# Mobility- and Energy-Aware Cooperative Edge Offloading for Dependent Computation Tasks +
## 5 Solution of Dynamic Computation Offloading Optimization Problem
### Energy-Efficient Task Offloading (EETO) Algorithm
In scenarios with a high density of potential helper nodes in the vicinity of a UE that seeks to offload tasks, the helper nodes could be thinned out randomly. Alternatively, the pre-selection could be based on the distance from the UE to the helper and MEC server nodes or the wireless channel conditions. The pre-selection could be aided by machine learning strategies. For the development of such machine learning strategies, the formal optimization model and QCQP formulation and solution could be utilized to generate solution data sets for training. A related machine learning strategy could learn a direct mapping, i.e., a mapping from a given set of task properties as well as available communication and computing resources to a set of task offloading decisions. A model trained with solution data sets obtained from the QCQP solutions could potentially provide such a direct mapping with a simple neural network forward pass. The present study has focused on developing a general mathematical model for the offloading of dependent computation tasks to mobile helper nodes, whereby the task properties as well as available communication and computation resources are characterized through parsimonious model parameters. A future research direction is to conduct end-to-end evaluations of the task offloading in testbeds with popular distributed computing applications, such as distributed data management and database operations [68; 69], distributed Internet of Things data analytics [70; 71], distributed video analytics [72; 73; 74], and general distributed artificial intelligence and data analytics [75; 76]. Moreover, popular user-oriented applications, such as augmented and virtual reality [77; 78; 79; 80; 81; 82] and online gaming [83], are important application domains to examine in the context of cooperative task offloading to sliced networked computing resources. Such testbed evaluations could examine the typical values of the model parameters, e.g., for the task complexity \(c_{k}\), as well the impacts of real-world wireless bandwidth fluctuations and network congestion. Another important direction for future work is to investigate incentive mechanisms that promote fair cooperation between users. Conceptualization, M.M., G.P.K., X.G., M.R., and F.H.P.F. ; methodology, M.M., S.S., G.P.K., X.G., M.R., and F.H.P.F. ; software, M.M., S.S., and Y.H. ; validation, M.M., S.S., V.L., and Y.H. ; formal analysis, M.M., S.S., and M.R. ; investigation, S.S. and Y.H. ; resources, V.L. and F.H.P.F; data curation, S.S., Y.H., and V.L. ; writing--original draft preparation, M.M. and M.R. ; writing--review and editing, M.M. and M.R. ; visualization, V.L. ; supervision, G.P.K., X.G., M.R., and F.H.P.F. ; project administration, G.P.K., X.G., and F.H.P.F. ; funding acquisition, G.P.K., X.G., and F.H.P.F. All authors have read and agreed to the published version of the manuscript. This work was supported in part by the European Union H2020 research and innovation program under grant agreement H2020-MCSA-ITN-2016-SECRET 722424 [84] and in part by the German Research Foundation (DFG, Deutsche Forschungsgemeinschaft) as part of Germany's Excellence Strategy-EXC2050/1--Project ID 390696704--Cluster of Excellence \"Centre for Tactile Internet with Human-in-the-Loop\" (CeIT) of Technische Universitat Dresden. Conflicts of Interest: The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results. ## References

* [1] [PERSON] The tactile internet: Applications and challenges. _IEEE Veh. Technol. Mag._**2014**, \(9\), 64-70. [CrossRef]
* [2] [PERSON]; [PERSON]; [PERSON]; [PERSON]; [PERSON] Internet of things: A survey on enabling technologies, protocols, and applications. _IEEE Commun. Surv. Tutor._**2015**, _17_, 2347-2376.