Uncertainty-aware Wireless Sensor Networks

Mal-Sarkar, Sanchita; Sikder, Iftikhar U.; Yu, Chansu; Konangi, V.K.

doi:10.1504/ijmc.2009.023675

Cited by 14 publications

(9 citation statements)

References 37 publications

(35 reference statements)

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“…In the future, compressive wideband spectrum sensing can be introdued to the cognitive radio for muti-antenna system and wireless sesnor networks [33] [34]. Besides, some more apriori information of spectrum distribution can be incorparted to enhance the spectrum sensing accurancy.…”

Section: Discussionmentioning

confidence: 99%

Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Liu¹,

Wan²

2011

IJMC

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Too high sampling rate is the bottleneck to wideband spectrum sensing for cognitive radio in mobile communication. Compressed sensing (CS) is introduced to transfer the sampling burden. The standard sparse signal recovery of CS does not consider the distortion in the analogue-to-information converter (AIC). To mitigate performance degeneration casued by the mismatch in least square distortionless constraint which doesn't consider the AIC distortion, we define the sparse signal with the sampling distortion as a bounded additive noise, and An anti-sampling-distortion constraint (ASDC) is deduced. Then we combine the ℓ 1 norm based sparse constraint with the ASDC to get a novel robust sparse signal recovery operator with sampling distortion. Numerical simulations demonstrate that the proposed method outperforms standard sparse wideband spectrum sensing in accuracy, denoising ability, etc.Index Terms -cognitive radio, wideband spectrum sensing, compressive sensing, sparse signal recovery, mobile communication, analogue to information converter, sampling distortion.

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Section: Discussionmentioning

confidence: 99%

Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Liu¹,

Wan²

2011

IJMC

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“…• Transmission technology: Wired and wireless technologies are the main transmission methods of data transfer [39]. Issues related to transmission are considered as an issue regarding transfer speeds and delays in the delivery of data [34].…”

Section: Sources Of Uncertainty In Iotmentioning

confidence: 99%

Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

et al. 2021

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Adaptivity is the ability of the system to change its behavior whenever it does not achieve the system requirements. Self-adaptive software systems (SASS) are considered a milestone in software development in many modern complex scientific and engineering fields. Employing self-adaptation into a system can accomplish better functionality or performance; however, it may lead to unexpected system behavior and consequently to uncertainty. The uncertainty that results from using SASS needs to be tackled from different perspectives. The Internet of Things (IoT) that utilizes the attributes of SASS presents great development opportunities. Because IoT is a relatively new domain, it carries a high level of uncertainty. The goal of this work is to highlight more details about self-adaptivity in software systems, describe all possible sources of uncertainty, and illustrate its effect on the ability of the system to fulfill its objectives. We provide a survey of state-of-the-art approaches coping with uncertainty in SASS and discuss their performance. We classify the different sources of uncertainty based on their location and nature in SASS. Moreover, we present IoT as a case study to define uncertainty at different layers of the IoT stack. We use this case study to identify the sources of uncertainty, categorize the sources according to IoT stack layers, demonstrate the effect of uncertainty on the ability of the system to fulfill its objectives, and discuss the state-of-the-art approaches to mitigate the sources of uncertainty. We conclude with a set of challenges that provide a guide for future study.

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“…{S c (f): s(f) C S ∈ }represents a set of parameters derived from each sensor in the cluster [16]. For example, S C (T) is the air temperature index, S C (P) is the barometric pressure index, S C (M) is the soil moisture index, and S C (H) represents the relative humidity of the air.…”

Section: A Dominance-based Rough Set Approach For Forest Fire Detementioning

confidence: 99%

Application of wireless sensor networks in forest fire detection under uncertainty

Mal-Sarkar

Sikder

Konangi

2010

2010 13th International Conference on Computer and Information Technology (ICCIT)

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Uncertainty-aware Wireless Sensor Networks

Cited by 14 publications

References 37 publications

Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Anti-sampling-distortion compressive wideband spectrum sensing for Cognitive Radio

Toward Management of Uncertainty in Self-Adaptive Software Systems: IoT Case Study

Application of wireless sensor networks in forest fire detection under uncertainty

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