Mobile Information Systems

Deep Learning Models in Multi-Core Embedded Wireless Sensor Networks for Cellular Networks

Publishing date
01 Jun 2023
Submission deadline
10 Feb 2023

1Anna University, Chennai, India

2University of Paris, Paris, France

3Birmingham City University (BCU), Birmingham, UK

This issue is now closed for submissions.

Deep Learning Models in Multi-Core Embedded Wireless Sensor Networks for Cellular Networks

This issue is now closed for submissions.


Fast cellular networks provide speed, multi-MIMO capability, reliability, energy efficiency, and acquire the most effective results when combining data and voice performance. In order to develop feasible next-generation networks, analysis and development of standards for wireless connectivity need to be optimized. Data analytics and new networks (5G and 6G) enable end-to-end applications, and user requirements are integral to their design. As mobile technology advances, it may make use of distributed radio access networks (RAN) more efficiently and reduce latency, traffic, and energy consumption in core networks. Deep learning is a game-changing technology with advanced algorithms and frameworks in the cellular network industry. In order to create a connected world, deep learning must be integrated with embedded wireless sensor networks.

To achieve high performance and manage complexity, integrated wireless sensor networks have been developed through advances in integrated systems, wireless technologies, sensors, etc., and there has been a transition from single-core to multi-core. Multi-core embedded wireless sensor networks and their architectures make the processors more efficient and cost-effective, allowing them to handle computationally demanding tasks and automate, monitor, and remotely control systems. In addition, multicore embedded systems have many challenges such as tracing data from diverse cores, infrastructure, programming, debugging, etc. New technologies with novel network architectures will be required by cellular networks to overcome challenges such as global coverage, energy, cost, spectrum utilization, security, and intelligence. The development of deep learning models, prediction techniques, algorithms, and integrated wireless sensor frameworks may provide a possible solution. Innovative and powerful tools will be developed to enhance cellular networks and ensure sustainability.

This Special Issue bridges the gap between deep learning and multi-core embedded wireless sensor networks to optimize cellular networks. We welcome research focusing on state-of-the-art deep learning techniques with potential applications and platforms that facilitate the efficient deployment of deep learning onto the cellular system. Both original research and review papers are welcome.

Potential topics include but are not limited to the following:

  • Current progress in deep learning for multi-core embedded wireless sensor endpoint security and energy efficiency
  • Deep learning strategies and algorithms in multi-core embedded cellular technology for massive IoT applications
  • Deep learning models in cellular networks for energy-saving and ultra-low latency applications in rural and remote areas
  • Deep learning-based 5G/6G network slicing in the multi-core embedded wireless sensor for industrial applications
  • Deep learning-based smart embedded cellular wireless technologies for grid optimizations
  • Deep learning-based applications in embedded systems and mobile networks for developing intelligent robots
  • Deep learning in evolving cellular networks for connected autonomous systems in real-time
  • Novel hardware-software designs and embedded wireless framework for cellular network applications in healthcare
  • Resilient deep learning-based multi-core architectures for cyber-physical systems in cellular networks
  • New deep learning models and algorithms for effective integration of cellular and satellite networks for spectral efficiency
  • Testbeds in deep learning-vulnerability prediction models for cellular networks
Mobile Information Systems
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