4 research outputs found
Placement of Social Digital Twins at the Edge for Beyond 5G IoT Networks
As the fifth-generation (5G) and beyond (5G+/6G)
networks move forward, and a wide variety of new advanced
Internet of Things (IoT) applications are offered, effective
methodologies for discovering time-relevant information, services,
and resources are being demanded. To this end, computing-,
storage-, and battery-constrained IoT devices are progressively
augmented via digital twins (DTs) hosted on edge servers.
According to recent research results, a further feature these
devices may acquire is social behavior; this latter offers enormous
possibilities for fast and trustworthy service discovery, although
it requires new orchestration policies of DTs at the network edge.
This work addresses the dynamic placement of DTs with social
capabilities [social digital twins (SDTs)] at the edge, by providing
an optimal solution under IoT device mobility and by accounting
for edge network deployment specifics, types of devices, and their
social peculiarities. The optimization problem is formulated as
a particular case of the quadratic assignment problem (QAP);
also, an approximation algorithm is proposed and two relaxation
techniques are applied to reduce computation complexity. Results
show that the proposed placement policy ensures a latency among
SDTs up to 1.4 times lower than the one obtainable with a
traditional proximity-based only placement while still guaranteeing appropriate proximity between physical devices and their
virtual counterparts. Moreover, the proposed heuristic closely
approximates the optimal solution while guaranteeing the lowest
computational time