# 5G Mobile Services and Scenarios: Challenges and Solutions
## 3 A Brief Review of Articles of this Special Issue
[31] proposed a user-offloading algorithm for Evolved Node B (eNB) hardware that smartly allocates the deprived LTE User-Equipment (UEs) and assigns the LWA service to an optimal number of UEs without degrading the QoS for existing WLAN UEs. With LWA, UEs supporting both LTE and WLAN can utilize both LTE and WLAN links simultaneously. Thus, UEs under the coverage of an LWA network will be surrounded by multiple standards, such as LTE, WLAN, and LWA, along with cells of different sizes and coverage. Providing the LWA service to all UEs unconditionally may lead to serious intra-cell unfairness, degradation of system-level QoS, and a reduction in system resource utilization. Hence, to resolve this issue, two important challenges need to be addressed: Which LTE UEs should be transferred, and how many LTE UEs need to be transferred. The proposed LWA admission control mechanism for eNBs finds the optimum number of LTE UEs for LWA activation. It addresses two important challenges: which LTE UEs should be transferred, and how many LTE UEs need to be transferred. Through numerical simulations, this paper proves that the proposed algorithm contributes to maximizing network throughput and performance by optimally assigning LWA service to poor UEs without degrading the QoS of existing WLAN UEs. The paper shows that the proposed scheme maximizes the throughput performance of the whole network. A Smart Grid (SG) [32; 33] is a modernized electric grid that enhances the reliability, efficiency, sustainability, and economics of electricity services and plays a vital role in modern energy infrastructure. The core challenge faced by SGs is how to efficiently utilize different kinds of front-end smart devices, such as smart meters and power assets, and in what manner to process the enormous volume of data received from these devices. Furthermore, cloud and fog computing provide on-demand resources for computation, which is a good solution to overcome SG hurdles. Fog-based cloud computing has numerous good characteristics, such as cost-saving, energy-saving, scalability, flexibility, and agility.Resource management is one of the big issues in SGs. This paper proposes a cloud-fog-based model for resource management in SGs. The key idea of the proposed work [34] is to determine a hierarchical structure of cloud-fog computing to provide different types of computing services for SG resource management. In this paper, four load balancing algorithms, namely Particle Swarm Optimization (PSO), Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and the proposed Hybrid Artificial Bee Ant Colony Optimization (HABACO) algorithm, are used to manage SGs resources optimally in a cloud and fog based environment, and their results are compared with each other. For this purpose, two scenarios are considered. Four load-balancing algorithms are implemented in both scenarios. Simulation results show that the overall performance of HABACO is better as compared to the other techniques presented in Scenario 1. In Scenario 2, the performance of the proposed algorithm is also found to be better as compared to PSO, ABC, and ACO. From the numerical results, the paper concludes that, in Scenario 1, the response time of proposed algorithm outperformed PSO by up to 60%, ACO by up to 90% and it enhances its efficiency by up to 36% in the case of the ABC using two VMs in Scenario 1. The proposed algorithm also outperforms the other algorithms in terms of average processing time: 7.8% as compared to PSO, 7.07% as compared to ABC and 66% as compared to ACO for average processing time using one fog server and two VMs. In this manner, the proposed HABACO algorithm outperforms its counterpart algorithms due to its capability of finding the both global and local optimal solutions. Named Data Networking (NDN) [35] is an innovative paradigm that provides the location independent data communications in which client's requests are satisfied regardless of the content's location.