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The Precision Time Protocol (PTP) as described in IEEE 1588–2019 provides a sophisticated mechanism to achieve microsecond or even sub-microsecond synchronization of computer clocks in a well-designed and managed network, therefore... more
The Precision Time Protocol (PTP) as described in IEEE 1588–2019 provides a sophisticated mechanism to achieve microsecond or even sub-microsecond synchronization of computer clocks in a well-designed and managed network, therefore meeting the needs of even the most time-sensitive industrial and financial applications. However, PTP is prone to many security threats that impact on a correct clock synchronization, leading to potentially devastating consequences. Here, the most vicious attacks are internal attacks, where a threat actor has full access to the infrastructure including any cryptographic keys used. This paper builds on existing research on the impact of internal attack strategies on PTP networks. It shows limitations of existing security approaches to tackle internal attacks and proposes a new security approach using a trusted supervisor node (TSN), in line with prong D as specified in IEEE 1588–2019. A TSN collects and analyzes delay and offset outputs of monitored slaves...
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In this experiment we tested if a novel Mobile-EEG Cloud System recording over 4G network based on the OpenBCI board could produce comparable results to an equivalent EEG Local System recording over BLE based on the g.tec Unicorn Naked... more
In this experiment we tested if a novel Mobile-EEG Cloud System recording over 4G network based on the OpenBCI board could produce comparable results to an equivalent EEG Local System recording over BLE based on the g.tec Unicorn Naked under controlled conditions. Three subjects were requested to walk briskly at 120 steps per minute for 2 ½ minutes and cycle at 60 Revolution per Minute for 5 minutes on the exercise bike. In the experiments 48 Trials were conducted divided between the g.tec and OpenBCI based systems.
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Accurate 3D head pose estimation from a 2D image frame is an essential component of modern consumer technology (CT). It enables a better determination of user attentiveness and engagement and can support immersive audio and AR... more
Accurate 3D head pose estimation from a 2D image frame is an essential component of modern consumer technology (CT). It enables a better determination of user attentiveness and engagement and can support immersive audio and AR experiences. While deep learning methods have improved the accuracy of head pose estimation models, these depend on the accurate annotation of training data. The acquisition of real-world head pose data with a large variation of yaw, pitch and roll is a very challenging task. Available head-pose datasets often have limitations in terms of the number of data samples, image resolution, annotation accuracy and sample diversity (gender, race, age). In this work, a rendering pipeline is proposed to generate pixel-perfect synthetic 2D headshot images from high-quality 3D facial models with accurate pose angle annotations. A diverse range of variations in age, race, and gender are provided. The resulting dataset includes more than 300k pairs of RGB images with the corresponding head pose annotations. For every hundred 3D models there are multiple variations in pose, illumination and background. The dataset is evaluated by training a state-of-the-art head pose estimation model and testing against the popular evaluation dataset BIWI. The results show training with purely synthetic data produced by the proposed methodology can achieve close to state-of-the-art results on the head pose estimation task and is better generalized for age, gender and racial diversity than solutions trained on ‘real-World’ datasets.
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Until recently Wi-Fi-based user profiling and passive surveillance of the public was straight forward and caused therefore huge privacy concerns. Here Wi-Fi probe request bursts transmitted by a user device, e.g. a mobile phone, were... more
Until recently Wi-Fi-based user profiling and passive surveillance of the public was straight forward and caused therefore huge privacy concerns. Here Wi-Fi probe request bursts transmitted by a user device, e.g. a mobile phone, were sorted by the device's static MAC address and linked across location and time, therefore giving a detailed location and movement profile of the device's user. To combat this, device manufacturers introduced MAC address randomization in 2014, whereby a device changes its MAC address to a random value between transmissions of probe requests. Devices also switched to a more passive form of scanning for known networks, in which they wait for a beacon frame from a saved network rather than broadcasting the SSID of the network and awaiting a response. While both mechanisms reduce the attack surface and make it harder to track users, it does not fully solve the problem. This paper presents an access point honeypot system that works around both enhancements, therefore allowing tracking and profiling of the public with little effort or expense.
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Electromagnetic flowmeters with DC excitation are used for a wide range of fluid measurement tasks, but are rarely found in dosing applications with short measurement cycles due to the achievable accuracy. This paper will identify a... more
Electromagnetic flowmeters with DC excitation are used for a wide range of fluid measurement tasks, but are rarely found in dosing applications with short measurement cycles due to the achievable accuracy. This paper will identify a number of factors that influence the accuracy of this sensor type when used for short-term measurements. Based on these results a new signal-processing algorithm will be described that overcomes the identified problems to some extend. This new method allows principally a higher accuracy of electromagnetic flowmeters with DC excitation than traditional methods.
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Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures... more
Spectrum sensing in a cognitive radio network involves detecting when a primary user vacates their licensed spectrum, to enable secondary users to broadcast on the same band. Accurately sensing the absence of the primary user ensures maximum utilization of the licensed spectrum and is fundamental to building effective cognitive radio networks. In this paper, we address the issues of enhancing sensing gain, average throughput, energy consumption, and network lifetime in a cognitive radio-based Internet of things (CR-IoT) network using the non-sequential approach. As a solution, we propose a Dempster–Shafer theory-based throughput analysis of an energy-efficient spectrum sensing scheme for a heterogeneous CR-IoT network using the sequential approach, which utilizes firstly the signal-to-noise ratio (SNR) to evaluate the degree of reliability and secondly the time slot of reporting to merge as a flexible time slot of sensing to more efficiently assess spectrum sensing. Before a global ...
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High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in... more
High accurate time synchronization is very important for many applications and industrial environments. In a computer network, synchronization of time for connected devices is provided by the Precision Time Protocol (PTP), which in principal allows for device time synchronization down to microsecond level. However, PTP and network infrastructures are vulnerable to cyber-attacks, which can de-synchronize an entire network, leading to potentially devastating consequences. This paper will focus on the issue of internal attacks on time synchronization networks and discuss how counter-measures based on public key infrastructures, trusted platform modules, network intrusion detection systems and time synchronization supervisors can be adopted to defeat or at least detect such internal attacks.
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In the field of autonomous drone or micro air vehicle (MAV) research, much of the existing literature focuses on novel approaches to MAV automation and navigation. Whilst discovering these new approaches has scientific merit, these works... more
In the field of autonomous drone or micro air vehicle (MAV) research, much of the existing literature focuses on novel approaches to MAV automation and navigation. Whilst discovering these new approaches has scientific merit, these works rarely focus on the impact that the deployment of such systems have in terms of the operational time, power consumption or efficiency of the MAV. This work sets out to review the parallel tracking and mapping algorithm (PTAM) as applied to MAV control systems. Through experimentation, the limits of this algorithm are found in an attempt to determine the minimum computational and power requirements for a computer to have, in order to run PTAM effectively. This work demonstrates that it is feasible with current available technology, to operate PTAM on a 5 watt computer by limiting the parameters that add computational overhead to the system.
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The IEEE 1588 Precision Time Protocol (PTP) is very important for many financial and industrial applications, as it can provide highly accurate time synchronisation down to microsecond level. However, any PTP infrastructure is vulnerable... more
The IEEE 1588 Precision Time Protocol (PTP) is very important for many financial and industrial applications, as it can provide highly accurate time synchronisation down to microsecond level. However, any PTP infrastructure is vulnerable to cyber-attacks that can de-synchronise some or all network devices, causing potentially destructive consequences. This paper will focus on how two of these attacks, the asymmetric delay and the byzantine attack, can be implemented in a PTP network, analyses their impact on slave clocks, and investigates how these attacks can be detected.
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The utilization of renewable sources of energy is growing all over the world due to pressure for sustainable solutions. It brings benefits to the environment, but also adds complexity to the electricity grid, which faces energy balancing... more
The utilization of renewable sources of energy is growing all over the world due to pressure for sustainable solutions. It brings benefits to the environment, but also adds complexity to the electricity grid, which faces energy balancing challenges caused by an intermittent production from this kind of generation. Having a good energy prediction is essential to avoid losses and improve the quality and efficiency of the energy systems. There are many machine learning (ML) methods that can be used in these predictions; however, every consumer is different and will behave in a distinct way. Therefore, the objective of this article is to compare the application of different ML methods, aiming to predict PV energy production and energy consumption for residential users. Four different ML methods were applied in a real dataset from the RESPOND project: Linear Regression, Decision Forest regression, Boosted Decision Tree Regression and Neural Network. After the simulation, the predicted va...
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Honeypots are deployed to capture cyber attack data for analysis of attacker behavior. This paper analyses a honeypot dataset to establish attack types and corresponding temporal patterns. It calculates the probability of each attack type... more
Honeypots are deployed to capture cyber attack data for analysis of attacker behavior. This paper analyses a honeypot dataset to establish attack types and corresponding temporal patterns. It calculates the probability of each attack type occurring at a particular time of day and tests these probabilities with a random sample from the honeypot dataset. Attacks can take many forms and can come from different geographical sources. Temporal patterns in attacks are often observed due to the diurnal nature of computer usage and thus attack types captured on a honeypot will also reflect these patterns. We propose that it is possible to determine the probability of differing attack types occurring at certain times of the day. Understanding attack behavior informs the implementation of more robust security measures. The paper also proposes automating this process to create dynamic and adaptive honeypots. An adaptive honeypot that can modify its security levels, can increase the attack vector at different times of the day. This will improve data collection for analysis that ultimately will lead to better cyber defenses.
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Bipolar Disorder (BD) is a recurrent psychiatric condition characterised by periods of depression and (hypo)mania, it affects more than 1% of the world’s population [1]. However, accurate diagnosis can be difficult due to the lack of... more
Bipolar Disorder (BD) is a recurrent psychiatric condition characterised by periods of depression and (hypo)mania, it affects more than 1% of the world’s population [1]. However, accurate diagnosis can be difficult due to the lack of diagnostic tools available to practitioners. To address this knowledge gap this paper aims to understand how the application of transfer learning, in the context of machine learning techniques, can be used to improve
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A smart city system will contain diverse heterogeneous smart objects. Their complexity will range from simple reduced function devices (RFD) acting as common nodes, to full function devices (FFD) acting as coordinators and controlling... more
A smart city system will contain diverse heterogeneous smart objects. Their complexity will range from simple reduced function devices (RFD) acting as common nodes, to full function devices (FFD) acting as coordinators and controlling actuators. As part of the Internet of Things, web facing devices can be remotely accessed for monitoring, control and data exchange. This makes them vulnerable to cyber attacks and compromise. To analyse such attacks, honeynets and honeypots are deployed to attract attackers and capture their activity for behavioural analysis. Designing honeynets is difficult due to the broad engineering scope of smart cities as a concept, and consequent diversity of smart object characteristics such as communication channels, interaction, data exchange and embedded security. This paper brings order to this diversity and scope by taking a data-centric view of smart city devices. The data-centric view assesses smart devices for their criticality, security and complexity. It presents a framework using this view, for adaptive honeynet development. It then validates the new framework by categorizing smart objects using the data centric view and applying them to the framework.
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The Internet of Things (IoT) enables the connectivity of disparate devices and the exchange of real-time data among those devices. It requires large amounts of bandwidth to ensure the quality of service (QoS), but sufficient bandwidth is... more
The Internet of Things (IoT) enables the connectivity of disparate devices and the exchange of real-time data among those devices. It requires large amounts of bandwidth to ensure the quality of service (QoS), but sufficient bandwidth is not always available due to the limited frequency spectrum allocated for wireless data communication. Cognitive radio (CR) is a promising technology that enhances the utilization of the spectrum by allowing unlicensed users/secondary users (SU) access to the licensed primary users (PU) spectrum under certain conditions. The CR based IoT (CR-IoT) network can overcome the spectrum scarcity problem in a conventional IoT network. In a CR-IoT network, energy efficiency must be considered for avoiding interference between the PU and the SU, as the conventional energy detection (ED) technologies consume significant energy for CR operations. To mitigate this problem, we propose a novel energy efficient sequential ED spectrum sensing technique which enhances the sensing duration of each unlicensed CR-IoT user/SU by utilizing the reporting time slot when compared to the conventional non-sequential ED spectrum sensing scheme. In addition, each unlicensed CR-IoT user calculates the weight factor based on the Kullback Liebler Divergence score, which enhances the detection performance. Thereafter, each CR-IoT user in the CR-IoT network sequentially passes on both the local sensing result and the weight factor to the corresponding fusion center (FC) via the allocated reporting channel, which extends the sensing time duration of the CR-IoT user. The FC uses the local sensing result and the weight factor of each CR-IoT user to make a global decision by using the soft fusion rule. The results obtained through simulations show that the proposed sequential ED spectrum sensing scheme achieves a better sensing performance, an enhanced sum rate, an enhanced energy and spectral efficiency when compared to the conventional non-sequential ED spectrum sensing scheme with interference constraints.
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The rapidly increasing number of cellular mobile users and services increases the cellular mobile spectrum scarcity problem of the fixed cellular mobile spectrum (CMS). Cognitive radio (CR) is a new promising technology which aims to... more
The rapidly increasing number of cellular mobile users and services increases the cellular mobile spectrum scarcity problem of the fixed cellular mobile spectrum (CMS). Cognitive radio (CR) is a new promising technology which aims to overcome this spectrum scarcity problem by enhancing the utilization of the cellular mobile communication spectrum. The channel allocation and re-allocation is a vital part of cellular mobile networks (CMNs) to enhance the utilization of the CMS. In this paper we propose a new channel allocation and reallocation scheme to enhance the spectrum utilization of CMNs by integrating cognitive radio technology and a new channel allocation scheme called Channel Allocation with Cognitive Radio (CACR). MATLAB simulations have been used to evaluate the performance of CACR with particular emphasis on probability of call blocking and handover failure. The simulation results show that the proposed channel allocation scheme enhances the cellular mobile spectrum utilization.
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The IEEE 1588 Precision Time Protocol (PTP) is a widely used mechanism to provide time synchronization of computer clocks down to microsecond accuracy as required by many financial and industrial applications ("IEEE Standard for a... more
The IEEE 1588 Precision Time Protocol (PTP) is a widely used mechanism to provide time synchronization of computer clocks down to microsecond accuracy as required by many financial and industrial applications ("IEEE Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems," 2008). However, PTP is vulnerable to infrastructure cyber-attacks that reduce the desired accuracy. IEEE 1588 defined an experimental security extension (Annex K) in order to protect a PTP network, but various drawbacks have been discovered, resulting in further improvements including the use of public-key encryption ( Itkin & Wool, 2020 ) and reduce the three-way handshake mechanism to one way authentication ( Önal & Kirrmann, 2012 ). Today Annex K is deprecated in favor of L2 / L3 security mechanisms. Further on, in 2020 a backwards compatible IEEE 1588 version (v2.1) will be introduced, that contains a new security extension called Annex S. Annex S consists of four prongs as follows ("IEEE Draft Standard for a Precision Clock Synchronization Protocol for Networked Measurement and Control Systems," 2019): • Prong (A) PTP Integrated Security Mechanism describes an authentication type-length-value (TLV) that is aligned with and integrated into the PTP message. • Prong (B) PTP External Transport Security Mechanisms describes the current external security mechanisms that can be used to provide protection to PTP message i.e., IPsec and MACsec. • Prong (C) Architecture Guidance describes a redundant time system, redundant grandmaster, and redundant paths. • Prong D (Monitoring and Management Guidance) suggests monitoring the slaves’ synchronization process.
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Abstract The use of machine learning techniques has been proven to be a viable solution for smart home energy management. These techniques autonomously control heating and domestic hot water systems, which are the most relevant loads in a... more
Abstract The use of machine learning techniques has been proven to be a viable solution for smart home energy management. These techniques autonomously control heating and domestic hot water systems, which are the most relevant loads in a dwelling, helping consumers to reduce energy consumption and also improving their comfort. Moreover, the number of houses equipped with renewable energy resources is increasing, and this is a key element for energy usage optimization, where coordinating loads and production can bring additional savings and reduce peak loads. In this regard, we propose the development of a deep reinforcement learning (DRL) algorithm for indoor and domestic hot water temperature control, aiming to reduce energy consumption by optimizing the usage of PV energy production. Furthermore, a methodology for a new dynamic indoor temperature setpoint definition is presented, thus allowing greater flexibility and savings. The results show that the proposed DRL algorithm combined with the dynamic setpoint achieved on average 8% of energy savings compared to a rule-based algorithm, reaching up to 16% of savings over the summer period. Moreover, the users’ comfort has not been compromised, as the algorithm is calibrated to not exceed more than 1% of the time out the specified temperature setpoints. Additional analysis shows that further savings could be achieved if the time out of comfort is increased, which could be agreed according to users’ needs. Regarding demand side management, the DRL control shows efficiency by anticipating and delaying actions for a PV self-consumption optimization, performing over 10% of load shifting. Finally, the renewable energy consumption is 9.5% higher for the DRL-based model compared to the rule-based, which means less energy consumed from the grid.
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The IEEE 1588 precision time protocol (PTP) is very important for many industrial sectors and applications that require time synchronization accuracy between computers down to microsecond and even nanosecond levels. Nevertheless, PTP and... more
The IEEE 1588 precision time protocol (PTP) is very important for many industrial sectors and applications that require time synchronization accuracy between computers down to microsecond and even nanosecond levels. Nevertheless, PTP and its underlying network infrastructure are vulnerable to cyber-attacks, which can stealthily reduce the time synchronization accuracy to unacceptable and even damage-causing levels for individual clocks or an entire network, leading to financial loss or even physical destruction. Existing security protocol extensions only partially address this problem. This paper provides a comprehensive analysis of strategies for advanced persistent threats to PTP infrastructure, possible attacker locations, and the impact on clock and network synchronization in the presence of security protocol extensions, infrastructure redundancy, and protocol redundancy. It distinguishes between attack strategies and attacker types as described in RFC7384, but further distingui...
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Summary Objectives: As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended... more
Summary Objectives: As wearable sensors take the consumer market by storm, and medical device manufacturers move to make their devices wireless and appropriate for ambulatory use, this revolution brings with it some unintended consequences, which we aim to discuss in this paper. Methods: We discuss some important unintended consequences, both beneficial and unwanted, which relate to: modifications of behavior; creation and use of big data sets; new security vulnerabilities; and unforeseen challenges faced by regulatory authorities, struggling to keep pace with recent innovations.Where possible, we proposed potential solutions to unwanted consequences. Results: Intelligent and inclusive design processes may mitigate unintended modifications in behavior. For big data, legislating access to and use of these data will be a legal and political challenge in the years ahead, as we trade the health benefits of wearable sensors against the risk to our privacy. The wireless and personal natur...
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... The Wi-Fi device connected directly to a merchant provided access point and all communication with ... The Forum has established a Preferred Payment Architecture (PPA). ... C. Merchant Terminal To ease integration of the proposed... more
... The Wi-Fi device connected directly to a merchant provided access point and all communication with ... The Forum has established a Preferred Payment Architecture (PPA). ... C. Merchant Terminal To ease integration of the proposed SWiFT system, the merchant terminal would be ...
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This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression... more
This paper presents a diagnostic system for cardiac arrhythmias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimental results presented in this paper show that more than 90% prediction accuracy may be obtained using the improved ...
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The IEEE 1588 precision time protocol (PTP) is used by many time-sensitive applications and systems, as it achieves sub-microsecond time synchronization between computer clocks. However, a PTP network is vulnerable to cyber-attacks that... more
The IEEE 1588 precision time protocol (PTP) is used by many time-sensitive applications and systems, as it achieves sub-microsecond time synchronization between computer clocks. However, a PTP network is vulnerable to cyber-attacks that can reduce the protocol accuracy to unacceptable levels for some or all clocks in a network with potentially devastating consequences. Of particular concern are advanced persistent threats (APT), where an actor infiltrates a network and operates stealthily and over extended periods of time before being discovered. This paper investigates the impact of the most important APT strategies on a PTP network, i.e., the delay attack, packet modification or transparent clock attack, and time reference attack, using a fully programable and customizable man in the middle device, thereby considering the two most popular PTP slave daemons PTPd and PTP4l. In doing so, it determines suitable attack patterns and parameters to compromise the time synchronization cove...
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ABSTRACT This paper presents an intelligent honeypot that uses reinforcement learning to proactively engage with and learn from attacker interactions. It adapts its behaviour for automated malware to optimise the volume of data collected.... more
ABSTRACT This paper presents an intelligent honeypot that uses reinforcement learning to proactively engage with and learn from attacker interactions. It adapts its behaviour for automated malware to optimise the volume of data collected. Malware employs highly automated methods to create a global botnet. These automated methods are used to self-propagate and compromise hosts. Honeypots have been deployed to capture these automated interactions. Machine-learning techniques have previously been employed to retrospectively model botnet interactions. We develop a honeypot that uses reinforcement learning with a specific state action space formalism to interact with automated malware. It compares functionality with similar intelligent honeypots which target human interaction. It also demonstrates that datasets collected from an intelligent honeypot deployment are considerably larger than standard high interaction deployments and existing adaptive honeypots.
The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a... more
The cognitive radio relay plays a vital role in cognitive radio networking (CRN), as it can improve the cognitive sum rate, extend the coverage, and improve the spectral efficiency. However, cognitive relay aided CRNs cannot obtain a maximal sum rate, when the existing sensing approach is applied to a CRN. In this paper, we present an enhanced sum rate in the cluster based cognitive radio relay network utilizing a reporting framework in the sequential approach. In this approach a secondary user (SU) extends its sensing time until right before the beginning of its reporting time slot by utilizing the reporting framework. Secondly all the individual measurement results from each relay aided SU are passed on to the corresponding cluster head (CH) through a noisy reporting channel, while the CH with a soft-fusion report is forwarded to the fusion center that provides the final decision using the n-out-of-k-rule. With such extended sensing intervals and amplified reporting, a better sens...
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Abstract This paper presents a diagnostic system for cardiac arrhyth mias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic... more
Abstract This paper presents a diagnostic system for cardiac arrhyth mias from ECG data, using an Artificial Neural Network (ANN) classifier based on a Bayesian framework. The Bayesian ANN Classifier is built by the use of a logistic regression model and the back propagation algorithm. A dual threshold method is applied to determine the diagnosis strategy and suppress false alarm signals. The experimen tal results presented in this paper show that more than 90% prediction accuracy may be obtained using the improved ...
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The exponential growth of connected autonomous embedded devices (aka the IoT) will pose huge challenges with regard to device- and inter-device communication security. Features like encrypted communication, device authentication and... more
The exponential growth of connected autonomous embedded devices (aka the IoT) will pose huge challenges with regard to device- and inter-device communication security. Features like encrypted communication, device authentication and device authorisation must be appropriately addressed in order to provide a reliable and penetration-resistant device network. However, recent examples of cyber-attacks on such networks and the current lack of generic standards have shown that there is still a long way to go. Public key infrastructures (PKI) and digital certificates are key elements of today's secure Internet infrastructure. This paper analyses the benefits, limitations and suitability of both concepts for IoT deployments in combination with secure communication protocols. Based on this assessment it proposes an adopted PKI architecture that provides and manages customised X.509 digital certificates.
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This paper considers the problem of using miniature low-cost robots for real-world tasks. The issues of low quality sensor data, inaccurate odometry, low processing capacity and limited power are approached through the adoption of a... more
This paper considers the problem of using miniature low-cost robots for real-world tasks. The issues of low quality sensor data, inaccurate odometry, low processing capacity and limited power are approached through the adoption of a flexible, distributed control system. The system is employed in the context of col-laborative multi-robot exploration. A market framework used to support efficient task selection and formation of coalitions between robots, where collaboration is employed to increase the accuracy of generated maps.
The advent of the IoT with an estimated 50 billion internet enabled devices by the year 2020 raises questions about the suitability and scalability of existing mechanisms to provide privacy, data integrity and end-entity authentication... more
The advent of the IoT with an estimated 50 billion internet enabled devices by the year 2020 raises questions about the suitability and scalability of existing mechanisms to provide privacy, data integrity and end-entity authentication between communicating peers. In this paper we present a new protocol that combines zero-knowledge proofs and key exchange mechanisms to provide secure and authenticated communication in static M2M networks, therefore addressing all the above problems. The protocol is suitable for devices with limited computational resources and can be deployed in wireless sensor networks. While the protocol requires an a-priori knowledge about the network setup and structure, it guarantees perfect forward secrecy.
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This paper describes a UDP over IP based, server- oriented redundant host configuration protocol (RHCP) that can be used by collaborating embedded systems in an ad-hoc network to acquire a dynamic IP address. The service is provided by a... more
This paper describes a UDP over IP based, server- oriented redundant host configuration protocol (RHCP) that can be used by collaborating embedded systems in an ad-hoc network to acquire a dynamic IP address. The service is provided by a single network device at a time and will be dynamically reassigned to one of the other network clients if the primary
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ABSTRACT The Internet has evolved to a stage where the number of Internet enabled devices exceeds the global population. With much of the required connectivity over wireless, contention for bandwidth is an important issue that has to be... more
ABSTRACT The Internet has evolved to a stage where the number of Internet enabled devices exceeds the global population. With much of the required connectivity over wireless, contention for bandwidth is an important issue that has to be addressed. In this context, applications that have certain quality of service (QoS) requirements must be protected. WiFi-enabled mobile devices such as smartphones and tablets are commonly used by both personal and business users for voice over IP (VoIP) communications, while also supporting conventional data applications such as email, file transfer and web access. Previous research has shown that time synchronized endpoints can provide better QoS by calculating accurate mouth-to-ear delays, and using this information to better inform buffer playout strategies. The IEEE 802.11e protocol extends 802.11 by providing different traffic priorities based on traffic type. However, it cannot distinguish between traffic streams within the same category. In this paper, we present an access point-centred mechanism for further distinguishing between streams within 802.11e categories. The mechanism predicts the VoIP quality for multiple sessions using the ITU-T E-Model. Delay values are determined from RTCP packet timing information. We provide implementation details on a real world proof-of-concept and present results that correlate well with previous NS-3 based simulations. In addition, we address scalability issues for large scale deployments. Copyright © 2015 John Wiley & Sons, Ltd.
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An analog DCC of the MIB (Medical Information Bus) with the ability of transmitting fast analog signals is described. The data to be transmitted will be compressed by either redundancy or relevance reduction methods. This compression is... more
An analog DCC of the MIB (Medical Information Bus) with the ability of transmitting fast analog signals is described. The data to be transmitted will be compressed by either redundancy or relevance reduction methods. This compression is being performed by a transputer architecture. Cost benefit considerations are being carried out.
ABSTRACT The presented passive optical architecture is based on wave division multiplexing passive optical network (WDM-PON), which provides a solution to problems associated with current PONs. The architecture inherently reduces issues... more
ABSTRACT The presented passive optical architecture is based on wave division multiplexing passive optical network (WDM-PON), which provides a solution to problems associated with current PONs. The architecture inherently reduces issues like bottleneck bandwidth, reduced security and distance limitations. Results from testing non-return-to-zero (NRZ) and return-to-zero (RZ) encoding scheme over distances between 50 km and 80 km are also briefly discussed.
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ABSTRACT This paper describes the development of a system for mapping the features of the human face to sound. In order to determine how best to express these qualities, magnitude estimation experiments are performed with young visually... more
ABSTRACT This paper describes the development of a system for mapping the features of the human face to sound. In order to determine how best to express these qualities, magnitude estimation experiments are performed with young visually impaired students. The data dimensions used are overall head ratio (as a size measure) and distance between key facial features. The display dimensions are frequency and tempo.
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ABSTRACT WiFi-enabled mobile devices such as smartphones and tablets are being increasingly used by both personal and business users for Voice over IP (VoIP) communications, while also supporting conventional data applications such as... more
ABSTRACT WiFi-enabled mobile devices such as smartphones and tablets are being increasingly used by both personal and business users for Voice over IP (VoIP) communications, while also supporting conventional data applications such as email, file transfer and web access. Previous research has shown that time synchronized endpoints can provide better QoS by utilizing the Real-time Transport Control Protocol (RTCP) Sender and Receiver Reports (SR/RR) to calculate accurate Mouth to Ear (M2E) delays. The IEEE 802.11e protocol extends 802.11 by providing different traffic priorities based on traffic type. However it cannot distinguish between traffic streams within the same category. In this paper we propose an Access Point (AP)-centred mechanism for further distinguishing between streams within 802.11 categories. It is based on calculating E-Model R-factors for multiple VoIP sessions from delay values that are calculated using RTCP packet information. We detail a real world test-bed and present initial results that correlate well with a previous NS-3 based implementation.
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ABSTRACT WiFi-enabled mobile handheld devices are being increasingly used for Voice over IP (VoIP) as well as supporting conventional data applications such as email, file transfer and web access. Wireless LANs (WLANs) are thus... more
ABSTRACT WiFi-enabled mobile handheld devices are being increasingly used for Voice over IP (VoIP) as well as supporting conventional data applications such as email, file transfer and web access. Wireless LANs (WLANs) are thus increasingly required to support Quality of Service (QoS)-centric applications, which are delay sensitive and require a certain level of throughput. While 802.11e goes some way towards meeting this need, severe congestion leading to unacceptable delays and packet loss can still occur. In the existing 802.11e standard, all VoIP sessions contend within the same prioritization Access Category (AC), despite potentially having very different, and varying one-way (M2E - Mouth to Ear) delays. In this paper we provide a detailed analysis on simulations that demonstrate how VoIP endpoints that are time synchronized can help optimize 802.11e EDCA in order to prioritize VoIP sessions that have relatively large M2E delays and thus distinguish between VoIP sessions. Using the NS-3 Network Simulator, we quantify the benefits achievable through synchronization of an 802.11e network handling multiple VoIP calls in the presence of other TCP traffic. We present a heuristic EDCA tuning algorithm which uses the ITU-T E-Model R-Factor QoS planning tool as the basis of control.
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ABSTRACT In hospitals drug dosage calculation is a complex process which involves multiple steps when calculating a single dose. This multi-step process is error-prone, as it requires experience and the full attention of physicians or... more
ABSTRACT In hospitals drug dosage calculation is a complex process which involves multiple steps when calculating a single dose. This multi-step process is error-prone, as it requires experience and the full attention of physicians or nurses involved. The potential for error is high and the consequences are potentially serious. Software technologies can offer much added value in ensuring that patients receive the correct dose of a drug based on their individual needs. This paper describes a distributed drug management system that resolves critical drug dosing problems.