Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits deep learning models to extract hardware impairments to uniquely identify wireless devices. Recent studies show deep learning-based RFFI is vulnerable to adversarial attacks. However, effective adversarial attacks against different types of RFFI classifiers have not yet been explored. In this paper, we carried out a comprehensive investigations into different adversarial attack methods on RFFI systems using various deep learning models. Three specific algorithms, fast gradient sign method (FGSM), projected gradient descent (PGD), and universal adversarial perturbation (UAP), were analyzed. The attacks were launched to LoRa-RFFI and the experimental results showed the generated perturbations were effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU). We further used UAP to launch practical attacks. Special factors were considered for the wireless context, including implementing real-time attacks, the effectiveness of the attacks over a period of time, etc. Our experimental evaluation demonstrated that UAP can successfully launch adversarial attacks against the RFFI, achieving a success rate of 81.7% when the adversary almost has no prior knowledge of the victim RFFI systems.

This study addresses the persistent challenge of cross-dataset generalisability in intrusion detection systems by both assessing whether concatenating datasets improves generalisability and proposing AlignAD-VAE, a new unsupervised variational autoencoder model augmented with maximum mean discrepancy (MMD)-based alignment. The model aims to reduce the distribution shift between datasets by aligning their latent representations in a common feature space. We systematically evaluate AlignAD-VAE against modern architectures such as autoencoder and variational autoencoder baselines across multiple cross-dataset configurations using the CIC-IDS2017, CSE-CIC-IDS2018, and CIC-DDoS2019 datasets. Our experiments cover both single-dataset training and concatenated multidataset training, assessing model performance on completely unseen datasets. Concatenating training datasets improves generalisability by up to 10%, as it exposes models to a broader range of normal patterns and traffic variations, thereby reducing overfitting to dataset-specific artefacts. While all models benefit from the richer training data, AlignAD-VAE outperforms the VAE baseline by up to 2%, indicating that the integration of MMD-based domain alignment provides additional, although modest, improvements in cross-domain adaptation, as reflected in AUC-ROC, F1-score, and accuracy metrics. These findings highlight that combining diverse datasets with domain alignment can make IDS more robust to unseen network environments, a critical requirement for real-world deployment.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that is based on intrinsic hardware impairments. Internet of things (IoT) devices can be identified and classified based on their wireless signals using RFFI. Developing a robust RFFI system that can maintain high classification accuracy across diverse communication scenarios is a critical challenge. In this paper, we proposed a contrastive learning-based RFFI approach to establish a channel-robust system using the spectrogram. Specifically, we leverage contrastive learning in the training stage, which has been implemented with data augmentation techniques to mitigate the influence of channels on RFFI. We carried out extensive experimental evaluations involving a public dataset and a self-collected dataset, both with ten LoRa devices. Utilizing these datasets, the performance of the system has been tested in various channel environments, including stationary, mobile, line-of-sight (LOS) and non-line-of-sight (NLOS) scenarios. The results demonstrated that our approach is effective and robust to channel variation, achieving 93% and 82% on static and dynamic channels. respectively.

Node identification is the first line of defense for the security of wireless Internet-of-Things (IoT), which prevents illegal devices from accessing the network and launching attacks. Hardware features originating from innate hardware manufacturing imperfections are considered promising fingerprints for identification; among which, the hardware clock feature has been put under the spotlight due to its practicality and ease of extraction. However, current extractions of hardware clock features over wireless networks rely on the transmissions of time information, which, per se, enable significant vulnerabilities such as spoofing and replay attacks. In this paper, we propose a covert method to extract the hardware clock features, which does not rely on the insecure time information transmissions that are adopted in most existing schemes. We also analyze the security of the proposed covert extraction. We further propose SNICK, a secure node identification scheme based on our tailored implementation of covert clock feature extraction and machine learning. We implement and evaluate the proposed approach on a real IoT testbed consisting of a Long Range (LoRa) gateway and heterogeneous end nodes. We conduct experiments to prove the security of the proposed scheme and evaluate the proposed scheme under three scenarios: short-term, long-term, and cross-environment. Experimental results of three scenarios demonstrate average identification accuracies of 98.53%, 85.9%, and 88.3%. We further reveal the identification performance under parameter and environmental variations.

Radio frequency fingerprint identification (RFFI) is an emerging method for authenticating Internet of Things (IoT) devices. RFFI exploits the intrinsic and unique hardware imperfections for classifying IoT devices. Deep learning-based RFFI has shown excellent performance. However, there are still remaining research challenges, such as limited public training datasets as well as impacts of channel and receive effects. In this paper, we proposed a three-stage RFFI approach involving contrastive learning-enhanced pretraining, Siamese network-based classification network training, and inference. Specifically, we employed spectrogram as signal representation to decouple the transmitter impairments from channel effects and receiver impairments. We proposed an unsupervised contrastive learning method to pretrain a channel-robust RFF extractor. In addition, the Siamese network-based scheme is enhanced by data augmentation and contrastive loss, which is capable of jointly mitigating the effects of channel and receiver impairments. We carried out a comprehensive experimental evaluation using three public LoRa datasets and one self-collected LoRa dataset. The results demonstrated that our approach can effectively and simultaneously mitigate the effects of channel and receiver impairments. We also showed that pretraining can significantly reduce the required amount of the fine-tuning data. Our proposed approach achieved an accuracy of over 90% in dynamic non-line-of-sight (NLOS) scenarios when there are only 20 packets per device.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique, which exploits the hardware characteristics of the RF front-end as device identifiers. The receiver hardware impairments interfere with the feature extraction of transmitter impairments, but their effect and mitigation have not been comprehensively studied. In this paper, we propose a receiver-agnostic RFFI system by employing adversarial training to learn the receiver-independent features. Moreover, when there are multiple receivers, collaborative inference are designed to enhance classification accuracy. Finally, we show how it is possible to leverage fine-tuning for further improvement with fewer collected signals. To validate the approach, we have conducted extensive experimental evaluation by applying the approach to a LoRaWAN case study involving ten LoRa devices and 20 software-defined radio (SDR) receivers. The results show that receiver-agnostic training enables the trained neural network to become robust to changes in receiver characteristics. The collaborative inference improves classification accuracy by up to 20% beyond a single-receiver RFFI system and fine-tuning can bring a 40% improvement for underperforming receivers. The system is further evaluated on a more practical testbed. By making additional use of online augmentation and multi-packet inference, the identification accuracy is improved from 50% to 90% at 10 dB.

Radio frequency fingerprint identification (RFFI) is an emerging technique for the lightweight authentication of wireless Internet of things (IoT) devices. RFFI exploits unique hardware impairments as device identifiers, and deep learning is widely deployed as the feature extractor and classifier for RFFI. However, deep learning is vulnerable to adversarial attacks, where adversarial examples are generated by adding perturbation to clean data for causing the classifier to make wrong predictions. Deep learning-based RFFI has been shown to be vulnerable to such attacks, however, there is currently no exploration of effective adversarial attacks against a diversity of RFFI classifiers. In this paper, we report on investigations into white-box attacks (non-targeted and targeted) using two approaches, namely the fast gradient sign method (FGSM) and projected gradient descent (PGD). A LoRa testbed was built and real datasets were collected. These adversarial examples have been experimentally demonstrated to be effective against convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and gated recurrent units (GRU).

Radio frequency fingerprint identification (RFFI) can classify wireless devices by analyzing the signal distortions caused by intrinsic hardware impairments. Recently, state-of-the-art neural networks have been adopted for RFFI. However, many neural networks, e.g., multilayer perceptron (MLP) and convolutional neural network (CNN), require fixed-size input data. In addition, many IoT devices work in low signal-to-noise ratio (SNR) scenarios but the RFFI performance in such scenarios is often unsatisfactory. In this paper, we analyze the reason why MLP- and CNN-based RFFI systems are constrained by the input size. To overcome this, we propose four neural networks that can process signals of variable lengths, namely flatten-free CNN, long short-term memory (LSTM) network, gated recurrent unit (GRU) network, and transformer. We adopt data augmentation during training which can significantly improve the model’s robustness to noise. We compare two augmentation schemes, namely offline and online augmentation. The results show the online one performs better. During the inference, a multi-packet inference approach is further leveraged to improve the classification accuracy in low SNR scenarios. We take LoRa as a case study and evaluate the system by classifying 10 commercial-off-the-shelf LoRa devices in various SNR conditions. The online augmentation can boost the low-SNR classification accuracy by up to 50% and the multi-packet inference approach can further increase the accuracy by over 20%.

Radio frequency fingerprint identification (RFFI) is a promising device authentication technique based on transmitter hardware impairments. The device-specific hardware features can be extracted at the receiver by analyzing the received signal and used for authentication. In this paper, we propose a scalable and channel-robust RFFI framework achieved by deep learning powered radio frequency fingerprint (RFF) extractor and channel independent features. Specifically, we leverage deep metric learning to train an RFF extractor, which has excellent generalization ability and can extract RFFs from previously unseen devices. Any devices can be enrolled via the pre-trained RFF extractor and the RFF database can be maintained efficiently for allowing devices to join and leave. Wireless channel impacts the RFF extraction and is tackled by exploiting channel independent features and data augmentation. We carried out extensive experimental evaluation involving 60 commercial off-the-shelf LoRa devices and a USRP N210 software defined radio platform. The results have successfully demonstrated that our framework can achieve excellent generalization abilities for rogue device detection and device classification as well as effective channel mitigation.

Distributed virtual environments (DVEs) are challenging to create as the goals of consistency and responsiveness become contradictory under increasing latency. DVEs have been considered as both distributed transactional databases and force-reflection systems. Both are good approaches, but they do have drawbacks. Transactional systems do not support Level 3 (L3) collaboration: manipulating the same degree-of-freedom at the same time. Force-reflection requires a client-server architecture and stabilisation techniques. With Consensus Based Networking (CBN), we suggest DVEs be considered as a distributed data-fusion problem. Many simulations run in parallel and exchange their states, with remote states integrated with continous authority. Over time the exchanges average out local differences, performing a distribued-average of a consistent, shared state. CBN aims to build simulations that are highly responsive, but consistent enough for use cases such as the piano-movers problem. CBN's support for heterogeneous nodes can transparently couple different input methods, avoid the requirement of determinism, and provide more options for personal control over the shared experience. Our work is early, however we demonstrate many successes, including L3 collaboration in room-scale VR, 1000's of interacting objects, complex configurations such as stacking, and transparent coupling of haptic devices. These have been shown before, but each with a different technique; CBN supports them all within a single, unified system.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on intrin-sic hardware characteristics of wireless devices. We designed an RFFI scheme for Long Range (LoRa) systems based on spectrogram and convolutional neural network (CNN). Specifically, we used spectrogram to represent the fine-grained time-frequency characteristics of LoRa signals. In addition, we revealed that the instantaneous carrier frequency offset (CFO) is drifting, which will result in misclassification and significantly compromise the system stability; we demonstrated CFO compensation is an effective mitigation. Finally, we designed a hybrid classifier that can adjust CNN outputs with the estimated CFO. The mean value of CFO remains relatively stable, hence it can be used to rule out CNN predictions whose estimated CFO falls out of the range. We performed experiments in real wireless environments using 20 LoRa devices under test (DUTs) and a Universal Software Radio Peripheral (USRP) N210 receiver. By comparing with the IQ-based and FFT-based RFFI schemes, our spectrogram-based scheme can reach the best classification accuracy, i.e., 97.61% for 20 LoRa DUTs.

Radio frequency fingerprint identification (RFFI) is an emerging device authentication technique that relies on the intrinsic hardware characteristics of wireless devices. This paper designs a deep learning-based RFFI scheme for Long Range (LoRa) systems. Firstly, the instantaneous carrier frequency offset (CFO) is found to drift, which could result in misclassification and significantly compromise the stability of the deep learning-based RFFI system. CFO compensation is demonstrated to be effective mitigation. Secondly, three signal representations for deep learning-based RFFI are investigated in time, frequency, and time-frequency domains, namely in-phase and quadrature (IQ) samples, fast Fourier transform (FFT) results and spectrograms, respectively. For these signal representations, three deep learning models are implemented, i.e., multilayer perceptron (MLP), long short-term memory (LSTM) network and convolutional neural network (CNN), in order to explore an optimal framework. Finally, a hybrid classifier that can adjust the prediction of deep learning models with the estimated CFO is designed to further increase the classification accuracy. The CFO will not change dramatically over several continuous days, hence it can be used to correct predictions when the estimated CFO is much different from the reference one. Experimental evaluation is performed in real wireless environments involving 25 LoRa devices and a Universal Software Radio Peripheral (USRP) N210 platform. The spectrogram-CNN model is found to be optimal for classifying LoRa devices which can reach an accuracy of 96.40% with the least complexity and training time.

Based on the reverse asymmetric time synchronization framework, we have proposed several schemes with a major focus on the energy efficiency and computational complexity of a large number of battery-powered, low-cost sensor nodes in wireless sensor networks (WSNs). To address the cumulative end-to-end synchronization error, we have also introduced an idea of compensating for the processing delays at packet-relaying gateways as an energy-efficient way of multi-hop extension of WSN time synchronization schemes. In this paper, we present a comprehensive analysis of the multi-hop extension of WSN time synchronization schemes based on packet-relaying gateways with the per-hop delay compensation and the results of extensive experiments for the energy-efficient time synchronization schemes based on the reverse asymmetric time synchronization framework together with the flooding time synchronization protocol as a representative of existing schemes. Experimental results based on a real testbed demonstrate that the multi-hop extension based on packet-relaying gateways with the per-hop delay compensation greatly improves the performance of time synchronization of all the schemes considered compared to the multi-hop extension based on the conventional time-translating gateways.

Device authentication is essential for securing Internet of things. Radio frequency fingerprint identification (RFFI) is an emerging technique that exploits intrinsic and unique hardware impairments as the device identifier. The existing RFFI literature focuses on experimental exploration but comprehensive modelling is missing. This paper systematically models impairments of transmitter and receiver in narrowband systems and carries out extensive experiments and simulations to evaluate their effects on RFFI. The modelled impairments include oscillator imperfections, imbalance of inphase (I) and quadrature (Q) branches of mixers and power amplifier (PA) nonlinearity. We then propose a convolutional neural network-based RFFI protocol. We carry out experimental measurements over three months and demonstrate that oscillator imperfections are not suitable for RFFI due to their unpredictable time variation caused by temperature change. Our simulation results show that our protocol can classify 50 and 200 devices with uniformly and randomly distributed IQ imbalances and PA nonlinearities with high accuracy, namely 99% and 89%, respectively. We also show that the RFFI has some tolerance on different receiver imbalances during training and classification. Specifically, the accuracy is shown to degrade less than 20% when the residual receiver's gain and phase imbalances are small. Based on the experimental and simulation results, we made recommendations for designing a robust RFFI protocol, namely compensate carrier frequency offset and calibrate IQ imbalances of receivers.

Mobile HMDs must sacrifice compute performance to achieve ergonomic and power requirements for extended use. Consequently, applications must either reduce rendering and simulation complexity - along with the richness of the experience - or offload complexity to a server. Within the context of edge-computing, a popular way to do this is through render streaming. Render streaming has been demonstrated for desktops and consoles. It has also been explored for HMDs. However, the latency requirements of head tracking make this application much more challenging. While mobile GPUs are not yet as capable as their desktop counterparts, we note that they are becoming more powerful and efficient. With the hard requirements of VR, it is worth continuing to investigate what schemes could optimally balance load, latency and quality. We propose an alternative we call edge-physics: streaming at the scene-graph level from a simulation running on edge-resources, analogous to cluster rendering. Scene streaming is not only straightforward, but compute and bandwidth efficient. The most demanding loops run locally. Jobs that hit the power-wall of mobile CPUs are off-loaded, while improving GPUs are leveraged, maximising compute utilisation. In this paper we create a prototypical implementation and evaluate its potential in terms of fidelity, bandwidth and performance. We show that an effective system which maintains high consistencies on typical edge-links can be easily built, but that some traditional concepts are not applicable, and a better understanding of the perception of motion is required to evaluate such a system comprehensively.

We consider a multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in which a source node, Alice, communicates with an energy-harvesting destination node, Bob, in the presence of a passive eavesdropper. To secure the wireless transmission, Alice generates a hybrid artificial noise (AN) in both frequency and time domains. Moreover, in order to collect more energy, Bob splits the received signal power of the cyclic prefix of each OFDM block. We then propose two non-convex optimization problems to balance both the need for security and the need for harvesting energy at Bob. While one considers maximizing the secrecy rate, the other approach aims at maximizing the harvested energy. Path-following algorithms of low computational complexity are developed and evaluated. Our numerical results show the gain of our proposed scheme and the effectiveness of our proposed algorithms.

The ever-increasing number of WSN deployments based on a large number of battery-powered, low-cost sensor nodes, which are limited in their computing and power resources, puts the focus of WSN time synchronization research on three major aspects of accuracy, energy consumption, and computational complexity. In the literature, the latter two aspects haven't received much attention compared to the accuracy of WSN time synchronization. Especially in multi-hop WSNs, intermediate gateway nodes are overloaded with tasks for not only relaying messages but also a variety of computations for their offspring nodes as well as themselves. Therefore, not only minimizing the energy consumption but also lowering the computational complexity while maintaining the synchronization accuracy is crucial to the design of time synchronization schemes for resource-constrained sensor nodes. In this paper, focusing on the three aspects of WSN time synchronization, we introduce a framework of reverse asymmetric time synchronization for resource-constrained multi-hop WSNs and propose a beaconless energy-efficient time synchronization scheme based on reverse one-way message dissemination. Experimental results with a WSN testbed based on TelosB motes running TinyOS demonstrate that the proposed scheme conserves up to 95% energy consumption compared to the flooding time synchronization protocol while achieving microsecond-level synchronization accuracy.

Recently, TCP SYN flood has been the most common and serious type of Distributed Denial of Service attack that causes outages of server resource of Internet Service Providers. In another aspect, Software Defined Networking (SDN) has emerged as a new networking paradigm to increase network agility and programmability. SDN is also a promising architecture to deal with the network security issue where we can flexibly change security rules and control incoming flows. In this article, we design an Openflow/SDN network remedy to combat specifically TCP SYN flood. We show security threats for the SDN architecture and exploit SDN capabilities and features to design a SDN-based SYN Proxy (SSP) paradigm to mitigate such TCP SYN threats. Our SSP is proved to be a network-based solution to protect application servers in terms of decreasing number of Half-Open Connections at an application server and increasing probability of successful establishment for a TCP flow connection under TCP SYN Flood attack. Using SSP to support application servers is shown to outperform the case where the servers adopt only the protection scheme of Microsoft Windows server reference model without utilizing SSP. SSP also shows that it can reduce the time a flow entry occupies the switch resource by 94% in comparison with the Avant-Guard solution. In addition, SSP improves the successful connection rate and average connection retrieval time in comparison with the standard Openflow solution.

A multi-input multi-output (MIMO) orthogonal frequency-division multiplexing (OFDM) network in the presence of a passive eavesdropper is considered. The deployment of radio frequency power transfer at the receiver and the use of hybrid artificial noise at the transmitter are simultaneously taken into account. At the legal receiver, the cyclic prefix of each OFDM block is used for the purpose of harvesting energy. In parallel, the power-splitting SWIPT technique is additionally used. We then propose a trade-off problem to maximize the secrecy rate of the network while keeping the harvested energy above a given threshold. Throughout the numerical results, the performance of our proposed secure scheme is evaluated.

Real-time interactive systems such as virtual environments have high performance requirements, and profiling is a key part of the optimisation process to meet them. Traditional techniques based on metadata and static analysis have difficulty following causality in asynchronous systems. In this paper we explore a new technique for such systems. Timestamped samples of the system state are recorded at instrumentation points at runtime. These are assembled into a graph, and edges between dependent samples recovered. This approach minimises the invasiveness of the instrumentation, while retaining high accuracy. We describe how our instrumentation can be implemented natively in common environments, how its output can be processed into a graph describing causality, and how heterogeneous data sources can be incorporated into this to maximise the scope of the profiling. Across three case studies, we demonstrate the efficacy of this approach, and how it supports a variety of metrics for comprehensively bench-marking distributed virtual environments.

This paper studies the security aspect of a recently introduced “cell-free massive MIMO” network under a pilot spoofing attack. First, a simple method to recognize the presence of this type of an active eavesdropping attack to a particular user is shown. In order to deal with this attack, we consider the problem of maximizing the achievable data rate of the attacked user or its achievable secrecy rate. The corresponding problems of minimizing the power consumption subject to security constraints are also considered in parallel. Path-following algorithms are developed to solve the posed optimization problems under different power allocation to access points (APs). Under equip-power allocation to APs, these optimization problems admit closed-form solutions. Numerical results show their efficiency.

This paper investigates the impact of a uniform circular array (UCA) in the context of wireless security via exposure region-based beamforming. An improvement is demonstrated for the security metric proposed in our previous paper, namely, the spatial secrecy outage probability (SSOP), by optimizing the configuration of the UCA. Our previous paper focused on formalizing the SSOP concept and exploring its applicability using a uniform linear array example. This paper proposes the UCA as a superior candidate because it is more robust against the effects of mutual coupling. The UCA's SSOP configuration is explored and a special expression is derived from the general expression for the first time, and a closed-form upper bound is then generated to facilitate analysis. By carefully designing the UCA structure particularly the radius, an SSOP optimization algorithm is derived and explored for mutual coupling. It is shown that the information leakage to eavesdroppers is reduced while the legitimate user's received signal quality is enhanced due to the use of beamforming.

With the increasing number of antennae in base stations, there is considerable interest in using beamforming to improve physical layer security, by creating an “exposure region” that enhances the received signal quality for a legitimate user and reduces the possibility of leaking information to a randomly located passive eavesdropper. This paper formalizes this concept by proposing a novel definition for the security level of such a legitimate transmission, called the spatial secrecy outage probability (SSOP). By performing a theoretical and numerical analysis, it is shown how the antenna array parameters can affect the SSOP and its analytic upper bound. While this approach may be applied to any array type and any fading channel model, it is shown here how the security performance of a uniform linear array varies in a Rician fading channel by examining the analytic SSOP upper bound.

Energy Harvesting Wireless Sensor Networks (EH-WSNs) have been attracting increasing interest in recent years. Most current EH-WSN approaches focus on sensing and networking algorithm design, and therefore only consider the energy consumed by sensors and wireless transceivers for sensing and data transmissions respectively. In this paper, we incorporate CPU-intensive edge operations that constitute in-network data processing (e.g. data aggregation/fusion/compression) with sensing and networking; to jointly optimize their performance, while ensuring sustainable network operation (i.e. no sensor node runs out of energy). Based on realistic energy and network models, we formulate a stochastic optimization problem, and propose a lightweight on-line algorithm, namely Recycling Wasted Energy (RWE), to solve it. Through rigorous theoretical analysis, we prove that RWE achieves asymptotical optimality, bounded data queue size, and sustainable network operation. We implement RWE on a popular IoT operating system, Contiki OS, and evaluate its performance using both real-world experiments based on the FIT IoT-LAB testbed, and extensive trace-driven simulations using Cooja. The evaluation results verify our theoretical analysis, and demonstrate that RWE can recycle more than 90% wasted energy caused by battery overflow, and achieve around 300% network utility gain in practical EH-WSNs.

Key generation from the randomness of wireless channels is a promising technique to establish a secret cryptographic key securely between legitimate users. This paper proposes a new approach to extract keys efficiently from the channel responses of individual orthogonal frequency-division multiplexing (OFDM) subcarriers. The efficiency is achieved by: 1) fully exploiting randomness from time and frequency domains and 2) improving the cross-correlation of the channel measurements. Through the theoretical modeling of the time and frequency autocorrelation relationship of the OFDM subcarrier's channel responses, we can obtain the optimal probing rate and use multiple uncorrelated subcarriers as random sources. We also study the effects of non-simultaneous measurements and noise on the cross-correlation of the channel measurements. We find that the cross-correlation is mainly impacted by noise effects in a slow fading channel and use a low-pass filter to reduce the key disagreement rate and extend the system's working signal-to-noise ratio range. The system is evaluated in terms of randomness, key generation rate, and key disagreement rate, verifying that it is feasible to extract randomness from both time and frequency domains of the OFDM subcarrier's channel responses.

In this paper, we introduce Insistent Spectrum Sensing Data Falsification (ISSDF) as a new practical and destructive attack model aimed at distributed cooperative spectrum sensing schemes that are based on iterative average consensus. We compare various linear iteration-based and iterative gossip-based schemes in terms of primary user detection performance and convergence speed under this attack. Moreover, we devise a trust management scheme to mitigate the attack and we propose a practical trust-aware consensus-based scheme for distributed cooperative spectrum sensing which is resilient to ISSDF. Finally, we quantify the performance improvement due to trust management through extensive simulations.

In physical layer security systems there is a clear need to exploit the radio link characteristics to automatically generate an encryption key between two end points. The success of the key generation depends on the channel reciprocity, which is impacted by the non-simultaneous measurements and the white nature of the noise. In this paper, an OFDM subcarriers' channel responses based key generation system with enhanced channel reciprocity is proposed. By theoretically modelling the OFDM subcarriers' channel responses, the channel reciprocity is modelled and analyzed. A low pass filter is accordingly designed to improve the channel reciprocity by suppressing the noise. This feature is essential in low SNR environments in order to reduce the risk of the failure of the information reconciliation phase during key generation. The simulation results show that the low pass filter improves the channel reciprocity, decreases the key disagreement, and effectively increases the success of the key generation.

Security in the Internet of Things (IoT) is now considered a priority, and trust in machine-to-machine (M2M) communications is expected to play a key role. This paper presents a mechanism to detect an emerging threat in M2M systems whereby an attacker may create multiple fake embedded machines using virtualized or emulated systems, in order to compromise either a targeted IoT device, or the M2M network. A new trust method is presented that is based on a characterisation of the behaviours of real embedded machines, and operates independently of their architectures and operating systems, in order to detect virtual and emulated systems. A range of tests designed to characterise embedded and virtual devices are presented, and the results underline the efficiency of the proposed solution for detecting these systems easily and quickly.

We present a novel approach to network security against passive eavesdroppers by employing a configurable beam-forming technique to create tightly defined regions of coverage for targeted users. In contrast to conventional encryption methods, our security scheme is developed at the physical layer by configuring antenna array beam patterns to transmit the data to specific regions. It is shown that this technique can effectively reduce vulnerability of the physical regions to eavesdropping by adapting the antenna configuration according to the intended user's channel state information. In this paper we present the application of our concept to 802.11n networks where an antenna array is employed at the access point, and consider the issue of minimizing the coverage area of the region surrounding the targeted user. A metric termed the exposure region is formally defined and used to evaluate the level of security offered by this technique. A range of antenna array configurations are examined through analysis and simulation, and these are subsequently used to obtain the optimum array configuration for a user traversing a coverage area.

A good scheduling algorithm in wireless networks is the key for the efficient use of wireless medium. This paper presents a hybrid scheduling algorithm, called Contention-Vector based Hybrid Scheduling (CVHS) algorithm, for wireless networks wherein stations can sense each other. Examples of such wireless networks include wireless LANs and small wireless ad hoc networks. CVHS offers collision-free transmissions and QoS support. In addition, the proposed solution is free from hidden node problems, exhibits fairness across the network, and is adaptive to network dynamics such as nodes leaving and joining, changes in packet size and arrival rate. Analyses and simulations demonstrate that CVHS has high performance in terms of network throughput, delay, jitter, and fairness.

This paper analyses and proposes a novel detection strategy for the 'Chameleon’ WiFi AP-AP virus. Previous research has considered virus construction, likely virus behaviour and propagation methods. The research here describes development of an objective measure of virus success, the impact of product susceptibility, the acceleration of infection and the growth of the physical area covered by the virus. An important conclusion of this investigation is that the connectivity between devices in the victim population is a more significant influence on virus propagation than any other factor. The work then proposes and experimentally verifies the application of a detection method for the virus. This method utilises layer 2 management frame information which can detect the attack while maintaining user privacy and user confidentiality, a key requirement in many security solutions.

A queue manager (QM) is a core traffic management (TM) function used to provide per-flow queuing in access and metro networks; however current designs have limited scalability. An on-demand QM (OD-QM) which is part of a new modular field-programmable gate-array (FPGA)-based TM is presented that dynamically maps active flows to the available physical resources; its scalability is derived from exploiting the observation that there are only a few hundred active flows in a high speed network. Simulations with real traffic show that it is a scalable, cost-effective approach that enhances per-flow queuing performance, thereby allowing per-flow QM without the need for extra external memory at speeds up to 10 Gbps. It utilizes 2.3%-16.3% of a Xilinx XC5VSX50t FPGA and works at 111 MHz.

With the development of wireless network applications, wireless networks need more interactions between many entities, and a rapidly increasing requirement for designing secure applications among these entities is trust management. Therefore, a lot of attacks against distributed environment are aimed at the trust management. This article presents a new trust management framework (TMF) for Mobile Ad hoc Networks. The proposed framework calculates a node's trust value based on observations from neighbour nodes by using Grey theory and Fuzzy sets. The TMF chooses multiple rather than a single parameter to obtain trust values. Simulations conducted in a Mobile Ad hoc Network (MANET, 802.11-based) with Random Waypoint Mobility Mode show what the proposed framework can do is not only detecting abnormal trust behaviour, but also discovering which parameter for forming trust values of a mobile node is abnormal, that means it can identify the possible attack strategy in both static and mobile environment. The TMF has shown good performance in calculating trust values of mobile wireless nodes under normal and abnormal (attack) conditions, and hence can be considered as an effective trust framework for MANETs.

A key issue in the design of next-generation Internet routers and switches will be provision of Traffic Manager (TM) functionality in the datapaths of their high-speed switching fabrics. A new architecture that allows dynamic deployment of different TM functions is presented. By considering the processing requirements of operations such as policing and congestion, queuing, shaping, and scheduling, a solution has been derived that is scalable with a consistent programmable interface. Programmability is achieved using a function computation unit which determines the action (e.g., drop, queue, remark, forward) based on the packet attribute information and a memory storage part. Results of a Xilinx Virtex-5 FPGA reference design are presented.

The effective transmission of traffic involving the sense of touch (haptics) presents a significant challenge to the current Internet architecture. It is now accepted that the Quality of Service (QoS) needed to support haptic feedback in networks will be significantly different from that used to support conventional real-time traffic such as voice or video, primarily because this traffic originates from a different human sense and has specific tolerances to delay and loss of the force and position that is reflected back to the user. Haptic applications therefore require a stringent QoS from the network for successful interaction in Distributed Virtual Environments (DVEs). Network impairments such as time delay, jitter and packet loss each have different (and severe) impacts on the user's haptic experience for haptic interaction. Network delay reduces the fidelity when touching a virtual object due to reduction of force response. Jitter and packet loss can cause abrupt force feedback. All of these can cause system instability in the haptic feedback loop between the user and the virtual environment being rendered. This paper describes some of the issues and challenges that are presented whenever remote haptic interactions with virtual environments are considered, and identifies a number of QoS parameters and techniques to compensate for the network impairments in the network and in the end applications, that can be used to improve performance of such systems.

Traditional Time Division Multiple Access (TDMA) protocol provides deterministic periodic collision-free data transmissions. However, TDMA lacks flexibility and exhibits low efficiency in dynamic environments such as wireless LANs. On the other hand, contention-based MAC protocols such as the IEEE 802.11 DCF are adaptive to network dynamics but are generally inefficient in heavily loaded or large networks. To take advantage of the both types of protocols, a D-CVDMA protocol is proposed. It is based on the k-round elimination contention (k-EC) scheme, which provides fast contention resolution for Wireless LANs. D--CVDMA uses a contention mechanism to achieve TDMA-like collision-free data transmissions, which does not need to reserve time slots for forthcoming transmissions. These features make the D-CVDMA robust and adaptive to network dynamics such as node leaving and joining, changes in packet size and arrival rate, which, in turn, make it suitable for the delivery of hybrid traffic including multimedia and data content. D-CVDMA is designed for single-hop wireless ad hoc networks. Analyses and simulations demonstrate that D-CVDMA outperforms the IEEE 802.11 DCF and k-EC in terms of network throughput, delay, jitter, and fairness.

This paper describes LocMAC, a new location aware power saving MAC layer for target tracking applications in smart space environments. Target tracking applications focus on providing proximity based local services to mobile users within a fixed deployment of wireless nodes. Conventional wireless sensor MAC layers increase the lifespans of battery powered wireless nodes by scheduling sleep periods for the entire network, which are either of fixed length, or vary based upon current network metrics. LocMAC bases its sleep schedule on the relative physical location of network nodes and allows target tracking service to be guaranteed. It propagates user location updates, and uses this knowledge to perform 'location based sleep scheduling' (LBSS). LBSS describes the use of wireless node and network target positions to maximise the potential sleep periods. Adding knowledge of user mobility allows those nodes which will not be required by the network application to sleep for longer periods of time, waking up just in time to service network user requirements. The paper describes the operation of LocMAC and compares its performance against existing sleep scheduling MAC protocols through analysis and simulations of typical application scenarios. The results show that LocMAC performs significantly better for target tracking applications and is particularly appropriate for use in future Smart Spaces.

Denial of service (DoS) attacks, and jamming in particular, present a significant threat to wireless networks because they are easy to mount and difficult to detect and prevent. We present and analyze a special type of DoS attack, called random packet destruction (RPD) that works by transmitting short periods of noise signals. The RPD DoS attack can effectively shut down a wireless network. Since the attacker does not need to pretend to be a legal user participating in the network, current anti-attack measures such as encryption, authentication and authorization cannot prevent these types of attacks. RPD DoS attacks are pervasive in nature and can potentially be launched against any wireless networks that are detectable. An attacker can launch RPD attacks against wireless networks used for mission critical systems to inflict serious damages on lives or properties. The paper presents for the first time, both theoretical analysis and performance simulations of WLANs when operating under RPD DoS attacks for a range of types of network traffic.

Currently all interactions that occur between ourselves and communications networks involve only two senses (aural and visual). Moreover all these networks, including the Internet, have been designed to carry application information pertaining to these two senses (e.g. telephony, video, graphics, and text). It is clear that by introducing into networks the ability to carry information relating to other senses opens up an enormous potential for new and improved applications. However it is also clear that the network service needed to support other senses such as touch (haptics) will be significantly different from that which currently exists. The effective transmission of the sense of touch (haptics) presents a significant challenge to the current Internet architecture. Haptic information originates from a different human sense; therefore the quality of service (QoS) required to support this type of traffic is significantly different from that used to support conventional real-time traffic such as voice or video. To date there has been no specific provision of QoS parameters for haptic interaction, and each type of network impairment has different (and severe) impacts on the userpsilas haptic experience. This paper describes some of the issues and challenges that are presented whenever remote haptic interactions with virtual environments are considered, and identifies a number of techniques, in the network and in the end applications, that can be used to improve performance of such systems.

This paper introduces a new flow monitoring architecture that is based on FPGA technology offering high speed and programmability for monitoring in the next generation Internet. Two monitoring functions are also presented that can be implemented on the proposed architecture. The functions are based on Internet traffic characteristics and can increase monitoring efficiency and compute additional network statistics. The two functions illustrate how the programmable nature of the architecture allows for flexible, intelligent and on-demand monitoring. The first function classifies flows based on size and speed and the second function, measures flow traffic bursts.

The contention resolution scheme is a key component in carrier sense based wireless MAC protocols. It has a major impact on MAC’s performance metrics such as throughput, delay, and jitter. The IEEE 802.11 DCF adopts a simple contention resolution scheme, namely Binary Exponential Backoff (BEB) scheme. The BEB achieves a reasonable performance for transmitting best-effort packets in small-size wireless networks. However, as the network size increases, it suffers from inefficiency because of the medium contention, which leads to reduced performance. The main reason is that the BEB mechanism incurs an everincreasing collision rate as the number of contending nodes increases. We devise a novel contention resolution scheme, a k-round elimination contention (k-EC) scheme. The k-EC scheme exhibits high efficiency and robustness during the collision resolution. More importantly, it is insensitive to the number of contending nodes. This feature makes it feasible for use in networks of different sizes. Simulation results show that the k-EC scheme offers a powerful remedy to medium contention resolution. It significantly outperforms the IEEE 802.11 DCF scheme in all the MAC’s performance metrics, and also exhibits better fairness.

Virtual environments using haptic devices have proved useful for assembly/disassembly simulation of mechanical components. To date most haptic virtual environments are stand-alone. Collaborative haptic virtual environments (CHVEs) are distributed across a number of users via a network, such as the Internet. These present new challenges to the designer, such as consistency of the virtual environments, user-user haptic interaction, and scalability. The system described in this paper considers the CHVEs to be distributed over a packet-switched network such as the Internet. It gives priority to the validation of interactions between objects grasped by users; guarantees consistency across different users' virtual environments. The paper explains the components used and the consistency-maintenance scheme that guarantees the consistency of the virtual scene in the remote nodes. Consistency and force feedback results are also discussed. Results presented show the system maintains a consistent and satisfactory response when network incurs delay or packet jitter

Space division switching fabrics, like the buffered crossbar, are required to meet the forwarding capacity requirements of Gigabit and Terabit packet switches. These fabrics use fast-operating arbitration algorithms to achieve high utilization under heavy traffic loads. However, Next Generation Network (NGN) switches also need to provide throughput guarantees. A new scheduling system is proposed in this paper that can achieve both of these goals. Primarily, backlogged traffic is serviced in a manner that meets throughput guarantees, after which, the excess capacity is serviced in a manner that achieves high switch utilization.

In 802.11 WLANs, active intrusion attacks on the MAC layer causes changes in the management frame distributions. This paper focuses on detecting intrusions by analyzing the management traffic patterns. Experimental results are presented that describe the patterns generated by two well-known active attacks on 802.11 WLANs: deauthentication denial-of service (DoS) and man-in-the-middle (MITM). By clustering the management frame bursts over a certain period of time, we observe that the active attacking traffic patterns can be classified through calculation of a cluster content value (CCV). Our results show that when any station in a WLAN experiences clustering in its management traffic distribution, the CCV can be used to detect and classify the attacks the station is experiencing

Provision of an efficient communication infrastructure is a critical task for wireless sensor networks, and Virtual backbones have been proposed as an efficient mechanism for providing this. In this paper, we propose an energy-aware virtual backbone tree (EVBT) for general-purpose communications in wireless sensor networks. The EVBT has some salient features: the overhead for constructing an EVBT is very low; the delivery of data along the tree depletes minimum energy from the network; and the tree nodes are those with high energy levels. In addition to these, the proposed algorithm is adaptive in nature - it automatically adapts to the addition and removal of the sensor nodes. The use of multiple EVBTs improves the robustness of the communications infrastructure. Simulation results are presented that demonstrate the efficiency of the proposed algorithm.

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.

The performance of network management applications is becoming an increasingly important factor as the scale of the networks they have to manage increases. This paper presents a series of comparative performance tests on two common platforms used for network management: SNMP and CORBA. In terms of network performance, the two platforms are used to achieve the same network management tasks. The bandwidth consumed and the time taken to complete the tasks is compared. Both systems were tested by manipulating different types of management objects (single or tabular) in different network environments. The results highlight the bottlenecks in each system and the reasons for them. The paper compares both systems and identifies their respective advantages and disadvantages. The work shows that their different features will decide what kind of application they are most suited to.

This paper reports on the quality of service (QoS) requirements and the performance of virtual environment (VE) applications deployed in IP networks. We are interested specifically in systems that support communication with end-user through force feedback devices known as haptic interfaces. Little is known about networked haptic interfaces in VE. Our goal is to understand such application QoS requirements as well as the effect of other traffic when they co-exist in the same network. In this paper, we compare the peer-to-peer model with the client-server model. Our motivation is to deploy distributed haptic VE applications (DHVE) over a network connecting two departments of Queen's University of Belfast. This deployment will be used for educational purposes and for implementing further research in this area, and will be evaluated by end-users; hence, the essential realization of network-based DHVE under realistic network conditions. A set of experiments was conducted to achieve this aim and their results are presented in this paper.

The worst-case fairness index (WFI) has proved to be an important metric in the provision of fairness and bounded delay in both wired and wireless networks. Worst case fair WFQ (WF/sup 2/Q) is better than weighted fair queuing (WFQ) in that it can provide bounded WFI. However, simulations show that in some circumstances WF/sup 2/Q has higher WFI and queuing delay than WFQ. This paper firstly analyzes the reasons why WF/sup 2/Q sometimes experiences higher WFI, and based on this, an adaptive packet queuing algorithm termed delay optimized WF/sup 2/Q (DO-WF/sup 2/Q), is proposed. This algorithm aims to improve the delay index (DI) while maintaining the WFI bound provided by WF/sup 2/Q. Verifications and simulations show that DO-WF/sup 2/Q can reduce DI while not destroying the WFI bound, and at the same time maintain low complexity.

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).

This paper analyses the performance of a new MAC layer protocol proposed for use in the return channel of a cellular broadband network which has fixed, or cordless access to multiple services classes such as voice and video-on-demand. The asymmetrical nature of cellular broadband networks means that the return channel must be optimized to support a dynamically changing traffic load. Any MAC layer protocol must therefore support multiple connections that may have different service characteristics. A dynamic TDMA slot allocation protocol is proposed which provides telephony, Internet and VoD services in the wireless return channel. Computer simulations allowing the performance of the proposed MAC layer to be analyzed are presented. The results highlight the capability of the protocol to support a range of multiple traffic service classes.

The emergence of new FPGA families such as the Xilinx 6200 FPGA family and the Atmel 40000 series has been an important development in the FPGAs for Custom Computing Machines (FCCMs). These devices have number of appealing features when compared to other technologies such as the Xilinx 4000 series SRAM technology. These can be characterised as follows: faster reconfiguration (typically m/spl mu/ s or /spl mu/s), support for partial reconfiguration, dedicated microprocessor interface. An approach for run-time reconfiguration can be achieved by considering a range of functions collectively and developing the specific circuit architectures for each so that a high degree of commonality exists between them in terms of their structure, wiring and cell function. This is done by representing the functions or algorithms using Signal Flow Graphs (SFGs) and manipulating them to produce similar graphs for different functions. This basic concept can only be exploited through the development of an efficient hardware system. This revolves around the concept of virtual hardware which is integrated within the operating system and is supported by programming languages such as C and C++. The reconfigurable designs which allow partial re-configuration, are stored within a configuration data graph. Whilst this allows the configuration data to be efficiently stored, reconfiguration state graphs are used for high speed reconfiguration. The entire software hardware system for fast partial reconfiguration is illustrated.

In this paper, an analysis of the traffic load presented to the Synchronous Digital Hierarchy (SDH) management channel is described. Through a study of operational SDH networks, the traffic patterns on the management channel have been found to conform generally to a number of generic profiles. Computer simulation models of SDH network elements and their associated manager have been developed and used to characterize the behaviour of the management network. The paper also describes the simulation results for the performance of the management network when the traffic network is operating under normal and alarm conditions, for a ring topology of different network sizes.