# Key Technologies for 6G-Enabled Smart Sustainable City
## 4 Key Candidate Technologies for Future SSC
### Prime Technologies for SSC
#### 4.2.2 Advanced Mobile Edge Computing
**Uncompromising quality of service**: The selection of the edge node to compute contributes to measuring QoS. The primary objective of advanced mobile edge computing is to perform the computing anywhere near the collected data. To evaluate network performance, latency is a vital QoS metric which is not only determined by computation time. The computation of the workload should be performed at a closer layer. In addition, this layer must have enough computation capability to perform the calculations at the edge network. In the following, we elaborate on the potential benefits of leveraging advanced mobile edge computing for 6G-enabled SSC. * _Real-time interaction_: By placing edge computing servers near UEs, it enables the efficient utilization of resources and data distribution according to the UE's computing capability. This approach offers high-speed communication compared to traditional centralized computing and can reduce the latency between servers and devices to 10 ms or less. Consequently, it ensures low latency and high QoS for delay-sensitive applications and real-time immersive services [43]. * _Enhanced data security_: In traditional centralized cloud computing, all information is concentrated in a centralized data center, which can be exposed to hacking and cyber-crimes. In contrast, advanced mobile edge computing has a decentralized structure and can provide more robust data security. Further, in IoT-based SSC environments, an appropriate privacy rule for each IoT device at the network edge can be provided in real time based on the ontology model containing privacy information [44], providing more robust and efficient data security. * _Overload reduction_: Traditional centralized cloud computing tends to concentrate all computational and data processing requests in a central data center, resulting in high bandwidth demands and overload. In contrast, advanced mobile edge computing enables local processing since UE's requests can be processed on edge servers, reducing the need for large data transfers to central data centers and the amount ofnetwork traffic [43]. This reduces bandwidth demands and then mitigates bottlenecks, improving the network performance by alleviating the overload. * _Preventing repetitive traffic_: In conventional telecommunication networks, backhaul links often experience congestion caused by repetitive traffic requests for the same content. The caching technique offers a solution to alleviate congestion by storing frequently requested content in various locations on the network, ensuring low-latency access. Edge computing systems, integrated with the caching technique, can effectively deliver resource-intensive smart services, encompassing smart transportation, smart tourism, and smart industry [90]. Furthermore, intelligent edge caching based on machine learning and optimization is expected to significantly reduce the latency of real-time applications [42]. MEC networks can contribute to the delivery of a variety of SSC services. Firstly, healthcare systems, assisted by edge computing architectures, can provide more personalized and expedited healthcare services. The data processing architecture between healthcare-related devices (such as smartwatches) and edge nodes must be reevaluated to satisfy the ultra-low latency, energy efficiency, and high levels of security conditions required for mission-critical healthcare applications [91]. Secondly, the two-tier edge computing-based traffic management model can enhance the accuracy and efficiency of traffic detection processes [92]. Large volumes of monitoring data collected for traffic congestion and accident detection can be partitioned and processed at edge nodes located near the detection cameras. This eliminates the need to forward the data all the way to the traffic management center for processing. In addition, it enables more effective urban traffic management by adjusting additional lanes and regulating the movement of autonomous vehicles.