The human digital twin (HDT) is a new paradigm that possesses the ability to revolutionize the current healthcare systems. With HDT, ensuring an efficient connectivity scheme between each human-virtual twin pair remains a significant problem. As the concept of HDT is new, conventional connectivity schemes cannot meet the unique requirements of HDT in terms of reliability, security and privacy. This paper thus proposes an edge-assisted connectivity scheme for HDT and adopts an integrated blockchain and federated learning techniques to ensure security and privacy. To minimize long-term average connectivity cost, we formulated the connectivity problem as a Markov decision process and adopted the deep deterministic policy gradient (DDPG) algorithm to learn the optimal connectivity policy in terms of connectivity cost. The obtained results were then compared with the conventional deep Q-network-based solution. The results show that the proposed DDPG-based connectivity solution is feasible to perform the connectivity process better by optimally allocating system resources, thus reducing the overall connectivity cost, while ensuring data security and privacy.


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    Title :

    Edge-assisted human-to-virtual twin connectivity scheme for human digital twin frameworks


    Contributors:


    Publication date :

    2022-06-01


    Size :

    1118481 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



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