  The freshness of information, measured as Age of Information (AoI), is
critical for many applications in next-generation wireless sensor networks
(WSNs). Due to its high bandwidth, millimeter wave (mmWave) communication is
seen to be frequently exploited in WSNs to facilitate the deployment of
bandwidth-demanding applications. However, the vulnerability of mmWave to user
mobility typically results in link blockage and thus postponed real-time
communications. In this paper, joint sampling and uploading scheduling in an
AoI-oriented WSN working in mmWave band is considered, where a single human
blocker is moving randomly and signal propagation paths may be blocked. The
locations of signal reflectors and the real-time position of the blocker can be
detected via wireless sensing technologies. With the knowledge of blocker
motion pattern, the statistics of future wireless channels can be predicted. As
a result, the AoI degradation arising from link blockage can be forecast and
mitigated. Specifically, we formulate the long-term sampling, uplink
transmission time and power allocation as an infinite-horizon Markov decision
process (MDP) with discounted cost. Due to the curse of dimensionality, the
optimal solution is infeasible. A novel low-complexity solution framework with
guaranteed performance in the worst case is proposed where the forecast of link
blockage is exploited in a value function approximation. Simulations show that
compared with several heuristic benchmarks, our proposed policy, benefiting
from the awareness of link blockage, can reduce average cost up to 49.6%.
