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Age of Information Optimization

Age of Information Optimization is a research topic within Computer Networks and Communications. Science Explorer counts 6.9k research works in it since 1966. 27.5% of them reached the world's top 10% most cited for their field and year.

This cluster of papers focuses on optimizing the freshness of information in communication networks, particularly in the context of real-time status updates, wireless networks, energy harvesting, and scheduling policies. The research explores age of information metrics, multi-hop networks, IoT monitoring systems, and queue management to minimize the age of information and improve network performance.

  • Age of Information
  • Real-time Status Updates
  • Wireless Networks
  • Energy Harvesting
  • Scheduling Policies
  • Multi-hop Networks
  • IoT Monitoring Systems
  • Queue Management
  • Packet Management
  • Networked Control Systems
Research works
6.9k
fractional, since 1966
In the world top 10%
1.9k
per year above
Top-10% rate
27.5%
share of its works in the world top 10%
Growth, 2013–17 → 2018–22
+242%
the tick is no change

Which countries lead Age of Information Optimization research?

By volume, China and the United States publish the most (1.2k and 370 works in 2022–2025).

By volume, 2022–2025

  1. 1 China 1.2k works
  2. 2 United States 370 works
  3. 3 India 167 works
  4. 4 Canada 94 works
  5. 5 South Korea 86 works
  6. 6 United Kingdom 80 works
  7. 7 Germany 77 works
  8. 8 Italy 74 works
  9. 9 France 56 works
  10. 10 Hong Kong 49 works

How concentrated that is

The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.

China: 53.1%United States: 16.5%India: 7.4%Canada: 4.2%6 others listed: 18.8%53%largest
China1,189 · 53.1%United States370 · 16.5%India167 · 7.4%Canada94 · 4.2%6 others listed422 · 18.8%

Shares of the rows listed above, not of the whole node.

Which institutions lead Age of Information Optimization research?

By volume in 2022–2025, Beijing University of Posts and Telecommunications publishes the most Age of Information Optimization research, followed by Tsinghua University and Sun Yat-sen University.

Who are the leading researchers in Age of Information Optimization?

The most-cited researchers publishing on Age of Information Optimization include H. Vincent Poor, Rajkumar Buyya and Xuemin Shen.

  1. 1 H. Vincent Poor United States 9.5k citations
  2. 2 Rajkumar Buyya Australia 7.8k citations
  3. 3 Xuemin Shen Canada 5.1k citations
  4. 4 Zhu Han United States 4.7k citations
  5. 5 Dusit Niyato Singapore 4.5k citations

Ranked by citations received across their whole record, among researchers with at least three works on this topic.

Where is Age of Information Optimization research done?

The largest centres of Age of Information Optimization research in 2022–2025 are Beijing (China), Nanjing (China), Guangzhou (China) and Shanghai (China). Among places with at least 20 works in it, it is an unusually large share of all research in College Park, Padova and Shenzhen.

Largest cities, 2022–2025

  1. 1 Beijing China 277 works
  2. 2 Nanjing China 120 works
  3. 3 Guangzhou China 76 works
  4. 4 Shanghai China 75 works
  5. 5 Xi'an China 63 works
  6. 6 Shenzhen China 58 works
  7. 7 Seoul South Korea 48 works
  8. 8 Chongqing China 47 works
  9. 9 Changsha China 42 works
  10. 10 Hong Kong China 37 works

Where it is the local speciality

  1. College ParkUS · 21.9 works13×
  2. PadovaIT · 20.7 works8.5×
  3. ShenzhenCN · 57.7 works6.1×
← less than its size predictsmore →

Location quotient: how much more of its research is in Age of Information Optimization than the world average.

See Age of Information Optimization on the map

Where is the best place to study Age of Information Optimization?

Among universities, judged by research, Beijing University of Posts and Telecommunications, Xidian University and University of Maryland, College Park score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.

0%20%40%mean 32.66%fractional works in this node (log) →share in the world top 10% →Beijing University of Posts and Telecommunications: 62, 32.0%Xidian University: 22, 32.8%University of Maryland, College Park: 22, 24.9%Nanjing University of Posts and Telecommunications: 19, 27.5%Chongqing University of Posts and Telecommunications: 21, 40.9%Tsinghua University: 34, 30.2%Beijing Jiaotong University: 26, 20.9%Singapore University of Technology and Design: 10, 35.3%PLA Army Engineering University: 9, 41.3%Nanyang Technological University: 14, 40.8%Xidian UniversityBeijing University o…Nanjing University o…University of Maryla…
above the meannear itbelow it

One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.

#UniversityScoreTop 10%SpecialisationWorksGrowth
1 Beijing University of Posts and TelecommunicationsChina 81.532.0%36.3×62 +358.2%
2 Xidian UniversityChina 72.532.8%11.0×22 +648.7%
3 University of Maryland, College ParkUnited States 68.124.9%14.7×22 +216.4%
4 Nanjing University of Posts and TelecommunicationsChina 66.927.5%15.9×19 +223.5%
5 Chongqing University of Posts and TelecommunicationsChina 66.840.9%26.2×21 +33.3%
6 Tsinghua UniversityChina 65.330.2%6.3×34 +223.3%
7 Beijing Jiaotong UniversityChina 65.220.9%15.1×26 +421.7%
8 Singapore University of Technology and DesignSingapore 64.135.3%39.5×10
9 PLA Army Engineering UniversityChina 64.041.3%27.2×9 +140.0%
10 Nanyang Technological UniversitySingapore 62.640.8%6.4×14 +80.0%

Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.

Is Age of Information Optimization research growing?

Output in 2018–2022 was 242% higher than in 2013–2017, peaking in 2023. The fastest-growing topics are Age of Information Optimization.

19801990200020102020
grewheldshrank

The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.

Which topics inside it are moving

Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.