Uncertainty Modeling in Wireless Sensor Networks

Doğan, Gülüstan; Brown, Ted

doi:10.1145/3175684.3175692

Cited by 6 publications

(3 citation statements)

References 20 publications

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“…If the distribution of possibilities is nonnormalized (inconsistent), we can define a new measure Inc(π) ∈ [0, 1] as the measure of inconsistency of this distribution (30):…”

Section: Description Of the Proposed Modelmentioning

confidence: 99%

A fuzzy/possibility approach for area coverage in wireless sensor networks

et al. 2023

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Deterministic methods used to address the coverage problem in an uncertain deployment environment have not proven to be very successful. For this purpose, the original idea of this paper is to deal with the coverage problem in an uncertain environment with uncertain theories. We consider the imperfection in the deployment environment and in the characteristics of the sensor nodes. The selection of a minimum number of nodes for a minimum number of clusters to guarantee coverage in WSN is uncertain. As a consequence, this paper proposes a hybrid Fuzzy-Possibilistic model to schedule the Active/ Passive state of sensor nodes. This model helps to plan the scheduling of node states (Active / Passive) based on the possibilistic information fusion to make a possibilistic decision for the node activation at each period. We evaluated the proposed model with (a) a running example, (b) a statistical evaluation (calculation of the confidence interface), and (c) a comparison with maximum sensing coverage region problem (MSCR), Coverage Maximization with Sleep Scheduling (CMSS), Spider Canvas Strategy, Semi-Random Deployment Strategy (SRDP) and PEAS with location information protocols. The simulation results highlight the benefits of using the fuzzy and possibility theories for treating the area coverage problem.

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“…If the distribution of possibilities is nonnormalized (inconsistent), we can define a new measure Inc(π) ∈ [0, 1] as the measure of inconsistency of this distribution (30):…”

Section: Description Of the Proposed Modelmentioning

confidence: 99%

A fuzzy/possibility approach for area coverage in wireless sensor networks

et al. 2023

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“…There are models of uncertainty specifically developed for the purpose of sensor networks. For example, [ 39 ] has developed a model of uncertainty for wireless sensors that takes into account, among others, the impact of environmental factors such as temperature and battery status, as well as possible failures.…”

Section: Uncertainty—an Overviewmentioning

confidence: 99%

A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks

Cofta

Karatzas

Orłowski

2021

Sensors

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The growing popularity of inexpensive IoT (Internet of Things) sensor networks makes their uncertainty an important aspect of their adoption. The uncertainty determines their fitness for purpose, their perceived quality and the usefulness of information they provide. Nevertheless, neither the theory nor the industrial practice of uncertainty offer a coherent answer on how to address uncertainty of networks of this type and their components. The primary objective of this paper is to facilitate the discussion of what progress should be made regarding the theory and the practice of uncertainty of IoT sensor networks to satisfy current needs. This paper provides a structured overview of uncertainty, specifically focusing on IoT sensor networks. It positions IoT sensor networks as contrasted with professional measurement and control networks and presents their conceptual sociotechnical reference model. The reference model advises on the taxonomy of uncertainty proposed in this paper that demonstrates semantic differences between various views on uncertainty. This model also allows for identifying key challenges that should be addressed to improve the theory and practice of uncertainty in IoT sensor networks.

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“…Uncertainty-aware graph-based data, occurring in applications like business and finance [36,94], social and information sciences [6], sensor networks [21,86], bioinformatics [93] and cybersecurity [2], can be visualized by a variety of approaches. These approaches are usually based on uncertainty-aware graph-drawing algorithms.…”

Section: Data Typesmentioning

confidence: 99%

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

Maack

Scheuermann

Hagen

et al. 2021

Preprint

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In many applications, Visual Analytics(VA) has developed into a standard tool to ease data access and knowledge generation. Unfortunately, many data sources, used in the VA process, are affected by uncertainty. In addition, the VA cycle itself can introduce uncertainty to the knowledge generation process. The classic VA cycle does not provide a mechanism to handle these sources of uncertainty. In this manuscript, we aim to provide an extended VA cycle that is capable of handling uncertainty by quantification, propagation, and visualization. Different data types and application scenarios that can be handled by such a cycle, examples, and a list of open challenges in the area of uncertainty-aware VA are provided.

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Uncertainty Modeling in Wireless Sensor Networks

Cited by 6 publications

References 20 publications

A fuzzy/possibility approach for area coverage in wireless sensor networks

A fuzzy/possibility approach for area coverage in wireless sensor networks

A Conceptual Model of Measurement Uncertainty in IoT Sensor Networks

Uncertainty-aware Visual Analytics - Scope, Oppertunities and Challenges

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