Call for Papers

The IEEE Open Journal of Communications Society (OJ-COMS) invites manuscript submissions in the area of AI Powered Wireless Emergency Communications Networks.

Wireless emergency communications networks assist in establishing stability during crises and manage their impact on the community. Researchers worldwide are taking large strides in developing intelligent systems to deal with various types of emergencies. Yet, the underpinning concept behind every emergency application includes wireless emergency communication networks. An appropriate wireless emergency communication system can help individuals, irrespective of their boundaries, with critical information and notification instantly. It helps to respond, recover, and mitigate from the potential harm. However, achieving such objectives is highly complicated, as the wireless communications systems fall in several contexts, such as lack of interoperability, often fail to support crucial broadband data transfer, and become easily overloaded in emergencies.  Despite the extensive use of modern devices such as smartphones and computers, there is no guarantee that messages have reached the end-users during emergencies. For a wireless emergency communication network to be successful, it is essential to consider the critical components of intelligent network communication systems.

In the case of emergency circumstances, every second is vital. Fast and efficient response in an optimized manner is important, and this is where exactly the role of artificial intelligence (AI) comes into the picture of wireless emergency communication networks. The major functions of AI in wireless emergency communications include network optimization, robotic process automation (RPA), virtual assistant and preventive maintenance. Machine learning and deep learning techniques promise end-to-end optimization of wireless networks while they commoditize physical and signal processing designs and assist in overcoming radio frequency complexity during emergencies. It further improves channel monitoring, spectrum monitoring, and antenna sensitivity. Simultaneously, AI invokes data analysis to train the network efficiently with reduced power consumption and computational requirements. Also, the AI-assisted training models facilitate better situational awareness and ensure end-to-end learning for creating an optimal wireless emergency communication network. In short, emergency network automation and intelligence will enable better root cause analysis, prediction of network issues, and increasingly help manage, optimize, and maintain the network infrastructure and the end-user support operations. More advanced research in this background is inevitable as it underpins more strategic goals and helps to deal with emergency scenarios. 

This special issue brings together leading research experts from industry and academia to present their novel and original contributions on novel methodologies, applications, standards, and protocols for applying AI techniques for wireless emergency communication networks.

The topics of interest for the special issue include, but not limited to, the following:

• Drone assisted wireless emergency communications systems
• Advances in medium access control techniques for wireless emergency communication systems
• 5G assisted intelligent network optimization standards and protocols for wireless emergency communications systems
• AI assisted virtual assistants to deal with pandemic situations
• Robotic process automation in wireless emergency communications networks
• Securing wireless emergency communications networks with AI and blockchain techniques
• Resource allocation and service management in wireless emergency communications systems
• Ultra-reliable low-latency communications systems for wireless emergency networks
• Trends in massive machine type communication systems for wireless emergency communications networks
• Network planning and scheduling for intelligent wireless networks
• Next wireless frontier in emergency communications networks with deep learning and machine learning algorithms
• Benefits and challenges of implementing intelligent communication networks for emergency situations

Submission Guidelines

Prospective authors should submit their manuscripts following the IEEE OJ-COMMS guidelines. Authors should submit a manuscript to Manuscript Central.

Lead Guest Editor

Gunasekaran Manogaran, Howard University, Washington D.C., USA

Guest Editors

Ching-Hsien Hsu, Asia University, Taiwan.
Mamoun Alazab, Charles Darwin University, Australia.
Syed Hassan Ahmed, JMA Wireless, USA.
Oscar Sanjuán Martínez, Universidad Internacional de la Rioja (UNIR), Logroño, Spain.
Joel Rodrigues, Federal University of Piauí (UFPI), Teresina - PI, Brazil

For inquiries regarding this Special Issue, please contact the Lead Guest Editor.