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

Cuevas, Antonio; Urueña, Manuel; Romeral, Ricardo

doi:10.1049/iet-com.2010.0574

Cited by 6 publications

(3 citation statements)

References 22 publications

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“…To the best of our knowledge, the WSANs-based Edge computing systems on data storage and information discovery are emerging research areas, and not much research has to date been carried out [ 42 , 43 , 44 , 45 , 46 , 47 , 48 , 49 , 50 , 51 , 52 , 53 , 54 ]. Those existing schemes mainly follow two basic models: the query-driven model and the event-driven model.…”

Section: Analysis Of the Data-storage And Information-discovery Scmentioning

confidence: 99%

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

Liang

Liu

et al. 2018

Sensors

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In the post-Cloud era, the proliferation of Internet of Things (IoT) has pushed the horizon of Edge computing, which is a new computing paradigm with data processed at the edge of the network. As the important systems of Edge computing, wireless sensor and actuator networks (WSANs) play an important role in collecting and processing the sensing data from the surrounding environment as well as taking actions on the events happening in the environment. In WSANs, in-network data storage and information discovery schemes with high energy efficiency, high load balance and low latency are needed because of the limited resources of the sensor nodes and the real-time requirement of some specific applications, such as putting out a big fire in a forest. In this article, the existing schemes of WSANs on data storage and information discovery are surveyed with detailed analysis on their advancements and shortcomings, and possible solutions are proposed on how to achieve high efficiency, good load balance, and perfect real-time performances at the same time, hoping that it can provide a good reference for the future research of the WSANs-based Edge computing systems.

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Section: Analysis Of the Data-storage And Information-discovery Scmentioning

confidence: 99%

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

Liang

Liu

et al. 2018

Sensors

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“…In a large-scale, heterogeneous distributed storage environment, multi-replication of data is an important concern for reducing overall network traffic (Cuevas et al, 2011) because the locality of an application can be increased if data are replicated across all geographically dispersed networks in which the application may be accessing data. Thus, the locality of data access can be preserved by effectively maintaining replicas, but the major problem becomes how to maintain the integrity and consistency of the replicas in the distributed storage environment.…”

Section: Related Studiesmentioning

confidence: 99%

Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment

Mishra

Chang

Lin

2018

IJSNET

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Large-scale sensor data distributions and knowledge inferences are major challenges for cognitive-based distributed storage environments. Cognitive storage sinks play an essential role in addressing these challenges. In a data-concentrated distributed cognitive sensor environment, cognitive storage sinks regulate the data distribution operations and infer knowledge from the large amounts of sensor data that are distributed across the conventional sensors. Embedding cognitive functions in conventional sensors is unreasonable, and the knowledge-processing limitations of conventional sensors create a serious problem. To overcome this problem, we propose a cognitive co-sensor platform across a large-scale distributed environment. Further, we propose a distributed data distribution framework (DDD-framework) for effective data distributions and a distributed knowledge inference framework (DKIframework) that infers useful patterns for building knowledge intelligence. The analysis and discussion demonstrate that these frameworks can be adequately instigated for the purpose of optimal data distribution and knowledge inference within the horizon of a real-time distributed environment.

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“…In [38], the authors propose four application profiles where two are aggregation profiles called ConsAggr and ProdAggr. Data aggregation is proposed using replication nodes proposed in QAR, see Section 3.7.…”

Section: Data Aggregationmentioning

confidence: 99%

Techniques and Challenges of Data Centric Storage Scheme in Wireless Sensor Network

Ahmed

Gregory

2012

JSAN

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Storing, collecting and querying data across miniaturized battery powered Wireless Sensor Networks (WSN) is a key research focus today. Distributed Data-Centric Storage (DCS), an alternate to External Storage (ES) and Local Storage (LS), is thought 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 the range query mechanism, similarity search, load balancing, multi-dimensional data search, as well as limited and constrained resources have driven this line of research. In this paper, an extensive state-of-the-art study is provided including the prime WSN DCS schemes, challenges that inspired these schemes, as well as drawbacks and shortcomings of existing solutions. In contrast to previous surveys that briefly discuss the contribution of a few WSN DCS mechanisms, we provide a thematic taxonomy in which schemes are classified according to the problems dealt with including range query, similarity search, data aggregation, sensor network field non-uniformity, multi-replication, load balancing and routing algorithm

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

Cited by 6 publications

References 22 publications

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

A Survey on Data Storage and Information Discovery in the WSANs-Based Edge Computing Systems

Sensor data distribution and knowledge inference framework for a cognitive-based distributed storage sink environment

Techniques and Challenges of Data Centric Storage Scheme in Wireless Sensor Network

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