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Related Concept Videos

Reducing Line Loss01:18

Reducing Line Loss

226
In a three-phase circuit, line loss is an indicator of energy dissipated as heat due to the resistance of transmission lines. To address this, incorporating transformers into the system—a step-up transformer at the source and a step-down transformer at the load—is a strategic solution. Two three-phase transformers are introduced to improve this.
With a step-up transformer at the source, the voltage is increased, thereby reducing the current in the transmission lines since power loss...
226
Distributed Loads: Problem Solving01:21

Distributed Loads: Problem Solving

849
Beams are structural elements commonly employed in engineering applications requiring different load-carrying capacities. The first step in analyzing a beam under a distributed load is to simplify the problem by dividing the load into smaller regions, which allows one to consider each region separately and calculate the magnitude of the equivalent resultant load acting on each portion of the beam. The magnitude of the equivalent resultant load for each region can be determined by calculating...
849
Distributed Loads01:19

Distributed Loads

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Distributed loads are a common type of load that engineers and scientists encounter in various practical situations. Distributed loads often refer to a type of load spread over a surface or a structure and can be modeled as continuous force per unit area.
For example, consider a bookshelf filled with books stacked vertically adjacent to each other. The weight of the books is evenly distributed over the length of the shelf. As a result, the pressure at different locations on the surface of the...
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Downsampling01:20

Downsampling

336
When considering a sampled sequence with zero values between sampling instants, one can replace it by taking every N-th value of the sequence. At these integer multiples of N, the original and sampled sequences coincide. This process, known as decimation, involves extracting every N-th sample from a sequence, thereby creating a more efficient sequence.
The Fourier transform of the decimated sequence reveals a combination of scaled and shifted versions of the original spectrum. This...
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Lossy Lines and Overvoltages01:22

Lossy Lines and Overvoltages

146
Transmission-line series resistance and shunt conductance cause three primary effects: attenuation, distortion, and power losses.
Attenuation
When constant series resistance and shunt conductance are present, voltage and current equations are modified. The propagation constant indicates that voltage and current waves consist of both forward and backward traveling components. These waves attenuate as they propagate, with the attenuation factor related to the resistance and conductance. In a...
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Issues And Trends In Healthcare Delivery System01:29

Issues And Trends In Healthcare Delivery System

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The issues and trends in healthcare delivery are constantly changing. The COVID-19 pandemic is one recent issue that wreaked havoc on healthcare systems, causing a shortage of healthcare workers, high demand for medicines and supplies, and increased medical expenditure due to a lack of insurance. Other issues include rising healthcare costs and care fragmentation.
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Related Experiment Videos

The Deep Learning Solutions on Lossless Compression Methods for Alleviating Data Load on IoT Nodes in Smart Cities.

Ammar Nasif1, Zulaiha Ali Othman1, Nor Samsiah Sani1

  • 1Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science & Technology, University Kebangsaan Malaysia, Bangi 43600, Malaysia.

Sensors (Basel, Switzerland)
|July 2, 2021
PubMed
Summary

This study enhances Internet of Things (IoT) data compression for smart cities by improving adaptive Huffman coding with deep learning. The proposed method aims for better compression ratios, addressing IoT network limitations.

Keywords:
IoTIoT marketcompressioncompression methoddata trafficdeep learningdictionary codingentropy codinginflationinternet of thingmemorynetworkpoolingpopulationproblempruningsmart city

Related Experiment Videos

Area of Science:

  • Computer Science
  • Electrical Engineering
  • Network Engineering

Background:

  • Smart city development relies heavily on robust networking infrastructure, particularly the Internet of Things (IoT).
  • Increasing numbers of IoT devices generate massive data flows, posing significant challenges for network capacity and storage.
  • Existing IoT networks often struggle with insufficient memory to manage large volumes of transaction data.

Purpose of the Study:

  • To investigate and propose an effective data compression method for reducing Internet of Things (IoT) network data traffic.
  • To identify the most suitable lossless compression algorithms for IoT specifications.
  • To develop a novel compression technique by integrating deep learning with adaptive Huffman coding for enhanced IoT data compression.

Main Methods:

  • A chronological review of factors influencing smart city development and IoT network infrastructure.
  • Investigation of various lossless compression algorithms, including entropy-based (Huffman, Adaptive Huffman) and dictionary-based (LZ77, LZ78).
  • Experimental compression of five distinct IoT data traffic datasets using selected algorithms.
  • Conceptualization of a deep learning-enhanced adaptive Huffman algorithm incorporating neural network concepts like weights, pruning, and pooling.

Main Results:

  • Adaptive Huffman coding demonstrated superior performance among the tested traditional algorithms for alleviating IoT data traffic.
  • The study identified adaptive Huffman as the most effective compression algorithm for current IoT data traffic.
  • The proposed deep learning-enhanced adaptive Huffman method is expected to achieve a superior compression ratio.

Conclusions:

  • Adaptive Huffman coding is a viable solution for reducing IoT data traffic, but further improvements are needed.
  • A novel conceptual compression method, enhancing adaptive Huffman with deep learning, shows promise for better IoT data compression ratios.
  • Challenges related to IoT device memory and processor limitations must be addressed for the successful implementation of advanced compression techniques in IoT networks.