Artificial intelligence (AI) is being pursued to empower machines with the capabilities equivalent to the human brain and achieve what most humans cannot, i.e., rational thinking and decision-making. By far, many applications have evolved for AI, especially building systems capable of dynamic decision-making for different areas like computer vision, natural language processing, voice and speech interpretation, and expert system. Irrespective of the application being discussed, one thing is common for all of them, gathering large amounts of data, and the resultant system will be as good as the collected data. So, one possible approach which has been used extensively until recently is, making one central system and feeding all data to that system which involves enormous amounts of data travelling across the network, resulting in the creation of a centralized architecture. The centralized systems have flaws and limitations, involving single points of failure, network choking points, and extensive infrastructural requirements. Hence, the shift is now towards finding solutions for distributed computing in AI, which has resulted in EdgeAI being formed due to the amalgamation of edge computing and AI. The idea is to build AI applications that can be deployed on network edge devices, e.g., autonomous driving vehicles, dynamic traffic lights and many more. Gartner says combining edge computing and AI provides significant opportunities to survive in an increasingly disruptive world.

Further, according to M&M, the expected edge computing market in the next five years will be around US$ 111 Billion. TMR predicts the market of EdgeAI to grow to US$ 76 Billion in the next seven years. However, the deployment of AI on edge has its challenges, like deploying the resource-intensive tasks on edge, where the hardware resources may not be available in abundance like on cloud platforms; this brings the issues of deciding what tasks can be done at the edge as part of the system. Further, once we decide on the data to be kept on the edge and the processing required at the edge, customized algorithms are required to do different tasks in the EdgeAI scenario, especially if the model is to be made more successful than the commercial services offering requirements also needs further exploration. Nevertheless, this is not the end; the problem starts from here in the context of privacy of the shared data along with participants in distributed scenarios along with transparent access control logging requirements so that participants build trust in the system and willfully contribute data for the betterment of the system. Moreover, whatever systems are designed must be adaptable to the magnitude of the demand and supply. Hence, as per needs, the system needs to scale together with being sustainable. 

The aim of this workshop is to provide a platform for industrial practitioners and academic researchers to think beyond the boundaries to submit the new developments related to EdgeAI for distributed systems and networks. Moreover, our focus is intended to discuss the near-term and long-term confluence of the EdgeAI and DIstributed Systems and Networks. 

The workshop will be help in conjunction with the 25th International Conference on Distributed Computing & Networking (ICDCN 2024) - during 4 - 7 January, 2024 in Madras, India. 

Accepted paper in EdgeAI 2024 

• Deepanshu Garg (Chandigarh University, India) and Rasmeet Singh Bali (Chandigarh University, India). Edge Supported QoS based Secure Data Communication mechanism for Software Defined Vehicular Networks.

• Joy Lal Sarkar (Techno College of Engineering Agartala), Sheng-Lung Peng (National Taipei University of Business, Taiwan), Subhrajyoti Deb , Muthaiah U (Amrita School of Computing, Amrita Vishwa Vidyapeetham, Chennai, India), Ayan Mondal (IIT Indore) and Gaddam Venu Gopal (Koneru Lakshmaiah Education Foundation, AP, India). SAHA: An SDN Enabled Fog Architecture for Healthcare Applications.

• Anjum Mohd Aslam (Bennett University, India), Aditya Bhardwaj (Bennett University, India), Rajat Chaudhary (Bennett University, India) and Ishan Budhiraja (Bennett University, India). A Cooperative Game Approach for Multi-lane Merging Decision-making Algorithm for CAVs.

• Shilpi Mittal (Chandigarh University, India) and Rasmeet Singh Bali (Chandigarh University, India). Intelligent Resource Orchestration for Task Distribution in Hybrid Vehicular Networks.

• Kavish Shah (DA-IICT, Gujrat, India), Ayush Gandhi  (DA-IICT, Gujrat, India), Kalgi Gandhi  (DA-IICT, Gujrat, India) and Minal Bhise  (DA-IICT, Gujrat, India). Workload Prediction for Edge Computing.