  Wireless traffic is exploding, due to the myriad of new connections and the
exchange of capillary data at the edge of the networks to operate real-time
processing and decision making. The latter especially affects the uplink
traffic, which will grow in 6G and beyond networks, calling for new
optimization metrics that include energy, service delay, and electromagnetic
field (EMF) exposure (EMFE). To this end, reconfigurable intelligent surfaces
(RISs) represent a promising solution to mitigate the EMFE, thanks to their
ability of shaping and manipulating the impinging electromagnetic waves. In
line with this vision, this paper proposes an online adaptive method to
mitigate the EMFE under end-to-end delay constraints of a computation
offloading service, in the context of RIS and multi-access edge computing
(MEC)-aided wireless networks. The goal is to minimize the long-term average of
the EMF human exposure under such constraints, investigating the advantages of
RISs towards blue (i.e. low EMFE) communications. A multiple-input
multiple-output (MIMO) system is investigated as part of the visions towards
6G. Focusing on a typical scenario of computation offloading, the method
jointly and adaptively optimizes user precoding, transmit power, RIS
reflectivity parameters, and receiver combiner, with theoretical guarantees on
the desired long-term performance. Besides the theoretical results, numerical
simulations assess the performance of the proposed algorithm, when exploiting
accurate antenna patterns, thus showing the advantage of the RIS and that of
our method, compared to benchmark solutions.
