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Related Concept Videos

Data Collection by Survey01:07

Data Collection by Survey

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The systematic method of obtaining and analyzing accurate information of a population is called data collection. A survey is a standard method of data collection that involves collecting information from a target human population about their experience, opinion, or knowledge of a product, service, or process. The responses are recorded and interpreted. The most common survey examples are written questionnaires, face-to-face or telephonic conversations, focus groups, and electronic (e-mail or...
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Sensation01:21

Sensation

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Sensory receptors are specialized neurons that respond to specific types of external stimuli, initiating the process known as sensation. This occurs when sensory input, such as light entering the eye, is detected by these receptors, causing chemical changes in the cells of the retina. These cells then convert the sensory stimulus into action potentials that are transmitted to the central nervous system, a process termed transduction.
Absolute thresholds can quantify the sensitivity of sensory...
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Sensory Perception: Organization of the Somatosensory System01:11

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The somatosensory system is the central and peripheral nervous system component that senses and processes touch, pressure, pain, temperature, and body position or proprioception. The process of sensation takes place at three levels:
The receptor level:
The receptor level is the first stage of sensation. It involves the detection of a stimulus by specialized sensory receptors. The stimulus must arrive within the receptor's receptive field. Next, the receptor converts the energy of the...
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Convenience Sampling Method00:55

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Sampling is a technique to select a portion (or subset) of the larger population and study that portion (the sample) to gain information about the population. Data are the result of sampling from a population. The sampling method ensures that samples are drawn without bias and accurately represent the population.
Convenience sampling is a non-random method of sample selection; this method selects individuals that are easily accessible and may result in biased data. For example, a marketing...
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Somatosensation01:33

Somatosensation

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The somatosensory system relays sensory information from the skin, mucous membranes, limbs, and joints. Somatosensation is more familiarly known as the sense of touch. A typical somatosensory pathway includes three types of long neurons: primary, secondary, and tertiary. Primary neurons have cell bodies located near the spinal cord in groups of neurons called dorsal root ganglia. The sensory neurons of ganglia innervate designated areas of skin called dermatomes.
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Data Collection by Observations01:08

Data Collection by Observations

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Data collection refers to a systematic way of obtaining, observing, measuring, and analyzing accurate information. Observational studies are one of the most widely used methods of data collection. It involves collecting data by observing the behavior and physical characteristics of a sample without making any modifications to the sample.
An astronomer viewing the motion and brightness of stars in the sky and recording the data is an example of observational data collection. A botanist recording...
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Approximate Sensory Data Collection: A Survey.

Siyao Cheng1, Zhipeng Cai2, Jianzhong Li3

  • 1School of Computer Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. csy@hit.edu.cn.

Sensors (Basel, Switzerland)
|March 14, 2017
PubMed
Summary
This summary is machine-generated.

The explosive growth of sensory data from Internet of Things (IoTs) and wireless sensor networks (WSNs) necessitates approximate data collection. This survey reviews model-based, compressive sensing, and query-driven algorithms for efficient data handling.

Keywords:
approximate computationinternet of thingssensory data collectionwireless sensor networks

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Area of Science:

  • Computer Science
  • Electrical Engineering
  • Data Science

Background:

  • The proliferation of Internet of Things (IoTs) and wireless sensor networks (WSNs) has led to an exponential increase in sensory data volume, posing significant challenges for traditional data collection methods.
  • Limitations in bandwidth and energy within IoT and WSN systems make exact data collection often infeasible and unaffordable.

Purpose of the Study:

  • To provide a comprehensive survey of state-of-the-art approximate data collection algorithms for IoT and WSN environments.
  • To categorize and analyze existing approximate data collection techniques, highlighting their strengths, weaknesses, and future research directions.

Main Methods:

  • Classification of approximate data collection algorithms into three main categories: model-based, compressive sensing-based, and query-driven.
  • Detailed review and comparative analysis of algorithms within each category, discussing their advantages and disadvantages.

Main Results:

  • Identification of key challenges and unsolved problems in approximate data collection for large-scale sensory data.
  • Forecasting of future research prospects and potential advancements in the field.

Conclusions:

  • Approximate data collection is crucial for managing the massive data generated by IoTs and WSNs due to resource constraints.
  • The survey provides a structured overview to guide future research and development in efficient and scalable data collection strategies.