SDS: Distributed Spatial-Temporal Similarity Data Storage in Wireless Sensor Networks

Shen, Haiying; Li, Ting; Zhao, Lianyu; Li, Ze

doi:10.1109/icccn.2009.5235333

Cited by 5 publications

(7 citation statements)

References 22 publications

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“…In previous literature [12,16], DCS schemes adopt standard hash function to determine the data storage location. But the standard hash function maps the data to each zone uniformly, regardless of how many sensors in this zone.…”

Section: Determination Of Storage Locationmentioning

confidence: 99%

“…It recursively divides the plane so as to assign address to sensors and then hashes metadata to that address. Shen et al [16] proposed a distributed data storage scheme with spatial-temporal similarity, which provides an efficient spatial-temporal and similarity data searching based on location, time, and locality-sensitive hash [17]. Albano et al [18] provided an approach dealing with data storage for non-uniform distribution WSN.…”

Section: Introductionmentioning

confidence: 99%

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Energy‐efficient big data storage and retrieval for wireless sensor networks with nonuniform node distribution

Guo

Xiao

et al. 2015

Concurrency and Computation

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SUMMARYDistributed data-centric storage in wireless sensor networks (WSNs) is considered as a promising big data storage approach, because it contributes to reducing the communication overhead inside the networks. However, most of the existing distributed methods rely on locating systems, which consume more energy, and assume that sensors are uniformly distributed, which is clearly not applicable for the scenarios with nonuniform sensor distribution. To address these issues, in this paper, we propose a big data storage and retrieval algorithm for WSNs with nonuniform node distribution, which aims at estimating the real distribution and the addresses of sensor nodes. In particular, we consider the data redundancy among neighbor nodes in the proposed algorithm and exploit a simple routing based on the algorithm. Experimental results show that our approach outperforms other approaches in terms of data querying efficiency and data loss rate.

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Section: Determination Of Storage Locationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Energy‐efficient big data storage and retrieval for wireless sensor networks with nonuniform node distribution

Guo

Xiao

et al. 2015

Concurrency and Computation

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“…In [105] a distributed spatial-temporal Similarity Data Storage (SDS) scheme is provided. For that, the monitoring area is partitioned into zones based on the geographical layout.…”

Section: Data-centric Data/query Disseminationmentioning

confidence: 99%

Distributed Database Management Techniques for Wireless Sensor Networks

Diallo

Rodrigues

Sène

et al. 2015

IEEE Trans. Parallel Distrib. Syst.

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Abstract-In sensor networks, the large amount of data nsor reWireless Se composed of a large nu activities are sometimes not negligible in energy consumption generated by sensors greatly influences the lifetime of the network. In order to manage this amount of sensed data in an energy-efficient way, new methods of storage and data query are needed. In this way, the distributed database approach for sensor networks is proved as one of the most energy-efficient data storage and query techniques. This paper surveys the state of the art of the techniques used to manage data and queries in wireless sensor networks based on the distributed paradigm. A classification of these techniques is also proposed. The goal of this work is not only to present how data and query management techniques have advanced nowadays, but also show their benefits and drawbacks, and to identify open issues providing guidelines for further contributions in this type of distributed architectures.

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“…Data-Centric Storage (DCS) [12,13], an alternate to External Storage (ES) and Local Storage (LS), is considered to be a promising and efficient storage and search mechanism. There has been a growing interest in understanding and optimizing WSN DCS schemes in recent years, where a various number of query mechanisms such as point query, range query, similarity searching, top-k query and skyline query can be applied in a consolidated framework.…”

Section: Temperaturementioning

confidence: 99%

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

Ahmed

Nafi

Gregory

2016

JSAN

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Dynamic skyline query is one of the most popular and significant variants of skyline query in the field of multi-criteria decision-making. However, designing a distributed dynamic skyline query possesses greater challenge, especially for the distributed data centric storage within wireless sensor networks (WSNs). In this paper, a novel Enhanced Distributed Dynamic Skyline (EDDS) approach is proposed and implemented in Disk Based Data Centric Storage (DBDCS) architecture. DBDCS is an adaptation of magnetic disk storage platter consisting tracks and sectors. In DBDCS, the disc track and sector analogy is used to map data locations. A distance based indexing method is used for storing and querying multi-dimensional similar data. EDDS applies a threshold based hierarchical approach, which uses temporal correlation among sectors and sector segments to calculate a dynamic skyline. The efficiency and effectiveness of EDDS has been evaluated in terms of latency, energy consumption and accuracy through a simulation model developed in Castalia.

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SDS: Distributed Spatial-Temporal Similarity Data Storage in Wireless Sensor Networks

Cited by 5 publications

References 22 publications

Energy‐efficient big data storage and retrieval for wireless sensor networks with nonuniform node distribution

Energy‐efficient big data storage and retrieval for wireless sensor networks with nonuniform node distribution

Distributed Database Management Techniques for Wireless Sensor Networks

Enhanced Distributed Dynamic Skyline Query for Wireless Sensor Networks

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