Blockchain Technology for Managing Edge Computing Resources

Different aspects involving the autonomous operation of robots and vehicles will change when they have access to next-generation mobile networks. 5G and beyond connectivity is bringing together a myriad of technologies and industries under its umbrella. High-bandwidth, low-latency edge computing services through network slicing have the potential to support novel application scenarios in different domains including robotics, autonomous vehicles, and the Internet of Things. In particular, multi-tenant applications at the edge of the network will boost the development of autonomous robots and vehicles offering computational resources and intelligence through reliable offloading services. The integration of more distributed network architectures with distributed robotic systems can increase the degree of intelligence and level of autonomy of connected units. We argue that the last piece to put together a services framework with third-party integration will be next-generation low-latency blockchain networks. Blockchains will enable a transparent and secure way of providing services and managing resources at the Multi-Access Edge Computing (MEC) layer.

Publications

[1] Zhuo Zou, Yi Jin, Paavo Nevalainen, Yuxiang Huan, Jukka Heikkonen, Tomi Westerlund, "Edge and Fog Computing Enabled AI for IoT - An Overview", International Conference on Artificial Intelligence Circuits and Systems (AICAS) , IEEE (2019) . (View) (Download) (Researchgate)

[2] Anum Nawaz, Tuan Nguyen Gia, Jorge Peña Queralta and Tomi Westerlund, "Edge AI and Blockchain for Privacy-Critical and Data-Sensitive Applications", Twelfth International Conference on Mobile Computing and Ubiquitous Network (ICMU) , IEEE (2019) . (View) (Download) (Researchgate)

[3] Jorge Peña Queralta, Li Qingqing, Zhuo Zou, and Tomi Westerlund, "Enhancing Autonomy with Blockchain and Multi-Acess Edge Computing in Distributed Robotic Systems", FMEC 2020: International Conference on Fog and Mobile Edge Computing , IEEE (2020) . (View) (Download) (Researchgate)