Distributed Relation Discovery in Internet of Things

Zhu, Wei-Ping; Lü, Hongliang; Cui, Xiaohui

doi:10.1109/ccbd.2014.31

Cited by 1 publication

(5 citation statements)

References 23 publications

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“…The details are given in Algorithms 1, 2, and 3. Notably, DRDA extends our previous approach proposed in [8] by using breadth-first traversal strategy and enabling automatic change of the depth of the routing tree (in Algorithm 1), supporting more time modalities (in Algorithm 2), and optimizing the tree building process for multiple relation detection tasks (in Algorithm 3). Algorithm 1 is responsible for building a routing tree for each relation in a distributed manner.…”

Section: Detailed Algorithmsmentioning

confidence: 99%

“…Second, the objects involved in a relation may start routing tree building concurrently, but a single routing tree should be formed eventually. Compared with our previous work [8], we change the tree-building strategy from depth-first to breadth-first, because we observed that the breadth-first tree has a less depth and more compact structure comparing with the depth-first tree.…”

Section: Detailed Algorithmsmentioning

confidence: 99%

“…We compared the energy consumption of DRDA with that of the centralized approach proposed by Raychoudhury et al in [4] (denoted as the centralized approach) and the approach proposed in our previous work [8] (denoted as DRDA-D). Energy consumption is measured by the number of packages transmitted during the relation detection.…”

Section: Performance Evaluationmentioning

confidence: 99%

“…If this is the case, a time interval is composed that includes the value of the attribute, the start time, and the end time. The start time is set to current timestamp , and the end Function: sendAttributeInterval(String attributeID, Data value, Relation r, Timestamp t) (1) if attributeID ∈ r.attributes then (2) if events.get(attributeID) ̸ = ⌀ then (3) Interval interval = events.get(attributeID) (4) interval.endTime = t (5) send Info(r.ID, attributeID, interval) (6) end (7) Interval interval = (value, t, ⊥) (8) events.get(attributeID).add(interval) (9) end Function: receiveInterval(Interval , NodeID , Relation r) (1) .enqueue( ) (2) if .size=1 then (3) updatedQueue.add( ) (4) end (5) while updatedQueues ̸ = ⌀ do (6) foreach ∈ updatedQueues do…”

Section: Detailed Algorithmsmentioning

confidence: 99%

“…Section 5 reviews the related works and finally Section 6 concludes the paper. This paper is based on our conference paper [8]. In this version, we extend the approach to automatically change the depth of the routing tree based on breadth-first traversal and optimize the process among multiple relation detection tasks to further reduce the energy consumption.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Distributed Relation Detection Approach in the Internet of Things

Zhu

Lü

Cui

et al. 2017

Mobile Information Systems

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In the Internet of Things, it is important to detect the various relations among objects for mining useful knowledge. Existing works on relation detection are based on centralized processing, which is not suitable for the Internet of Things owing to the unavailability of a server, one-point failure, computation bottleneck, and moving of objects. In this paper, we propose a distributed approach to detect relations among objects. We first build a system model for this problem that supports generic forms of relations and both physical time and logical time. Based on this, we design the Distributed Relation Detection Approach (DRDA), which utilizes a distributed spanning tree to detect relations using in-network processing. DRDA can coordinate the distributed treebuilding process of objects and automatically change the depth of the routing tree to a proper value. Optimization among multiple relation detection tasks is also considered. Extensive simulations were performed and the results show that the proposed approach outperforms existing approaches in terms of the energy consumption.

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Section: Detailed Algorithmsmentioning

confidence: 99%

Section: Detailed Algorithmsmentioning

confidence: 99%