Impact Score 3.13
Junaid Shuja CUI, Abbottabad Campus, Pakistan ([email protected]).
Mehdi Sookhak: Illinois State University, Normal, IL, USA.
Ali Kashif Bashir: Manchester Metropolitan University, United Kingdom.
Jun Wu” Shanghai Jiao Tong University, China.
Conventional cloud and content delivery networks (CDNs) are geographically distant from end-users resulting in high access and round trip times. On the contrary, the increasing number of high-speed internet users and social media applications demand for solutions with low latency, energy efficiency, and high bandwidth. Such requirements cannot be fulfilled by CDNs and geo-dispersed cloud due to high logistic distance. Therefore, it is required to migrate cache and compute facilities near to the end-user for better Quality of Experience (QoE) in emerging applications of social media, video-on-demand, live streaming, augmented, etc. The emerging 5G/6G networks are being implemented worldwide to cater for increasing user QoE demands. With the emergence of high speed networks, Mobile Edge Computing (MEC), Content Centric Networks (CCN), Fog computing, and cloudlet paradigms have gained attention for bringing compute and storage resources in user proximity. Generally, these approaches can be called as an Edge Network or In-network computing as they bring resources to the user end (edge) of the network while increasing QoE. The main research challenges in edge computing are what, where, and when to cache as users are mobile with variable content preferences. Edge caching and computing needs to be strengthened by federated, collaborative, and distributed intelligence to solve complex problems of user mobility prediction, user-BS associations, proactive caching, edge specific content popularity, and social community detection to name a few. AI and nature inspired algorithms can be applied to solve the challenges of edge computing ranging from privacy, security, resource management, service placement, computation offloading, and automation, etc. The increase in the number of distributed nodes (network devices, high-end smartphones) has made application of compute-intensive AI/ML algorithms feasible within the network. The application of edge caching and computing are necessary for smart homes, smart grids, intelligent traffic systems, surveillance systems, smart agriculture, and IoTs in general.
The aim of this special issue is to focus on state-of-the-art edge caching and computing techniques with focus on ML and nature inspired algorithms. The topics covered by the special issue are
Edge caching and computing: Novel frameworks for MEC, Fog, CCN and Fog with application of AI/MLSocial community detection for in network applicationsGeographic recommendations systemsApplication of ML towards privacy and security in MEC.Clustering and classification algorithms for MECFederated learning for Edge computingCyber-Physical systems for In network cachingSmart traffic signaling, monitoring and surveillance systems in EdgeSDN and NFV solutions for Edge Voice/speaker recognition for smart homes in EdgeMultimedia analytics for Edge cachingSimulation tools, testbeds, models, and datasets for edge caching and performance evaluationPrivacy preserving and security approaches for Edge computingValidation of security and privacy protection methods in real-world applications
Submission Deadline (Tentative):
Submission deadline: 15 October 2021
First round notification: 15 December 2021
Second round due: 15 February 2022
Final notification: 15 March 2022