Fault perturbations in building sensor network data streams

Bigrigg, Michael W.; Matthews, H. Scott; Garrett, James H.

doi:10.1504/ijsnet.2010.033117

Cited by 7 publications

(2 citation statements)

References 15 publications

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“…Commonly, the DQ dimensions are used to derive more specific DQ metrics such as outlier [9,23] and anomaly detection [3]. The problem with the existing metrics is, however, that they are often developed on an ad-hoc basis to solve specific problems, and fundamental principles necessary for developing general DQ metrics in practice have not been defined [13].…”

Section: Data Quality Assessmentmentioning

confidence: 99%

Towards altruistic data quality assessment for mobile sensing

Ferreira

2017

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 AC

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High-quality data is a necessity for successful research and development endeavors. In this article, we review relevant literature for data quality (DQ) assessment methods in different domains and discuss the possibilities, challenges and constraints of applying them to mobile sensing. We identify DQ dimensions directly applicable to sensor data: believability (comparison with the correct operating bounds), completeness (missing values), free-of-error (erroneous values), consistency (over time), timeliness (delay), accuracy (deviation from true value) and precision (granularity of readings) are core aspects of high-quality sensor data. We also emphasize that sensor data must be representative of the originating type of sensor. We propose an altruistic approach to DQ assessment for sensor data that facilitates aggregating and sharing of domain knowledge through a community-contributed library of DQ assessment methods organized by sensor type.

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Section: Data Quality Assessmentmentioning

confidence: 99%

Towards altruistic data quality assessment for mobile sensing

Ferreira

2017

Proceedings of the 2017 ACM International Joint Conference on Pervasive and Ubiquitous Computing and Proceedings of the 2017 AC

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“…In practice, it is necessary to introduce the fault tolerance into the WSN (Snoussi and Richard, 2007;Bein et al, 2009;Bigrigg et al, 2010;Mishra et al, 2011). However, most of the existing proposed topology control algorithms do not take this issue into consideration which will have a significant impact on performance and reliability of wireless networks and upper level applications.…”

Section: Introductionmentioning

confidence: 99%

Distributed k-connected fault-tolerant topology control algorithms with PSO in future autonomic sensor systems

Guo

Xiong

Vasilakos

et al. 2012

IJSNET

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Fault-tolerant topology control in Wireless Sensor Networks (WSNs) has drawn a significant amount of research interest and become a hot point. In this paper, we first propose a centralised k-connected fault-tolerant topology control algorithm with Particle Swarm Optimisation (PSO) called CKFTC-PSO. In CKFTC-PSO, we take both issues of node failure and power efficiency into consideration and give the mathematical model of k-connected faulttolerant topology control problem. Inspired by physics of Genetic Algorithm (GA), the principles of mutation and crossover operator in GA are incorporated into the proposed CKFTC-PSO algorithm to achieve a better diversity and break away from local optima. Based on CKFTC-PSO, we then propose a distributed k-connected fault-tolerant topology control algorithm with PSO called DKFTC-PSO. DKFTC-PSO has better performance than CKFTC-PSO in powerefficiency while preserve k-connectivity. Simulation results are presented to demonstrate the effectiveness of the proposed algorithms. in 2007. His major research fields include computation intelligence, computer networks, information security, etc. Until now he has published about 80 research papers.Chaolong Yu is a Graduate student studying in the college of mathematics and computer sciences, Fuzhou University, China. His interests include mobile computing and wireless sensor networks.

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Sensor Distribution on Coverage in Sensor Networks

Peng

Xiao

Chen

et al. 2012

Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering

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Abstract.In this paper, we study the impacts of sensor node distributions on network coverage. We first show the impacts on network coverage by adopting different sensor node distributions through both analytical and simulation studies. Then, we adopt a distribution-free approach to study network coverage, in which no assumption of probability distribution of sensor node locations is needed. The proposed approach has yielded good estimations of network coverage.

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Fault perturbations in building sensor network data streams

Cited by 7 publications

References 15 publications

Towards altruistic data quality assessment for mobile sensing

Towards altruistic data quality assessment for mobile sensing

Distributed k-connected fault-tolerant topology control algorithms with PSO in future autonomic sensor systems

Sensor Distribution on Coverage in Sensor Networks

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