APDA: Adaptive pruning &amp; data aggregation algorithms for query based wireless sensor networks

Sarode, Prachi; Nandhini, R.

doi:10.1109/icgtspicc.2016.7955301

Cited by 2 publications

(2 citation statements)

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“…Research targeting the aggregation of queries and the prediction of queries has been relatively prolific, and have sought to improve and optimize the performance of search, index, and storage systems [44], [45], [106]. With respect to query aggregation, An [44] developed a method for query aggregation in wireless sensor networks that combines query simplification and merging.…”

Section: B Query Aggregation and Predictionmentioning

confidence: 99%

“…With respect to query aggregation, An [44] developed a method for query aggregation in wireless sensor networks that combines query simplification and merging. In addition, Sarode and Nandhini [45] developed adaptive pruning and data aggreagation (APDA) for query-based wireless sensor networks. Their work seeks to minimize query response time by establishing Dominator nodes that perform adaptive pruning and aggregation to perform the task of finding the highest-K values (application dependent) and source sensor IDs.…”

Section: B Query Aggregation and Predictionmentioning

confidence: 99%

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Towards Efficient and Intelligent Internet of Things Search Engine

et al. 2021

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The Internet of Things (IoT) has created a novel ecosystem for sensing and actuation throughout our world, enabling intelligently controlled autonomous systems to conserve energy, water crops, manage factories, and provide situation awareness on an unprecedented scale. As IoT progresses, the interest in IoT search engines, that is, search engines to find IoT devices and retrieve IoT data, has grown. While basic examples of IoT search engines exist, considerable challenges prevent the full realization of an efficient and intelligent IoT search engine that provides universal data service, scalable data communication and retrieval, and efficient querying of massively distributed heterogeneous devices and data. In this article, we first propose a generic framework for the IoT search engine, and then present a naming service for the IoT system, an essential component for an effective IoT search engine. We also outline some research challenges and possible solutions for building efficiency and intelligence in the IoT search engine. Further, we present a case study and seek to address a particular aspect of the query process for IoT search, namely efficient and timely query processing. Given the now obvious advances in machine learning, the potential for deep learning-based prediction to improve resource use, and thus query retrieval, is clear. In detail, we utilize Long-Short-Term Memory (LSTM) neural network architecture to predict aggregated query volumes to be preemptively applied and stored for immediate response. Combining several realistic IoT datasets, we explore the efficacy of simultaneously predicting multiple targets for predictive query retrieval.INDEX TERMS Machine learning, Internet of Things, search engine, edge intelligence, applications.

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Section: B Query Aggregation and Predictionmentioning

confidence: 99%

Section: B Query Aggregation and Predictionmentioning

confidence: 99%