Dear Colleagues, We cordially invite you to submit you work to Springer Collection on Discovery Internet of Things Special issue on Unlocking the Potential of Internet of Things through Edge Computing and Artificial Intelligence The Internet of Things (IoT) has allowed any type of device to be connected to the Internet and exchange data with other devices and the traditional Internet infrastructure. This has allowed the introduction of myriads of new applications covering practically every aspect of our life. IoT has revolutionized the service offering by making existing applications more intelligent, while empowering vendors, manufacturers and service providers to reinvent their product portfolio. However, to fully reap the benefits of IoT and grant the so called fourth industrial revolution, there are a number of challenges to be resolved. IoT networks are characterized as low power and lossy networks, while IoT devices are limited in terms of computational resources and energy capabilities. Edge Computing has been proven to be a viable solution for IoT by repositioning computational and communication resources closer to the IoT devices. However, the amalgamation of IoT and Edge resources is still facing serious problems of how the resources should be scheduled and allocated to serve the unprecedented data generated from the numerous of IoT applications that may coexist. At the same time, the focus has shifted from a simple connection aspect to a data one. IoT data are the core component of the applications, which help in automating their operation and extract meaningful knowledge for the IoT users and providers. Thus, Artificial Intelligence approaches will be an inherent component of next generation IoT systems. AI can be used to predict user and device behaviors, data generation models, and network communication conditions to maximize the benefit of an IoT/Edge Computing interplay. Finally, recent trends in cellular communications as 5G and 6G are targeting IoT networks through the new massive machine-type communication models. Accordingly, other challenges arise in terms of scalability, integration of Network Function Virtualization (NFV) and network slicing with the IoT and 5G and beyond paradigms and so on. To this end, this Topical Collection is soliciting conceptual, theoretical, and experimental contributions to a set of currently unresolved challenges in the area of IoT, while leveraging complementary to IoT paradigms such as, Edge Computing, AI, NFV, and 5G and beyond. Keywords: * Resource allocation and scheduling in IoT networks * Task Offloading, resource allocation, and scheduing in an IoT/Edge interplay * Energy-aware resource allocation in IoT/Edge * IoT network management and orchestration systems * Data analytics in IoT * Traffic characterization and classification in IoT/Edge * QoS management through AI in IoT/Edge * Automation through Monitoring and Service Assurance in IoT/Edge * 5G and beyond enabled IoT through mMTC models * NFV enabled IoT/Edge * 5G Network Slicing in IoT * Testbeds and Experimental facilities reports * Control of devices and networks over IoT * Malware propagation in IoT networks * Information diffusion in IoT networks For more information on submission guidelines please visit: https://link.springer.com/collections/adediacfgb Aris Leivadeas | Associate Professor Department of Software and IT Engineering École de technologie supérieure | University of Quebec 1100, rue Notre-Dame Ouest, office A-3415 Montréal (Québec) H3C 1K3 Tel. 514 396-8860 | etsmtl.ca<http://etsmtl.ca/> Find us on Facebook<http://www.facebook.com/etsmtl> - Twitter<http://twitter.com/etsmtl> - YouTube<http://www.youtube.com/user/etsmtl> - LinkedIn<https://www.linkedin.com/school/etsmtl/>