https://doi.org/10.1016/j.eij.2022.03.003

Smart cities: Fusion-based intelligent traffic congestion control system for vehicular networks using machine learning techniques

A 5G campus network is a 5G network for the users affiliated with the campus organization, e.g., an industrial campus, covering a prescribed geographical area. A 5G campus network can operate as a so-called 5G non-standalone (NSA) network (which requires 4G Long-Term Evolution (LTE) spectrum access) or as a 5G standalone (SA) network (without 4G LTE spectrum access). 5G campus networks are envisioned to enable new use cases, which require cyclic delay-sensitive industrial communication, such as robot control. We design a rigorous testbed for measuring the one-way packet delays between a 5G end device via a radio access network (RAN) to a packet core with sub-microsecond precision as well as for measuring the packet core delay with nanosecond precision. With our testbed design, we conduct detailed measurements of the one-way download (downstream, i.e., core to end device) as well as one-way upload (upstream, i.e., end device to core) packet delays and losses for both 5G SA and 5G NSA hardware and network operation. We also measure the corresponding 5G SA and 5G NSA packet core processing delays for download and upload. We find that typically 95% of the SA download packet delays are in the range from 4–10 ms, indicating a fairly wide spread of the packet delays. Also, existing packet core implementations regularly incur packet processing latencies up to 0.4 ms, with outliers above one millisecond. Our measurement results inform the further development and refinement of 5G SA and 5G NSA campus networks for industrial use cases. We make the measurement data traces publicly available as the IEEE DataPort 5G Campus Networks: Measurement Traces dataset (DOI 10.21227/xe3c-e968).

/pdf/5g-campus-networks-a-first-measurement-study-51v05sz56d.pdf

5G Campus Networks: A First Measurement Study

Supply Chain Collaboration is the network of various entities that work cohesively to make up the entire process. The supply chain organizations’ success is dependent on integration, teamwork, and the communication of information. Every day, supply chain and business players work in a dynamic setting. They must balance competing goals such as process robustness, risk reduction, vulnerability reduction, real financial risks, and resilience against just-in-time and cost-efficiency. Decision-making based on shared information in Supply Chain Collaboration constitutes the recital and competitiveness of the collective process. Supply Chain Collaboration has prompted companies to implement the perfect data analytics functions (e.g., data science, predictive analytics, and big data) to improve supply chain operations and, eventually, efficiency. Simulation and modeling are powerful methods for analyzing, investigating, examining, observing and evaluating real-world industrial and logistic processes in this scenario. Fusion-based Machine learning provides a platform that may address the issues/limitations of Supply Chain Collaboration. Compared to the Classical probable data fusion techniques, the fused Machine learning method may offer a strong computing ability and prediction. In this scenario, the machine learning-based Supply Chain Collaboration model has been proposed to evaluate the propensity of the decision-making process to increase the efficiency of the Supply Chain Collaboration. 

https://www.techscience.com/iasc/v31n3/44835/pdf

Fusion-Based Supply Chain Collaboration Using Machine Learning Techniques

Device-to-device (D2D) communication is one of the most promising technologies in wireless cellular networks that can be employed to improve spectral and energy efficiency, increase data rates, and reduce links latency. This paper investigates fifth generation and beyond (5GB) networks, the basics of D2D communication, applications, and classification. Herein, D2D in in-band (IBD) and out-band (OBD) modes are discussed. This paper also presents the integration of D2D communication with other prominent technologies and demonstrates the importance of integration with possible solutions in improving network performance. We further investigate the challenges of D2D communication, opportunities, and future research directions of D2D in 5GB networks. In addition, D2D communication in 6G network challenges and open research areas are introduced. 

Survey on Device to Device (D2D) Communication for 5GB/6G Networks: Concept, Applications, Challenges, and Future Directions

With the tremendous demand for connectivity anywhere and anytime, existing network architectures should be modified. To cope with the challenges that arise due to the increasing flood of devices/users and a diverse range of application requirements, new technologies and concepts must be integrated to enable their benefits. Service providers and business companies are looking for new areas of research to enhance overall system performance. This article gives a detailed survey about the recent 5G technologies, the solutions they provide, and the effect caused by their addition to current cellular networks. It is based on the three most important 5G concepts: Device to Device (D2D), Network Slicing (NS), and Mobile Edge Computing (MEC). This study proposes to design the future 5G networks by the integration of all three technologies. It is believed that spectrum efficiency, energy efficiency, and overall throughput will be greatly improved by using D2D. The system delay and computational load will be reduced as tasks will be handled by edge routers located at the base stations. Thus offloading the core network and the system capital expenses and operational expenses will be reduced significantly by slicing the network.

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

Fifth Generation (5G) cellular networks aim to overcome the pressing demands posed by dynamic Quality of Service (QoS) constraints, which have primarily remained unaddressed using conventional network infrastructure. Cellular networks of the future necessitate the formulation of efficient resource allocation schemes that readily meet throughput requirements. The idea of combining Device-to-Device (D2D), Mobile Edge Computing (MEC), and Network slicing (NS) can improve spectrum utilization with better performance and scalability. This work presents a spectrum efficiency optimization problem in D2D based 5G-Heterogeneous Cellular Network (5G-HCN) with NS. Owing to the shortage of resources, we propose an underlay model where macro-cell users (MUs), small-cell users (SUs), and D2D users (DUs) reuse the resources while considering the effects of interference. The goal is to maximize the average network spectrum efficiency (SE) and throughput without degrading the system performance. The problem at hand is naturally a non-convex mixed-integer non-linear programming (MINLP) problem that is intractable. Therefore, we have suggested a distributed resource allocation strategy with an edge computing (DRA-EC) approach to find the sub-optimal solution. In distributed augmented Lagrange method, each edge router located at BS will solve its problem locally, and the consensus algorithm will find the global solution using these local estimates. The central slice controller will cut the customized network slices according to the bandwidth requirements of each user type with optimized spectrum information. The simulation outcomes prove that our proposed method is near the central optimization scheme with low computational complexity. It is much better because it reduces the computational time and system overhead.

/pdf/efficient-resource-allocation-using-distributed-edge-148smupay3.pdf

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

In the above article [1] , reference [2] was missing. The corrections are made in Section VI of [1] . Figures 5 and 7 of [1] are also cited.

/pdf/corrections-to-integration-of-d2d-network-slicing-and-mec-in-28vol3vb7p.pdf

Lubna Nadeem

Papers

Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges

Efficient Resource Allocation Using Distributed Edge Computing in D2D Based 5G-HCN With Network Slicing

Corrections to “Integration of D2D, Network Slicing, and MEC in 5G Cellular Networks: Survey and Challenges”