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Sustainability 2018, 10(10), 3626; doi:10.3390/su10103626
5G Mobile Services and Scenarios: Challenges and Solutions
Department of Information and Communication Engineering, Yeungnam University, 280 Daehak-Ro, Gyeongsan, Gyeongbuk 38541, Korea
COMSATS University, Islamabad, Wah Campus, Wah Cantt 47010, Pakistan
GoPerception Laboratory, Cornell University, Ithaca, 14850 NY, USA
Waterford Institute of Technology, X91 K236 Waterford, Ireland
Author to whom correspondence should be addressed.
Received: 8 October 2018 / Accepted: 9 October 2018 / Published: 11 October 2018
The Fifth generation (5G) network is projected to support large amount of data traffic and massive number of wireless connections. Different data traffic has different Quality of Service (QoS) requirements. 5G mobile network aims to address the limitations of previous cellular standards (i.e., 2G/3G/4G) and be a prospective key enabler for future Internet of Things (IoT). 5G networks support a wide range of applications such as smart home, autonomous driving, drone operations, health and mission critical applications, Industrial IoT (IIoT), and entertainment and multimedia. Based on end users’ experience, several 5G services are categorized into immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. In this paper, we present a brief overview of 5G technical scenarios. We then provide a brief overview of accepted papers in our Special Issue on 5G mobile services and scenarios. Finally, we conclude this paper.
Keywords:5G; immersive 5G service; intelligent 5G service; omnipresent 5G service; autonomous 5G service; public 5G services
We are now heading towards the fourth industrial revolution, which is based on digital revolution and marked by emerging technology breakthroughs in artificial intelligence, quantum computing, robotics, nanotechnology, biotechnology, etc. . This new wave of technological revolution is changing the way we work, live, and communicate with each other. We can see emergence of unprecedented applications and services such as artificial intelligence, smart home, autonomous vehicles, drone-based delivery systems, smart cities, smart factories, etc. The co-existence of machine and human centric services will diversify the future wireless environments with the cost of challenges. In the near future, mobile communication will penetrate every part of society and create a multidimensional, user-centric information ecosystem. In addition, a fully mobile and connected society is expected, which will be characterized by an incredible amount of growth in connectivity, traffic volume, and a much broader range of usage scenarios . The traditional business model of mobile communications has been disrupted by Mobile Broadband (MBB) service, enabling unprecedented user experiences and making a deep impact on every aspect of people’s life and work. Accompanying the large-scale commercialization of 4G, the fifth generation of mobile communications (5G) is expected to be commercialized around 2020 and has become a central point for global research and development. Regardless of advanced 4G network, it is difficult to provide mobile services that need high speed, high reliability, rapid response, and energy efficiency. Existing 4G networks cannot deliver instant cloud services, enhanced Vehicle-to-Everything (eV2X), Internet of Things (IoT) , tactile internet [4,5], and robots and drone communication, considering the quality of experience (QoE) to mobile users. These characteristics have become vital necessities for future services in the 5G era. The recent research on 5G services and their technical requirements have been performed by the International Telecommunication Union-Radio communication Sector (ITU-R), the Next Generation Mobile Networks (NGMN) Alliance and the 3rd Generation Partnership Project (3GPP). In the ITU-R Working Party (WP) 5D, 5G is defined by the name of International Mobile Telecommunications-2020 (IMT-2020) .
Based on end users’ experience, several 5G services are categorized into five groups. The new 5G services are as follows: immersive 5G services, intelligent 5G services, omnipresent 5G services, autonomous 5G services, and public 5G services. As mobile multimedia content streaming and social networks have become more widespread, the demand for mobile traffic has continuously increased. Table 1 summarizes the five service categories of 5G. In addition, new types of multimedia services such as augmented reality, virtual reality, etc., which all require huge traffic volumes, have emerged .
The vision of 5G is to provide super ultra-low latency user experience, fiber-like access data rate, connect more than 100 billion devices and deliver a consistent experience across diverse scenarios with enhanced energy and cost efficiency.
2. 5G Technical Scenarios
This section presents 5G vision and requirements, and summarizes the main technical scenarios, challenges, and key enabling technologies for 5G. The key features and core technologies are extracted to form 5G concept. IoT and Mobile Internet will become the main driving force for the evolution of mobile communications towards 2020 and beyond. In the future, 5G will need to fulfill diverse service requirements in all aspects of life such as work, leisure, and transportation. Ultimate service experiences include Super Ultra-High Definition (S-UHD) videos, augmented reality, virtual reality, cloud desktop, edge cloud computing, and online gaming. Moreover, 5G will penetrate the IoT and achieve Internet of Everything (IoE) to accomplish diverse service requirements of transportation, manufacturing, medicine, and other vertical industries.
5G will solve the challenges stemmed from the drastically different performance requirements in a wide range of diversified scenarios, where different Key Performance Indicators (KPI) such as user experienced data rate, traffic volume density, latency, energy efficiency, and connection density might be challenging. Four typical technical scenarios of 5G are derived from the main application scenarios, service requirements, and key challenges of mobile internet and IoT: high-capacity hot-spot scenario, low-power massive-connections scenario, seamless wide-area coverage scenario, and low latency high-reliability scenario [8,9]. Along with this, the above-mentioned technical scenarios have key issues, which are given in Table 2.
Further use cases are expected to emerge, which are currently not anticipated. For future 5G, flexibility will be necessary to adapt to new use cases that come with a wide range of requirements . The research forums and other external organizations wishing to contribute to the future development of 5G are encouraged to focus especially in the following key areas :
3. A Brief Review of Articles of this Special Issue
In this Special Issue, we have accepted papers in the domain of 5G Mobile Services and Scenarios.
Most existing approaches for categorizing 5G services are presented from the perspective of network operators and service providers . However, Yu et al.  categorized with the aim of maximizing the satisfaction of end-users. The megatrends in the upcoming 5G era are analyzed. Then, the new 5G services are classified on the basis of these trends, with the consideration of end-users’ experience. Essential technical requirements for realizing the 5G services are suggested. In addition, a competitiveness analysis on 5G services/devices/network industries and the current condition of 5G technologies have been explained in the paper.
Ali et al.  studied the IEEE 802.11ax High-Efficiency Wireless local area network (HEW) as the foundation technology for the 5G networks on unlicensed bands (5G-U) . The paper identifies the future use cases of HEW deployments in 5G-U networks, and highlights the challenge of performance degradation in case of high number of Wireless Local Area Networks (WLANs). The authors proposed a self-scrutinized Channel Observation-based Scaled Back Off (COSB) mechanism to handle the high-density contention problem. The performance efficiency of the proposed solution is analyzed through the analytical and simulation results, and is compared with the binary Exponential Back Off (BEB) mechanism. The proposed mechanism guarantees the enhanced QoE (high throughput and low delay) in a high-density environment by reducing the number of collisions during the channel access mechanism.
Sodhro et al.  examined the integration of Fog computing and 5G based IoT devices [21,22]. The author highlighted the shortcoming of Fog computing in terms of less storage capacity and more power drain. Unlike other related literature, the paper investigates the quality of service (QoS) domain of communications between IoT and Fog computing. The authors proposed the Transmission Power Control (TPC) based QoS optimization algorithm (QoS-TPC). The proposed Fog-IoT-TPC-QoS architecture considers the static and dynamic behavior of the channel. Importantly, a comparison with the conventional adaptive TPC (ATPC), Team Game (TG) and constant TPC methods is shown in the proposed work. However, the proposed work carry certain limitations including requirement of high Received Signal Strength Indicator (RSSI) values, and high delay and jitter values.
Song et al.  discussed the challenges and the possible performance degradation of Transmission Control Protocol (TCP) due to path heterogeneity in highly integrated 5G systems . The paper presents a performance analysis model for the TCP over multiple heterogeneous paths, with the consideration of link quality asymmetry (both delay and bandwidth). The accuracy of the proposed model is obtained by the comparison of simulation study with field experiment. The authors developed the interesting conclusion that the delay asymmetry between the multiple links is more dominant factor in the performance of TCP. The proposed model, no doubt, can provide guidance to the design of novel Concurrent Multipath Transfer (CMT) solutions for 5G mobile services.
Li et al.  proposed a theoretical model for partitioning and allocating an arbitrarily divisible application, i.e., Optical Character Recognition (OCR), among a group of available resources, with the objective of reducing the completion time. The effectiveness of the proposed model for partitioning and allocating divisible applications in opportunistic computing environments is demonstrated through both the simulations and real experiments. The proposed work only focus on the benefit of offloading in terms of completion time, and the inclusion of the incurred energy consumption is suggested for the future work.
Khalid and Yu  evaluated the 5G based Cognitive Radio Networks (5G-CRN)  as one of the potential candidates for enhancing the spectrum utilization efficiency. The paper highlights the need of efficient handover management for the 5G-CRN as multi-tier network layout for 5G requires the Primary User (PU) to be more active. This paper focuses on the formulation and analysis of the sum utilization of spectrum policy for multi-channel scenarios under different PU and Secondary User (SU) co-existing network topologies, with the aid of sensing and handoff capabilities. The proposed work demonstrates that the different sets of channels, PU activities, and sensing outcomes have a significant impact on the sum utilization of spectrum associated with a specific network topology.
Tanoli et al.  investigated experimental performance analysis of Cooperative Communication (CC)  over Direct Communication (DC) in the lab environment to overcome channel fading and to improve channel capacity. The experimental setup is built using Universal Software Radio Peripheral (USRP) and Laboratory Virtual Instrument Engineering Workbench (LabVIEW). A text message is transmitted by using Phase Shift Keying (PSK) modulation schemes. The setup uses Amplify and Forward (AF) relaying mode and two time slot transmission protocols. The Maximum Ratio Combining (MRC) technique is used for combining Signal to Noise Ratio (SNR) at the receiver. The CC can significantly enhance the system performance in terms of reliability and channel capacity. Due to the high data rates and cost effective solutions, CC is considered as one of the most efficient technique, and it can be exploited for the 5G wireless networks as well. In this paper , extensive experiments are carried out for capacity gain of CC over DC in a lab environment. Experimental performance analysis shows that CC significantly improves the performance of the system than DC. Experimental analysis also shows that the increase in the transmit antenna gain results in improving the SNR at receiver. High SNR provides more channel capacity by using higher order PSK modulation. The performance of CC significantly increases the system reliability and the channel capacity of the system with and without obstacle environment. It is also analyzed that channel capacity increases by placing the relay at center between source and destination.
To alleviate the spectrum scarcity problem in 5G networks, traditional mobile data offloading schemes from Long-Term Evolution (LTE) to WLANs have been revised by the third-Generation Partnership Project (3GPP) in release 13, which is known as LTE-WLAN Aggregation (LWA) . Bajracharya et al.  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  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)  is an innovative paradigm that provides the location independent data communications in which client’s requests are satisfied regardless of the content’s location. In fact, this concept provides unique and location-independent content, in-network caching and name-based routing. These features give NDN the potential to become a key technology for data dissemination in IoT. The NDN concept has abilities to mitigate the significant communication overhead caused by the distribution of commonly accessed contents. The Information-Centric Networking (ICN) [36,37] concept is used to decrease the substantial communication overhead caused by the dissemination of analogously accessed contents. In-networking caching arranges a content replica that is cached at different locations to increase the availability of the desired data. Caching is a crucial component that makes ICN more beneficial than the present Internet architecture. It significantly minimizes the huge volume of data traffic and diminishes the bottleneck caused by publishing data in a unique location. Moreover, caches reduce the distance required for data retrieval. The cache performance totally depends on cache management schemes, which are divided into two mechanisms, namely, content placement strategies and replacement policies. Naeem et al.  addressed the caching strategies with the aim of recognizing which caching strategy is the most suitable for IoT networks. The simulations indicate the impact of different popularity-based caching strategies; of these, periodic caching is the most appropriate strategy for IoT environments in terms of a stretch that results in decreasing the content retrieval latency and improving the cache-to-hit ratio. Moreover, it is useful for all smart city applications, as it reduces the latency and improves the content response time.
Cisco first introduced the term fog computing. The aim is to provide data storage, processing, and analysis capabilities at the edge devices instead of the cloud. Consequently, it improves the efficiency and performance of the overall system by reducing network load and delay. Integration of fog computing and IoT opens up new research venues, challenges and opportunities for the practitioners and researchers [39,40,41]. Dealing with the issue of energy crisis in SG is a challenging task. Load management using load balancing is one of the technique to cater this challenge. Virtual Machines (VMs) on the physical system is one of the techniques to manage resources efficiently and is also feasible for SGs management. Bakhsh et al.  addressed the issue of dynamic VM placement to reduce the computational cost, increase the system performance and reduce the delay for SG. Optimized Response Time Policy (ORTP) is used to route the request to fog. Thereafter, First Come First Serve (FCFS) or Ant Colony Optimization (ACO) policy assigns the request to the VM. The result shows that proposed system improves the overall system in terms of response time, processing time and the computation cost. It is interesting to see comprehensive analysis and evaluation by applying Best Fit (BF), First Fit (FF), First Fit Decreasing (FFD), and Best Fit Decreasing (BFD) heuristic algorithms in the future work.
Twelve papers in this Special Issue provide state-of-the-art research trend in the area of 5G Mobile Services and Scenarios. The papers present interesting discussion and novel ideas for the readers. The Guest Editors would like to show appreciation to authors and thank all the anonymous reviewers for providing constructive feedback to improve the overall quality of all the accepted papers. We would also like to thank Editor-in-Chief Prof. Dr. Marc A. Rosen and Managing Editor Leanne Fan for the invaluable help and productive advice in finalizing this Special Issue.
Conceptualization, Y.B.Z. and M.K.A.; Writing—Original Draft Preparation, Y.B.Z.; Writing—Review and Editing, Y.B.Z., S.W.K., M.K.A., H.W., and M.H.R.; and Supervision, S.W.K.
This research received no external funding.
This research was supported in part by the MSIT(Ministry of Science, ICT), Korea, under the ITRC (Information Technology Research Center) support program (IITP-2018-2016-0-00313) supervised by the IITP (Institute for Information & communications Technology Promotion) and in part by Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2015R1D1A1A01058751).
Conflicts of Interest
The authors declare no conflict of interest.
The following abbreviations are used in this manuscript:
|3GPP||3rd Generation Partnership Project|
|5G-CRN||5G Based Cognitive Radio Networks|
|5G-U||5G Networks on Unlicensed Bands|
|ABC||Artificial Bee Colony|
|ACO||Ant Colony Optimization|
|ATPC||Adaptive Transmission Power Control|
|BBUs||Baseband Processing Units|
|BEB||Binary Exponential Back Off|
|BFD||Best Fit Decreasing|
|CMT||Concurrent Multipath Transfer|
|COSB||Channel Observation-based Scaled Back Off|
|FCFS||First Come First Serve|
|FFD||First Fit Decreasing|
|HABACO||Hybrid Artificial Bee Ant Colony Optimization|
|IMT-2020||International Mobile Telecommunications-2020|
|IoE||Internet of Everything|
|IoT||Internet of Things|
|ITU-R||International Telecommunication Union-Radio communication Sector|
|KPI||Key Performance Indicators|
|LabVIEW||Laboratory Virtual Instrument Engineering Workbench|
|MRC||Maximum Ratio Combining|
|NDN||Named Data Networking|
|NGMN||Next Generation Mobile Networks|
|OCR||Optical Character Recognition|
|ORTP||Optimized response time policy|
|PSK||Phase Shift Keying|
|PSO||Particle Swarm Optimization|
|QoE||Quality of Experience|
|QoS||Quality of Service|
|RRHs||Remote Radio Heads|
|RSSI||Received Signal Strength Indicator|
|S-UHD||Super Ultra-high Definition|
|TCP||Transmission Control Protocol|
|TPC||Transmission Power Control|
|USRP||Universal Software Radio Peripheral|
|WLANs||Wireless Local Area Networks|
- Davis, N. What is the fourth industrial revolution? World Econ. Forum 2016, 1, 2016. [Google Scholar]
- Liu, G.; Jiangl, D. 5G: Vision and Requirements for Mobile Communication System towards Year 2020. Chin. J. Eng. 2016, 2016. [Google Scholar] [CrossRef]
- Zikria, Y.B.; Yu, H.; Afzal, M.K.; Rehmani, M.H.; Hahm, O. Internet of Things (IoT): Operating System, Applications and Protocols Design, and Validation Techniques. Future Gener. Comput. Syst. 2018, 88, 699–706. [Google Scholar] [CrossRef]
- Fettweis, G.P. The Tactile Internet: Applications and Challenges. IEEE Veh. Technol. Mag. 2014, 9, 64–70. [Google Scholar] [CrossRef]
- Simsek, M.; Aijaz, A.; Dohler, M.; Sachs, J.; Fettweis, G.P. 5G-Enabled Tactile Internet. IEEE J. Sel. Areas Commun. 2016, 34, 460–473. [Google Scholar] [CrossRef]
- International Telecommunication Union (ITU). IMT Vision—Framework and Overall Objectives of the Future Development Of IMT for 2020 and Beyond M Series Mobile, Radiodetermination, Amateur and Related Satellite Services; International Telecommunication Union (ITU): Geneva, Switzerland, 2015. [Google Scholar]
- Cisco, T. Cisco Visual Networking Index: Global Mobile Data Traffic Forecast Update, 2016–2021. Cisco 2017, 3, 2017. [Google Scholar]
- IMT-2020, P.G. 5G Concept. IMT-2020 (5G) Promotional Group, China. Volume 2, 2015. Available online: http://www.imt-2020.org.cn/en/documents/download/3 (accessed on 11 October 2018).
- Bajracharya, R.; Shrestha, R.; Zikria, Y.B.; Kim, S.W. LTE in the Unlicensed Spectrum: A Survey. IETE Techn. Rev. 2018, 35, 78–90. [Google Scholar] [CrossRef]
- Al-Shehri, S.; Loskot, P.; Numanoglu, T.; Mert, M. Virtual cells for infrastructureless MANETs. In Proceedings of the 2018 International Conference on Communication information and Computing Technology (ICCICT), Mumbai, India, 2–3 February 2018; pp. 1–6. [Google Scholar]
- Busari, S.A.; Mumtaz, S.; Al-Rubaye, S.; Rodriguez, J. 5G Millimeter-Wave Mobile Broadband: Performance and Challenges. IEEE Commun. Mag. 2018, 56, 137–143. [Google Scholar] [CrossRef]
- Bajracharya, R.; Shrestha, R.; Kim, S.W. Impact of Contention based LAA on Wi-Fi Network. Information 2017, 20, 827–836. [Google Scholar] [CrossRef]
- Bajracharya, R.; Shrestha, R.; Zikria, Y.B.; Kim, S.W. LTE or LAA: Choosing Network Mode for My Mobile Phone in 5G Network. In Proceedings of the 2017 IEEE 85th Vehicular Technology Conference (VTC Spring), Sydney, Australia, 4–7 June 2017; pp. 1–4. [Google Scholar]
- Nuggehalli, P. LTE-WLAN aggregation [Industry Perspectives]. IEEE Wirel. Commun. 2016, 23, 4–6. [Google Scholar] [CrossRef]
- Morgado, A.; Huq, K.M.S.; Mumtaz, S.; Rodriguez, J. A survey of 5G technologies: Regulatory, standardization and industrial perspectives. Dig. Commun. Netw. 2018, 4, 87–97. [Google Scholar] [CrossRef]
- Yu, H.; Lee, H.; Jeon, H. What is 5G? Emerging 5G Mobile Services and Network Requirements. Sustainability 2017, 9, 1848. [Google Scholar] [CrossRef]
- Afrin, M.; Razzaque, M.A.; Anjum, I.; Hassan, M.M.; Alamri, A. Tradeoff between User Quality-of-Experience and Service Provider Profit in 5G Cloud Radio Access Network. Sustainability 2017, 9, 2127. [Google Scholar] [CrossRef]
- Ali, R.; Shahin, N.; Bajracharya, R.; Kim, B.S.; Kim, S.W. A Self-Scrutinized Backoff Mechanism for IEEE 802.11ax in 5G Unlicensed Networks. Sustainability 2018, 10, 1201. [Google Scholar] [CrossRef]
- Mumtaz, S.; Al-Dulaimi, A.; Frascolla, V.; Niyato, D.; Briggs, K. Dynamic Spectrum Management for 5G. IEEE Wirel. Commun. 2017, 24, 12–13. [Google Scholar] [CrossRef]
- Sodhro, A.H.; Pirbhulal, S.; Sangaiah, A.K.; Lohano, S.; Sodhro, G.H.; Luo, Z. 5G-Based Transmission Power Control Mechanism in Fog Computing for Internet of Things Devices. Sustainability 2018, 10, 1258. [Google Scholar] [CrossRef]
- Li, S.; Xu, L.D.; Zhao, S. 5G Internet of Things: A survey. J. Ind. Inf. Integr. 2018, 10, 1–9. [Google Scholar] [CrossRef]
- Mouradian, C.; Naboulsi, D.; Yangui, S.; Glitho, R.H.; Morrow, M.J.; Polakos, P.A. A Comprehensive Survey on Fog Computing: State-of-the-Art and Research Challenges. IEEE Commun. Surv. Tutor. 2018, 20, 416–464. [Google Scholar] [CrossRef]
- Song, J.; Dong, P.; Zhou, H.; Zheng, T.; Du, X.; Guizani, M. A Performance Analysis Model of TCP over Multiple Heterogeneous Paths for 5G Mobile Services. Sustainability 2018, 10, 1337. [Google Scholar] [CrossRef]
- Polese, M.; Jana, R.; Zorzi, M. TCP and MP-TCP in 5G mmWave Networks. IEEE Internet Comput. 2017, 21, 12–19. [Google Scholar] [CrossRef]
- Li, B.; He, M.; Wu, W.; Sangaiah, A.K.; Jeon, G. Computation Offloading Algorithm for Arbitrarily Divisible Applications in Mobile Edge Computing Environments: An OCR Case. Sustainability 2018, 10, 1611. [Google Scholar] [CrossRef]
- Khalid, W.; Yu, H. Sum Utilization of Spectrum with Spectrum Handoff and Imperfect Sensing in Interweave Multi-Channel Cognitive Radio Networks. Sustainability 2018, 10, 1764. [Google Scholar] [CrossRef]
- Kakalou, I.; Psannis, K.E.; Krawiec, P.; Badea, R. Cognitive Radio Network and Network Service Chaining toward 5G: Challenges and Requirements. IEEE Commun. Mag. 2017, 55, 145–151. [Google Scholar] [CrossRef]
- Tanoli, S.A.K.; Rehman, M.; Khan, M.B.; Jadoon, I.; Ali Khan, F.; Nawaz, F.; Shah, S.A.; Yang, X.; Nasir, A.A. An Experimental Channel Capacity Analysis of Cooperative Networks Using Universal Software Radio Peripheral (USRP). Sustainability 2018, 10, 1983. [Google Scholar] [CrossRef]
- Panagopoulos, A.D.; Karagiannidis, G.K.; Vázquez-Castro, M.n. Recent advances in cooperative communications for wireless systems. Phys. Commun. 2011, 4, 141–143. [Google Scholar] [CrossRef]
- Laselva, D.; Lopez-Perez, D.; Rinne, M.; Henttonen, T. 3GPP LTE-WLAN Aggregation Technologies: Functionalities and Performance Comparison. IEEE Commun. Mag. 2018, 56, 195–203. [Google Scholar] [CrossRef]
- Bajracharya, R.; Shrestha, R.; Kim, S.W. An Admission Control Mechanism for 5G LWA. Sustainability 2018, 10, 1999. [Google Scholar] [CrossRef]
- Fang, X.; Misra, S.; Xue, G.; Yang, D. Smart Grid—The New and Improved Power Grid: A Survey. IEEE Commun. Surv. Tutor. 2012, 14, 944–980. [Google Scholar] [CrossRef]
- Kabalci, Y. A survey on smart metering and smart grid communication. Renew. Sustain. Energy Rev. 2016, 57, 302–318. [Google Scholar] [CrossRef]
- Zahoor, S.; Javaid, S.; Javaid, N.; Ashraf, M.; Ishmanov, F.; Afzal, M.K. Cloud–Fog–Based Smart Grid Model for Efficient Resource Management. Sustainability 2018, 10, 2079. [Google Scholar] [CrossRef]
- Saxena, D.; Raychoudhury, V.; Suri, N.; Becker, C.; Cao, J. Named Data Networking: A survey. Comput. Sci. Rev. 2016, 19, 15–55. [Google Scholar] [CrossRef]
- Xylomenos, G.; Ververidis, C.N.; Siris, V.A.; Fotiou, N.; Tsilopoulos, C.; Vasilakos, X.; Katsaros, K.V.; Polyzos, G.C. A Survey of Information-Centric Networking Research. IEEE Commun. Surv. Tutor. 2014, 16, 1024–1049. [Google Scholar] [CrossRef]
- Fang, C.; Yao, H.; Wang, Z.; Wu, W.; Jin, X.; Yu, F.R. A Survey of Mobile Information-Centric Networking: Research Issues and Challenges. IEEE Commun. Surv. Tutor. 2018, 20, 2353–2371. [Google Scholar] [CrossRef]
- Naeem, M.A.; Ali, R.; Kim, B.S.; Nor, S.A.; Hassan, S. A Periodic Caching Strategy Solution for the Smart City in Information-Centric Internet of Things. Sustainability 2018, 10, 2576. [Google Scholar] [CrossRef]
- Atlam, H.F.; Walters, R.J.; Wills, G.B. Fog Computing and the Internet of Things: A Review. Big Data Cogn. Comput 2018, 2, 10. [Google Scholar] [CrossRef]
- Aazam, M.; Zeadally, S.; Harras, K.A. Fog Computing Architecture, Evaluation, and Future Research Directions. IEEE Commun. Mag. 2018, 56, 46–52. [Google Scholar] [CrossRef]
- Mukherjee, M.; Shu, L.; Wang, D. Survey of Fog Computing: Fundamental, Network Applications, and Research Challenges. IEEE Commun. Surv. Tutor. 2018, 20, 1826–1857. [Google Scholar] [CrossRef]
- Bakhsh, R.; Javaid, N.; Fatima, I.; Khan, M.I.; Almejalli, K.A. Towards efficient resource utilization exploiting collaboration between HPF and 5G enabled energy management controllers in smart homes. Sustainability 2018, 10, 3592. [Google Scholar] [CrossRef]
Table 1. Summary of five service categories of 5G.
|Immersive 5G Service||Massive Contents streaming|
|Virtual Augmented Reality|
|Intelligent 5G Service||Crowded Area Service|
|Omnipresent 5G Service||Smart Personal Devices/Health|
|Smart City/Smart Factory Systems|
|Autonomous 5G service||Smart Transportation/Teleoperation|
|Drone-based 3D connectivity|
|Public 5G Services||Private security and Public Safety|
Table 2. 5G Technical Scenarios and Key Issues.
|Technical Scenarios||Key Issues|
|High-capacity hot-spot||Traffic volume density: Tens of Tbps/km|
|User experienced data rate: 1 Gbps|
|Peak data rate: Tens of Gbps|
|Low-power massive-connections||Connection density: 106/km|
|Low power consumption and low cost|
|Seamless wide-area coverage||100 Mbps user experienced data rate|
|Low latency high-reliability||Air interface latency: 1 ms|
|End-to-end latency: ms level|
|Reliability: nearly 100%|
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