a special message upwards through the DAG, and the intermediate nodes (if they operate in the so-called storing mode) add an entry for this destination in the routing table created specifically for downward routing. The size of each such table is potentially O(n), where n is the size of the subtree rooted at the routing node. Given that memory space may be highly constrained in low-power wireless networks, all routes often cannot be stored, and packets must therefore be dropped. This results in high message loss and high memory footprint. In order to reduce the size of the routing table, it would be desirable to aggregate several addresses of closely located destination nodes in a single routing table entry. However, because IPv...

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CBFR: Bloom filter routing with gradual forgetting for tree-structured wireless sensor networks with mobile nodes

Cited by 21 publications

References 21 publications

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

Optimizing Bloom Filter: Challenges, Solutions, and Comparisons

RoCoCo: Receiver-Initiated Opportunistic Data Collection and Command Multicasting for WSNs

IPv6 Multihop Host Configuration for Low-Power Wireless Networks

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