Outlier Detection Techniques for Wireless Sensor Networks: A Survey

Zhang, Yang; Meratnia, Nirvana; Havinga, Paul J.M.

doi:10.1109/surv.2010.021510.00088

Cited by 630 publications

(85 citation statements)

References 33 publications

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“…Future research involves consideration of fading channel in the distributed sensor networks [20] [21]. Designing the appropriate threshold is one of our future works [22].…”

Section: Discussionmentioning

confidence: 99%

Data Prediction in Distributed Sensor Networks Using Adam Bashforth Moulton Method

Islam¹,

Nazi²,

Hossain³

et al. 2018

JST

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Information collection from remote location is very important for several tasks such as temperate monitoring, air quality investigation, and wartime surveillance. Wireless sensor network is the first choice to complete these types of tasks. Basically, information prediction scheme is an important feature in any sensor nodes. The efficiency of the sensor network can be improved to large extent with a suitable information prediction scheme. Previously, there were several efforts to resolve this problem, but their accuracy is decreased as the prediction threshold reduces to a small value. Our proposed Adams-Bashforth-Moulton algorithm to overcome this drawback was compared with the Milne Simpson scheme. The proposed algorithm is simulated on distributed sensor nodes where information is gathered from the Intel Berkeley Research Laboratory. To maximize the power saving in wireless sensor network, our adopted method achieves the accuracy of 60.28 and 59.2238 for prediction threshold of 0.01 for Milne Simpson and Adams-Bashforth-Moulton algorithms, respectively.

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“…Future research involves consideration of fading channel in the distributed sensor networks [20] [21]. Designing the appropriate threshold is one of our future works [22].…”

Section: Discussionmentioning

confidence: 99%

Data Prediction in Distributed Sensor Networks Using Adam Bashforth Moulton Method

Islam¹,

Nazi²,

Hossain³

et al. 2018

JST

View full text Add to dashboard Cite

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“…For example, for a smart phone, low battery levels could lead to infrequent GPS position updates, thus resulting in geo-tagging errors. Approaches to ensure data quality often rely on oversampling and filtering of outlier values [70], reputation schemes that provide trustworthy sensing for public safety [71], or trust network-based human intervention approaches.…”

Section: Target Scenarios For Mission-critical Iotmentioning

confidence: 99%

A Review on Internet of Things for Defense and Public Safety

Fraga‐Lamas

Fernández‐Caramés

Suárez-Albela

et al. 2016

Sensors

192

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The Internet of Things (IoT) is undeniably transforming the way that organizations communicate and organize everyday businesses and industrial procedures. Its adoption has proven well suited for sectors that manage a large number of assets and coordinate complex and distributed processes. This survey analyzes the great potential for applying IoT technologies (i.e., data-driven applications or embedded automation and intelligent adaptive systems) to revolutionize modern warfare and provide benefits similar to those in industry. It identifies scenarios where Defense and Public Safety (PS) could leverage better commercial IoT capabilities to deliver greater survivability to the warfighter or first responders, while reducing costs and increasing operation efficiency and effectiveness. This article reviews the main tactical requirements and the architecture, examining gaps and shortcomings in existing IoT systems across the military field and mission-critical scenarios. The review characterizes the open challenges for a broad deployment and presents a research roadmap for enabling an affordable IoT for defense and PS.

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“…In other words, k-means minimizes the distance between each object and cluster centroid to which it belongs. According to Meratnia and Havinga (2010), calculating the distances that define the similarity between the elements of the groups requires a large computational effort for the limited nodes in a WSN. Therefore, this step should happen outside the network, and it can also be understood as part of data preparation.…”

Section: Obtaining Thermal Patternsmentioning

confidence: 99%