# The Challenges and Compatibility of Mobility Management Solutions for Future Networks
## 3 Qualitative Benchmark for Analysis
### Criteria for Requirement Mapping for Energy Efficiency  
The following criteria (requirements 1 and 12 of Table 1) are used for requirement mapping to fulfill the energy utilization parameters PL1, PL2, and PL3 congruently because adding numerous power-sensitive activities to the edge server will increase the battery life by 70 percent, as stated in requirement 1 of Table 1. Similarly, various offloading algorithms and applications, such as a genetic procedure-based and multi-objective workflow-based computation offloading framework that takes care of task completion constraints and energy usage, will try to decrease energy usage. Similarly, identity is another multimedia-based framework that can handle 50 times more computational load than other applications by manipulating the task of offloading, as mentioned in requirement 12 of Table 1. Next, meeting the criteria for parameter PL4 of the power sensitivity provides handover options at different levels of the network and usage of SDN, MEC, and NFV platforms that are needed for the efficient application of requirement 12 mentioned in Table 1. Likewise, dynamic multipath optimization (DMPO) reduces the offloading path after user movement. It forecasts the next position and user data size for the short term but does not perform well when the distance among the stations (base stations) is irregular.  
Similarly, enterprise mobility management (EMM) segregates information into two distinct categories. The first category covers the channel state information, and the second covers the geographical position, number of base stations, and user task size. The energy usage at the time of handoff is decreased based on these mentioned categories, but it performs worse when multi-user systems are involved, as mentioned in 12 of Table 1.  
Finally, when parameter PL5 is considered, it assists in achieving requirement 13 of Table 1. The rationale is that heuristics-based methods are most appropriate for high-speed vehicles.  
Additionally, according to the above-mentioned mapping, it can be concluded that the selected criteria for our qualitative analysis seemed to be more comprehensive than earlier work in this domain. Further, we considered the key performance indicator (KPI) [33] of the 5G network. These indicators can be fulfilled with the help of dependability and by the use of efficient energy utilization metrics during mobility scenarios. Hence, more than 90 percent dependability and 25 percent battery saving are ensured. Similarly, with the help of dependability and adaptability metrics, the latency can be reduced for virtual reality, broad-based applications, and connected cars up to 10 ms and 5 ms, respectively. The dependability metric also helps to provide reliable link selection and traffic conditions on the network. In addition, there are approximately 0.75 million devices/km\({}^{2}\) with multiple applications and mobility profiles that can ensure the criteria of extensibility and battery-saving. Now, in the following sections, we can further evaluate and judge the criteria of the aforementioned qualitative analysis.