In this paper, we consider an Internet-of-Things network supported by several satellites and multiple cache-assisted unmanned aerial vehicles (UAVs). Due to the long-distance transmission and detrimental effects from the transmission environment, the latency can be extremely high, especially in the presence of backhaul congestion. Therefore, we formulate an optimisation problem with the aim of minimising the total network latency. To reduce the complexity of the original problem, it is divided into three sub-problems, namely, clustering ground users associated with UAVs, cache placement in UAVs (to support the network in avoiding backhaul congestion), and power allocation for satellites and UAVs. We propose a distributed optimisation method consisting of: a non-cooperative game is designed to obtain the solution to the clustering problem; a genetic algorithm, which is powerful in the scenario of many variables, is employed to obtain the optimal solution to the high-complexity caching problem; and a quick estimation technique is used for power allocation. Additionally, a centralised optimisation method is presented as a benchmark. Simulation results show that although the distributed method leads to network latency of approximately 30% higher than the centralised method, it takes significantly less time to execute and is suitable for systems requiring strict real-time computing constraints. Furthermore, the numerical results prove the efficiency of our methods compared with other conventional ones.

Abstract A digital twin (DT) framework for Internet‐of‐thing (IoT) networks is proposed where unmanned aerial vehicles (UAVs) acting as flying mobile edge computing (MEC) servers support the task offloading on the fly. The considered DT model is very well suitable for industrial automation with the strict constraints of mission‐critical services' ultra‐reliable low‐latency communication (URLLC) links. To support low‐latency IoT devices, we formulate the end‐to‐end (e2e) latency minimisation problem of digital twin‐aided offloading UAV‐URLLC. Specifically, the minimised latency is obtained by jointly optimising both communication and computation parameters, namely power, offloading factors, and the processing rate of IoT devices and MEC‐UAV servers. Due to the highly non‐convex optimisation problem, we first consider the K‐means clustering algorithm to optimally deploy the on‐demand UAVs. Then, an alternative optimisation approach combined with appropriate inner approximations is effectively exploited to tackle this challenge. We demonstrate the effectiveness of the proposed DT framework through representative numerical results.

This article proposes an optimization framework for power and time resource allocation during time sharing non-orthogonal multiple access (TS-NOMA) transmissions performed by an unmanned aerial vehicle (UAV) in the context of a large-scale scenario. The objective of the proposed UAV-TS-NOMA system and optimization framework is to jointly maximize the energy efficiency (EE) and the downlink throughput fairness among users within the UAV communication range. The idea behind is to propose a communication system that: i) merges the advantages of UAV communications with the ones offered by the TS-NOMA paradigm and ii) maximizes the EE and the downlink fairness among users. The resulting model finds applicability in performing energy efficient and throughput fair transmissions into power-constrained communication scenarios. Performance investigations regarding the proposed framework in finding the optimal set of resources which maximizes jointly the above mentioned network metrics, have shown the advantage of the proposed two-step optimization framework in finding the optimal configuration of both power and time resources, respecting both the power constraints at the transmitter and the quality-of-service requirement of the users. In addition, it is shown how under particular conditions the proposed framework jointly optimizes the aforementioned network metrics in only one step.

In this paper, the secrecy performance of cooperative heterogeneous networks with unreliable backhaul over Nakagami- m fading channels is investigated. To secure the proposed system, a friendly jammer is considered to confuse eavesdroppers. To transmit the signals from the source to the destination, a two-phase transmitter/relay selection scheme is proposed. The best transmitter is selected when the signal-to-noise ratio at the relays is maximized. In the second phase, the best relay is chosen when the jamming signal-to-interference-plus-noise ratio of the eavesdroppers is minimized. To investigate the system performance, closed -form expressions are derived for the secrecy outage probability, ergodic capacity and non-zero achievable secrecy rate. In order to gain an insight into the system, asymptotic analysis is also provided. The results show that the degree of cooperative transmission and backhaul reliability are key parameters in the system and these parameters determine the secrecy performance.

In this research, the authors investigate the outage probability (OP) of the secondary network in a cognitive wireless powered communication network. Energy‐constrained secondary users harvest energy from a hybrid access‐point (H‐AP) and the primary transmitter in the first phase. In the second phase, they select a secondary user based on two different schemes, namely the best uplink channel selection (UCS) and the minimal interference channel selection (MICS), to transfer information to the H‐AP. The secondary network can share the same spectrum with the primary network ensuring that a desired OP constraint in the primary network is always met. This constraint represents the quality‐of‐service (QoS) of the primary network. The analytical expressions and asymptotic expressions of the OP of the secondary network are derived. The result shows that increasing the number of secondary users can considerably improve system performance. The transmit power of the selected secondary user, energy harvesting time and relaxing the QoS constraint of the primary network have a significant impact on the OP of the secondary network. The results show that UCS outperforms MICS.

In this paper, we investigate the effect of of the primary network on the secondary network when harvesting energy in cognitive radio in the presence of multiple power beacons and multiple secondary transmitters. In particular, the influence of the primary transmitter's transmit power on the energy harvesting secondary network is examined by studying two scenarios of primary transmitter's location, i.e., the primary transmitter's location is near to the secondary network and the primary transmitter's location is far from the secondary network. In the scenario where the primary transmitter locates near to the secondary network, although secondary transmitter can be benefit from the harvested energy from the primary transmitter, the interference caused by the primary transmitter suppresses the secondary network performance. Meanwhile, in both scenarios, despite the fact that the transmit power of the secondary transmitter can be improved by the support of powerful power beacons, the peak interference constraint at the primary receiver limits this advantage. In addition, the deployment of multiple power beacons and multiple secondary transmitters can improve the performance of the secondary network. The analytical expressions of the outage probability of the secondary network in the two scenarios are also provided and verified by numerical simulations.

Internet Protocol Television (IPTV) is a key growth application for Internet traffic. The combination of real-time services such as video and voice along with best effort Internet access introduces challenging Quality of Service (QoS) issues. In order to meet the QoS requirements, smart resource allocation is required, in particular for IPTV provision across wireless networks. In existing research, IPTV is generally described as a video application. This supports the high bandwidth characteristic of IPTV but not the low latency requirement of realtime transmission. In this work, we present a real-time IPTV utility function describing the application with respect to the stage of transmission. This accurately represents IPTV with high bandwidth, low latency characteristics. The function describes the relationship between allocated channel time and the user perceived quality of the transmission. In an example wireless networking scenario, the resource allocation solution illustrates accurate prioritization in accordance with the individual user requirements.

This article introduces a resource allocation solution capable of handling mixed media applications within the constraints of a 60 GHz wireless network. The challenges of multimedia wireless transmission include high bandwidth requirements, delay intolerance and wireless channel availability. A new Channel Time Allocation Particle Swarm Optimization (CTA-PSO) is proposed to solve the network utility maximization (NUM) resource allocation problem. CTA-PSO optimizes the time allocated to each device in the network in order to maximize the Quality of Service (QoS) experienced by each user. CTA-PSO introduces network-linked swarm size, an increased diversity function and a learning method based on the personal best, Pbest, results of the swarm. These additional developments to the PSO produce improved convergence speed with respect to Adaptive PSO while maintaining the QoS improvement of the NUM. Specifically, CTA-PSO supports applications described by both convex and non-convex utility functions. The multimedia resource allocation solution presented in this article provides a practical solution for real-time wireless networks.

Throughput maximization is generally the major objective when allocating resources in orthogonal frequency division multiple access (OFDMA)networks. Traditionally, dynamic allocation methods were developed to exploit multi-user diversity in these networks. These techniques achieved significant gain in throughput by adopting relaxed convex models to define system upper bound capacity. Frequency diversity, on the other hand, is only considered to a certain extent in order to meet user service constraints. The vast majority of existing research relies on these techniques. Separately, research considering combining frequency diversity and multi-user diversity in full has been scarce. The results of our research in this paper show that using this dual diversity combining can substantially maximize system capacity and resource efficiency, and minimize outage probability whilst users' quality of service (QoS) demands are maintained.

The authors propose a novel and practical approach to estimate resources and perform a distributed admission control in multi-hop ad hoc networks based on multi-rate enabled IEEE 802.11 technology. The main challenge is to determine if there exist sufficient resources [i.e. the available bandwidth (AB)] for a new incoming flow to be admitted rather than quantifying the exact amount of existing resources. In order to determine the AB along a multi-hop path, the authors take into consideration the channel rate at each hop as well as the channel idle ratio of relevant neighbouring nodes. Furthermore, the admission control is performed at the same time as the AB is determined which minimises overhead. The proposed approach can be applied hop-by-hop in a distributed manner by the end-user, thus being suitable for wireless ad hoc networks. Analysis and simulation based on the Network Simulator version 2 (NS2) platform verify the proposed approach.

Multimedia applications place high demands on resources in wireless networks. The 60 GHz frequency band has been adopted to support high data rate applications in wireless personal area networks (WPANs). In the IEEE 802.15.3c standard for millimeter-wave-based high-rate WPANs, devices are allocated a dedicated transmission time called a CTA (channel time allocation) based on their request to transmit. While the duration of a CTA may be adjusted, the standard does not specify how resource allocation should be achieved. High-Definition (HD) video with variable bit rate (VBR) is a potential application for the WPAN. In this work, an IEEE 802.15.3c network is simulated and the throughput losses due to static resource provisioning for a series of H.264/SVC (MPEG-4 Part 10) HD video sources are identified. It is demonstrated that the high data rates proposed for the 60 GHz frequency band will not be achieved without dynamic resource allocation.

To determine the end-to-end throughput capacity of a multi-hop route in wireless networks, existing work either use a simplistic approach to divide the 1-hop throughput capacity by the number of contending links in the bottleneck region, which has limitations in terms of accuracy, or rely on complicated non-linear equations, which is impractical to solve for a large number of hops. In this paper, we present an optimization methodology to analytically calculate the end-to-end throughput capacity of IEEE 802.11-based multi-hop wireless networks. The calculation considers the interference due to neighboring nodes and assess the impact of hidden node collision as well as multi-rate terminals (i.e., nodes can transmit at different rates) on throughput capacity. The proposed methodology provides a very accurate calculation of the end-to-end throughput capacity when compared to existing work, and yet it is more practical to implement.

A distributed approach to perform admission control in multi-hop ad hoc networks is proposed. Existing algorithms normally compute the total amount of available resources in the network, however our method evaluates if there are sufficient resources to satisfy the bandwidth demand of a new incoming flow rather than quantifying the total amount, which is impractical when considering implementation. Our methodology also considers multi-rate scenarios and can be implemented hop-by-hop in a distributed manner, which makes the approach scalable and suitable for wireless ad hoc networks. The distributed admission control algorithm introduced in this paper is assessed via analysis and simulation.

The identification and classification of network traffic and protocols is a vital step in many quality of service and security systems. Traffic classification strategies must evolve, alongside the protocols utilising the Internet, to overcome the use of ephemeral or masquerading port numbers and transport layer encryption. This research expands the concept of using machine learning on the initial statistics of flow of packets to determine its underlying protocol. Recognising the need for efficient training/retraining of a classifier and the requirement for fast classification, the authors investigate a new application of k-means clustering referred to as ‘two-way’ classification. The ‘two-way’ classification uniquely analyses a bidirectional flow as two unidirectional flows and is shown, through experiments on real network traffic, to improve classification accuracy by as much as 18% when measured against similar proposals. It achieves this accuracy while generating fewer clusters, that is, fewer comparisons are needed to classify a flow. A ‘two-way’ classification offers a new way to improve accuracy and efficiency of machine learning statistical classifiers while still maintaining the fast training times associated with the k-means.

Data identification and classification is a key task for any Internet Service Provider (ISP) or network administrator. As port fluctuation and encryption become more common in P2P applications wishing to avoid identification, new strategies must be developed to detect and classify their flows. This article introduces a method of separating P2P and standard web traffic that can be applied as part of an offline data analysis process, based on the activity of the hosts on the network. Heuristics are analyzed and a classification system proposed that focuses on classifying those “long” flows that transfer most of the bytes across a network. The accuracy of the system is then tested using real network traffic from a core Internet router showing misclassification rates as low as 0.54% of flows in some cases. We expand on this proposed strategy to investigate its relevance to real-time, early classification problems. New proposals are made and the results of real-time experiments are compared to those obtained in the offline analysis. It is shown that classification accuracies in the real-time strategy are similar to those achieved in offline analysis with a large portion of the total web and P2P flows correctly identified.

With the increasing quantity and varying nature of traffic crossing the internet, coupled with techniques such as fluctuating port numbers and transport layer encryption, the identification of individual packet flows is becoming more difficult. We introduce and investigate a new method for the detec

In this paper we present the hardware architecture and implementation of a tag computation circuit for a credit based Self-Clocked Fair Queuing (SCFQ) scheduler specifically targeted for packet scheduling in Broadband Wireless Access (BWA) as described in the recently released IEEE 802.16 Standards. Our objective is the implementation of a configurable scheduler that is based on the principles of weighted fair queuing combined with a credit based bandwidth reallocation scheme. The implementation provides the hardware platform for a runtime configurable scheduling architecture that is able to reallocate bandwidth on the fly if particular links should suffer packet loss due to unexpected noise or channel quality degradation. The system is implemented using FPGA technology and provides extended programmability to adapt the tag computation to a range of custom scheduling schemes. The hardware architecture is parallel and pipelined enabling an aggregated throughput rate of 180 million tag computations per second. The throughput performance is ideal for BWA nodes, allowing room for relatively complex computations in QoS aware adaptive scheduling. The high-level system breakdown is described and synthesis results for Xilinx FPGA technology are presented.

Summary form only given. We present the hardware architecture and implementation of a reconfigurable tag computation circuit for terabit packet scheduling for future QoS aware core routers. The presented implementation provides a platform for a runtime configurable scheduling architecture that is able to reallocate bandwidth on the fly. The system is implemented using FPGA technology and provides extended programmability to adapt the tag computation to a range of custom packet scheduling policies. The hardware architecture is parallel and pipelined enabling an aggregated throughput rate of 175 million tag computations per second, easily out performing current QoS router solutions. The high-level system breakdown is described and synthesis results for Altera FPGA technology are presented.

Providing guaranteed quality of service (QoS) in wireless asynchronous transfer mode (WATM) networks is especially challenging due to the characteristically poor quality of the transmission media. The provision and prediction of QoS, which is necessary in order to satisfy the requirements of the interconnected fixed ATM network, and the many services it must support, is directly influenced by the design of the lower protocol layers such as the ATM and MAC layers. Identification of the functional requirements of these layers for supporting QoS are described and more specifically, the QoS degradation due to buffering delays. However the dynamically changing and multi-service class nature of WATM traffic also has a major effect upon the QoS. A novel modelling approach is presented whereby a call level-cell level model allows us to study the performance of different buffering techniques in the WATM access points (APs).