Real-time kinematic (RTK) positioning is a widely used technique that improves positioning accuracy to the centimeter level. In RTK systems, users can utilize the correction data provided by a reference station (RS) to offset their location errors. However, due to the limited deployment and high cost of RS, some places are not covered by physical RS (within 30 kilometers). Therefore, a virtual reference station (VRS) RTK service is provided, where a server generates VRS for users and provides seamless cloud-based network RTK corrections without the need of a real RS. Despite the benefits of VRS services, it poses a risk to user location privacy, as users need to continuously report their standalone calculated positions to the server to obtain the service. In this paper, we propose a privacy-preserving approach for VRS services by multicasting correction data over mobile networks, where joined users can receive valid correction data from the base station in either inactive or connected mode. Experiment results show that the network delay is less than 40ms, throughput is about 16kbps, and the RTK positioning error is 4cm. Other qualitative analyses (such as service continuity and stability) also prove the feasibility and effectiveness of the proposed method.

With the advancement of Internet of Things (IoT) applications, it is essential to utilize an IoT platform to facilitate data exchange and application deployment. Existing platforms are typically either data-cloud-based or data-centralized, relying on servers as repeaters to exchange data. However, these architectures often face limitations related to triangle routing, network bottlenecks, and data scalability challenges, particularly in AIoT (Artificial Intelligence of Things) applications that require the fusion of numerous high-volume data streams. These challenges can be significantly mitigated through data-decentralized direct sender-to-receiver exchanges with a remote 'Agent', which is responsible for connectivity management. This work presents a prototype data-decentralized AIoT platform (DAIoTtalk) featuring peer-to-peer communications empowered by customized gRPC remote procedure calls based on the publish-subscribe (pub-sub) paradigm. An extension of IoTtalk, DAIoTtalk ensures device management with more adaptable node networking and offers a test bed for low-code development with decentralized communications. We demonstrate through extensive experiments that our design achieves at least 3 times more efficiency than a data-centralized approach. We also develop a case study to showcase the flexibility of our platform.

Train delays have been a serious persisting problem in the UK and also many other countries. Due to increasing demand, rail networks are running close to their full capacity. As a consequence, an initial delay can cause many knock-on delays to other trains, and this is the main reason for the overall deterioration in the performance of the rail networks. Therefore, it is really useful to have an AI-based method that can predict delays accurately and reliably, to help train controllers to make and apply alternative plans in time to reduce or prevent further delays, when a delay occurs. However, existing machine learning models are not only inaccurate but more importantly unreliable. In this study, we have proposed a new approach to build heterogeneous ensembles with two novel model selection methods based on accuracy and diversity. We tested our heterogeneous ensembles using the real-world data and the results indicated that they are more accurate and robust than single models and state-of-the-art homogeneous ensembles, e.g. Random Forest and XGBoost. We then verified their performances with an independent dataset from a different train operating company and found that they achieved the consistent and accurate results.

Thanks to mobility and large coverage, 6G mobile networks introduce satellites and unmanned aerial vehicles as aerial base stations (ABS) in the 6G era. Instead of using a wired backhaul in 5G and its predecessor, an ABS leverages a wireless channel to a core network (CN). However, such a wireless channel design introduces new security challenges. In this paper, we present that passive attackers could sniff the ABS-CN wireless channel and identify what users are doing based on deep learning methods. We collect GTP protocol data on our testbed and use convolutional neural networks to classify 5 types of encrypted App traffic, like IG and TikTok. Experiment results proved the effectiveness of the proposed method, revealing the confidential data leakage problem on the 6G wireless ABS-CN channel.

Learning discriminative and invariant feature representation is the key to visual image categorization. In this article, we propose a novel invariant deep compressible covariance pooling (IDCCP) to solve nuisance variations in aerial scene categorization. We consider transforming the input image according to a finite transformation group that consists of multiple confounding orthogonal matrices, such as the D4 group. Then, we adopt a Siamese-style network to transfer the group structure to the representation space, where we can derive a trivial representation that is invariant under the group action. The linear classifier trained with trivial representation will also be possessed with invariance. To further improve the discriminative power of representation, we extend the representation to the tensor space while imposing orthogonal constraints on the transformation matrix to effectively reduce feature dimensions. We conduct extensive experiments on the publicly released aerial scene image data sets and demonstrate the superiority of this method compared with state-of-the-art methods. In particular, with using ResNet architecture, our IDCCP model can reduce the dimension of the tensor representation by about 98% without sacrificing accuracy (i.e., < 0.5%).

Most Information Technology (IT) services nowadays rely on one or multiple data centers. In this paper, we propose a Quality of Service (QoS) scheme for Intra/Inter data center communications systems. The proposed scheme is based on differentiation of traffic into different class categories. The differentiation is based on the source of traffic as well as the specific traffic's requirements. We conduct a comprehensive performance study for the proposed scheme. Our results demonstrate the benefits of deploying a QoS scheme in data centers especially if there is a considerable number of live virtual machine migrations among its servers. By completing such migration quickly, the Quality of Experience (QoE) will not degrade and the workload will be kept balanced among all the servers in the Data Center. Additionally, our proposed scheme considers the live virtual machine migrations among different Data Centers which is currently rapidly increasing.

Connected Communities (CCs) are socio-technical systems that rely on an information and communication technology (ICT) infrastructure to integrate people and organizations (companies, schools, hospitals, universities, local and national government agencies) willing to share information and perform joint decision-making to create sustainable and equitable work and living environments. We discuss a research agenda considering CCs from three distinct but complementary points of view: CC metaphors, models, and services.

We focus on feature extraction and selection to best represent texture and shape properties of plant diseases in an image-based leaf monitoring system implemented in a mobile-cloud environment. A number of textural and region-based features are aggregated from previous studies; also we introduce mean and peak indices of histogram-of-shape as disease property representations along with the proposed and enhanced shape features based on diseased regions. A total of 260 colour-based attributes and 163 shape attributes are searched to find the best potential features based on different aspects: probability of feature error, correlation, targeted-class relevancy and the separability quality of a feature. Experimental results show that the best selected feature set which combines colour-based and shape features yields high classification accuracy on wheat disease images captured by a smartphone camera and also provides insights into potential sets of features to be further implemented as a lightweight standalone mobile application.

Future 5G networks have stringent end-user requirements on data rate and error performance. In order to satisfy these requirements, innovative wireless networking technologies and models need be researched. One particular example is the two-way relaying channel, which can have as much as 100% higher theoretical data rate than current systems where transmissions are arranged in an orthogonal manner. However, benefits of this model cannot be achieved without the application of proper relaying protocols. This paper proposes a novel protocol that directly addresses the problems of existing protocols of two-way relaying models, e.g. analogy network coding and physical network coding, and has improved performance. By combining direct and differential demodulation-forward schemes based on wireless channel qualities and signal to noise ratio, a new hybrid protocol is created. Theoretical analysis and numerical experiments show that the proposed solution has lower error rate than the existing ones, and can thus be applied to support future 5G networks.

Two-Way Relay Channel (TWRC) plays an important role in relay networks, and efficient relaying protocols are particularly important for this model. However, existing protocols may not be able to realize the potential of TWRC if the two independent fading channels are not carefully handled. In this paper, a Hybrid DeModulate-Forward (HDMF) protocol is proposed to address such a problem. We first introduce the two basic components of HDMF - direct and differential DMF, and then propose the key decision criterion for HDMF based on the corresponding log-likelihood ratios. We further enhance the protocol so that it can be applied independently from the modulation schemes. Through extensive mathematical analysis, theoretical performance of the proposed protocol is investigated. By comparing with existing protocols, the proposed HDMF has lower error rate. A novel scheduling scheme for the proposed protocol is introduced, which has lower length than the benchmark method. The results also reveal the protocol's potential to improve spectrum efficiency of relay channels with unbalanced bilateral traffic.

Mobile cloud computing aims at improving user experience through enhancing the ability of mobile applications by doing intensive tasks in the cloud. In this paper we consider an environment similar to a hybrid cloud in which the mobile device works as a private cloud. Given that the mobile phone has both limited processing resources and battery time, the proposed mobile application architecture has been designed with the capability of sending specified data/parameters to the cloud. This data is subsequently used for further processing/mining and visualization to assist in inferring further information through mapreduce. This information gives details about resource and battery consumption which will help in optimizing the relationship between the mobile device and cloud. It will also be beneficial to the optimization of the mobile application through the trends visualized in the cloud. In this paper we created an activity recognition health application as an example and helped the user about his health along with giving an insight into abnormal behavior and lifestyle trends.

The essential broadcasting feature of radio propagation channels provides an opportunity for multiple nodes to exchange information and work cooperatively, where each node, for example, mobile sensor or robot, plays both the role of transmitter and receiver. Especially in such machine‐to‐machine communication scenarios, the network performance and redundancy of information from different providers highly affect the work efficiency of each individual node and the whole system. To address these problems, a relay assisted centralised network model with physical layer network coding implemented in the relay is proposed in this study. This structure has the advantage of flexible data exchange and the capability to reduce redundancy in information. Its theoretical performance is analysed by the diversity multiplexing tradeoff, which proves the proposed model is versatile in reliable and high spectral‐efficiency information exchange. Experiments of multiple nodes in a machine‐to‐machine scenario – unmanned aerial vehicles, further reveal its potential in improving efficiency of communication and cooperation.

The efficient management of networks and the provisioning of services with desired QoS guarantees is a challenge which needs to be addressed through autonomous mechanisms which are intelligent, lightweight and scalable. Recent focus on applying Machine Learning approaches to model the network and service behavioural patterns have proved to be quite effective in fulfilling the objectives of autonomous management. To this end, this paper advances on the idea of implementing a distributed management solution which harnesses the predictive capability of Bayesian Networks (BN). A multi-node distributed Call Admission Control solution (termed as BNDAC) is proposed and implemented to demonstrate the modelling and prediction power of BN. A thorough evaluation of BNDAC is presented in terms of its prediction accuracy, algorithmic complexity and decision-making speed. In an online setup, performance of BNDAC is evaluated and compared with a centralised scenario, to demonstrate its superior performance for Call Blocking Probability and QoS provisioning. Simulation results based on Opnet Modeler and Hugin Researcher show the feasibility and applicability of BNDAC solution for real-time operation and management of real world networks such as the NGN.

The papers in this special issue focus on research and field trials of unmanned autonomous vehicles on land, in the air and underwater. The suite of selected papers covers key challenges that impact on the communications dynamics and behaviour of unmanned autonomous vehicles of varying size and resource capability and address UAV bridging, topology maintenance,path planning and link performance optimisation in highly changeable deployments.

Resource allocation in the cloud is usually driven by performance predictions, such as estimates of the future incoming load to the servers or of the quality-of-service(QoS) offered by applications to end users. In this context, characterizing web workload fluctuations in an accurate way is fundamental to understand how to provision cloud resources under time-varying traffic intensities. In this paper, we investigate the Markovian Arrival Processes (MAP) and the related MAP/MAP/1 queueing model as a tool for performance prediction of servers deployed in the cloud. MAPs are a special class of Markov models used as a compact description of the time-varying characteristics of workloads. In addition, MAPs can fit heavy-tail distributions, that are common in HTTP traffic, and can be easily integrated within analytical queueing models to efficiently predict system performance without simulating. By comparison with traced riven simulation, we observe that existing techniques for MAP parameterization from HTTP log files often lead to inaccurate performance predictions. We then define a maximum likelihood method for fitting MAP parameters based on data commonly available in Apache log files, and a new technique to cope with batch arrivals, which are notoriously difficult to model accurately. Numerical experiments demonstrate the accuracy of our approach for performance prediction of web systems.

This paper reports on an investigation into bit-rate adjustment on high capability metro and core routers. The bit-rate is controlled in response to traffic load using EWMA (Exponential Weighted Moving Average), with parameters set to minimize the performance impact of the adjustments. The purpose of the adjustment is to enable energy savings. Our calculations show that 22.5% power saving is achievable by bit rate adjustment without significant performance degradation.

The advent of IP-based Next Generation Network (NGN) and its guaranteed QoS promise has attracted significant attention from both service providers and subscribers. However, to fulfil the said promise, there is a need to provide effective Call Admission Control (CAC) based QoS provisioning solutions which are autonomous, intelligent and scalable.

Given the growing complexity and energy consumption of customer premises equipment (CPE) in smart home networks and the consequent need for optimal energy usage, there is a need for better coordination of the power operational states of the CPE devices. In particular, this paper presents an energy model to synchronise the behaviour of a Home-Gateway (HG) with the CPE such as a set-top box with exemplar data flow such as IPTV. In addition, we explore the policy engine to monitor and control the energy consumption as a function of application QoS, traffic load, availability and link health across the networks, paying particular attention to the CPE device transitional power states. Our approach is, therefore, better suited for providing guaranteed quality and at the same time is more energy efficient.

Hybrid optical and wireless technology integrations have been considered as one of the most promising candidates for the next generation broadband access networks for quite some time. The integration scheme provides the bandwidth advantages of the optical networks and mobility features of the wireless networks for Subscriber Stations (SSs). It also brings economic efficiency to the network providers particularly in rural area where the existing wired telecommunication infrastructures such as Digital Subscriber Line (DSL), Cable Modem (CM), T-1/E-1 networks or fibre deployments are either costly or unreachable. For successful integration of the optical and wireless technologies there are some technical issues which need to be addressed efficiently in order to provide End-to-End (ETE) and diverse Quality of Service (QoS) for various service classes. This paper investigates the possible challenging issues for the integrated structure of the Time Division Multiplexing and Wavelength Division Multiplexing Ethernet Passive Optical Networks (TDM EPON and WDM EPON ) with the Worldwide Interoperability for Microwave Access and Wireless Fidelity (WiMAX and Wi-Fi) networks. To reduce the ETE delay and provide the QoS for diverse service classes, we have compared six existing upstream scheduling mechanisms in two levels which are distributed on Access Points (APs) from Wi-Fi domain and Base Stations (BSs) from WiMAX domain. Performance evaluations of the existing scheduling techniques for three popular service classes (Quad-play) have been studied which show the strong impact of using the efficient up-link scheduler in converged scenario. We have also proposed a dynamic scheduling algorithm for optical and wireless integration scheme, which is under the implementation and evaluation process.

Carbon emissions are receiving increased attention and scrutiny in all walks of life and the ICT sector is no exception. With the increase in on-demand applications and services together with on-demand compute/storage facilities in server farms or data centres there are self-evident increases in the power requirements to maintain such systems. Proponents of the impact of increased carbon emissions when powering electrical systems in general however, regularly impress negative side-effects such as influence on climate change. Action is subsequently being encouraged to halt further environmental damage. The problem is explored in this paper from the point of view of carbon emissions from data centre operations and the development of energy-aware management and energy-efficient networking solutions. Data centre energy consumption costs drive the evaluation process within a Data Centre Energy-Efficient Context-Aware Broker (DCe-CAB) algorithm designed as an original solution to this significant carbon-contributing network scenario. In this paper, performance requirements and objectives of the DCe-CAB are defined, along with case study demonstration of the way in which it optimises selection and operation of data centres using context-awareness.

As fixed and wireless access networks converge towards Internet Protocol based transport in next generation networks, the requirement for effective and scalable control and management solutions to address the complexity introduced by their heterogeneity becomes ever more critical. Over the years, policy-based management has emerged as a viable tool to address this challenge. Within the IU-ATC project, we are developing a policy-based framework, (Converged Networks QoS Framework, CNQF) for end-to-end QoS control and resource management in converged next generation networks. CNQF is designed to support QoS control and context-driven resource management policies across all networks (access, metro, core) on the end-to-end transport layer of converged networks. This paper describes CNQF and develops an exemplary use case scenario for policy-based resource management based on our CNQF architecture. The paper also presents the development of stochastic-analytic models to characterise and evaluate the impact of policies on operational performance. The model is used to analyse the performance of dynamic CNQF context-driven policies for admission control case study.

Enterprise IT environments have seen a sharp growth in content use due to the popularity of on-demand data-intensive applications. In turn, the huge demand in content has spawned off major developments such as growth and distribution of computing nodes as well as the adoption of various implementation technologies. Given the complexity brought to the makeup of business computing environments in addressing the above-mentioned factors, the critical planning task of determining the appropriate infrastructure sizes for supporting firm Quality of Service (QoS) guarantees becomes a very challenging undertaking to fulfil. Benchmarking methods are widely employed in calibrating attainable performance in IT solutions, but these have the drawback of presenting output performance metrics as composite measurements that only give an end-to-end perspective. As an enhancement to benchmarking approaches, we explore the use of Performance Monitoring Counters (PMCs) in obtaining detailed operational performance of CPU and memory hardware. Performance Monitoring Counters (PMCs) are onchip registers found on most modern processor hardware. We use PMC-derived measurements to validate cache performance trends that have been derived analytically, and in the course of validations, PMC data is also used to investigate the nature and character of surges in cache miss events, which emerge as the memory load generated by runtime processes increases.

The importance of providing guaranteed Quality of Service (QoS) cannot be overemphasised, especially in the NGN environment which supports converged services on a common IP transport network. Call Admission Control (CAC) mechanisms do provide QoS to class-based services in a proactive manner. However, due to the factors of complexity, scale and dynamicity of NGN, Machine Learning techniques are favoured to analytical approaches for providing autonomous CAC. This paper is an effort to compare the performance of two such approaches - Neural Networks (NN) and Bayesian Networks (BN), to model the network behaviour and to estimate QoS metrics to be used in the CAC algorithm. It provides a way to find the optimum model training size for accurate predictions. Performance comparison is based on a wide range of experiments through a simulated network in Opnet. The outcome of this comparative study provides some interesting insights into the behaviour of NN and BN models and how they can be utilised for better CAC implementations.

This paper presents a framework for context-driven policy-based QoS control and end-to-end resource management in converged next generation networks. The Converged Networks QoS Framework (CNQF) is being developed within the IU-ATC project, and comprises distributed functional entities whose instances co-ordinate the converged network infrastructure to facilitate scalable and efficient end-to-end QoS management. The CNQF design leverages aspects of TISPAN, IETF and 3GPP policy-based management architectures whilst also introducing important innovative extensions to support context-aware QoS control in converged networks. The framework architecture is presented and its functionalities and operation in specific application scenarios are described.

The idea of carrying voice traffic on IP networks has been found to be very lucrative. However to achieve a quality similar to that offered by the existing telephone networks, the IP network should be in a position to honour the stringent quality of service (QoS) requirements of voice traffic. If traffic in excess of the network capacity is admitted into the network, QoS may be violated resulting in performance degradation. One way of providing sustained and consistent QoS is by regulating the number of voice calls that are admitted into the network such that the load on the network is less than or equal to the capacity of the network. This mechanism is known as call admission control. This paper proposes a scalable and distributed call admission control algorithm that operates at the network edge and factors the local passive measurements into the admission decision. Performance evaluation of the proposed algorithm through ns2 simulations reveals that it is successful in detecting rate mismatches (input rate greater than output rate) and subsequently rejecting admission requests (as long as input rate is greater than output rate) thereby delivering on the QoS guarantees demanded by voice applications. Moreover, it is simple and lightweight from an implementation perspective.

The quest for better resource control has been the driving force behind Active Queue Management (AQM) research. Random Early Detection (RED), the defacto standard and its variants have been proposed as simple solutions to the AQM problem. These approaches, however, are known to suffer from problems like parameter sensitivity and inability to capture input traffic load fluctuations accurately, thereby resulting in instability. This paper presents a proactive queue management algorithm called PAQMAN that captures input traffic load fluctuations accurately and regulates the queue size around the desirable level. PAQMAN draws from the predictability in the underlying traffic by employing the Recursive Least Squares (RLS) algorithm to forecast the average queue size over the next prediction interval using the average queue size information of the past intervals. The packet drop probability is then computed as a function of this predicted average queue size. The performance of PAQMAN has been evaluated and compared against existing AQM schemes through ns-2 simulations that encompass varying network conditions for networks comprising of single as well as multiple bottleneck links. Simulation results demonstrate that PAQMAN maintains a relatively low queue size, while at the same time achieving high link utilization and low packet loss. Moreover, the computational overhead of PAQMAN is negligible (lightweight) which further justifies its use.

Running data grid applications such as high energy nuclear physics (HENP) and weather modelling experiments involves working with huge data sets possibly of hundreds of Terabytes to Petabytes in size often kept over wide area networks. Data replication is a useful technique for reducing latency across communication networks over which the source data are accessed. As a starting point towards developing a multifaceted optimisation solution for data grids, this paper considers the effect of replication and storage parameter settings on data grid performance. The simulation results we obtained suggest that replication at local (Tier2) nodes has significant impact on data grid performance while cache settings at remote (Tier 1) node result in minimal performance improvement.

We present a novel Service Level Agreement (SLA)-driven service provisioning architecture, which enables dynamic and flexible bandwidth reservation schemes on a per-user or per-application basis. Various session level SLA negotiation schemes involving bandwidth allocation, service start time and service duration parameters are introduced and analyzed. The results show that these negotiation schemes can be utilized for the benefit of both end users and network providers in achieving the highest individual SLA optimization in terms of key Quality of Service (QoS) metrics and price. The inherent characteristics of software agents such as autonomy, adaptability and social abilities offer many advantages in this dynamic, complex, and distributed network environment especially when performing Service Level Agreements (SLA) definition negotiations and brokering tasks. This article also presents a service broker prototype based on Fujitsu's Phoenix Open Agent Mediator (OAM) agent technology, which was used to demonstrate a range of SLA brokering scenarios.

This paper is to introduce the WLAN mobile networks and analyze the handover problem in WLAN mobile networks. We will propose a new solution of the integration of WLAN and mobile IPv6, which can realize the seamless mobile communications. In order to evaluate our solution, we had set up a physical environment of IPv6 WLAN for testing mobile communications. The testing results show that our new solution is stable and qualified for mobile communications running in WLAN.

Chameleon is 100% Java middleware for multimedia streaming to heterogeneous mobile clients, which allows the dynamic configuration of protocols with respect to application requirements and available network resources. We evaluate the dynamic reconfigurability of the middleware in order to demonstrate runtime adaptation. We specially concentrate on the secondary quality transformation technique (SQT) of the middleware which enables clients to not only subscribe to media groups in accordance with available resources and network capacity but to also adapt media quality within each quality group dynamically in accordance to resource constraints.

Fixed end-system protocols are unable to support the wide range of applications requirements on top of current networks without adding overhead in the form of unnecessary functionality for multiple combinations of application requirements and networks. The Real-time Wide Area Network Dissemination Architecture (RWANDA) protocol paradigm dynamically configures protocol stacks to support a wide range of application requirements and to increase performance. RWANDA provides an asynchronous foundation that is necessary for developing reliable large-scale Internet applications requiring timely guarantees such as remote surgery, teleconferencing and Internet telephony.

Executing a protocol P, a sending host S decides, through P's routing mechanism, that it wants to transmit to a target host T located somewhere on a connected piece of 10 Mbit Ethernet cable which conforms to IEEE 802.3. To actually transmit the Ethernet packet a 48-bit Ethernet/hardware address must be generated. The addresses assigned to hosts within protocol P are not always compatible with the corresponding Ethernet address (being different address space byte orderings or values). A protocol is presented which allows dynamic distribution of the information required to build tables that translate a host's address in protocol P's address space into a 48 bit Ethernet address. An extension is incorporated to allow such a protocol to be flexible enough to exist in a Transparent Bridge, or generic Host. The capability of the Bridge to detect host reboot conditions in a multi-LAN environment is also discussed, emphasising particularly the effect on channel bandwidth. To illustrate the operation of the protocol mechanisms, the ARPA Internet Protocol (IP) is used as a benchmark.6, 8* An introduction to Address Resolution is presented, followed by a discussion of a reboot detection process.