Edge-Assisted Connectivity Framework for HDT

One of the key challenges in implementing HDT is establishing an efficient connectivity framework between each human-virtual pair. Given the novelty of HDT, traditional connectivity approaches fall short of meeting its unique requirements, which encompass reliability, security, and privacy. This chapter introduces a novel connectivity solution for HDT, leveraging edge computing while integrating blockchain and federated learning techniques to enhance security and privacy measures. To minimize the long-term average connectivity costs, the connectivity problem is formulated as a Markov decision process. At the same time, the deep deterministic policy gradient (DDPG) algorithm is employed to learn the optimal connectivity policy, primarily focusing on time and energy cost-efficiency. The results clearly demonstrate the effectiveness of the DDPG algorithm to provide solutions to the formulated problem.