  Compared to the generations up to 4G, whose main focus was on broadband and
coverage aspects, 5G has expanded the scope of wireless cellular systems
towards embracing two new types of connectivity: massive machine-type
communication (mMTC) and ultra-reliable low-latency communications (URLLC).
This paper will discuss the possible evolution of these two types of
connectivity within the umbrella of 6G wireless systems. The paper consists of
three parts. The first part deals with the connectivity for a massive number of
devices. While mMTC research in 5G was predominantly focused on the problem of
uncoordinated access in the uplink for a large number of devices, the traffic
patterns in 6G may become more symmetric, leading to closed-loop massive
connectivity. One of the drivers for this is distributed learning/inference.
The second part of the paper will discuss the evolution of wireless
connectivity for critical services. While latency and reliability are tightly
coupled in 5G, 6G will support a variety of safety critical control
applications with different types of timing requirements, as evidenced by the
emergence of metrics related to information freshness and information value.
Additionally, ensuring ultra-high reliability for safety critical control
applications requires modeling and estimation of the tail statistics of the
wireless channel, queue length, and delay. The fulfillment of these stringent
requirements calls for the development of novel AI-based techniques,
incorporating optimization theory, explainable AI, generative AI and digital
twins. The third part will analyze the coexistence of massive connectivity and
critical services. We will consider scenarios in which a massive number of
devices need to support traffic patterns of mixed criticality. This will be
followed by a discussion about the management of wireless resources shared by
services with different criticality.
