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Pazhani.A, Azhagu Jaisudhan, P. Gunasekaran, Vimal Shanmuganathan, Sangsoon Lim, Kaliappan Madasamy, Rajesh Manoharan, and Amit Verma. "Peer–Peer Communication Using Novel Slice Handover Algorithm for 5G Wireless Networks." Journal of Sensor and Actuator Networks 11, no. 4 (November 29, 2022): 82. http://dx.doi.org/10.3390/jsan11040082.
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The goal of 5G wireless networks is to address the growing need for network services among users. User equipment has progressed to the point where users now expect diverse services from the network. The latency, reliability, and bandwidth requirements of users can all be classified. To fulfil the different needs of users in an economical manner, while guaranteeing network resources are resourcefully assigned to consumers, 5G systems plan to leverage technologies like Software Defined Networks, Network Function Virtualization, and Network Slicing. For the purpose of ensuring continuous handover among network slices, while catering to the advent of varied 5G application scenarios, new mobility management techniques must be adopted in Sliced 5G networks. Users want to travel from one region of coverage to another region without any fading in their network connection. Different network slices can coexist in 5G networks, with every slice offering services customized to various QoS demands. As a result, when customers travel from one region of coverage to another, the call can be transferred to a slice that caters to similar or slightly different requirements. The goal of this study was to develop an intra- and inter-slice algorithm for determining handover decisions in sliced 5G networks and to assess performance by comparing intra- and inter-slice handovers. The proposed work shows that an inter-slice handover algorithm offers superior quality of service when compared to an intra-slice algorithm.
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An, Namwon, Yonggang Kim, Juman Park, Dae-Hoon Kwon, and Hyuk Lim. "Slice Management for Quality of Service Differentiation in Wireless Network Slicing." Sensors 19, no. 12 (June 19, 2019): 2745. http://dx.doi.org/10.3390/s19122745.
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Network slicing is a technology that virtualizes a single infrastructure into multiple logical networks (called slices) where resources or virtualized functions can be flexibly configured by demands of applications to satisfy their quality of service (QoS) requirements. Generally, to provide the guaranteed QoS in applications, resources of slices are isolated. In wired networks, this resource isolation is enabled by allocating dedicated data bandwidths to slices. However, in wireless networks, resource isolation may be challenging because the interference between links affects the actual bandwidths of slices and degrades their QoS. In this paper, we propose a slice management scheme that mitigates the interference imposed on each slice according to their priorities by determining routes of flows with a different routing policy. Traffic flows in the slice with the highest priority are routed into shortest paths. In each lower-priority slice, the routing of traffic flows is conducted while minimizing a weighted summation of interference to other slices. Since higher-priority slices have higher interference weights, they receive lower interference from other slices. As a result, the QoS of slices is differentiated according to their priorities while the interference imposed on slices is reduced. We compared the proposed slice management scheme with a naïve slice management (NSM) method that differentiates QoS among slices by priority queuing. We conducted some simulations and the simulation results show that our proposed management scheme not only differentiates the QoS of slices according to their priorities but also enhances the average throughput and delay performance of slices remarkably compared to that of the NSM method. The simulations were conducted in grid network topologies with 16 and 100 nodes and a random network topology with 200 nodes. Simulation results indicate that the proposed slice management increased the average throughput of slices up to 6%, 13%, and 7% and reduced the average delay of slices up to 14%, 15%, and 11% in comparison with the NSM method.
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Ren, Zhe, Xinghua Li, Qi Jiang, Qingfeng Cheng, and Jianfeng Ma. "Fast and Universal Inter-Slice Handover Authentication with Privacy Protection in 5G Network." Security and Communication Networks 2021 (January 31, 2021): 1–19. http://dx.doi.org/10.1155/2021/6694058.
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In a 5G network-sliced environment, mobility management introduces a new form of handover called inter-slice handover among network slices. Users can change their slices as their preferences or requirements vary over time. However, existing handover-authentication mechanisms cannot support inter-slice handover because of the fine-grained demand among network slice services, which could cause challenging issues, such as the compromise of service quality, anonymity, and universality. In this paper, we address these issues by introducing a fast and universal inter-slice (FUIS) handover authentication framework based on blockchain, chameleon hash, and ring signature. To address these issues, we introduce an anonymous service-oriented authentication protocol with a key agreement for inter-slice handover by constructing an anonymous ticket with the trapdoor collision property of chameleon hash functions. In order to reduce the computation overhead of the user side in the process of authentication, a privacy-preserving ticket validation with a ring signature is designed to finish in the consensus phase of the blockchain in advance. Thanks to the edge computing capabilities in 5G, distributed edge nodes help to store the anonymous ticket information, which guarantees that the legal users can finish authentication swiftly during handover. Our scheme's performance is evaluated through simulation experiments to testify the efficiency and feasibility in a 5G network-sliced environment. The results show that compared to other authentication schemes of the same type, the overall inter-slice handover delay has been reduced by 97.94%.
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Hurtado Sánchez, Johanna Andrea, Katherine Casilimas, and Oscar Mauricio Caicedo Rendon. "Deep Reinforcement Learning for Resource Management on Network Slicing: A Survey." Sensors 22, no. 8 (April 15, 2022): 3031. http://dx.doi.org/10.3390/s22083031.
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Network Slicing and Deep Reinforcement Learning (DRL) are vital enablers for achieving 5G and 6G networks. A 5G/6G network can comprise various network slices from unique or multiple tenants. Network providers need to perform intelligent and efficient resource management to offer slices that meet the quality of service and quality of experience requirements of 5G/6G use cases. Resource management is far from being a straightforward task. This task demands complex and dynamic mechanisms to control admission and allocate, schedule, and orchestrate resources. Intelligent and effective resource management needs to predict the services’ demand coming from tenants (each tenant with multiple network slice requests) and achieve autonomous behavior of slices. This paper identifies the relevant phases for resource management in network slicing and analyzes approaches using reinforcement learning (RL) and DRL algorithms for realizing each phase autonomously. We analyze the approaches according to the optimization objective, the network focus (core, radio access, edge, and end-to-end network), the space of states, the space of actions, the algorithms, the structure of deep neural networks, the exploration–exploitation method, and the use cases (or vertical applications). We also provide research directions related to RL/DRL-based network slice resource management.
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Al-Yassari, Mohammed Mousa Rashid, and Nadia Adnan Shiltagh Al-Jamali. "Automatic Spike Neural Technique for Slicing Bandwidth Estimated Virtual Buffer-Size in Network Environment." Journal of Engineering 29, no. 6 (June 1, 2023): 87–97. http://dx.doi.org/10.31026/j.eng.2023.06.07.
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The Next-generation networks, such as 5G and 6G, need capacity and requirements for low latency, and high dependability. According to experts, one of the most important features of (5 and 6) G networks is network slicing. To enhance the Quality of Service (QoS), network operators may now operate many instances on the same infrastructure due to configuring able slicing QoS. Each virtualized network resource, such as connection bandwidth, buffer size, and computing functions, may have a varied number of virtualized network resources. Because network resources are limited, virtual resources of the slices must be carefully coordinated to meet the different QoS requirements of users and services. These networks may be modified to achieve QoS using Artificial Intelligence (AI) and machine learning (ML). Developing an intelligent decision-making system for network management and reducing network slice failures requires reconfigurable wireless network solutions with machine learning capabilities. Using Spiking Neural Network (SNN) and prediction, we have developed a 'Buffer-Size Management' model for controlling network load efficiency by managing the slice's buffer size. To analyze incoming traffic and predict the network slice buffer size; our proposed Buffer-Size Management model can intelligently choose the best amount of buffer size for each slice to reduce packet loss ratio, increase throughput to 95% and reduce network failure by about 97%.
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Koutlia, K., R. Ferrús, E. Coronado, R. Riggio, F. Casadevall, A. Umbert, and J. Pérez-Romero. "Design and Experimental Validation of a Software-Defined Radio Access Network Testbed with Slicing Support." Wireless Communications and Mobile Computing 2019 (June 12, 2019): 1–17. http://dx.doi.org/10.1155/2019/2361352.
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Network slicing is a fundamental feature of 5G systems to partition a single network into a number of segregated logical networks, each optimized for a particular type of service or dedicated to a particular customer or application. The realization of network slicing is particularly challenging in the Radio Access Network (RAN) part, where multiple slices can be multiplexed over the same radio channel and Radio Resource Management (RRM) functions shall be used to split the cell radio resources and achieve the expected behaviour per slice. In this context, this paper describes the key design and implementation aspects of a Software-Defined RAN (SD-RAN) experimental testbed with slicing support. The testbed has been designed consistently with the slicing capabilities and related management framework established by 3GPP in Release 15. The testbed is used to demonstrate the provisioning of RAN slices (e.g., preparation, commissioning, and activation phases) and the operation of the implemented RRM functionality for slice-aware admission control and scheduling.
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Kim, Yohan, Sunyong Kim, and Hyuk Lim. "Reinforcement Learning Based Resource Management for Network Slicing." Applied Sciences 9, no. 11 (June 9, 2019): 2361. http://dx.doi.org/10.3390/app9112361.
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Network slicing to create multiple virtual networks, called network slice, is a promising technology to enable networking resource sharing among multiple tenants for the 5th generation (5G) networks. By offering a network slice to slice tenants, network slicing supports parallel services to meet the service level agreement (SLA). In legacy networks, every tenant pays a fixed and roughly estimated monthly or annual fee for shared resources according to a contract signed with a provider. However, such a fixed resource allocation mechanism may result in low resource utilization or violation of user quality of service (QoS) due to fluctuations in the network demand. To address this issue, we introduce a resource management system for network slicing and propose a dynamic resource adjustment algorithm based on reinforcement learning approach from each tenant’s point of view. First, the resource management for network slicing is modeled as a Markov Decision Process (MDP) with the state space, action space, and reward function. Then, we propose a Q-learning-based dynamic resource adjustment algorithm that aims at maximizing the profit of tenants while ensuring the QoS requirements of end-users. The numerical simulation results demonstrate that the proposed algorithm can significantly increase the profit of tenants compared to existing fixed resource allocation methods while satisfying the QoS requirements of end-users.
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Wang, Qian, Yanan Zhang, and Xuanzhong Wang. "Resource Allocation Optimization Algorithm of Power 5G Network Slice Based on NFV and SDN." Journal of Physics: Conference Series 2476, no. 1 (April 1, 2023): 012085. http://dx.doi.org/10.1088/1742-6596/2476/1/012085.
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Abstract To realize 5G network slicing, the virtual resource scheduling and allocation method based on NFV and SDN is designed. The scheme comprehensively considers 5G communication technology and network slice structure, and establishes the optimal mathematical model of network slice operation economy. Then the network functions and resources are virtualized by NFV and SDN, the greedy algorithm is adopted to solve the mapping problem of network slices, and the specific implementation algorithm is provided. The simulation results show that the allocation algorithm can customize the management of network slice resources, achieve flexible control and sharing of network traffic and resources, and significantly promote the construction, optimization and promotion of 5G mobile communication network architecture.
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Abbas, Khizar, Muhammad Afaq, Talha Ahmed Khan, Adeel Rafiq, and Wang-Cheol Song. "Slicing the Core Network and Radio Access Network Domains through Intent-Based Networking for 5G Networks." Electronics 9, no. 10 (October 18, 2020): 1710. http://dx.doi.org/10.3390/electronics9101710.
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The fifth-generation mobile network presents a wide range of services which have different requirements in terms of performance, bandwidth, reliability, and latency. The legacy networks are not capable to handle these diverse services with the same physical infrastructure. In this way, network virtualization presents a reliable solution named network slicing that supports service heterogeneity and provides differentiated resources to each service. Network slicing enables network operators to create multiple logical networks over a common physical infrastructure. In this research article, we have designed and implemented an intent-based network slicing system that can slice and manage the core network and radio access network (RAN) resources efficiently. It is an automated system, where users just need to provide higher-level network configurations in the form of intents/contracts for a network slice, and in return, our system deploys and configures the requested resources accordingly. Further, our system grants the automation of the network configurations process and reduces the manual effort. It has an intent-based networking (IBN) tool which can control, manage, and monitor the network slice resources properly. Moreover, a deep learning model, the generative adversarial neural network (GAN), has been used for the management of network resources. Several tests have been carried out with our system by creating three slices, which shows better performance in terms of bandwidth and latency.
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Singh, Sushil Kumar, Mikail Mohammed Salim, Jeonghun Cha, Yi Pan, and Jong Hyuk Park. "Machine Learning-Based Network Sub-Slicing Framework in a Sustainable 5G Environment." Sustainability 12, no. 15 (August 3, 2020): 6250. http://dx.doi.org/10.3390/su12156250.
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Nowadays, 5G network infrastructures are being developed for various industrial IoT (Internet of Things) applications worldwide, emerging with the IoT. As such, it is possible to deploy power-optimized technology in a way that promotes the long-term sustainability of networks. Network slicing is a fundamental technology that is implemented to handle load balancing issues within a multi-tenant network system. Separate network slices are formed to process applications having different requirements, such as low latency, high reliability, and high spectral efficiency. Modern IoT applications have dynamic needs, and various systems prioritize assorted types of network resources accordingly. In this paper, we present a new framework for the optimum performance of device applications with optimized network slice resources. Specifically, we propose a Machine Learning-based Network Sub-slicing Framework in a Sustainable 5G Environment in order to optimize network load balancing problems, where each logical slice is divided into a virtualized sub-slice of resources. Each sub-slice provides the application system with different prioritized resources as necessary. One sub-slice focuses on spectral efficiency, whereas the other focuses on providing low latency with reduced power consumption. We identify different connected device application requirements through feature selection using the Support Vector Machine (SVM) algorithm. The K-means algorithm is used to create clusters of sub-slices for the similar grouping of types of application services such as application-based, platform-based, and infrastructure-based services. Latency, load balancing, heterogeneity, and power efficiency are the four primary key considerations for the proposed framework. We evaluate and present a comparative analysis of the proposed framework, which outperforms existing studies based on experimental evaluation.
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Shariat, Mehrdad, Ömer Bulakci, Antonio De Domenico, Christian Mannweiler, Marco Gramaglia, Qing Wei, Aravinthan Gopalasingham, et al. "A Flexible Network Architecture for 5G Systems." Wireless Communications and Mobile Computing 2019 (February 11, 2019): 1–19. http://dx.doi.org/10.1155/2019/5264012.
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In this paper, we define a flexible, adaptable, and programmable architecture for 5G mobile networks, taking into consideration the requirements, KPIs, and the current gaps in the literature, based on three design fundamentals: (i) split of user and control plane, (ii) service-based architecture within the core network (in line with recent industry and standard consensus), and (iii) fully flexible support of E2E slicing via per-domain and cross-domain optimisation, devising inter-slice control and management functions, and refining the behavioural models via experiment-driven optimisation. The proposed architecture model further facilitates the realisation of slices providing specific functionality, such as network resilience, security functions, and network elasticity. The proposed architecture consists of four different layers identified as network layer, controller layer, management and orchestration layer, and service layer. A key contribution of this paper is the definition of the role of each layer, the relationship between layers, and the identification of the required internal modules within each of the layers. In particular, the proposed architecture extends the reference architectures proposed in the Standards Developing Organisations like 3GPP and ETSI, by building on these while addressing several gaps identified within the corresponding baseline models. We additionally present findings, the design guidelines, and evaluation studies on a selected set of key concepts identified to enable flexible cloudification of the protocol stack, adaptive network slicing, and inter-slice control and management.
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Hsiao, Chiu-Han, Yean-Fu Wen, Frank Yeong-Sung Lin, Yu-Fang Chen, Yennun Huang, Yang-Che Su, and Ya-Syuan Wu. "An Optimization-Based Orchestrator for Resource Access and Operation Management in Sliced 5G Core Networks." Sensors 22, no. 1 (December 24, 2021): 100. http://dx.doi.org/10.3390/s22010100.
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Network slicing is a promising technology that network operators can deploy the services by slices with heterogeneous quality of service (QoS) requirements. However, an orchestrator for network operation with efficient slice resource provisioning algorithms is essential. This work stands on Internet service provider (ISP) to design an orchestrator analyzing the critical influencing factors, namely access control, scheduling, and resource migration, to systematically evolve a sustainable network. The scalability and flexibility of resources are jointly considered. The resource management problem is formulated as a mixed-integer programming (MIP) problem. A solution approach based on Lagrangian relaxation (LR) is proposed for the orchestrator to make decisions to satisfy the high QoS applications. It can investigate the resources required for access control within a cost-efficient resource pool and consider allocating or migrating resources efficiently in each network slice. For high system utilization, the proposed mechanisms are modeled in a pay-as-you-go manner. Furthermore, the experiment results show that the proposed strategies perform the near-optimal system revenue to meet the QoS requirement by making decisions.
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Walia, Jaspreet Singh, Heikki Hämmäinen, Kalevi Kilkki, Hannu Flinck, Marja Matinmikko-Blue, and Seppo Yrjölä. "Network Slice Provisioning Approaches for Industry Verticals." International Journal of Business Data Communications and Networking 17, no. 2 (July 2021): 1–15. http://dx.doi.org/10.4018/ijbdcn.286700.
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Network slicing is widely studied as an essential technological enabler for supporting diverse use case specific services through network virtualization. Industry verticals, consisting of diverse use cases requiring different network resources, are considered key customers for network slices. However, different approaches for network slice provisioning to industry verticals and required business models are still largely unexplored and require further work. Focusing on technical and business aspects of network slicing, this article develops three new business models, enabled by different distributions of business roles and management exposure between business actors. The feasibility of the business models is studied in terms of; the costs and benefits to business actors, mapping to use cases in various industry verticals, and the infrastructure costs of common and dedicated virtualization infrastructures. Finally, a strategic approach and relevant recommendations are proposed for major business actors, national regulatory authorities, and standards developing organizations.
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Abbas, Khizar, Talha Ahmed Khan, Muhammad Afaq, and Wang-Cheol Song. "Network Slice Lifecycle Management for 5G Mobile Networks: An Intent-Based Networking Approach." IEEE Access 9 (2021): 80128–46. http://dx.doi.org/10.1109/access.2021.3084834.
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Guijarro, Luis, Jose Vidal, and Vicent Pla. "Competition in Service Provision between Slice Operators in 5G Networks." Electronics 7, no. 11 (November 12, 2018): 315. http://dx.doi.org/10.3390/electronics7110315.
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Network slicing is gaining an increasing importance as an effective way to introduce flexibility in the management of resources in 5G networks. We envision a scenario where a set of network operators outsource their respective networks to one Infrastructure Provider (InP), and use network slicing mechanisms to request the resources as needed for service provision. The InP is then responsible for the network operation and maintenance, while the network operators become Virtual Network Operators (VNOs). We model a setting where two VNOs compete for the users in terms of quality of service, by strategically distributing its share of the aggregated cells capacity managed by the InP among its subscribers. The results show that the rate is allocated among the subscribers at each cell in a way that mimics the overall share that each VNO is entitled to, and that this allocation is the Nash equilibrium of the strategic slicing game between the VNOs. We conclude that network sharing and slicing provide an attractive flexibility in the allocation of resources without the need to enforce a policy through the InP.
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Guda Blessed, Ibrahim Aliyu, James Agajo, Thiago Lima Sarmento, Cleverson Veloso Nahum, Lucas Novoa, Rebecca Aben-Athar, et al. "Network resource allocation for emergency management based on closed-loop analysis." ITU Journal on Future and Evolving Technologies 3, no. 2 (September 22, 2022): 175–201. http://dx.doi.org/10.52953/hvpi8935.
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The telecommunication system being a critical pillar of emergency management, intelligent deployment and management of slices in an affected area will help emergency responders. Techniques such as automated management of Machine Learning (ML) pipelines across the edge and emergency responder devices, usage of hierarchical closed-loops, and offloading inference tasks closer to the edge can minimize latencies for first responders in case of emergencies. This study describes the major results from building a Proof of Concept (PoC) for network resource allocation for emergency management using a hierarchical autonomous Artificial Intelligence (AI)/ML-based closed-loops in the mobile network, organized by the Internal Telecommunication Union Focus Group on Autonomous Networks (ITU FG-AN). The background scenario for this PoC included the interaction between a higher closed-loop in the Operations Support System (OSS) and a lower closed-loop in Radio Access Network (RAN) to intelligently share RAN resources between the public and the emergency responder slice. Representation of closed-loop "controllers" in a declarative fashion (intent), triggering "imperative actions" in the "underlay" based on the intent, setup of a data pipeline between various components, and methods of "influencing" lower layer loops using specific logic/models, were some of the essential aspects investigated by various teams. The main conclusions are summarised in this paper, including the significant observations and limitations from the PoC as well as future directions.
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Bojović, Petar D., Teodor Malbašić, Dušan Vujošević, Goran Martić, and Živko Bojović. "Dynamic QoS Management for a Flexible 5G/6G Network Core: A Step toward a Higher Programmability." Sensors 22, no. 8 (April 7, 2022): 2849. http://dx.doi.org/10.3390/s22082849.
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The academic and professional community has recently started to develop the concept of 6G networks. The scientists have defined key performance indicators and pursued large-scale automation, ambient sensing intelligence, and pervasive artificial intelligence. They put great efforts into implementing new network access and edge computing solutions. However, further progress depends on developing a more flexible core infrastructure according to more complex QoS requirements. Our research aims to provide 5G/6G core flexibility by customizing and optimizing network slices and introducing a higher level of programmability. We bind similar services in a group, manage them as a single slice, and enable a higher level of programmability as a prerequisite for dynamic QoS. The current 5G solutions primarily use predefined queues, so we have developed highly flexible, dynamic queue management software and moved it entirely to the application layer (reducing dependence on the physical network infrastructure). Further, we have emulated a testbed environment as realistically as possible to verify the proposed model capabilities. Obtained results confirm the validity of the proposed dynamic QoS management model for configuring queues’ parameters according to the service management requirements. Moreover, the proposed solution can also be applied efficiently to 5G core networks to resolve complex service requirements.
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Bega, Dario, Marco Gramaglia, Andres Garcia-Saavedra, Marco Fiore, Albert Banchs, and Xavier Costa-Perez. "Network Slicing Meets Artificial Intelligence: An AI-Based Framework for Slice Management." IEEE Communications Magazine 58, no. 6 (June 2020): 32–38. http://dx.doi.org/10.1109/mcom.001.1900653.
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Ampririt, Phudit, Yi Liu, Makoto Ikeda, Keita Matsuo, Leonard Barolli, and Makoto Takizawa. "Effect of Slice Priority for admission control in 5G Wireless Networks: A comparison study for two Fuzzy-based systems considering Software-Defined-Networks." Journal of High Speed Networks 26, no. 3 (November 27, 2020): 169–83. http://dx.doi.org/10.3233/jhs-200637.
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The Fifth Generation (5G) networks are expected to be flexible to satisfy demands of high-quality services such as high speed, low latencies and enhanced reliability from customers. Also, the rapidly increasing amount of user devices and high user’s requests becomes a problem. Thus, the Software-Defined Network (SDN) will be the key function for efficient management and control. To deal with these problems, we propose a Fuzzy-based SDN approach. This paper presents and compares two Fuzzy-based Systems for Admission Control (FBSAC) in 5G wireless networks: FBSAC1 and FBSAC2. The FBSAC1 considers for admission control decision three parameters: Grade of Service (GS), User Request Delay Time (URDT) and Network Slice Size (NSS). In FBSAC2, we consider as an additional parameter the Slice Priority (SP). So, FBSAC2 has four input parameters. The simulation results show that the FBSAC2 is more complex than FBSAC1, but it has a better performance for admission control.
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Liu, Xian, Jian Lu, Zeyang Cheng, and Xiaochi Ma. "A Dynamic Bayesian Network-Based Real-Time Crash Prediction Model for Urban Elevated Expressway." Journal of Advanced Transportation 2021 (May 13, 2021): 1–12. http://dx.doi.org/10.1155/2021/5569143.
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Traffic crash is a complex phenomenon that involves coupling interdependency among multiple influencing factors. Considering that interdependency is critical for predicting crash risk accurately and contributes to revealing the underlying mechanism of crash occurrence as well, the present study attempts to build a Real-Time Crash Prediction Model (RTCPM) for urban elevated expressway accounting for the dynamicity and coupling interdependency among traffic flow characteristics before crash occurrence and identify the most probable risk propagation path and the most significant contributors to crash risk. In this study, Dynamic Bayesian Network (DBN) was the framework of the RTCPM. Random Forest (RF) method was employed to identify the most important variables, which were used to build DBN-based RTCPMs. The PC algorithm combined with expert experience was further applied to investigate the coupling interdependency among traffic flow characteristics in the DBN model. A comparative analysis among the improved DBN-based RTCPM considering the interdependency, the original DBN-based RTCPM without considering the interdependency, and Multilayer Perceptron (MLP) was conducted. Besides, the sensitivity and strength of influences analyses were utilized to identify the most probable risk propagation path and the most significant contributors to crash risk. The results showed that the improved DBN-based RTCPM had better prediction performance than the original DBN-based RTCPM and the MLP based RTCPM. The most probable risk influencing path was identified as follows: speed on current segment (V) (time slice 2)⟶V (time slice 1)⟶speed on upstream segment (U_V) (time slice 1)⟶Traffic Performance Index (TPI) (time slice 1)⟶crash risk on current segment. The most sensitive contributor to crash risk in this path was V (time slice 2), followed by TPI (time slice 1), V (time slice 1), and U_V (time slice 1). These results indicate that the improved DBN-based RTCPM has the potential to predict crashes in real time for urban elevated expressway. Besides, it contributes to revealing the underlying mechanism of crash and formulating the real-time risk control measures.
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Tipantuña, Christian, and Xavier Hesselbach. "Adaptive Energy Management in 5G Network Slicing: Requirements, Architecture, and Strategies." Energies 13, no. 15 (August 2, 2020): 3984. http://dx.doi.org/10.3390/en13153984.
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Energy consumption is a critical issue for the communications network operators, impacting deeply the cost of the services, as well as the ecological footprint. Network slicing architecture for 5G mobile communications enables multiple independent virtual networks to be created on top of a common shared physical infrastructure. Each network slice needs different types of resources, including energy, to fulfill the demands requested by each application, operator, or vertical market. The existing literature on network slicing is mainly targeted at the partition of network resources; however, the corresponding management of energy consumption is an unconsidered critical concern. This paper analyzes the requirements for an energy-aware 5G network slicing provisioning according to the 3GPP specifications, proposes an architecture, and studies the strategies to provide efficient energy consumption in terms of renewable and non-renewable sources. NFV and SDN technologies are the essential enablers and leverage the Internet of Things (IoT) connectivity provided by 5G networks. This paper also presents the technical 5G technology documentation related to the proposal, the requirements for adaptive energy management, and the Integer Linear Programming (ILP) formulation of the energy management model. To validate the improvements, an exact optimal algorithmic solution is presented and some heuristic strategies.
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Wu, Guomin, Guoping Tan, and Defu Jiang. "A Distributed E-Cross Learning Algorithm for Intelligent Multiple Network Slice Selection." Wireless Communications and Mobile Computing 2021 (May 16, 2021): 1–14. http://dx.doi.org/10.1155/2021/8875515.
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Recently, some technological issues in network slicing have been explored. However, most works focus on the physical resource management in this research field and less on slice selection. Different from the existing studies, we explore the problem of intelligent multiple slice selection, which makes some effort to dynamically obtain better user experience in a changeable state. Herein, we consider two factors about user experience: its throughput and energy consumption. Accordingly, a distributed E-cross learning algorithm is developed in the multiagent system where each terminal is regarded as an agent in the distributed network. Furthermore, its convergence is theoretically proven for the dynamic game model. In addition, the complexity of the proposed algorithm is discussed. A mass of simulation results are presented for the convergence and effectiveness of the proposed distributed learning algorithm. Compared with greedy algorithm, the proposed intelligent algorithm has a faster convergence speed. Besides, better user experience is attained effectively with multiple slice access.
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Wang, Jiao, Jay Weitzen, Oguz Bayat, Volkan Sevindik, and Mingzhe Li. "Performance Model for Video Service in 5G Networks." Future Internet 12, no. 6 (June 8, 2020): 99. http://dx.doi.org/10.3390/fi12060099.
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Network slicing allows operators to sell customized slices to various tenants at different prices. To provide better-performing and cost-efficient services, network slicing is looking to intelligent resource management approaches to be aligned to users’ activities per slice. In this article, we propose a radio access network (RAN) slicing design methodology for quality of service (QoS) provisioning, for differentiated services in a 5G network. A performance model is constructed for each service using machine learning (ML)-based approaches, optimized using interference coordination approaches, and used to facilitate service level agreement (SLA) mapping to the radio resource. The optimal bandwidth allocation is dynamically adjusted based on instantaneous network load conditions. We investigate the application of machine learning in solving the radio resource slicing problem and demonstrate the advantage of machine learning through extensive simulations. A case study is presented to demonstrate the effectiveness of the proposed radio resource slicing approach.
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Chagdali, Abdellatif, Salah Eddine Elayoubi, and Antonia Maria Masucci. "Slice Function Placement Impact on the Performance of URLLC with Multi-Connectivity." Computers 10, no. 5 (May 18, 2021): 67. http://dx.doi.org/10.3390/computers10050067.
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Network slicing has emerged as a promising technical solution to ensure the coexistence of various 5G services. While the 5G architecture evolution for supporting slicing has been exhaustively studied, the architectural option impacts on RAN resource allocation efficiency remain unclear. This article fills a gap in this area by evaluating the impact of architecture choices on the quality of service of different services in the new 5G ecosystem, focusing on ultra-reliable low-latency communication applications. We propose architectural options based on the placement of the entities responsible for implementing these functions. We then assess their impact on the radio resource allocation flexibility when slices span two radio access technologies with redundant coverage. Our numerical experiments showed that the slice management function placement plays a pivotal role in choosing an adequate radio resource allocation scheme for URLLC slices.
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Mumtaz, Tariq, Shahabuddin Muhammad, Muhammad Imran Aslam, and Irfan Ahmed. "Inter-slice resource management for 5G radio access network using markov decision process." Telecommunication Systems 79, no. 4 (January 24, 2022): 541–57. http://dx.doi.org/10.1007/s11235-021-00877-9.
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AlQahtani, Salman A. "An Evaluation of e-Health Service Performance through the Integration of 5G IoT, Fog, and Cloud Computing." Sensors 23, no. 11 (May 23, 2023): 5006. http://dx.doi.org/10.3390/s23115006.
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In recent years, Internet of Things (IoT) advancements have led to the development of vastly improved remote healthcare services. Scalability, high bandwidth, low latency, and low power consumption are all essential features of the applications that make these services possible. An upcoming healthcare system and wireless sensor network that can fulfil these needs is based on fifth-generation network slicing. For better resource management, organizations can implement network slicing, which partitions the physical network into distinct logical slices according to quality of service (QoS) needs. Based on the findings of this research, an IoT–fog–cloud architecture is proposed for use in e-Health services. The framework is made up of three different but interconnected systems: a cloud radio access network, a fog computing system, and a cloud computing system. A queuing network serves as a model for the proposed system. The model’s constituent parts are then subjected to analysis. To assess the system’s performance, we run a numerical example simulation using Java modelling tools and then analyze the results to identify the key performance parameters. The analytical formulas that were derived ensure the precision of the results. Finally, the results show that the proposed model improves eHealth services’ quality of service in an efficient way by selecting the right slice compared to the traditional systems.
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Riefi, Daifi Afrila, Teuku Yuliar Arif, and Syahrial. "Evaluasi Pengaruh Parameter TIM Berdasarkan Multirate Terhadap Konsumsi Energi Jaringan IEEE 802.11ah." Jurnal RESTI (Rekayasa Sistem dan Teknologi Informasi) 5, no. 4 (August 20, 2021): 713–20. http://dx.doi.org/10.29207/resti.v5i4.3224.
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WLAN IEEE 802.11ah is wireless standard technology which potentially used for IoT networking to provide longer range transmission than WPAN and LPWAN. MAC layer IEEE 802.11ah introduces TIM segmentation scheme that provides effective management toward STA in large amount to make the energy consumption efficiently. STA is organized in hierarchical structure that allows TIM segmentation to reduce the length of frame beacon contains TIM. In case there’s no segmentation in a network with many STA, the TIM would be longer and requires all STA to wake-up receiving beacon TIM including STA without downlink data. This research intends to evaluate and analyze the TIM optimal parameters. Those are Page Period, Page Slice Length and Page Slice Count toward IEEE 802.11ah energy efficiency based on multirate using simulator NS-3 implemented on IEEE 802.11ah. As the result of STA experiment shows that Non-TIM is only optimal on sleep duration while TIM is optimal on energy consumption and delay packet. In the experiment of impact of STA/Slot amount based on Page Slice Length shows that sleep duration and energy consumption is optimal depends on the amount of the STA/Slot and data rate used while the optimal packet delay varies for each Page Slice Length.
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Ma, Zhi, and Songlin Sun. "Research on Vehicle-Road Co-Location Method Oriented to Network Slicing Service and Traffic Video." Sustainability 13, no. 10 (May 11, 2021): 5334. http://dx.doi.org/10.3390/su13105334.
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The development of 5G network slicing technology, combined with the application scenarios of vehicle–road collaborative positioning, provides end-to-end, large-bandwidth, low-latency, and highly reliable flexible customized services for Internet of Vehicle (IoV) services in different business scenarios. Starting from the needs of the network in the business scenario oriented to co-location, we researched the application of 5G network slicing technology in the vehicle–road cooperative localization system. We considered scheduling 5G slice resources. Creating slices to ensure the safety of the system, provided an optimized solution for the application of the vehicle–road coordinated positioning system. On this basis, this paper proposes a vehicle–road coordinated combined positioning method based on Beidou. On the basis of Beidou positioning and track estimation, using the advantages of the volumetric Kalman model, a combined positioning algorithm based on CKF was established. In order to further improve the positioning accuracy, vehicle characteristics could be extracted based on the traffic monitoring video stream to optimize the service-oriented positioning system. Considering that the vehicles in the urban traffic system can theoretically only travel on the road, the plan can be further optimized based on the road network information. It was preliminarily verified by simulation that this research idea has improved the relative single positioning method.
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Gu, Rentao, Yuqi Xue, Yong Zhang, Zixuan Wang, Hao Zhang, Yi Yang, Yan Li, and Yuefeng Ji. "Routing and Timeslot Scheduling for SPN Fine-Granularity Slices." Photonics 10, no. 2 (January 27, 2023): 126. http://dx.doi.org/10.3390/photonics10020126.
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The integration of 5G and vertical industries promotes the development of the energy Ethernet while putting forward fine granularity, flexibility, high reliability, and deterministic low-latency service requirements for the smart grid and the ubiquitous power Internet of Things (UPIoT). As the bearer architecture supporting the next-generation optical transmission network, the Slicing Packet Network (SPN) slice granularity decreases from 5 Gbps to 10 Mbps fine granularity and the frame period of 5 Gbps large-granularity slices is short, so the non-deterministic delay caused by timeslot conflicts has a negligible impact on the end-to-end delay, and the timeslot scheduling is unnecessary. However, due to the reduction in timeslot granularity and the change in frame structure in 10 Mbps slices, the scheduling of conflicting timeslots and the complex device computing management problems need to be solved urgently. In this paper, we establish a model of routing embedded timeslot scheduling for the routing of fine-granularity slices and timeslot scheduling problems in SPN-based FlexE interfaces, for which we propose a deterministic timeslot allocation mechanism supporting end-to-end low-latency transmission. According to the timeslot symmetry, the mechanism can reduce the space of feasible solutions through ant colony optimization and unidirectional neighborhood search (ACO-UNS), so as to efficiently solve the scheduling of conflicting timeslots and provide end-to-end delay guarantee for delay-sensitive services. Finally, we make a comparison between the ACO-UNS algorithm and the timeslot random dispatching algorithm (ACO-RD); the results show that, relative to the ACO-RD, the reduction in the proposed ACO-UNS is 98.721% for the end-to-end delay of fine-granularity slices.
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Jain, Anshul, Tanya Singh, and Satyendra Kumar Sharma. "Security as a Solution: An Intrusion Detection System Using a Neural Network for IoT Enabled Healthcare Ecosystem." Interdisciplinary Journal of Information, Knowledge, and Management 16 (2021): 331–69. http://dx.doi.org/10.28945/4838.
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Aim/Purpose: The primary purpose of this study is to provide a cost-effective and artificial intelligence enabled security solution for IoT enabled healthcare ecosystem. It helps to implement, improve, and add new attributes to healthcare services. The paper aims to develop a method based on an artificial neural network technique to predict suspicious devices based on bandwidth usage. Background: COVID has made it mandatory to make medical services available online to every remote place. However, services in the healthcare ecosystem require fast, uninterrupted facilities while securing the data flowing through them. The solution in this paper addresses both the security and uninterrupted services issue. This paper proposes a neural network based solution to detect and disable suspicious devices without interrupting critical and life-saving services. Methodology: This paper is an advancement on our previous research, where we performed manual knowledge-based intrusion detection. In this research, all the experiments were executed in the healthcare domain. The mobility pattern of the devices was divided into six parts, and each one is assigned a dedicated slice. The security module regularly monitored all the clients connected to slices, and machine learning was used to detect and disable the problematic or suspicious devices. We have used MATLAB’s neural network to train the dataset and automatically detect and disable suspicious devices. The different network architectures and different training algorithms (Levenberg–Marquardt and Bayesian Framework) in MATLAB software have attempted to achieve more precise values with different properties. Five iterations of training were executed and compared to get the best result of R=99971. We configured the application to handle the four most applicable use cases. We also performed an experimental application simulation for the assessment and validation of predictions. Contribution: This paper provides a security solution for the IoT enabled healthcare system. The architectures discussed suggest an end-to-end solution on the sliced network. Efficient use of artificial neural networks detects and block suspicious devices. Moreover, the solution can be modified, configured and deployed in many other ecosystems like home automation. Findings: This simulation is a subset of the more extensive simulation previously performed on the sliced network to enhance its security. This paper trained the data using a neural network to make the application intelligent and robust. This enhancement helps detect suspicious devices and isolate them before any harm is caused on the network. The solution works both for an intrusion detection and prevention system by detecting and blocking them from using network resources. The result concludes that using multiple hidden layers and a non-linear transfer function, logsig improved the learning and results. Recommendations for Practitioners: Everything from offices, schools, colleges, and e-consultation is currently happening remotely. It has caused extensive pressure on the network where the data flowing through it has increased multifold. Therefore, it becomes our joint responsibility to provide a cost-effective and sustainable security solution for IoT enabled healthcare services. Practitioners can efficiently use this affordable solution compared to the expensive security options available in the commercial market and deploy it over a sliced network. The solution can be implemented by NGOs and federal governments to provide secure and affordable healthcare monitoring services to patients in remote locations. Recommendation for Researchers: Research can take this solution to the next level by integrating artificial intelligence into all the modules. They can augment this solution by making it compatible with the federal government’s data privacy laws. Authentication and encryption modules can be integrated to enhance it further. Impact on Society: COVID has given massive exposure to the healthcare sector since last year. With everything online, data security and privacy is the next most significant concern. This research can be of great support to those working for the security of health care services. This paper provides “Security as a Solution”, which can enhance the security of an otherwise less secure ecosystem. The healthcare use cases discussed in this paper address the most common security issues in the IoT enabled healthcare ecosystem. Future Research: We can enhance this application by including data privacy modules like authentication and authorisation, data encryption and help to abide by the federal privacy laws. In addition, machine learning and artificial intelligence can be extended to other modules of this application. Moreover, this experiment can be easily applicable to many other domains like e-homes, e-offices and many others. For example, e-homes can have devices like kitchen equipment, rooms, dining, cars, bicycles, and smartwatches. Therefore, one can use this application to monitor these devices and detect any suspicious activity.
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Arnold, Paul, and Dirk von Hugo. "Future integrated communication network architectures enabling heterogeneous service provision." Advances in Radio Science 16 (September 4, 2018): 59–66. http://dx.doi.org/10.5194/ars-16-59-2018.
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Abstract. This paper summarizes expectations and requirements towards future converged communication systems denoted by 5th Generation (5G). Multiple research and standardization activities globally contribute to the definition and specification of an Information and Communication Technology (ICT) to provide business customers and residential users with both, existing and future upcoming services which demand for higher data rates and granted performance figures in terms of QoS parameters, such as low latency and high reliability. Representative use case families are threefold and represented as enhanced Mobile Broadband (eMBB), massive Internet of Things (mIoT), and Critical Communication, i.e. Ultra-Low Latency (ULL)/Ultra-High Reliability (UHR). To deploy and operate a dedicated network for each service or use case separately would raise the expenses and service costs to an unduly high amount. Instead provision of a commonly shared physical infrastructure offering resources for transport, processing, and storage of data to several separated logical networks (slices) individually managed and configured by potentially multiple service providers is the main concept of this new approach. Beside a multitude of other initiatives the EU-funded 5G NORMA project (5G Novel Radio Multiservice adaptive network Architecture) has developed an architecture which enables not only network programmability (configurability in software), but also network slicing and Multi Tenancy (allowing independent 3rd parties to offer an end-to-end service tailored according to their needs) in a mobile network. Major aspects dealt with here are the selectable support of mobility (on-demand) and service-aware QoE/QoS (Quality of Experience/Service) control. Specifically we will report on the outcome of the analysis of design criteria for Mobility Management schemes and the result of an exemplary application of the modular mobility function to scenarios with variable service requirements (e.g. high-terminal speed vs. on-demand mobility or portability of devices). An efficient sharing of scarce frequency resources in new radio systems demands for tight coordination of orchestration and assignment (scheduling) of resources for the different network slices as per capacity and priority (QoS) demand. Dynamicity aspects in changing algorithms and schemes to manage, configure, and optimize the resources at the radio base stations according to slice specific Service Level Agreements (SLAs) are investigated. It has been shown that architectural issues in terms of hierarchy (centralized vs. distributed) and layering, i.e. separation of control (signaling) and (user) data plane will play an essential role to increase the elasticity of network infrastructures which is in focus of applying SDN (Software Defined Networking) and NFV (Network Function Virtualization) to next generation communication systems. An outlook towards follow-on standardization and open research questions within different SDOs (Standards Defining Organizations) and recently started cooperative projects concludes the contribution.
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Jain, Anshul, Tanya Singh, Satyendra Kumar Sharma, and Vikas Prajapati. "Implementing Security in IoT Ecosystem Using 5G Network Slicing and Pattern Matched Intrusion Detection System: A Simulation Study." Interdisciplinary Journal of Information, Knowledge, and Management 16 (2021): 001–38. http://dx.doi.org/10.28945/4675.
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Aim/Purpose: 5G and IoT are two path-breaking technologies, and they are like wall and climbers, where IoT as a climber is growing tremendously, taking the support of 5G as a wall. The main challenge that emerges here is to secure the ecosystem created by the collaboration of 5G and IoT, which consists of a network, users, endpoints, devices, and data. Other than underlying and hereditary security issues, they bring many Zero-day vulnerabilities, which always pose a risk. This paper proposes a security solution using network slicing, where each slice serves customers with different problems. Background: 5G and IoT are a combination of technology that will enhance the user experience and add many security issues to existing ones like DDoS, DoS. This paper aims to solve some of these problems by using network slicing and implementing an Intrusion Detection System to identify and isolate the compromised resources. Methodology: This paper proposes a 5G-IoT architecture using network slicing. Research here is an advancement to our previous implementation, a Python-based software divided into five different modules. This paper’s amplification includes induction of security using pattern matching intrusion detection methods and conducting tests in five different scenarios, with 1000 up to 5000 devices in different security modes. This enhancement in security helps differentiate and isolate attacks on IoT endpoints, base stations, and slices. Contribution: Network slicing is a known security technique; we have used it as a platform and developed a solution to host IoT devices with peculiar requirements and enhance their security by identifying intruders. This paper gives a different solution for implementing security while using slicing technology. Findings: The study entails and simulates how the IoT ecosystem can be variedly deployed on 5G networks using network slicing for different types of IoT devices and users. Simulation done in this research proves that the suggested architecture can be successfully implemented on IoT users with peculiar requirements in a network slicing environment. Recommendations for Practitioners: Practitioners can implement this solution in any live or production IoT environment to enhance security. This solution helps them get a cost-effective method for deploying IoT devices on a 5G network, which would otherwise have been an expensive technology to implement. Recommendation for Researchers: Researchers can enhance the simulations by amplifying the different types of IoT devices on varied hardware. They can even perform the simulation on a real network to unearth the actual impact. Impact on Society: This research provides an affordable and modest solution for securing the IoT ecosystem on a 5G network using network slicing technology, which will eventually benefit society as an end-user. This research can be of great assistance to all those working towards implementing security in IoT ecosystems. Future Research: All the configuration and slicing resources allocation done in this research was performed manually; it can be automated to improve accuracy and results. Our future direction will include machine learning techniques to make this application and intrusion detection more intelligent and advanced. This simulation can be combined and performed with smart network devices to obtain more varied results. A proof-of-concept system can be implemented on a real 5G network to amplify the concept further.
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Navarro do Amaral, Thiago A., Raphael V. Rosa, David F. Cruz Moura, and Christian Esteve Rothenberg. "Run-Time Adaptive In-Kernel BPF/XDP Solution for 5G UPF." Electronics 11, no. 7 (March 24, 2022): 1022. http://dx.doi.org/10.3390/electronics11071022.
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Flexibility is considered a key feature of 5G softwarization to deliver a timely response to changes in network requirements that may be caused by traffic variation, user mobility, dynamic network function chains, slice lifecycle management operations, among others. In this article, we evolve the upf-bpf1 open-source project by proposing a new design to improve its flexibility by reducing the run-time adaptation time. The project proposes an in-kernel solution based on BPF and eXpress Data Path (XDP) for 5G User Plane Function (UPF) implementations. The Just-In-Time (JIT) compilation may have a huge impact on the adaptation time due to the in-kernel verification of the BPF programs at run-time. Our results show latency improvements of around 95% to inject the BPF program into the Linux kernel. Furthermore, the solution keeps the same functionalities and delivers a packet processing performance of around 10–11 Mpps using 6 cores with almost 70% of the CPU utilization in downlink/uplink directions.
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Butova, Xenia, Sergey Shayakhmetov, Maxim Fedin, Igor Zolotukhin, and Sergio Gianesini. "Artificial Intelligence Evidence-Based Current Status and Potential for Lower Limb Vascular Management." Journal of Personalized Medicine 11, no. 12 (December 2, 2021): 1280. http://dx.doi.org/10.3390/jpm11121280.
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Consultation prioritization is fundamental in optimal healthcare management and its performance can be helped by artificial intelligence (AI)-dedicated software and by digital medicine in general. The need for remote consultation has been demonstrated not only in the pandemic-induced lock-down but also in rurality conditions for which access to health centers is constantly limited. The term “AI” indicates the use of a computer to simulate human intellectual behavior with minimal human intervention. AI is based on a “machine learning” process or on an artificial neural network. AI provides accurate diagnostic algorithms and personalized treatments in many fields, including oncology, ophthalmology, traumatology, and dermatology. AI can help vascular specialists in diagnostics of peripheral artery disease, cerebrovascular disease, and deep vein thrombosis by analyzing contrast-enhanced magnetic resonance imaging or ultrasound data and in diagnostics of pulmonary embolism on multi-slice computed angiograms. Automatic methods based on AI may be applied to detect the presence and determine the clinical class of chronic venous disease. Nevertheless, data on using AI in this field are still scarce. In this narrative review, the authors discuss available data on AI implementation in arterial and venous disease diagnostics and care.
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Nakandala, Dilupa, Henry Lau, and Andrew Ning. "A hybrid approach for cost-optimized lateral transshipment in a supply chain environment." Business Process Management Journal 22, no. 4 (July 4, 2016): 860–78. http://dx.doi.org/10.1108/bpmj-08-2015-0122.
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Purpose – When making sourcing decisions, both cost optimization and customer demand fulfillment are equally important for firm competitiveness. The purpose of this paper is to develop a stochastic search technique, hybrid genetic algorithm (HGA), for cost-optimized decision making in wholesaler inventory management in a supply chain network of wholesalers, retailers and suppliers. Design/methodology/approach – This study develops a HGA by using a mixture of greedy-based and randomly generated solutions in the initial population and a local search method (hill climbing) applied to individuals selected for performing crossover before crossover is implemented and to the best individual in the population at the end of HGA as well as gene slice and integration. Findings – The application of the proposed HGA is illustrated by considering multiple scenarios and comparing with the other commonly adopted methods of standard genetic algorithm, simulated annealing and tabu search. The simulation results demonstrate the capability of the proposed approach in producing more effective solutions. Practical implications – The pragmatic importance of this method is for the inventory management of wholesaler operations and this can be scalable to address real contexts with multiple wholesalers and multiple suppliers with variable lead times. Originality/value – The proposed stochastic-based search techniques have the capability in producing good-quality optimal or suboptimal solutions for large-scale problems within a reasonable time using ordinary computing resources available in firms.
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Zhang, Angela, Amil Khan, Saisidharth Majeti, Judy Pham, Christopher Nguyen, Peter Tran, Vikram Iyer, Ashutosh Shelat, Jefferson Chen, and B. S. Manjunath. "Automated Segmentation and Connectivity Analysis for Normal Pressure Hydrocephalus." BME Frontiers 2022 (January 10, 2022): 1–13. http://dx.doi.org/10.34133/2022/9783128.
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Objective and Impact Statement. We propose an automated method of predicting Normal Pressure Hydrocephalus (NPH) from CT scans. A deep convolutional network segments regions of interest from the scans. These regions are then combined with MRI information to predict NPH. To our knowledge, this is the first method which automatically predicts NPH from CT scans and incorporates diffusion tractography information for prediction. Introduction. Due to their low cost and high versatility, CT scans are often used in NPH diagnosis. No well-defined and effective protocol currently exists for analysis of CT scans for NPH. Evans’ index, an approximation of the ventricle to brain volume using one 2D image slice, has been proposed but is not robust. The proposed approach is an effective way to quantify regions of interest and offers a computational method for predicting NPH. Methods. We propose a novel method to predict NPH by combining regions of interest segmented from CT scans with connectome data to compute features which capture the impact of enlarged ventricles by excluding fiber tracts passing through these regions. The segmentation and network features are used to train a model for NPH prediction. Results. Our method outperforms the current state-of-the-art by 9 precision points and 29 recall points. Our segmentation model outperforms the current state-of-the-art in segmenting the ventricle, gray-white matter, and subarachnoid space in CT scans. Conclusion. Our experimental results demonstrate that fast and accurate volumetric segmentation of CT brain scans can help improve the NPH diagnosis process, and network properties can increase NPH prediction accuracy.
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Kirillova, Elena A., Alexey I. Lazarev, and Oleg P. Kultygin. "Neural network model to support decision-making on managing cooperative relations in innovative ecosystems." Journal Of Applied Informatics 17, no. 2 (March 31, 2022): 79–92. http://dx.doi.org/10.37791/2687-0649-2022-17-2-79-92.
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Currently, the specifics of external conditions and peculiarities of innovation activity main subjects development determine not only the need for close, long-term scientific and technical cooperation with the state for the sustainable development of territories, but also the need to develop and substantiate proposals for managing the development of innovation processes in such a system as a whole. The article proposes a model for the representation of scientific and industrial interaction in the implementation of regional innovation processes in the form of a three-dimensional "slice" of the triple helix as a resource VRIO-profile of cooperative formation, which allows to clearly demonstrate the system of relations, identify in which direction the problem area is, influencing which it will be possible to return the system to an equilibrium state of sustainable development in a strategic perspective. The analysis of modern scientific works shows the relevance, necessity and effectiveness of using methods based on neural networks to predict changes in the state of complex socio-economic systems, such as regional innovation systems. Existing approaches, as a rule, demonstrate a narrow focus and belonging to a separate enterprise or organization, and therefore do not meet all the requirements from both the implementation of the innovation process itself and the modification of the external environment. In this connection, the authors proposed an information and analytical solution for using the described model to support decision-making on the management of cooperative formations. The developed program is based on predicting the future state (position in a three-dimensional coordinate system) of the system using deep neural networks, namely recurrent. The described practical approbation of the model can in the future serve as a basis for decision-making on the choice of forms and directions of interaction of cooperative formations in the strategic perspective.
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Ma, Zhanming, Min Xia, Liguo Weng, and Haifeng Lin. "Local Feature Search Network for Building and Water Segmentation of Remote Sensing Image." Sustainability 15, no. 4 (February 7, 2023): 3034. http://dx.doi.org/10.3390/su15043034.
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Extracting buildings and water bodies from high-resolution remote sensing images is of great significance for urban development planning. However, when studying buildings and water bodies through high-resolution remote sensing images, water bodies are very easy to be confused with the spectra of dark objects such as building shadows, asphalt roads and dense vegetation. The existing semantic segmentation methods do not pay enough attention to the local feature information between horizontal direction and position, which leads to the problem of misjudgment of buildings and loss of local information of water area. In order to improve this problem, this paper proposes a local feature search network (DFSNet) application in remote sensing image building and water segmentation. By paying more attention to the local feature information between horizontal direction and position, we can reduce the problems of misjudgment of buildings and loss of local information of water bodies. The discarding attention module (DAM) introduced in this paper reads sensitive information through direction and location, and proposes the slice pooling module (SPM) to obtain a large receptive field in the pixel by pixel prediction task through parallel pooling operation, so as to reduce the misjudgment of large areas of buildings and the edge blurring in the process of water body segmentation. The fusion attention up sampling module (FAUM) guides the backbone network to obtain local information between horizontal directions and positions in spatial dimensions, provide better pixel level attention for high-level feature maps, and obtain more detailed segmentation output. The experimental results of our method on building and water data sets show that compared with the existing classical semantic segmentation model, the proposed method achieves 2.89% improvement on the indicator MIoU, and the final MIoU reaches 83.73%.
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Jakob, Manuel, and Monica Menendez. "Macroscopic Modeling of On-Street and Garage Parking: Impact on Traffic Performance." Journal of Advanced Transportation 2019 (November 24, 2019): 1–20. http://dx.doi.org/10.1155/2019/5793027.
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The short-term interactions between on-street and garage parking policies and the associated parking pricing can be highly influential to the searching-for-parking traffic and the overall traffic performance in the network. In this paper, we develop a macroscopic on-street and garage parking decision model and integrate it into a traffic system with an on-street and garage parking search model over time. We formulate an on-street and garage parking-state-based matrix that describes the system dynamics of urban traffic based on different parking-related states and the number of vehicles that transition through each state in a time slice. This macroscopic modeling approach is based on aggregated data at the network level over time. This leads to data collection savings and a reduction in computational costs compared to most of the existing parking/traffic models. This easy to implement methodology can be solved with a simple numerical solver. All parking searchers face the decision to drive to a parking garage or to search for an on-street parking space in the network. This decision is affected by several parameters including the on-street and garage parking fees. Our model provides a preliminary idea for city councils regarding the short-term impacts of on-street and garage parking policies (e.g., converting on-street parking to garage parking spaces, availability of garage usage information to all drivers) and parking pricing policies on: searching-for-parking traffic (cruising), the congestion in the network (traffic performance), the total driven distance (environmental impact), as well as the revenue created for the city by the hourly on-street and garage parking fee rates. This model can be used to analyze how on-street and garage parking policies can affect traffic performance; and how traffic performance can affect the decision to use on-street or garage parking. The proposed methodology is illustrated with a case study of an area within the city of Zurich, Switzerland.
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Sakboonyarat, Boonnatee, and Pinyo Taeprasartsit. "Discriminative Image Enhancement for Robust Cascaded Segmentation of CT Images." ECTI Transactions on Computer and Information Technology (ECTI-CIT) 15, no. 2 (April 19, 2021): 150–65. http://dx.doi.org/10.37936/ecti-cit.2021152.240112.
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Objective: Cascaded/attention-based neural network has become common in image segmentation. This work proposes to improve its robustness by adding discriminative image enhancement to its attention mechanism. Unlike prior work, this image enhancement can also be applied as data augmentation and easily adapted for existing models. Its generalization can improve accuracy across multiple segmentation tasks and datasets. Methods: The method first localizes a target organ in a 2D fashion to obtain a tight neighborhood of the organ in each slice. Next, the method computes an HU histogram of a region combined from multiple 2D neighborhoods. This allows the method to adaptively handle HU-range difference among images. Then, HUs are nonlinearly stretched through a parameterized mapping function providing discriminative features for neural network. Varying the function parameters creates different intensity distribution of the target region. This effectively enhances and augments image data at the same time. The HU-reassigned region is then fed to a segmentation model for training. Results: Our experiments on liver and kidney segmentation showed that even a simple cascaded 2D U-Net model could deliver competitive performance in a variety of datasets. In addition, cross-validation and ablation analysis indicated robustness of the method even when the number of original training samples was limited. Conclusion: With the proposed technique, a simple model with limited training data can deliver competitive performance. Significance: The method significantly improves robustness of a trained model and is ready for generalization to other segmentation tasks and attention-based models. Accurate models can be simpler to save computing resources.
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Pan, Wenfu, Li Chen, and Ruxing Zhang. "Automatic Recognition of Financial Instruments Based on Anisotropic Partial Differential Equations." Advances in Mathematical Physics 2021 (October 28, 2021): 1–13. http://dx.doi.org/10.1155/2021/6529859.
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In this paper, anisotropic partial differential equations are used to conduct an indepth study and analysis of automatic recognition of financial bills. Firstly, it obtains the invoices of the group enterprise, uses scanning technology and related image recognition technology to capture, process, compress and slice the paper bill content, and then carries out data identification and verification of the image. It classifies the obtained electronic data information into bills, converts it into electronic information related to bills according to the corresponding categories of the bill template, and stores it in the bill table of the database to achieve the management operation of formatted electronic files. After categorizing the bills according to the electronic information of bills to match the business scenarios, financial journal vouchers can be generated according to the preconfigured voucher templates of the corresponding business scenarios, and the financial journal vouchers are converted into voucher messages using XML technology. Finally, we use agent technology to design middleware for heterogeneous financial systems to realize the function of communicating voucher messages to each other in different business systems. The system automatically extracts the key information of invoices through OCR technology and performs real-time verification and cyclical feedback to the verification results to the suppliers. The system has realized the intelligent management of the power company’s VAT invoices, thus greatly enhancing the efficiency of VAT invoice verification and settlement. The automatic tax invoice recognition system adopts a network structured tax invoice recognition model, which eliminates the cumbersome steps of character decomposition and character classification in traditional OCR character recognition. After several trials, it has obtained better experimental results in terms of recognition accuracy, with an accuracy rate of over 93% in the recognition of tax invoice data set.
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Musser, Jeremy, Ezra Kissel, Martin Swany, Joe Breen, Jason Stidd, Shawn McKee, and Benjeman Meekhof. "Applying OSiRIS NMAL to Network Slices on SLATE." EPJ Web of Conferences 245 (2020): 07055. http://dx.doi.org/10.1051/epjconf/202024507055.
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The Network Management Abstraction Layer (NMAL) extends perfSONAR capabilities to include automated network topology discovery and tracking in the Unified Network Information Service (UNIS), and incorporate Software Defined Networking (SDN) into overall operations of the OSiRIS distributed Ceph infrastructure. We deploy perfSONAR components both within OSiRIS and at our “client” locations to allow monitoring and measuring the networks interconnecting science domain users and OSiRIS components. Topology discovery (using an SDN controller application) and Flange Network Orchestration (NOS) rules are used to dynamically manage network pathing in our testbed environments. NMAL components have been containerized to operate within the Services Layer at the Edge (SLATE) infrastructure, and we describe our experiences in packaging and deploying our services.
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Wang, Yujia, Yuan Tian, Binyu Yang, Jian Wang, Xiaowei Hu, and Shi An. "Planning Flexible Bus Service as an Alternative to Suspended Bicycle-Sharing Service: A Data-Driven Approach." Journal of Advanced Transportation 2023 (January 30, 2023): 1–15. http://dx.doi.org/10.1155/2023/3187654.
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Many free-floating bicycle-sharing (FFBS) operators in cold region cities will put bicycles in warehouses and suspend services in winter due to factors such as safety and maintenance costs, resulting in the corresponding travel demand no longer be met. Considering the short-distance and green travel characteristics of FFBS, the flexible bus, as a sustainable demand-responsive transit service, is a suitable alternative transportation mode. To operate such a flexible bus system, service area and operation time planning is a key stage, however, the planning methods in relevant studies are not suitable for this research scenario. In view of the above, this paper proposed a data-driven method to determine the service area and operation time of flexible buses based on FFBS data. Firstly, an FFBS trip path reconstruction algorithm consists of fine-grained road network modelling and trajectory matching is proposed. Then, in the defined time slice, incorporating the idea of ride-sharing routes generation, according to the density-based clustering principle and considering topology between trajectories, a path clustering algorithm PATHSCAN is developed to generate the one-day path clusters. After that, a frequent pattern mining algorithm is applied to the multiday path clusters, and frequent pattern results with spatio-temporal correlation will be merged into the final service area. The generated planning results will cover ride-matching trips and high-frequency riding paths. Detailed application analysis and verification are carried out by using the real data from Tianjin, China. Through the evaluation and verification under the relatively limited experimental data set, the proposed data-driven method shows ideal planning results. Flexible bus service can supplement the green short-distance travel mode after the suspension of FFBS and can avoid FFBS travel demands switched to unsustainable transportation modes to a large extent. This study will contribute to urban sustainable transportation development and improving greenness.
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Leonardi, Luca, Lucia Lo Bello, and Simone Aglianò. "Priority-Based Bandwidth Management in Virtualized Software-Defined Networks." Electronics 9, no. 6 (June 17, 2020): 1009. http://dx.doi.org/10.3390/electronics9061009.
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In Industrial Internet of Things (IoT) applications, when the network size increases and different types of flows share the bandwidth, the demand for flexible and efficient management of the communication network is compelling. In these scenarios, under varying workload and flow priorities, the combined use of Software-Defined Networking (SDN) and Network Virtualization (NV) is a promising solution, as such techniques allow to reduce the network management complexity. This work presents the PrioSDN Resource Manager (PrioSDN_RM), a resource management mechanism based on admission control for virtualized SDN-based networks. The proposed combination imposes bounds on the resource utilization for the virtual slices, which therefore share the network links, while maintaining isolation from each other. The presented approach exploits a priority-based runtime bandwidth distribution mechanism to dynamically react to load changes (e.g., due to alarms). The paper describes the design of the approach and provides experimental results obtained on a real testbed.
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Han, Bin, Ji Lianghai, and Hans D. Schotten. "Slice as an Evolutionary Service: Genetic Optimization for Inter-Slice Resource Management in 5G Networks." IEEE Access 6 (2018): 33137–47. http://dx.doi.org/10.1109/access.2018.2846543.
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Khodapanah, Behnam, Ahmad Awada, Ingo Viering, Andre Noll Barreto, Meryem Simsek, and Gerhard Fettweis. "Framework for Slice-Aware Radio Resource Management Utilizing Artificial Neural Networks." IEEE Access 8 (2020): 174972–87. http://dx.doi.org/10.1109/access.2020.3026164.
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Slawomir Kuklinski, Lechoslaw Tomaszewski, Robert Kolakowski, Anne-Marie Bosneag, Ashima Chawla, Adlen Ksentini, Sabra Ben Saad, et al. "AI-driven predictive and scalable management and orchestration of network slices." ITU Journal on Future and Evolving Technologies 3, no. 3 (November 16, 2022): 570–88. http://dx.doi.org/10.52953/ipui5221.
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The future network slicing enabled mobile ecosystem is expected to support a wide set of heterogenous vertical services over a common infrastructure. The service robustness and their intrinsic requirements, together with the heterogeneity of mobile infrastructure and resources in both the technological and the spatial domain, significantly increase the complexity and create new challenges regarding network management and orchestration. High degree of automation, flexibility and programmability are becoming the fundamental architectural features to enable seamless support for the modern telco-based services. In this paper, we present a novel management and orchestration platform for network slices, which has been devised by the Horizon 2020 MonB5G project. The proposed framework is a highly scalable solution for network slicing management and orchestration that implements a distributed and programmable AI-driven management architecture. The cognitive capabilities are provided at different levels of management hierarchy by adopting necessary data abstractions. Moreover, the framework leverages intent-based operations to improve its modularity and genericity. The mentioned features enhance the management automation, making the architecture a significant step towards self-managed network slices.
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Pei, Xinjun, Shengwei Tian, Long Yu, Huanhuan Wang, and Yongfang Peng. "A Two-Stream Network Based on Capsule Networks and Sliced Recurrent Neural Networks for DGA Botnet Detection." Journal of Network and Systems Management 28, no. 4 (July 20, 2020): 1694–721. http://dx.doi.org/10.1007/s10922-020-09554-9.
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Rafiq, Adeel, Asif Mehmood, Talha Ahmed Khan, Khizar Abbas, Muhammad Afaq, and Wang-Cheol Song. "Intent-Based End-to-End Network Service Orchestration System for Multi-Platforms." Sustainability 12, no. 7 (April 1, 2020): 2782. http://dx.doi.org/10.3390/su12072782.
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On-demand service is the main feature of the 5G network, and Network Function Virtualization (NFV) provides it by virtualizing the existing 5G network infrastructure. NFV crafts various virtual networks on a shared physical network, but one of the core challenges in future 5G networks is to automate the modeling of Virtualized Network Functions (VNFs) and end-to-end Network Service (NS) orchestration with less human interaction. Traditionally, the descriptor of VNF and NS is created manually, which requires expert-level skills. This manual approach has a big threat of human error, which can be avoided by using the Intent-Based Networking (IBN) approach. The IBN approach eliminates the requirement of expertise for designing VNFs and NS by taking users’ intentions as an input. In this paper, the proposed system presents the Intent Management System for VNF modeling and end-to-end NS orchestration for multi-platforms. This system takes the high-level information related to a specific service, configures it accordingly, and converts it into the selected platform. The proposed system is tested using Mobile Central Office Re-architected as Data Center (M-CORD) and Open-Source Management and Orchestration (OSM) orchestrators. The results section shows that the proposed system reduces the effort of the end-user in creating network slices and provides seamless end-to-end service orchestration.
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Carminati, Marco, Andrea Turolla, Lorenzo Mezzera, Michele Di Mauro, Marco Tizzoni, Gaia Pani, Francesco Zanetto, Jacopo Foschi, and Manuela Antonelli. "A Self-Powered Wireless Water Quality Sensing Network Enabling Smart Monitoring of Biological and Chemical Stability in Supply Systems." Sensors 20, no. 4 (February 19, 2020): 1125. http://dx.doi.org/10.3390/s20041125.
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A smart, safe, and efficient management of water is fundamental for both developed and developing countries. Several wireless sensor networks have been proposed for real-time monitoring of drinking water quantity and quality, both in the environment and in pipelines. However, surface fouling significantly affects the long-term reliability of pipes and sensors installed in-line. To address this relevant issue, we presented a multi-parameter sensing node embedding a miniaturized slime monitor able to estimate the micrometric thickness and type of slime. The measurement of thin deposits in pipes is descriptive of water biological and chemical stability and enables early warning functions, predictive maintenance, and more efficient management processes. After the description of the sensing node, the related electronics, and the data processing strategies, we presented the results of a two-month validation in the field of a three-node pilot network. Furthermore, self-powering by means of direct energy harvesting from the water flowing through the sensing node was also demonstrated. The robustness and low cost of this solution enable its upscaling to larger monitoring networks, paving the way to water monitoring with unprecedented spatio-temporal resolution.