  IoT devices recently are utilized to detect the state transition in the
surrounding environment and then transmit the status updates to the base
station for future system operations. To satisfy the stringent timeliness
requirement of the status updates for the accurate system control, age of
information (AoI) is introduced to quantify the freshness of the sensory data.
Due to the limited computing resources, the status update can be offloaded to
the mobile edge computing (MEC) server for execution to ensure the information
freshness. Since the status updates generated by insufficient sensing
operations may be invalid and cause additional processing time, the data
sensing and processing operations need to be considered simultaneously. In this
work, we formulate the joint data sensing and processing optimization problem
to ensure the freshness of the status updates and reduce the energy consumption
of IoT devices. Then, the formulated NP-hard problem is decomposed into the
sampling, sensing and computation offloading optimization problems. Afterwards,
we propose a multi-variable iterative system cost minimization algorithm to
optimize the system overhead. Simulation results show the efficiency of our
method in decreasing the system cost and dominance of sensing and processing
under different scenarios.
