Exploiting sensing diversity for confident sensing in wireless sensor networks

Keally, Matthew; Zhou, Gang; Xing, Guoliang; Wu, Jianxin

doi:10.1109/infcom.2011.5934969

Cited by 9 publications

(6 citation statements)

References 35 publications

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“…In order to obtain stringent accuracy requirements for target detection or classification while extending network lifetime as much as possible in the military vehicles monitoring [31], only a few sensor nodes are activated incessantly while most of sensor nodes are occasionally active. According to the experiment of the reference in [32], an average of 10 active sensor nodes can cover all critical locations in SensIT experiment. Thus we can construct a truncated dataset from the original SensIT dataset [31] including the sensing data of 23 nodes, which exits long-tail phenomena and is utilized to demonstrate effectiveness of the proposed method.…”

Section: A Experimental Methodology and Related Settings 1) Data Setsmentioning

confidence: 99%

Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

2019

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In practical wireless sensor networks (WSNs)-based applications, often only a few nodes are active while most of the others are activated occasionally due to limited resource of the WSNs. Thus most sensors only provide a few observations and only a few sensors make many observations, which often cause long-tail issue to undermine information fusion performance. So we present a confidence-aware information fusion scheme named CAIF to solve such a problem. In particular, we first make a quantitative study on the long-tail data phenomenon and the relationship between node-target distance and node sensing capability, which can provide a guideline to improve node weight estimation error caused by long-tail data. Then, we propose a truth discovery-based method in WSNs via incorporating node-target distance into the truth discovery optimization solution framework to infer the sensor node's fusion weight. In order to adapt to the distribution characteristic of the WSNs, we propose a distributed implementation to estimate the sensor nodes' weights via confidence level and node-target distance to further improve the fusion performance. Besides, the iterative process shown in CAIF converges to a stationary point of the optimization problem and its time complexity is linear with respect to the total number of observations. Finally, we conduct extensive experiments on real data to validate and evaluate CAIF. The experimental results demonstrate the superior performance of our method over existing solutions in terms of root-mean-square error and accuracy.

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Section: A Experimental Methodology and Related Settings 1) Data Setsmentioning

confidence: 99%

Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

2019

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“…Energy savings are achieved by keeping active only one node from each subgroup at a time. The authors of [28] explore the impact of sensing diversity on sensor collaboration with the goal to achieve confident sensing coverage. The study of sensing diversity is based on a sensor network deployment for vehicle detection.…”

Section: Related Workmentioning

confidence: 99%

On Rendezvous in Mobile Sensing Networks

Saukh

Hasenfratz

Walser

et al. 2013

Lecture Notes in Electrical Engineering

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A rendezvous is a temporal and spatial vicinity of two sensors. In this chapter, we investigate rendezvous in the context of mobile sensing systems. We use an air quality dataset obtained with the OpenSense monitoring network to explore rendezvous properties for carbon monoxide, ozone, temperature, and humidity processes. Temporal and spatial locality of a physical process impacts the number of rendezvous between sensors, their duration, and their frequency. We introduce a rendezvous connection graph and explore the trade-off between locality of a process and the amount of time needed for the graph to be connected. Rendezvous graph connectivity has many potential use cases, such as sensor fault detection. We successfully apply the proposed concepts to track down faulty sensors and to improve sensor calibration in our deployment.

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“…WolfPack [13] implements an online sensor clustering protocol that pursues high sensing confidence. Works such as [24] express the accuracy of context estimation through a quality of inference (QoINF) function that captures the dependence of estimation accuracy on the selected sensors.…”

Section: Related Workmentioning

confidence: 99%

AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

Keally

Zhou

et al. 2013

2013 IEEE 19th Real-Time and Embedded Technology and Applications Symposium (RTAS)

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Abstract-In a Body Sensor Network (BSN) activity recognition system, sensor sampling and communication quickly deplete battery reserves. While reducing sampling and communication saves energy, this energy savings usually comes at the cost of reduced recognition accuracy. To address this challenge, we propose AdaSense, a framework that reduces the BSN sensors sampling rate while meeting a user-specified accuracy requirement. AdaSense utilizes a classifier set to do either multi-activity classification that requires a high sampling rate or single activity event detection that demands a very low sampling rate. AdaSense aims to utilize lower power single activity event detection most of the time. It only resorts to higher power multi-activity classification to find out the new activity when it is confident that the activity changes. Furthermore, AdaSense is able to determine the optimal sampling rates using a novel Genetic Programming algorithm. Through this Genetic Programming approach, AdaSense reduces sampling rates for both lower power single activity event detection and higher power multi-activity classification. With an existing BSN dataset and a smartphone dataset we collect from eight subjects, we demonstrate that AdaSense effectively reduces BSN sensors sampling rate and outperforms a state-of-the-art solution in terms of energy savings.

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Exploiting sensing diversity for confident sensing in wireless sensor networks

Cited by 9 publications

References 35 publications

Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

Estimate Information Fusion Weight of WSNs Nodes Based on Truth Discovery Optimization Method Among Conflicting Sources of Data

On Rendezvous in Mobile Sensing Networks

AdaSense: Adapting sampling rates for activity recognition in Body Sensor Networks

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