Optimized communication in 5G-driven vehicular ad-hoc networks (VANETs)

University of Technology Sydney. Faculty of Engineering and Information Technology.Next generation Vehicular Ad-hoc Networks will be dominated by heterogeneous data and additional massive diffusion of Internet of Things (IoT) traffic. To meet these objectives, a radical rethink of current VANET architecture is essentially required by turning it into a more flexible and programmable fabric. This research endeavours to provide next generation 5G-driven VANET architecture, with solutions for efficient and optimized communication. This thesis first introduces an innovative 5G-driven VANET architecture to provide flexible network management, control and high resource utilization, leveraging the concepts of SDN, C-RAN and Fog Computing. A new Fog Computing (FC) framework (comprising of zones and clusters) is proposed at the edge of the network to support vehicles and end users with prompt responses, and to avoid frequent handovers between vehicles and RSUs. The key results are improved throughput, reduced transmission delay and minimized control overhead on the controller. Furthermore, a novel Evolutionary Game Theoretic (EGT) framework is presented to achieve stable and optimized clustering in the Fog Computing Framework. The solution of the game is presented to be an evolutionary equilibrium. The equilibrium point is also proven analytically and the existence of an evolutionary equilibrium is also verified using the Lyapunov function. The results are analysed for different number of clusters for different populations and speeds. An optimal cost is suggested that defines an optimum clustering thus reducing an overhead of frequent cluster reformation. In addition, this thesis provides a Hybrid-Fuzzy Logic guided Genetic Algorithm (H-FLGA) approach for the SDN controller, to support diversified quality of service (QoS) demands and dynamic resource requirements of mobile users in 5G-driven VANET architecture. The proposed Fuzzy Inference System (FIS) is used to optimize weights of multi-objectives, depending on the Type of Service (ToS) requirements of customers. The results proved that the proposed hybrid H-FLGA performs better than GA. The results improve spectral efficiency and optimizes connections while minimizing E2E delay and further facilitates the service providers to implement a more flexible customer-centric network infrastructure. Furthermore, an end-to-end (E2E) network slicing framework is proposed to support customized services by managing the cooperation of both the RAN and Core Network (CN), using SDN, NFV and Edge Computing technologies. A dynamic radio resource slice optimization scheme is proposed to slice the overall bandwidth resources for mission critical and non-mission critical demands. The results meet ultra reliability and E2E latency of mission-critical services

Dynamic traffic forecasting and fuzzy-based optimized admission control in federated 5G-open RAN networks

Providing connectivity to high-density traffic demand is one of the key promises of future wireless networks. The open radio access network (O-RAN) is one of the critical drivers ensuring such connectivity in heterogeneous networks. Despite intense interest from researchers in this domain, key challenges remain to ensure efficient network resource allocation and utilization. This paper proposes a dynamic traffic forecasting scheme to predict future traffic demand in federated O-RAN. Utilizing information on user demand and network capacity, we propose a fully reconfigurable admission control framework via fuzzy-logic optimization. We also perform detailed analysis on several parameters (user satisfaction level, utilization gain, and fairness) over benchmarks from various papers. The results show that the proposed forecasting and fuzzy-logic-based admission control framework significantly enhances fairness and provides guaranteed quality of experience without sacrificing resource utilization. Moreover, we have proven that the proposed framework can accommodate a large number of devices connected simultaneously in the federated O-RAN

Analysis of the state of the art of Soft Computing Techniques applied to network planning problems in 5G.

En el presente artículo se realizó una revisión del estado del arte de la aplicación de las técnicas de Soft Computing en la resolución de problemas de planificación de redes 5G, para lo cual se clasificó las diferentes técnicas de soft computing existentes (redes neuronales, lógica difusa, algoritmos evolutivos) y de los trabajos e investigaciones realizados sobre el tema según sus autores, los modelos planteados y los métodos de solución. Adicionalmente se describieron las investigaciones más relevantes en donde se especifican técnicas para dar solución a los problemas de arquitecturas y funcionalidades cruciales en el desarrollo de esta tecnología, entre los cuales se resalta: encontrar una posición óptima para una Estación Base (BS) en un área de interés determinada, operar en las bandas de frecuencias múltiples deseadas mientras se mantiene una alta ganancia, limitar el consumo de energía en las infraestructuras de red 5G y tratar de incrementar la calidad del servicio al disminuir la probabilidad de bloqueo de llamadas. Finalmente se concluyó que las técnicas de soft computing más aplicadas a la solución de problemas de planificación de las redes 5G son lógica difusa, para limitar el consumo de energía en las infraestructuras de red 5G, además de las redes neuronales artificiales y algoritmos genéticos para la admisión de llamadas en redes 5G con la finalidad de incrementar la calidad del servicio al disminuir las interferencias.The current article developed a review of the state of the art about the application of Soft Computing techniques in solving planning problems of 5G networks. To achieve this, the different existing Soft Computing techniques were classified (neural networks, fuzzy logic, evolutionary algorithms) and models proposed and methods of solution were considered from works and research developed about the subject according to their authors. Additionally, the most relevant investigations described techniques which are specified to solve problems of architectures and crucial functionalities in the development of this technology, highlighting the following: finding an optimal position for a Base Station (BS) in an area of particular interest, operating in the desired multiple frequency bands while maintaining high gain, limiting power consumption in 5G network infrastructures, and trying to increase quality of service by decreasing the probability of call blocking. To conclude, the most applied Soft Computing techniques in solving planning problems of 5G networks are fuzzy logic, in order to limit the energy consumption in 5G network infrastructures, in addition to artificial neural networks and genetic algorithms for admission of calls in 5G networks, increasing quality of service by reducing interference

Fog-Assisted Cooperative Protocol for Traffic Message Transmission in Vehicular Networks

Traffic information exchange between vehicles and city-wide traffic command center will enable various traffic management applications in future smart cities. These applications require a secure and reliable communication framework that ensures real-time data exchange. In this paper, we propose a Fog-Assisted Cooperative Protocol (FACP) that efficiently transmits uplink and downlink traffic messages with the help of fog Road Side Units (RSUs). FACP divides the road into clusters and computes cluster head vehicles to facilitate transmission between vehicles and traffic command center or fog RSUs. Using a combination of IEEE 802.11p and C-V2X wireless technologies, FACP minimizes the time required by a vehicle to retrieve traffic information. Furthermore, FACP also utilizes cooperative transmissions to improve the reliability of traffic messages. Simulations results show that FACP improves the reception rate and end-to-end delay of traffic messages

Adaptive Washout Filter Based on Fuzzy Logic for a Motion Simulation Platform With Consideration of Joints Limitations

Motion simulation platforms (MSPs) are widely used to generate driving/flying motion sensations for the users. The MSPs have a restricted workspace area due to the dynamical and physical restrictions of the Motion Platforms active joints as well as the physical limitations of its passive joints. The motion cueing algorithm (MCA) is the reproduction of the motion signal including linear accelerations and angular velocities. It aims to simultaneously respect the MSP's workspace limitations and make the same motion feeling for the user as a real vehicle. The Classical washout filter (WF) is a well-known type of MCA. The classical WF is easy to set-up, offers a low computational burden and high functionality but has some major drawbacks such as fixed WF parameters tuned according to worst-case scenarios and no consideration of the human vestibular system. As a result, adaptive WFs were developed to consider the human vestibular system and enhance the efficiency of the method using time-varying filters. The existing adaptive WFs only cogitate the boundaries of the end-effector in the Cartesian coordinate space as a substitute for the active and passive joints limitations, which is MSP's main limiting factor. This conservative assumption reduces the available workspace area of the MSP and increases the motion sensation error for the MSPs user. In this study, a fuzzy logic-based WF is developed, to consider the dynamical and physical boundaries of the active joints as well as the physical boundaries of the passive joints. A genetic algorithm is used to select the membership functions values of the active and passive joints boundaries. The model is designed using MATLAB /Simulink and the outcomes demonstrate the efficiency of the proposed method versus existing adaptive WFs

Admission Control Optimisation for QoS and QoE Enhancement in Future Networks

Recent exponential growth in demand for traffic heterogeneity support and the number of associated devices has considerably increased demand for network resources and induced numerous challenges for the networks, such as bottleneck congestion, and inefficient admission control and resource allocation. Challenges such as these degrade network Quality of Service (QoS) and user-perceived Quality of Experience (QoE). This work studies admission control from various perspectives. For example, two novel single-objective optimisation-based admission control models, Dynamica Slice Allocation and Admission Control (DSAAC) and Signalling and Admission Control (SAC), are presented to enhance future limited-capacity network Grade of Service (GoS), and for control signalling optimisation, respectively. DSAAC is an integrated model whereby a cost-estimation function based on user demand and network capacity quantifies resource allocation among users. Moreover, to maximise resource utility, adjustable minimum and maximum slice resource bounds have also been derived. In the case of user blocking from the primary slice due to congestion or resource scarcity, a set of optimisation algorithms on inter-slice admission control and resource allocation and adaptability of slice elasticity have been proposed. A novel SAC model uses an unsupervised learning technique (i.e. Ranking-based clustering) for optimal clustering based on users’ homogeneous demand characteristics to minimise signalling redundancy in the access network. The redundant signalling reduction reduces the additional burden on the network in terms of unnecessary resource utilisation and computational time. Moreover, dynamically reconfigurable QoE-based slice performance bounds are also derived in the SAC model from multiple demand characteristics for clustered user admission to the optimal network. A set of optimisation algorithms are also proposed to attain efficient slice allocation and users’ QoE enhancement via assessing the capability of slice QoE elasticity. An enhancement of the SAC model is proposed through a novel multi-objective optimisation model named Edge Redundancy Minimisation and Admission Control (E-RMAC). A novel E-RMAC model for the first time considers the issue of redundant signalling between the edge and core networks. This model minimises redundant signalling using two classical unsupervised learning algorithms, K-mean and Ranking-based clustering, and maximises the efficiency of the link (bandwidth resources) between the edge and core networks. For multi-operator environments such as Open-RAN, a novel Forecasting and Admission Control (FAC) model for tenant-aware network selection and configuration is proposed. The model features a dynamic demand-estimation scheme embedded with fuzzy-logic-based optimisation for optimal network selection and admission control. FAC for the first time considers the coexistence of the various heterogeneous cellular technologies (2G, 3G,4G, and 5G) and their integration to enhance overall network throughput by efficient resource allocation and utilisation within a multi-operator environment. A QoS/QoE-based service monitoring feature is also presented to update the demand estimates with the support of a forecasting modifier. he provided service monitoring feature helps resource allocation to tenants, approximately closer to the actual demand of the tenants, to improve tenant-acquired QoE and overall network performance. Foremost, a novel and dynamic admission control model named Slice Congestion and Admission Control (SCAC) is also presented in this thesis. SCAC employs machine learning (i.e. unsupervised, reinforcement, and transfer learning) and multi-objective optimisation techniques (i.e. Non-dominated Sorting Genetic Algorithm II ) to minimise bottleneck and intra-slice congestion. Knowledge transfer among requests in form of coefficients has been employed for the first time for optimal slice requests queuing. A unified cost estimation function is also derived in this model for slice selection to ensure fairness among slice request admission. In view of instantaneous network circumstances and load, a reinforcement learning-based admission control policy is established for taking appropriate action on guaranteed soft and best-effort slice requests admissions. Intra-slice, as well as inter-slice resource allocation, along with the adaptability of slice elasticity, are also proposed for maximising slice acceptance ratio and resource utilisation. Extensive simulation results are obtained and compared with similar models found in the literature. The proposed E-RMAC model is 35% superior at reducing redundant signalling between the edge and core networks compared to recent work. The E-RMAC model reduces the complexity from O(U) to O(R) for service signalling and O(N) for resource signalling. This represents a significant saving in the uplink control plane signalling and link capacity compared to the results found in the existing literature. Similarly, the SCAC model reduces bottleneck congestion by approximately 56% over the entire load compared to ground truth and increases the slice acceptance ratio. Inter-slice admission and resource allocation offer admission gain of 25% and 51% over cooperative slice- and intra-slice-based admission control and resource allocation, respectively. Detailed analysis of the results obtained suggests that the proposed models can efficiently manage future heterogeneous traffic flow in terms of enhanced throughput, maximum network resources utilisation, better admission gain, and congestion control

Service Provisioning in Edge-Cloud Continuum Emerging Applications for Mobile Devices

Disruptive applications for mobile devices can be enhanced by Edge computing facilities. In this context, Edge Computing (EC) is a proposed architecture to meet the mobility requirements imposed by these applications in a wide range of domains, such as the Internet of Things, Immersive Media, and Connected and Autonomous Vehicles. EC architecture aims to introduce computing capabilities in the path between the user and the Cloud to execute tasks closer to where they are consumed, thus mitigating issues related to latency, context awareness, and mobility support. In this survey, we describe which are the leading technologies to support the deployment of EC infrastructure. Thereafter, we discuss the applications that can take advantage of EC and how they were proposed in the literature. Finally, after examining enabling technologies and related applications, we identify some open challenges to fully achieve the potential of EC, and also research opportunities on upcoming paradigms for service provisioning. This survey is a guide to comprehend the recent advances on the provisioning of mobile applications, as well as foresee the expected next stages of evolution for these applications

Clustering algorithm for D2D communication in next generation cellular networks : thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Engineering, Massey University, Auckland, New Zealand

Next generation cellular networks will support many complex services for smartphones, vehicles, and other devices. To accommodate such services, cellular networks need to go beyond the capabilities of their previous generations. Device-to-Device communication (D2D) is a key technology that can help fulfil some of the requirements of future networks. The telecommunication industry expects a significant increase in the density of mobile devices which puts more pressure on centralized schemes and poses risk in terms of outages, poor spectral efficiencies, and low data rates. Recent studies have shown that a large part of the cellular traffic pertains to sharing popular contents. This highlights the need for decentralized and distributive approaches to managing multimedia traffic. Content-sharing via D2D clustered networks has emerged as a popular approach for alleviating the burden on the cellular network. Different studies have established that D2D communication in clusters can improve spectral and energy efficiency, achieve low latency while increasing the capacity of the network. To achieve effective content-sharing among users, appropriate clustering strategies are required. Therefore, the aim is to design and compare clustering approaches for D2D communication targeting content-sharing applications. Currently, most of researched and implemented clustering schemes are centralized or predominantly dependent on Evolved Node B (eNB). This thesis proposes a distributed architecture that supports clustering approaches to incorporate multimedia traffic. A content-sharing network is presented where some D2D User Equipment (DUE) function as content distributors for nearby devices. Two promising techniques are utilized, namely, Content-Centric Networking and Network Virtualization, to propose a distributed architecture, that supports efficient content delivery. We propose to use clustering at the user level for content-distribution. A weighted multi-factor clustering algorithm is proposed for grouping the DUEs sharing a common interest. Various performance parameters such as energy consumption, area spectral efficiency, and throughput have been considered for evaluating the proposed algorithm. The effect of number of clusters on the performance parameters is also discussed. The proposed algorithm has been further modified to allow for a trade-off between fairness and other performance parameters. A comprehensive simulation study is presented that demonstrates that the proposed clustering algorithm is more flexible and outperforms several well-known and state-of-the-art algorithms. The clustering process is subsequently evaluated from an individual user’s perspective for further performance improvement. We believe that some users, sharing common interests, are better off with the eNB rather than being in the clusters. We utilize machine learning algorithms namely, Deep Neural Network, Random Forest, and Support Vector Machine, to identify the users that are better served by the eNB and form clusters for the rest of the users. This proposed user segregation scheme can be used in conjunction with most clustering algorithms including the proposed multi-factor scheme. A comprehensive simulation study demonstrates that with such novel user segregation, the performance of individual users, as well as the whole network, can be significantly improved for throughput, energy consumption, and fairness

Fuzzy Logic in Surveillance Big Video Data Analysis: Comprehensive Review, Challenges, and Research Directions

CCTV cameras installed for continuous surveillance generate enormous amounts of data daily, forging the term “Big Video Data” (BVD). The active practice of BVD includes intelligent surveillance and activity recognition, among other challenging tasks. To efficiently address these tasks, the computer vision research community has provided monitoring systems, activity recognition methods, and many other computationally complex solutions for the purposeful usage of BVD. Unfortunately, the limited capabilities of these methods, higher computational complexity, and stringent installation requirements hinder their practical implementation in real-world scenarios, which still demand human operators sitting in front of cameras to monitor activities or make actionable decisions based on BVD. The usage of human-like logic, known as fuzzy logic, has been employed emerging for various data science applications such as control systems, image processing, decision making, routing, and advanced safety-critical systems. This is due to its ability to handle various sources of real world domain and data uncertainties, generating easily adaptable and explainable data-based models. Fuzzy logic can be effectively used for surveillance as a complementary for huge-sized artificial intelligence models and tiresome training procedures. In this paper, we draw researchers’ attention towards the usage of fuzzy logic for surveillance in the context of BVD. We carry out a comprehensive literature survey of methods for vision sensory data analytics that resort to fuzzy logic concepts. Our overview highlights the advantages, downsides, and challenges in existing video analysis methods based on fuzzy logic for surveillance applications. We enumerate and discuss the datasets used by these methods, and finally provide an outlook towards future research directions derived from our critical assessment of the efforts invested so far in this exciting field

Mobility-aware Software-Defined Service-Centric Networking for Service Provisioning in Urban Environments

Disruptive applications for mobile devices, such as the Internet of Things, Connected and Autonomous Vehicles, Immersive Media, and others, have requirements that the current Cloud Computing paradigm cannot meet. These unmet requirements bring the necessity to deploy geographically distributed computing architectures, such as Fog and Mobile Edge Computing. However, bringing computing close to users has its costs. One example of cost is the complexity introduced by the management of the mobility of the devices at the edge. This mobility may lead to issues, such as interruption of the communication with service instances hosted at the edge or an increase in communication latency during mobility events, e.g., handover. These issues, caused by the lack of mobility-aware service management solutions, result in degradation in service provisioning. The present thesis proposes a series of protocols and algorithms to handle user and service mobility at the edge of the network. User mobility is characterized when user change access points of wireless networks, while service mobility happens when services have to be provisioned from different hosts. It assembles them in a solution for mobility-aware service orchestration based on Information-Centric Networking (ICN) and runs on top of Software-Defined Networking (SDN). This solution addresses three issues related to handling user mobility at the edge: (i) proactive support for user mobility events, (ii) service instance addressing management, and (iii) distributed application state data management. For (i), we propose a proactive SDN-based handover scheme. For (ii), we propose an ICN addressing strategy to remove the necessity of updating addresses after service mobility events. For (iii), we propose a graph-based framework for state data placement in the network nodes that accounts for user mobility and latency requirements. The protocols and algorithms proposed in this thesis were compared with different approaches from the literature through simulation. Our results show that the proposed solution can reduce service interruption and latency in the presence of user and service mobility events while maintaining reasonable overhead costs regarding control messages sent in the network by the SDN controller