Data-Centric Storage in Non-Uniform Sensor Networks

Albano, Michele; Chessa, Stefano; Nidito, Francesco; Pelagatti, Susanna

doi:10.1007/978-0-387-09663-6_1

Cited by 4 publications

(6 citation statements)

References 11 publications

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“…Geographical hash table [3], [11], [14]- [16] is a general approach for in-network data-centric storage and retrieval. A basic geographical hash-table-based scheme hashes a datum by its type into geographic coordinates and stores at the sensor node geographically nearest to such coordinates.…”

Section: A Overview Of Distributed Information Storage and Retrievalmentioning

confidence: 99%

Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Yang

Jin

Zhao

et al. 2015

IEEE/ACM Trans. Networking

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Distributed in-network data-centric processing aims to reduce energy consumed for communication and establish a selfcontained data storage, retrieval, aggregation, and query sensor system that focuses more on the data itself rather than the identities of the individual network nodes. Double-ruling-based schemes support efficient in-network data-centric information storage and retrieval, especially for aggregated data, since all data with different types generated in a network can be conveniently retrieved along any single retrieval curve. Previous double-ruling-based research focuses on two-dimensional (2-D) wireless sensor networks where a 2-D planar setting is assumed. With increasing interests in deploying wireless sensors in three-dimensional (3-D) space for various applications, it is urgent yet fundamentally challenging to design double-ruling-based approach in general 3-D sensor networks because double-ruling-based schemes in general have much harder geometric constraints than other distributed in-network data-centric processing schemes. In this research, we propose a geographic location-free double-ruling-based approach for general 3-D sensor networks with possibly complicated topology and geometric shapes. Without the knowledge of the geographic location and the distance bound, a query simply travels along a simple curve with the guaranteed success to retrieve aggregated data through time and space with one or different types across the network. Extensive simulations and comparisons show the proposed scheme with low cost and a balanced traffic load.Index Terms-3-D sensor networks, data-centric, in-network, information storage and retrieval.

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Section: A Overview Of Distributed Information Storage and Retrievalmentioning

confidence: 99%

Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Yang

Jin

Zhao

et al. 2015

IEEE/ACM Trans. Networking

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“…The works in [7], [20] consider non-Uniform WSN, however their focus is on connectivity problems and broadcast protocols. Effects of non-uniformity in WSN data storage are taken into account in our preliminary works [2], [4]. In particular, [4] introduces a dispersal strategy that exploits a non-uniform hash function and that introduces the concept of QoS in the storage.…”

Section: Related Workmentioning

confidence: 99%

“…This fact is particularly limiting considering that the network distribution may change due to sensor faults. The works in [2] builds over [4] by introducing a primitive mechanism for the estimation of the density of the network. However this mechanism is intended only as a setup of the network and it is assumed that the network density does not vary with time.…”

Section: Related Workmentioning

confidence: 99%

Dealing with Nonuniformity in Data Centric Storage for Wireless Sensor Networks

Albano

Chessa

Nidito

et al. 2011

IEEE Trans. Parallel Distrib. Syst.

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In-network storage of data in Wireless Sensor Networks (WSNs) is considered a promising alternative to external storage since it contributes to reduce the communication overhead inside the network. Recent approaches to data storage rely on Geographic Hash Tables (GHT) for efficient data storage and retrieval. These approaches, however, assume that sensors are uniformly distributed in the sensor field, which is seldom true in real applications. Also they do not allow to tune the redundancy level in the storage according to the importance of the data to be stored. To deal with these issues, we propose an approach based on two mechanisms. The first is aimed at estimating the real network distribution. The second exploits a data dispersal method based on the estimated network distribution. Experiments through simulation show that our approach approximates quite closely the real distribution of sensors and that our dispersal protocol sensibly reduces data losses due to unbalanced data load.

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“…For instance, there are specific routing proposals for DCS such as pathDCS [17], HVGR [18] and Double Rulings [19]. In addition, there are two works, DELiGHT [20] and Q-NiGHT [21], which consider DCS in non-uniform networks. In particular, Q-NiGHT focuses on studying QoS for DCS networks.…”

Section: Data-centric Storagementioning

confidence: 99%

Modelling data-aggregation in multi-replication data centric storage systems for wireless sensor and actor networks

2011

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This paper studies data-centric storage (DCS) as a suitable system to perform data aggregation on wireless sensor and actor networks (WSANs), in which sensor and actor nodes collaborate together in a fully distributed way without any central base station that manages the network or provides connectivity to the outside world. The authors compare different multi-replication DCS proposals and choose the best one to be applied when studying data aggregation. In addition, the authors provide mathematical models for the production, consumption and overall network traffic for different application profiles. Those application profiles are based on the ability of a particular application to perform data aggregation and on what type of traffic is dominant, either the consumption or the production one. Furthermore, the authors provide closed formulas for each application profile that defines the optimal number of replicas that minimise the overall network traffic. Finally, the authors validate the proposed models via simulation.

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Data-Centric Storage in Non-Uniform Sensor Networks

Cited by 4 publications

References 11 publications

Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Distributed Information Storage and Retrieval in 3-D Sensor Networks With General Topologies

Dealing with Nonuniformity in Data Centric Storage for Wireless Sensor Networks

Modelling data-aggregation in multi-replication data centric storage systems for wireless sensor and actor networks

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