# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 2 Related Work
### Mobility Models  
User mobility, i.e., the movement of devices or vehicles in the area of an MEC network, introduces several challenges for task offloading in cooperative MEC networks, which have been considered in relatively few prior studies. A matching-based algorithm for choosing the proper task offloading method to road side units (RSUs) and nearby vehicles in vehicular edge computing has been proposed in [46]. The matching-based algorithm aims at optimizing the system utility in terms of latency as well as computing and communication costs. The vehicle locations in [46] are modeled by 2D Euclidean coordinates, while their mobility follows a constant velocity model which is predetermined along roadways; a similar roadways-based mobility model has been considered in [47] (various other studies, e.g., [48], have examined similar mobility models for offloading to RSUs only, not to other vehicles). The mobility models based on roadways are appropriate for the specific case of vehicular networks but are not suitable for general mobility scenarios that are not restricted to predetermined roadways. The study [49] considered cooperative task offloading with mobile MEC servers that are mounted on unmanned aerial vehicles (UAVs), and a non-cooperative version of this UAV-assisted MEC is studied in [50]. The flight path of the UAVs is optimized to effectively support the offloading of independent tasks that can be partitioned. In contrast to these preceding studies, we consider general mobility scenarios for the end devices and stationary MEC servers.  
The study [51] used a deep-learning-based algorithm to predict the user mobility trajectories and locations in general mobility scenarios to develop an online algorithm for the non-cooperative offloading of tasks from a mobile end device to stationary MEC servers. However, the study [51] did not consider the cooperation between adjacent end devices. In contrast, we consider cooperative task offloading to adjacent mobile end devices reached with D2D communication and to stationary MEC servers with cellular communication.  
The study [52] considered an elementary position and direction vector mobility model for cooperative task offloading. A generic three-layer cooperative edge computing network architecture is presented in [53] taking into account the mobility effects of the users by considering the sojourn time with exponential distribution for the coverage of fog nodes. The study [53] parameterizes the exponential distribution for the sojourn times via a Gaussian distribution and notes that a future work direction is to employ machine learning for mobility modeling. The study [54] considered the cooperative task offloading of independent tasks utilizing human pedestrian trajectories that are predicted via data mining techniques [55; 56]. In the present paper, we tackle the future work direction noted in [53] and advance the mobility modeling in [54] by formulating the task offloading model with the PECNet deep learning trajectory method, as elaborated in Section 3.5, in the context of dependent computation tasks.