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The freshness of information, measured as Age of Information (AoI), iscritical for many applications in next-generation wireless sensor networks(WSNs). Due to its high bandwidth, millimeter wave (mmWave) communication isseen to be frequently exploited in WSNs to facilitate the deployment ofbandwidth-demanding applications. However, the vulnerability of mmWave to usermobility typically results in link blockage and thus postponed real-timecommunications. In this paper, joint sampling and uploading scheduling in anAoI-oriented WSN working in mmWave band is considered, where a single humanblocker is moving randomly and signal propagation paths may be blocked. Thelocations of signal reflectors and the real-time position of the blocker can bedetected via wireless sensing technologies. With the knowledge of blockermotion pattern, the statistics of future wireless channels can be predicted. Asa result, the AoI degradation arising from link blockage can be forecast andmitigated. Specifically, we formulate the long-term sampling, uplinktransmission time and power allocation as an infinite-horizon Markov decisionprocess (MDP) with discounted cost. Due to the curse of dimensionality, theoptimal solution is infeasible. A novel low-complexity solution framework withguaranteed performance in the worst case is proposed where the forecast of linkblockage is exploited in a value function approximation. Simulations show thatcompared with several heuristic benchmarks, our proposed policy, benefitingfrom the awareness of link blockage, can reduce average cost up to 49.6%.

Dynamic Uploading Scheduling in mmWave-Based Sensor Networks via Mobile Blocker Detection