Software Defined Mobile Networks Research Articles

MSA-SDMN: multicast source authentication scheme for multi-domain software defined mobile networks

ABSTRACT Multicast services provide an efficient way of conserving resources and reducing network traffic for multicast senders. However, ensuring security, particularly source authentication, is crucial for applications such as online news, IPTV, video streaming, and stock quote distribution. Previous research efforts have attempted to provide source authentication for multicast applications, but they often struggle to handle the dynamic nature of multicast mobile receivers and multi-domain networks. This paper provides an analysis of research conducted over 22 years on source authentication mechanisms with non-repudiation. Most of these mechanisms fail to address the dynamic nature of mobile users and multi-domain networks. To address this issue, a new multicast source authentication scheme for multi-domain Software Defined Mobile Networks (SDMN) is proposed. This approach uses the global view of the controller in SDMN to operate in dynamic environments, provide non-repudiation, and tolerate packet loss. Simulation results indicate that the proposed mechanism uses resources efficiently, reduces communication and delay overhead, and performs well in multi-domain and dynamic networks.

Enhancing the Security of Software Defined Mobile Networks (SDMN) based on Blockchain Technology

The blockchain involves future developments and new technologies. The emergence of a blockchain is a challenge for the conventional social organization and mode of activity. Data latency and mobile network capacity would no longer be a limitation for mobile users in next-generation networks of Software Defined Mobile Networks (SDMN). But there are many benefits to Software Defined Mobile Networking, it also contributes to certain security problems like DDoS / DoS attacks, unauthorized access, and single data point error. To enhance the security and privacy of the SDMN control plane, this paper proposes a new SDMN-based “Simplified Byzantine Fault Tolerance (SBFT)” algorithm to send signals between controllers and also set up an analysis study to investigate SBFT's security and results. However, there are many benefits of Software Defined Mobile Networking (SDMN), and then it helps to resolve other security concerns including DDoS / DoS attacks, unauthorized access, and single point failure. Blockchain, an evolving revolutionary technology, will offer creative approaches to address security issues in Software-Defined Mobile networks in an efficient way. This study proposes an SDMN-based Simplified Byzantine Fault Tolerance (SBFT) for enhancing the mobile network's security and privacy.

Feedback Learning in Software-Defined Mobile Network for Resource Aware Load Balancing Under Fault Tolerance Conditions

Fault-tolerant-based load balance resource allocation can help in the explosive data flow in the mobile network. System parameter function, system load factors, network-level configuration, network characteristics, and routing parameters are all affected by volatile data. In today's era of Big Data, one of the most important areas of study in mobile communications is how to adapt to traffic flow. The accessibility of load balancing sensors helps eliminate delays, which in turn helps reduce energy consumption and shorten execution time. In this research, we present a load balancing method for software-defined mobile networks (SDMNs) that is known to maximize the utility of the sensors by considering both the processing power of the sensors and the requirements of their sources. A proactive action technique that takes advantage of the wireless facility is proposed, and a wireless load balancing design solution using the learning method is then utilized in the designed framework. To achieve high resource utilization, we use a method based on convergence. Intelligent resource utilization by multiple sensor devices can help to cope with high bandwidth applications such as multimedia in mobile networks. Compared to conventional methods, the model has the potential to achieve better results.

Enhanced whale optimization based traffic forecasting for SDMN based traffic

In today’s scenario the number of mobile devices is increasing by leaps and bounds and hence the networks are highly congested. To use the existing networks efficiently, the insight into the amount of traffic beforehand, would be highly beneficial. Mobile traffic forecasting would enable Software Defined Mobile Network (SDMN) to use network resources and bandwidth effectively. Therefore, this paper aims to forecast mobile traffic for SDMN using nature-inspired​ optimization techniques. The data taken is captured using Wireshark. In order to accurately predict network traffic, this paper considers Least Square Support Vector Machine (LSSVM) function estimation based training model with Whale Optimization Algorithm (WOA). The proposed technique increases the probability of global search by optimizing kernel parameters namely, regularization and kernel width. The parameters: Mean Square Error (MSE), True Positive Rate (TPR), True Negative Rate (TNR),​ accuracy and precision are evaluated for the proposed optimization algorithm. The results of the proposed algorithm Least Square Support Vector Machine-Enhanced Whale Optimization Algorithm (LSSVM-EWOA) for traffic forecasting are compared with Least Square Support Vector Machine-Whale Optimization Algorithm (LSSVM-WOA). The accuracy of prediction of the proposed model is 98.10%, which confirmed the effectiveness and the actual use of the proposed model.

SDMN Architecture in 5G

Abstract Due to the exponentially growing number of mobile devices connected to the Internet, the current 4G LTE-A mobile network will no longer be able to serve the nearly 5 billion mobile devices. With the advent of the fifth generation, however, the number of cybercrimes may increase. This requires building an architecture that can adequately protect against these attacks. For wired networks, the SDN-type architecture has been introduced for some time. As a result, a similar design concept has emerged, which is called Software Defined Mobile Networks (SDMN). This article describes this technology and how it helps prevents DoS, DDoS at-tacks, and IP source spoofing.

QoS aware and fault tolerant handovers in software defined LTE networks

In order to handle a huge number of mobile users connections and their requirements like higher throughput, lower delay and seamless mobility, telecom operators have started deploying 4G and 5G technologies. One of the major requirements of 4G and 5G is seamless mobility in high speed mobile networks. Especially in high speed mobile networks sudden groups of handovers can raise sudden handover failures, high traffic load and Quality of Service (QoS) dissatisfaction to users in the network. In this work, these issues are addressed using centralized software defined mobile networks. In particular, to handle sudden traffic load and handover failures due to sudden groups of handovers, we propose an integrated QoS aware prioritized handovers and load balance approach called QoS Aware Fault Tolerant Handovers (QAFT). Unlike existing works, in this work, we are proposing prioritized handovers for Guaranteed Bit Rate (GBR) User Equipments (UEs) over Non-GBR (N-GBR) UEs. Main objectives of our approach are minimizing the effect of N-GBR UEs on GBR UEs during sudden handovers in the network, prevent failures of handovers and distribution of sudden traffic load uniformly across available neighbor cells. In our test scenarios evaluations, it is found that on average prioritized handovers provide 44% higher throughput and 42% reduction in delay to QoS UEs compared to non-prioritized handovers approach. Besides the integration of load balance approach and prioritized handovers in QAFT, it is able to maintain around 80% GBR satisfaction to all network UEs.

Adaptive Telemetry for Software-Defined Mobile Networks

The forthcoming set of 5G standards will bring programmability and flexibility to levels never seen before. This has required introducing changes in the architecture of mobile networks, enabling different features such as the split of control and data planes, as required to support the rapid programming of heterogeneous data planes. Software Defined Networking (SDN) has emerged as a basic toolset for operators to manage their infrastructure, as it opens up the possibility of running a multitude of intelligent and advanced applications for network optimization purposes in a centralized network controller. However, the very basic nature that makes possible this efficient management and operation in a flexible way—the logical centralization—poses important challenges due to the lack of proper monitoring tools, suited for SDN-based architectures. In order to take timely and right decisions while operating a network, centralized intelligence applications need to be fed with a continuous stream of up-to-date network statistics. However, this is not feasible with current SDN solutions due to scalability and accuracy issues. This article first analyzes the monitoring issues in current SDN solutions and then proposes a telemetry framework for software defined mobile networks capable of adapting to the various 5G services. Finally, it presents an experimental validation that shows the benefits of the proposed solution at alleviating the load on the control and data planes, improving the reactiveness to network events, and providing better accuracy for network measurements.

Adaptive Video Streaming With Edge Caching and Video Transcoding Over Software-Defined Mobile Networks: A Deep Reinforcement Learning Approach

Both mobile edge cloud (MEC) and software-defined networking (SDN) are technologies for next generation mobile networks. In this paper, we propose to simultaneously optimize energy consumption and quality of experience (QoE) metrics in video streaming over software-defined mobile networks (SDMN) combined with MEC. Specifically, we propose a novel mechanism to jointly consider buffer dynamics, video quality adaption, edge caching, video transcoding and transmission. First, we assume that the time-varying channel is a discrete-time Markov chain (DTMC). Then, based on this assumption, we formulate two optimization problems which can be depicted as a constrained Markov decision process (CMDP) and a Markov decision process (MDP). Then, we transform the CMDP problem into regular MDP by deploying Lyapunov technique. We utilize asynchronous advantage actor-critic (A3C) algorithm, one of the model-free deep reinforcement learning (DRL) methods, to solve the corresponding MDP issues. Simulation results are presented to show that the proposed scheme can achieve the goal of energy saving and QoE enhancement with the corresponding constraints satisfied.

PRACTICAL IMPLEMENTATION OF THE SOFTWARE DEFINED 5G MOBILE NETWORK BASED ON CLOUD-RAN AND SDR TECHNOLOGIES

The paper is devoted to the practical implementation aspects of a software-defined mobile network based on the Cloud-RAN architecture and universal software radio peripheral National Instruments USRP 2900. We propose a multilayer network architecture, which consists of a radio access network (RAN) plane, a core plane, a control plane, an artificial intelligence (AI) plane, and a monitoring system that collects data about network performance. The RAN plane provides all functions related to channel scheduling, data encoding and signal processing and combines all macro and small cells, as well as Wi-Fi access points. The core plane provides functionality of the evolved packet core such as service and packet gateways, mobility management entities and edge routers. The control plane is responsible for load balancing over all cells, spectrum sharing, spectrum reallocation, infrastructure reconfiguration, and other management parameters. The AI plane is responsible for inference and decisions making about optimal network configuration taking into account instantaneous network conditions. Such decisions are supported by a set of machine learning models, which are trained over the available statistics, collected through a long-term period of network operation. External monitoring system provides the needed data for AI plane by using the MQTT (Message Queue Telemetry Transport) protocol to collect information from all base stations, Wi-Fi access points and mobile devices.The proposed system enables the logical network slicing into multiple isolated segments over a single physical infrastructure by using virtualization of radio resources. A new method of intelligent radio resource management is proposed to improve the efficiency of spectrum utilization by predicting traffic demand of particular network sliced using deep recurrent neural network. Simulation results show that for the case of network slicing the proposed method allows to double the capacity of the physical network infrastructure, while ensuring the target quality of service for all users.

Joint resource allocation and admission control mechanism in software defined mobile networks

Joint resource allocation and admission control mechanism in software defined mobile networks

ARBAT: A flexible network architecture for QoE-aware communications in 5G systems

The expansion of the service scope of cellular networks to include a wide variety of services such as mobile broadband, Internet of Things, and mission-critical machine-type communications has significantly shaped the evolution towards 5G and beyond systems. All these services impose divergent and often mutually exclusive requirements in terms of data rate, latency, and energy efficiency. To satisfy heterogeneous requirements, 5G systems should have properties such as Quality-of-Experience awareness, adaptability and flexibility, scalability and reliability, support for multiple RATs, and backward compatibility, all at a low CAPEX and OPEX. To this end, software-defined networking and network function virtualization have been envisioned as key enabling technologies for 5G, and represent a major paradigm shift for 5G systems. In recent years, a plethora of software-defined mobile network architectures have been introduced worldwide, each with their unique features and drawbacks. Within this context, this paper introduces a new architecture called ARBAT which has been designed to satisfy and exceed the requirements put forth by 5G. ARBAT is characterized by many innovative features such as the Universal Network Device and Unified Cellular Network concepts, multi-slice modular resource management with the AirHYPE wireless hypervisor, network-user application interaction through the xStream platform, and simplified multi-tenant orchestration through ServiceBRIDGE. The novel features of the ARBAT infrastructure plane, data plane, control plane and Management and Orchestration entity are also explained in the paper in detail. Furthermore, a qualitative evaluation and feature comparison of ARBAT with other state-of-the-art architectures is conducted to demonstrate that ARBAT satisfies the aforementioned objectives of the 5G systems.

Enhancing Video Rate Adaptation With Mobile Edge Computing and Caching in Software-Defined Mobile Networks

Recent advances in software-defined mobile networks (SDMNs), in-network caching, and mobile edge computing (MEC) can have significant effects on video services in next generation mobile networks. In this paper, we jointly consider SDMNs, in-network caching, and MEC to enhance the video service in next generation mobile networks. We use a new video experience evaluation standard called U-video mean opinion score (vMOS), which is a more advanced measurement of the video quality based on the well-known vMOS. With the objective of maximizing the mean U-vMOS, an optimization problem is formulated. Due to the coupling of video data rate, computing resource, and traffic engineering (bandwidth provisioning and paths selection), the problem becomes intractable in practice. Thus, we utilize a dual-decomposition method to decouple those three sets of variables. By this decoupling, video rate adaptation is performed at users with network assistants. End nodes can schedule computing resource independently. Traffic engineering is performed by the software-defined networking controller and base stations. Furthermore, to address the challenges of dynamic change of network status and the drawbacks caused by the frequent exchange of information, we design a decentralized algorithm based on alternating direction method of multipliers to solve the traffic engineering problem. Extensive simulations are conducted with different system configurations to show the effectiveness of the proposed scheme.

Review of Traffic Forecasting Approaches in Software Defined Mobile Networks

Review of Traffic Forecasting Approaches in Software Defined Mobile Networks

Handover with Buffering for Distributed Mobility Management in Software Defined Mobile Networks

The rapidly-growing number of mobile subscribers has led to the creation of a large number of signalling messages. This makes it difficult to efficiently handle the mobility of subscribers in mobile cellular networks. The long-term evolution (LTE) architecture provides software-defined networking (SDN) to meet the requirements of 5G networks and to forward massive mobile data traffic. The SDN solution proposes separation of the control and data planes of a network. Centralized mobility management (CMM) is widely used in current mobile network technologies, such as 4G networks. One of the problems related to CMM is a single point of failure. To solve the problems of CMM and in order to provide for efficient mobility management, IETF has developed a solution called distributed mobility management (DMM), in which mobility is handled via the nearest mobility anchor. In this paper, we propose a DMM solution with handover operations for SDN-enabled mobile networks. The advantage of the proposed solution is that intra and inter handover procedures are defined with the data buffering and forwarding processes between base stations and mobility anchors. We adopt a simulation model to evaluate and compare the proposed solution with the existing solution in terms of handover latency, packet loss and handover failures.

Handover with Buffering for Distributed Mobility Management in Software Defined Mobile Networks

The rapidly-growing number of mobile subscribers has led to the creation of a large number of signalling messages. This makes it difficult to efficiently handle the mobility of subscribers in mobile cellular networks. The long-term evolution (LTE) architecture provides software-defined networking (SDN) to meet the requirements of 5G networks and to forward massive mobile data traffic. The SDN solution proposes separation of the control and data planes of a network. Centralized mobility management (CMM) is widely used in current mobile network technologies, such as 4G networks. One of the problems related to CMM is a single point of failure. To solve the problems of CMM and in order to provide for efficient mobility management, IETF has developed a solution called distributed mobility management (DMM), in which mobility is handled via the nearest mobility anchor. In this paper, we propose a DMM solution with handover operations for SDN-enabled mobile networks. The advantage of the proposed solution is that intra and inter handover procedures are defined with the data buffering and forwarding processes between base stations and mobility anchors. We adopt a simulation model to evaluate and compare the proposed solution with the existing solution in terms of handover latency, packet loss and handover failures.

Downlink user association and uplink scheduling for energy harvesting users in software-defined mobile networks

In this paper we consider a heterogeneous network which consists of a macro base station and some pico base stations utilizing massive MIMO and MIMO techniques, respectively. A central software-defined mobile network (SDMN) controller is adopted in order to provide user association and energy scheduling. The users are considered battery limited and are capable of simultaneous wireless information and power transfer (SWIPT) in order to harvest energy and address the energy shortage issue. These users harvest energy from the received signals in the downlink and consume it via their uplink communications. This paper deals with the downlink user association by jointly optimizing the overall sum-rate of the network and the harvested energy by introducing an appropriate utility function. In this regard, the optimum user association and power splitting factor for each user are calculated via the downlink optimization stage. Then, the process of uplink scheduling is defined as choosing the best users in each time epoch to transfer data as well as optimizing their transmit power by solving Lyapunov drift-plus-penalty function. Simulation results are provided in order to confirm the optimality of the proposed algorithm in comparison with the previous user association and uplink scheduling approaches in terms of providing fairness and battery management among users.

A chaotic grey wolf controller allocator for Software Defined Mobile Network (SDMN) for 5th generation of cloud-based cellular systems (5G)

There has been a significant increase in mobile data traffic in recent years and by the advent of the IoT (Internet of Things), it will continue to rise. Since the current wireless systems cannot handle this amount of traffic, the next generation of the mobile standard is planning to overcome the obstacles by using new technologies such as Cloud Computing, Software Defined Networking (SDN), and Network Function Virtualization (NFV). Cloud Computing will provide sufficient resources, required for the mobile networks, to handle the ever-increasing amount of traffic. Additionally, using SDN as well as NFV in cloud datacenters can lead to better manageability in the networks and easier development of network applications in the future. Since cloud datacenters as well as mobile networks are inherently large-scale networks, a single SDN controller (traditional SDN) cannot handle decision-making processes of these networks and control plane must be distributed between several software SDN controllers. Therefore, allocating SDN controllers efficiently in these networks becomes important, which can lead to lesser energy consumption, lesser CAPEX, and lesser OPEX. In addition, the highly variable pattern of these networks makes it necessary to allocate controllers dynamically. However, by the growth of the networks, dynamic allocation of controllers can turn into an NP-Hard problem and using metaheuristic algorithms instead of deterministic ones to solve this problem can show better results as well as better computation time. In this study, we proposed a metaheuristic-based framework for dynamic controller allocation for the 5th generation of mobile technology (5G). We simulated our framework in MATLAB, compared our framework with static allocation technique, and compared our algorithm with another metaheuristic algorithm—PSO. Simulation results show that our algorithm ameliorates computation time and the computed solutions for different Quality of Services (QoS) are feasible, acceptable, and accurate.

Secure communication channel architecture for Software Defined Mobile Networks

A Software-Defined Mobile Network (SDMN) architecture is proposed to enhance the performance, flexibility, and scalability of today’s telecommunication networks. However, SDMN features such as centralized controlling, network programmability, and virtualization introduce new security challenges to telecommunication networks. In this article, we present security challenges related to SDMN communication channels (i.e., control and data channel) and propose a novel secure communication channel architecture based on Host Identity Protocol (HIP). IPsec tunneling and security gateways are widely utilized in present-day mobile networks to secure backhaul communication channels. However, the utilization of legacy IPsec mechanisms in SDMNs is challenging due to limitations such as distributed control, lack of visibility, and limited scalability. The proposed architecture also utilizes IPsec tunnels to secure the SDMN communication channels by eliminating these limitations. The proposed architecture is implemented in a testbed and we analyzed its security features. The performance penalty of security due to the proposed security mechanisms is measured on both control and data channels.

Enhancing Security of Software Defined Mobile Networks

Traffic volumes in mobile networks are rising and end-user needs are rapidly changing. Mobile network operators need more flexibility, lower network operating costs, faster service roll-out cycles, and new revenue sources. The 5th Generation (5G) and future networks aim to deliver ultra-fast and ultra-reliable network access capable of supporting the anticipated surge in data traffic and connected nodes in years to come. Several technologies have been developed to meet these emergent demands of future mobile networks, among these are software defined networking, network function virtualization, and cloud computing. In this paper, we discuss the security challenges these new technologies are prone to in the context of the new telecommunication paradigm. We present a multi-tier component-based security architecture to address these challenges and secure 5G software defined mobile network (SDMN), by handling security at different levels to protect the network and its users. The proposed architecture contains five components, i.e., secure communication, policy-based communication, security information and event management, security defined monitoring, and deep packet inspection components for elevated security in the control and the data planes of SDMNs. Finally, the proposed security mechanisms are validated using test bed experiments.

Opportunities and Challenges of Software-Defined Mobile Networks in Network Security

Software-defined mobile network (SDMN) architecture integrates software-defined networks, network functions virtualization, and cloud computing principles in mobile networking environments to transform rigid and disparate legacy mobile networks into scalable and dynamic ecosystems. However, because SDMN architecture separates the control and data planes, it will significantly change the way security is managed and applied for mobile networks. In this article, the authors discuss the security challenges, vulnerabilities, and opportunities that need to be investigated and addressed for future SDMNs. It also highlights how common security threats in IP networks such as the Internet are now applicable in new open and IP-based SDMNs.