An Analysis of Deductive-Query Processing Approaches for Logic Macroprograms in Wireless Sensor Networks

An energy-efficient sensor cloud model is proposed based on the combination of prediction and forecasting methods. The prediction using Artificial Neural Network (ANN) with single activation function and forecasting using Autoregressive Integrated Moving Average (ARIMA) models use to reduce the communication of data. The requests of the users generate in every second. These requests must be transferred to the wireless sensor network (WSN) through the cloud system in the traditional model, which consumes extra energy. In our approach, instead of one second, the sensors generally communicate with the cloud every 24 hours, and most of the requests reply using the combination of prediction and forecasting methods in the cloud system, which results in less communication and more battery life for the sensor. In our model, we used the ANN model initially, which had predicted the temperature for a given day with an accuracy of 92%. The results of ANN, together with the earlier real temperatures, are given as input to the ARIMA forecasting model, which provides an accuracy of 96% for one day in advance. Our simulation shows that the proposed method saves more energy compared to the traditional approach.

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“…In Ref. [19], the authors analyzed the query processing technique's characteristics in the WSNs. In Ref.…”

Section: Introductionmentioning

confidence: 99%

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

Das¹,

Das²,

Mohapatra³

2020

Karbala International Journal of Modern Science

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“…WNES macroprogramming has been explored earlier by several research efforts, including TinyDB [1], COUGAR [2,3], Semantic-Streams [17], and Sense2P [4,5,18]. The above node-independent abstractions propose programming WNES as a database.…”

Section: Related Workmentioning

confidence: 99%

Hybrid Macroprogramming Wireless Networks of Embedded Systems with Declarative Naming

Intanagonwiwat

2012

International Journal of Distributed Sensor Networks

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Wireless Networks of Embedded Systems (WNES) are notoriously difficult and tedious to program. The difficulty is mostly originated from low-level details in system and network programming. This includes distributedly managing and accessing resources from a dynamic set of nodes in hostile and volatile networks. To simplify WNES programming, we propose Declarative Resource Naming (DRN) that abstracts out the mentioned low-level details by programming a WNES in the large (i.e., macroprogramming). DRN provides programming simplicity, expressiveness, tunability, on-the-fly reprogrammability, and innetwork data aggregation for energy savings. None of existing macroprogramming paradigms supports all of the mentioned features. Furthermore, DRN is an integration of declarative and imperative programming. The low-level details are declaratively abstracted out, but the main algorithm remains procedural. This allows programming simplicity without an adverse impact on the expressiveness. We have implemented and evaluated DRN on two platforms: Smart Message and Maté. Our result indicates that DRN enables programmers to develop energy-efficient applications with the desired flexibility and quality.

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Effective Partitioning and Multiple RDF Indexing for Database Triple Store

Abburu¹,

Golla²

2015

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The capability of semantic technology leads to adaption of semantic technology to multiple applications of various domains. Due to vast number of applications, the size of RDF triple store is increasing. Effective semantic query execution has become a challenge due to the structure of RDF triple store. Effective indexing and partitioning leads to good sematic query performance against RDF triple store. The current research work has focused on various indexing techniques and proposed a predicate centric partitioning and multiple RDF indexing method for database triple store. A detailed analysis process is been executed to measure and compare the query performance. The current method is evaluated using standard benchmark and real datasets with various indexing techniques. Later the methodology is applied to R&D project management dataset. A set of twenty seven queries has been derived by considering various user requirements that cover most of the SPARQL constructs. The method is implemented and a detailed evaluation has been successfully carried out. The query time is evaluated on R&D project management dataset. The test results indicate that the proposed method provides considerable improvement in overall query performance.

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An Analysis of Deductive-Query Processing Approaches for Logic Macroprograms in Wireless Sensor Networks

Cited by 4 publications

References 15 publications

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

A Novel Energy-efficient Sensor Cloud Model using Data Prediction and Forecasting Techniques

Hybrid Macroprogramming Wireless Networks of Embedded Systems with Declarative Naming

Effective Partitioning and Multiple RDF Indexing for Database Triple Store

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