Guest Editors
Dr. Asif Ali Laghari, Sindh Maressatul Islam University, Pakistan.
Dr. Yin Shoulin, Shenyang Normal University, China.
Dr. Vania V. Estrela, Federal Fluminense University (UFF), Brazil.
Summary
Aims & Scope: Nowadays, the population of cities has increased rapidly. According to people's perceptions and needs, managing resources is a problem for governments, businesses, healthcare facilities, teaching-learning setups, entertainment industries, and service providers. Citizens used mobile phones/smartphones, and most smartphones can sense ambient light, noise (through the microphone), location (through the GPS), movement (through accelerometers), and more. Crowdsensing is sometimes referred to as mobile crowdsensing, surveillance, or reconnaissance. It is a technique where a large group of individuals can use their mobile computing resources, sensors, and actuators continuously to fulfill their needs with minimal stress and high satisfaction. Among computing frameworks, one can cite smartphones, assorted mobile equipment, wearables, GPSs, remote sensing, drones, gaming environments, virtual/augmented/mixed reality contexts, Human-computer interfaces, and in-body healthcare devices. These elements collectively gather, analyze and share data to extract meaningful information. The result of sensing schemes helps measure, map, analyze, estimate, infer (predict), perform decision-making, and start remediation procedures via actuators. In short, this means crowdsourcing sensor data from mobile devices engenders sentience and a more humane approach to healthcare. The Quality of Experience (QoE) and Quality of Service (QoS) concepts allow adjusting cyber-physical systems' behavior and performance dynamically to cater to the most significant stakeholders without compromising their lives. QoE gets user needs data, and quality of service (QoS) provides better services.
This special issue aims to receive high-quality papers that extend the current state of the art with innovative ideas and solutions in the broad area of resource management for QoS and better QoE crowdsensing from images, videos, and their associated text descriptions. Visual data, imagery databases, and user engagement are paramount to implement total adherence to medical treatment, distance education, and civilized human interactions in public places while conquering stakeholders' resistance to remote healthcare. Potential topics include but are not limited to:
● QoE/QoS for image crowdsensing
● QoE/QoS for video crowdsensing
● Multispectral/hyperspectral remote sensing
● Privacy and security of crowdsensing data
● Reliability of crowdsensing data
● Machine learning models for crowdsensing
1. Crowdsensing
2. Quality of Experience
3. Quality of Service
4. Security
5. Image, video processing, and related metadata
6. Data reliability
7. Image-to-text and text-to-image retrieval efficiency
8. QoS and QoE in public health
9. QoS and QoE in education
10. Blockchain as a supporting tool to guarantee/reinforce multimedia QoS and QoE
11. QoS and QoS in drone units and swarms
12. QoS and QoE in the entertainment and gaming industries
13. New QoS and QoE metrics
14. QoS and QoE and sentience
15. QoS and QoE and the semantic web
Keywords
Quality of Experience, Quality of Service, Crowdsesning, Image/Video processing, Blockchain, Public Health
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