  The edge intelligence (EI) has been widely applied recently. Spliting the
model between device, edge server, and cloud can improve the performance of EI
greatly. The model segmentation without user mobility has been investigated
deeply by previous works. However, in most use cases of EI, the end devices are
mobile. Only a few works have been carried out on this aspect. These works
still have many issues, such as ignoring the energy consumption of mobile
device, inappropriate network assumption, and low effectiveness on adaptiving
user mobility, etc. Therefore, for addressing the disadvantages of model
segmentation and resource allocation in previous works, we propose mobility and
cost aware model segmentation and resource allocation algorithm for
accelerating the inference at edge (MCSA). Specfically, in the scenario without
user mobility, the loop interation gradient descent (Li-GD) algorithm is
provided. When the mobile user has a large model inference task needs to be
calculated, it will take the energy consumption of mobile user, the
communication and computing resource renting cost, and the inference delay into
account to find the optimal model segmentation and resource allocation
strategy. In the scenario with user mobility, the mobiity aware Li-GD (MLi-GD)
algorithm is proposed to calculate the optimal strategy. Then, the properties
of the proposed algorithms are investigated, including convergence, complexity,
and approximation ratio. The experimental results demonstrate the effectiveness
of the proposed algorithms.
