Data loss and reconstruction in sensor networks

Kong, Linghe; Xia, Mingyuan; Liu, Xiaoyang; Wu, Min-You; Li, Xue

doi:10.1109/infcom.2013.6566962

Cited by 180 publications

(110 citation statements)

References 24 publications

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“…In addition to the contributions above, in Paper VI we show that our improved BP algorithm performs significantly better than the state of the art compressive sensing approach [71].…”

Section: Contributionsmentioning

confidence: 63%

“…In Paper VI, we extend the study in Paper V by improving the accuracy of our BP algorithm, and we compare our results with the state of the art compressive sensing approach [71].…”

Section: Discussionmentioning

confidence: 84%

“…Vi formulerar problemet med optimering av aktiva sensorer, och föreslår två metoder för att lösa det: villkorsprogrammering, respektive en heuristikbaserad girig algoritm. Vi jämför våra resultat med state-of-the-art inom compressive sensing [71], och visar att vår metod genererar markant färre fel i prediktion av data. En förväntad effekt av detta arbete är att det tillhandahåller ett protokoll för WSNs som förbättrar dataprediktion med vår teknik för BP-inferens.…”

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Constraint programming for wireless sensor networks

Bijarbooneh

2015

Constraints

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In recent years, wireless sensor networks (WSNs) have grown rapidly and have had a substantial impact in many applications. A WSN is a network that consists of interconnected autonomous nodes that monitor physical and environmental conditions, such as temperature, humidity, pollution, etc. If required, nodes in a WSN can perform actions to affect the environment.WSNs present an interesting and challenging field of research due to the distributed nature of the network and the limited resources of the nodes. It is necessary for a node in a WSN to be small to enable easy deployment in an environment and consume as little energy as possible to prolong its battery lifetime. There are many challenges in WSNs, such as programming a large number of nodes, designing communication protocols, achieving energy efficiency, respecting limited bandwidth, and operating with limited memory. WSNs are further constrained due to the deployment of the nodes in indoor and outdoor environments and obstacles in the environment.In this dissertation, we study some of the fundamental optimisation problems related to the programming, coverage, mobility, data collection, and data loss of WSNs, modelled as standalone optimisation problems or as optimisation problems integrated with protocol design. Our proposed solution methods come from various fields of research including constraint programming, integer linear programming, heuristic-based algorithms, and data inference techniques. I would like to say many thanks to Roland Bol and Ivan Christoff for accepting me to MSc studies at Uppsala University, which led to this PhD. Your decision brought me on a very long and joyful journey: without it, I would not have been here today. KeywordsI had the special opportunity to see the ASTRA group on constraint programming grow larger with new PhD students and post-doc fellows throughout my PhD. I enjoyed working at the ASTRA group. Especially my appreciation goes to Jun He, Joseph Scott, Maria Andreina Francisco Rodriguez, and JeanNoël Monette, who always supported me with their thoughts and constructive discussions, and have been great friends for me. I have also always looked forward to your seminars with delicious cakes and cookies. Many thanks to Prof. Christian Schulte, Guido Tack, Mats Carlsson for their discussions and many constructive comments during my seminars and on my publications.I would like to specially thank all members of my PhD parent project (ProFuN). Thank you for your constructive suggestions and discussions during the seminars.I have made many wonderful friends during my PhD, friends who always stood by my side and supported me. There are many names to mention and I wish I could name everyone here, but I believe you know who you are and you know how much I appreciate your precious friendship. Thank you so much for being my friends and helping me to improve. There has been a very difficult time during my PhD when my cat Niuniu had an extremely harsh accident. I must specifically thank Edith Ngai, Palle Raabjerg, Ryoko Asai, and V...

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“…In addition to the contributions above, in Paper VI we show that our improved BP algorithm performs significantly better than the state of the art compressive sensing approach [71].…”

Section: Contributionsmentioning

confidence: 63%

“…In Paper VI, we extend the study in Paper V by improving the accuracy of our BP algorithm, and we compare our results with the state of the art compressive sensing approach [71].…”

Section: Discussionmentioning

confidence: 84%

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Constraint programming for wireless sensor networks

Bijarbooneh

2015

Constraints

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“…Missing data reconstruction methods are further challenged by the variation in patterns of missing data [24,25]; for example, missing data completely at random, non-random missing data [26], or whole blocks of missing data [27] (Figure 1). The work in [10] formulates the spatio-temporal sensory data as a high-dimensional tensor, using the tensor completion method to recover missing values.…”

Section: Introductionmentioning

confidence: 99%

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Cheng

2017

IJGI

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Abstract:Missing data reconstruction is a critical step in the analysis and mining of spatio-temporal data; however, few studies comprehensively consider missing data patterns, sample selection and spatio-temporal relationships. As a result, traditional methods often fail to obtain satisfactory accuracy or address high levels of complexity. To combat these problems, this study developed an effective two-step method for spatio-temporal missing data reconstruction (ST-2SMR). This approach includes a coarse-grained interpolation method for considering missing patterns, which can successfully eliminate the influence of continuous missing data on the overall results. Based on the results of coarse-grained interpolation, a dynamic sliding window selection algorithm was implemented to determine the most relevant sample data for fine-grained interpolation, considering both spatial and temporal heterogeneity. Finally, spatio-temporal interpolation results were integrated by using a neural network model. We validated our approach using Beijing air quality data and found that the proposed method outperforms existing solutions in term of estimation accuracy and reconstruction rate.

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“…It can lead to complications with summary measures including, notably, underestimation of the standard deviation [185]. The missing value problem is fundamental in data sets [186] and many works have contributed in this field.…”

Section: Imputation Of Missing Valuesmentioning

confidence: 99%

Assessing biofilm development in drinking water distribution systems by Machine Learning methods

Martínez¹

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Data loss and reconstruction in sensor networks

Cited by 180 publications

References 24 publications

Constraint programming for wireless sensor networks

Constraint programming for wireless sensor networks

A Two-Step Method for Missing Spatio-Temporal Data Reconstruction

Assessing biofilm development in drinking water distribution systems by Machine Learning methods

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