• On textual analysis and machine learning for cyberstalking detection

      Frommholz, Ingo; al-Khateeb, Haider; Potthast, Martin; Ghasem, Zinnar; Shukla, Mitul; Short, Emma; University of Bedfordshire; Bauhaus-Universität Weimar (Springer, 2016-06-01)
      Cyber security has become a major concern for users and businesses alike. Cyberstalking and harassment have been identified as a growing anti-social problem. Besides detecting cyberstalking and harassment, there is the need to gather digital evidence, often by the victim. To this end, we provide an overview of and discuss relevant technological means, in particular coming from text analytics as well as machine learning, that are capable to address the above challenges. We present a framework for the detection of text-based cyberstalking and the role and challenges of some core techniques such as author identification, text classification and personalisation. We then discuss PAN, a network and evaluation initiative that focusses on digital text forensics, in particular author identification.
    • On the impact of mobility on battery-less RF energy harvesting system performance

      Munir, Bilal; Dyo, Vladimir (MDPI, 2018-10-23)
      The future of Internet of Things (IoT) envisions billions of sensors integrated with the physical environment. At the same time, recharging and replacing batteries on this infrastructure could result not only in high maintenance costs, but also large amounts of toxic waste due to the need to dispose of old batteries. Recently, battery-free sensor platforms have been developed that use supercapacitors as energy storage, promising maintenance-free and perpetual sensor operation. While prior work focused on supercapacitor characterization, modelling and supercapacitor-aware scheduling, the impact of mobility on capacitor charging and overall sensor application performance has been largely ignored. We show that supercapacitor size is critical for mobile system performance and that selecting an optimal value is not trivial: small capacitors charge quickly and enable the node to operate in low energy environments, but cannot support intensive tasks such as communication or reprogramming; increasing the capacitor size, on the other hand, enables the support for energy-intensive tasks, but may prevent the node from booting at all if the node navigates in a low energy area. The paper investigates this problem and proposes a hybrid storage solution that uses an adaptive learning algorithm to predict the amount of available ambient energy and dynamically switch between two capacitors depending on the environment. The evaluation based on extensive simulations and prototype measurements showed up to 40% and 80% improvement compared to a fixed-capacitor approach in terms of the amount of harvested energy and sensor coverage.
    • On the usage of history for energy efficient spectrum sensing

      Syed, Tazeen Shabana; Safdar, Ghazanfar Ali; University of Bedfordshire (IEEE, 2015-03-31)
      Spectrum sensing is one of the most challenging issues in cognitive radio networks. It provides protection to primary users (PUs) from interference and also creates opportunities of spectrum access for secondary users (SUs). It should be performed efficiently to reduce number of false alarms and missed detection. At the same time, spectrum sensing should be energy efficient to ensure the longevity of cognitive radio devices. This work presents a novel scheme which investigates the usage of history for energy efficient spectrum sensing in infrastructure cognitive radio networks. The presented scheme employs an iteratively developed history processing database. It is shown that usage of history helps predicting PU activity and results into reduced spectrum scanning by SUs thereby improving the sensing related energy consumption.
    • Ontology driven personal health knowledge discovery

      Yu, Hong Qing; Zhao, Xia; Deng, Zhikun; Dong, Feng; University of Bedfordshire (Springer Verlag, 2015-08-04)
      With fast development of smart sensor devices and mobile applications, all different kinds of information related to humans can be founded on the Internet that can be seen as a universal data repository or called Web of Data. Health or healthcare related data are not exceptional in the Web of Data age. The most important and valuable data comes from IoT such as sensors and mobile activity tracking applications to support developing self-health risk detection and management applications. This paper presents a comprehensive ontology driven knowledge discovery framework in personal health domain, which aims to reason and discover health knowledge from various data sources of IoT. The framework contains a sensor oriented Personal Wellness Knowledge Ontology and data integration architecture to complete a whole lifecycle of health knowledge detecting and reasoning path. In addition, a cloud computing based parallel semantic lifting algorithm is described for illustrating the semantic data generation process in detail.
    • OpenFlow 1.3 extension for OMNeT++

      Salih, Mukhald; Cosmas, John; Zhang, Yue; Brunel University; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-12-28)
      This paper presents an upgrade for OpenFlow 1.0 extension module [1]. The new module developed for the simulation of OpenFlow 1.3 based networks with OMNeT++, a well-known, widely-used modular simulation framework, which offers a high degree of experiment support. It focuses mainly on modelling the OpenFlow switch and controller with a particular attention on the aspects related to support load balancing, fault tolerance, Quality of Service, and the extensible match. Subsequently, we describe in detail the general architecture of the module and its components with particular emphasis on the switch module.
    • Optimization of real-time transmission reliability on wireless industrial automation networks

      Karimireddy, Thanmayee; Zhang, Sijing; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-07-01)
      Reliable transmission of the control packets is one of the key requirements of deploying an Industrial Wireless Network (IWN) for automation purposes. The use of error-prone wireless channel, transmission of control packets having hard real-time traffic over IWN have made reliable transmission assurance highly challenging. This paper intends in addressing this challenge through reliability optimization of the hard real-time traffic transmitted over IWN. ReliaMAC is a novel scheme that is proposed to support reliable transmission of hard real-time traffic using redundancy bits added to each double MAC frame. Optimization of transmission reliability is used to identify the optimum number of nodes in an IWN that can support reliable transmission of the hard real-time traffic to a maximum extent. Network simulation results revealed the effectiveness of ReliaMAC in improving reliability of hard real-time communication on IWNs.
    • Parallel marching blocks: a practical isosurfacing algorithm for large data on many-core architectures

      Liu, Baoquan; Clapworthy, Gordon J.; Dong, Feng; Wu, Enhua; University of Bedfordshire; University of Macau (Wiley, 2016-07-04)
      Interactive isosurface visualisation has been made possible by mapping algorithms to GPU architectures. However, current state-of-the-art isosurfacing algorithms usually consume large amounts of GPU memory owing to the additional acceleration structures they require. As a result, the continued limitations on available GPU memory mean that they are unable to deal with the larger datasets that are now increasingly becoming prevalent. This paper proposes a new parallel isosurface-extraction algorithm that exploits the blocked organisation of the parallel threads found in modern many-core platforms to achieve fast isosurface extraction and reduce the associated memory requirements. This is achieved by optimising thread co-operation within thread-blocks and reducing redundant computation; ultimately, an indexed triangular mesh could be produced. Experiments have shown that the proposed algorithm is much faster (up to 10×) than state-of-the-art GPU algorithms and has a much smaller memory footprint, enabling it to handle much larger datasets (up to 64×) on the same GPU. 
    • Parsec: a state channel for the Internet of Value

      Jaiswal, Amit Kumar (2018-07-30)
      We propose Parsec, a web-scale State channel for the Internet of Value to exterminate the consensus bottleneck in Blockchain by leveraging a network of state channels which enable to robustly transfer value off-chain. It acts as an infrastructure layer developed on top of Ethereum Blockchain, as a network protocol which allows coherent routing and interlocking channel transfers for trade-off between parties. A web-scale solution for state channels is implemented to enable a layer of value transfer to the internet. Existing network protocol on State Channels include Raiden for Ethereum and Lightning Network for Bitcoin. However, we intend to leverage existing web-scale technologies used by large Internet companies such as Uber, LinkedIn or Netflix. We use Apache Kafka to scale the global payment operation to trillions of operations per day enabling near-instant, low-fee, scalable, and privacy-sustainable payments. Our architecture follows Event Sourcing pattern which solves current issues of payment solutions such as scaling, transfer, interoperability, low-fees, micropayments and to name a few. To the best of knowledge, our proposed model achieve better performance than state-of-the-art lightning network on the Ethereum based (fork) cryptocoins.
    • Patient empowerment for cancer patients through a novel ICT infrastructure

      Kondylakis, Haridimos; Bucur, Anca; Crico, Chiara; Dong, Feng; Graf, Norbert; Hoffman, Stefan; Koumakis, Lefteris; Manenti, Alice; Marias, Kostas; Mazzocco, Ketti; et al. (Academic Press Inc., 2019-12-06)
      As a result of recent advances in cancer research and “precision medicine” approaches, i.e. the idea of treating each patient with the right drug at the right time, more and more cancer patients are being cured, or might have to cope with a life with cancer. For many people, cancer survival today means living with a complex and chronic condition. Surviving and living with or beyond cancer requires the long-term management of the disease, leading to a significant need for active rehabilitation of the patients. In this paper, we present a novel methodology employed in the iManageCancer project for cancer patient empowerment in which personal health systems, serious games, psychoemotional monitoring and other novel decision-support tools are combined into an integrated patient empowerment platform. We present in detail the ICT infrastructure developed and our evaluation with the involvement of cancer patients on two sites, a large-scale pilot for adults and a small-scale test for children. The evaluation showed mixed evidences on the improvement of patient empowerment, while ability to cope with cancer, including improvement in mood and resilience to cancer, increased for the participants of the adults′ pilot.
    • Patient-specific fibre-based models of muscle wrapping

      Kohout, Josef; Clapworthy, Gordon J.; Zhao, Youbing; Tao, Yubo; Gonzalez-Garcia, G.; Dong, Feng; Wei, Hui; Kohoutová, E.; University of West Bohemia; University of Bedfordshire (Royal Society, 2013-04-06)
      In many biomechanical problems, the availability of a suitable model for the wrapping of muscles when undergoing movement is essential for the estimation of forces produced on and by the body during motion. This is an important factor in the Osteoporotic Virtual Physiological Human project which is investigating the likelihood of fracture for osteoporotic patients undertaking a variety of movements. The weakening of their skeletons makes them particularly vulnerable to bone fracture caused by excessive loading being placed on the bones, even in simple everyday tasks. This paper provides an overview of a novel volumetric model that describes muscle wrapping around bones and other muscles during movement, and which includes a consideration of how the orientations of the muscle fibres change during the motion. The method can calculate the form of wrapping of a muscle of medium size and visualize the outcome within tenths of seconds on commodity hardware, while conserving muscle volume. This makes the method suitable not only for educational biomedical software, but also for clinical applications used to identify weak muscles that should be strengthened during rehabilitation or to identify bone stresses in order to estimate the risk of fractures.
    • Patterns-of-life aided authentication

      Zhao, Nan; Ren, Aifeng; Zhang, Zhiya; Zhu, Tianqiao; Ur-Rehman, Masood; Yang, Xiaodong; Hu, Fangming; Xidian University; University of Bedfordshire (MDPI, 2016-09-23)
      Wireless Body Area Network (WBAN) applications have grown immensely in the past few years. However, security and privacy of the user are two major obstacles in their development. The complex and very sensitive nature of the body-mounted sensors means the traditional network layer security arrangements are not sufficient to employ their full potential, and novel solutions are necessary. In contrast, security methods based on physical layers tend to be more suitable and have simple requirements. The problem of initial trust needs to be addressed as a prelude to the physical layer security key arrangement. This paper proposes a patterns-of-life aided authentication model to solve this issue. The model employs the wireless channel fingerprint created by the user’s behavior characterization. The performance of the proposed model is established through experimental measurements at 2.45 GHz. Experimental results show that high correlation values of 0.852 to 0.959 with the habitual action of the user in different scenarios can be used for auxiliary identity authentication, which is a scalable result for future studies.
    • PCF based sensor with high sensitivity, high birefringence and low confinement losses for liquid analyte sensing applications

      Ademgil, Huseyin; Haxha, Shyqyri; ; European University of Lefke; University of Bedfordshire (MDPI AG, 2015-12-13)
      In this paper, we report a design of high sensitivity Photonic Crystal Fiber (PCF) sensor with high birefringence and low confinement losses for liquid analyte sensing applications. The proposed PCF structures are designed with supplementary elliptical air holes in the core region vertically-shaped V-PCF and horizontally-shaped H-PCF. The full vectorial Finite Element Method (FEM) simulations performed to examine the sensitivity, the confinement losses, the effective refractive index and the modal birefringence features of the proposed elliptical air hole PCF structures. We show that the proposed PCF structures exhibit high relative sensitivity, high birefringence and low confinement losses simultaneously for various analytes.
    • Performance analysis of a novel decentralised MAC protocol for cognitive radio networks

      Alhakami, Wajdi; Mansour, Ali; Safdar, Ghazanfar Ali; University of Bedfordshire (IEEE, 2016-09-15)
      Due to the demand of emerging Cognitive Radio (CR) technology to permits using the unused licensed spectrum parts by cognitive users (CUs) to provide opportunistic and efficient utilisation of the white spaces. This requires deploying a CR MAC with the required characteristics to coordinate the spectrum access among CUs. Therefore, this paper presents the design and implementation of a novel Medium Access Control (MAC) protocol for decentralised CRNs (MCRN). The protocol provides efficient utilisations of the unused licensed channels and enables CUs to exchange data successfully over licensed channels. This is based on the observation procedure of sensing the status of the Licensed Users (LUs) are ON or OFF over the licensed channels. The protocol is validated with the comparison procedure against two different benchmark protocols in terms of the network performance; communication time and throughput. Therefore, performance analysis demonstrated that the proposed MCRN perform better and achieve higher throughput and time benefits than the benchmarks protocols.
    • Performance analysis of opportunistic relaying and opportunistic hybrid incremental relaying over fading channels

      Lateef, Hafiz Yasar; Dyo, Vladimir; Allen, Ben; University of Bedfordshire; University of Oxford (Institution of Engineering and Technology (IET), 2015-06-01)
      In this study, the authors develop and present a comprehensive analysis of two opportunistic cooperative relaying schemes for long term evolution (LTE)-advanced networks operating over generalised- K and Nakagami- m fading channels. They present and compare the performance of opportunistic relaying (OR) and opportunistic hybrid automatic repeat request incremental relaying (OHIR). They analyse performance in terms of the average symbol error rate for both conventional OR and OHIR LTE-advanced networks with the radio channel modelled as composite generalised- K fading (encompassing both fading and shadowing) and Nakagami- m fading channels. They also analyse the outage probability for OR operating over these channels. Both the theoretical analysis and simulations confirm that for conventional OR LTE-advanced networks operating over composite generalised- K fading channels, a diversity order of k ( N + 1) is achieved when shadowing is more severe than fading, and a diversity order of m ( N + 1) is achieved when fading is more severe than shadowing (where k and m represent the generalised- K distribution shape parameters and N represents the number of candidate relays for the OR selection). The simulation results confirm the accuracy of the analytical expressions developed in this study. It is evident from the theoretical analysis and simulations that, for a similar quality of service as that for OR, OHIR not only reduces the amount of required radio resources but also maintains the full diversity order.
    • Performance comparison of top N recommendation algorithms

      Mustafa, Ghulam; Frommholz, Ingo; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2015-10-26)
      In traditional recommender systems, services/items are recommended to the user based on the initial ratings while the results comes from the predicted rating values are not considered which further refers to top N recommendations. In top N recommendation algorithms, recommendation process is further enhanced by predicting the missing ratings where the basic objective is to find the items that might be interest of a user. Performance comparison and evaluation of different top N recommendation algorithms is quite challenging for large datasets where selection of an appropriate algorithm can help to improve the recommendation process by predicting missing ratings. Therefore, in this paper we analyse and evaluate the 6 different top N recommendation algorithms using accuracy metrics such as precision and recall on Movie-lense 100K dataset from the Group-lens. Our main finding is the selection of Top N recommendation algorithm that perform significantly better than other recommender algorithms in pursuing the top-N recommendation process.
    • Performance evaluation of string based malware detection methods

      Mira, Fahad; Huang, Wei; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2019-07-01)
      Conventional signature-based malware detection techniques have been used for many years because of their high detection rates and low false positive rates. However, signature-based detection techniques are regarded as ineffective due to their inability to detect unseen, new, polymorphic and metamorphic malware. To affect the weaknesses of the signature-based detection techniques, researchers have turned into behavioural-based detection techniques whereby a malware behavioural is constructed by capturing malware API calls during execution. In this context, API call sequences matching techniques are widely used to compute malware similarities. However, API call sequences matching techniques require large processing resources which make the process slow due to computational complexity and therefore, cannot scale to large API call sequences. To mitigate its problem, Longest Common Substring and Longest Common Subsequence have been used in this paper for strings matching in order to detect malware and their variants. In this paper we evaluate these two algorithms in the context of malware detection rate and false alarm rate.
    • Periodic antireflection surface structure fabricated on silicon by four-beam laser interference lithography

      Zhang, Ziang; Wang, Zuobin; Wang, Di; Ding, Yu-jie; Changchun University of Science and Technology; University of Bedfordshire (Laser Institute of America, 2013-12-31)
      Silicon surface structures with excellent antireflection property arouse wide interest. Chemical and physical methods such as femtosecond, nanosecond, and picosecond laser processing, wet-chemical etching, electrochemical etching, and reactive ion etching have been developed to fabricate them. However, the methods can only produce a quasi-ordered array of sharp conical microspikes on silicon surface. In this paper, we present a method to fabricate periodic silicon antireflection surface structures using direct four-beam laser interference lithography (LIL). With 1 atm ambient atmosphere of SF6 and the laser fluence of the four beams irradiated on the silicon surface at 0.64 J cm-2, the periodical conical spikes were generated. Changing the polarization directions of the opposite incident beam pairs in a four-beam LIL system could convert conical spikes structure into an array of holes. Antireflection in a wide spectral range was measured by a spectrophotometer from ultraviolet to near-infrared. The average reflectance of this periodic black silicon surface is less than 3.5%. © 2014 Laser Institute of America.
    • Personally identifiable information security in cloud computing

      Feng, Xiaohua; Zhang, Xiangrui (2020-12-18)
      A cyber security application in Personally identifiable information) PII is attracting more and more attention and related to majority people’s everyday activities. The paper is introduced the trends of cyber security in cloud computing and in particular, focus on the responsibility of data privacy, especially in European Union countries. As the impact is on data protection which includes organisation based in the union, or has branches in the union or provides services to the union residents. The paper is also introduced the updated recent development content in our society which caused the impact that we have to deliver ISO standards; for instance, ISO/IEC 27018 and so on. A consequence of the standard is that regular practices of risk assessment need to be carried out in a regular base; such as an annually assessment. Keywords- Data protection, personal privacy, cryptography, cloud computing, cyber security, security policy, Trustworthiness, data service, personally identifiable information (PII), and ISO 27018
    • Photonic crystal fiber based surface plasmon sensor design and analyze with elliptical air holes

      Yaşli, Ahmet; Akowuah, Emmanuel K.; Haxha, Shyqyri; Ademgil, Huseyin; European University of Lefke; Kwame Nkrumah University of Science and Technology; University of Bedfordshire (Institute of Electrical and Electronics Engineers Inc., 2016-11-24)
      A photonic crystal fiber (PCF) based surface plasmon resonance (SPR) sensor has been presented at this work. The sensor sensitivity for multiple analyte depending on operating wavelength and spectral interrogation mode has been investigated thoroughly. Our numerical results indicate that the sensitivity of the x-polarized fundamental mode is 4100nm/Refractive Index Unit (RIU) and 4400nm/RIU for the y-polarized fundamental mode with 2.4×10-5 RIU and 2.3×10-5 RIU sensor resolutions respectively.
    • Physical detection of misbehavior in relay systems with unreliable channel state information

      Lv, Tiejun; Yin, Yajun; Lu, Yueming; Yang, Shaoshi; Liu, Enjie; Clapworthy, Gordon J.; University of Bedfordshire; Beijing University of Posts and Telecommunications; Huawei Technolgies Co. (IEEE, 2018-04-09)
      We study the detection 1 of misbehavior in a Gaussian relay system, where the source transmits information to the destination with the assistance of an amplify-and-forward relay node subject to unreliable channel state information (CSI). The relay node may be potentially malicious and corrupt the network by forwarding garbled information. In this situation, misleading feedback may take place, since reliable CSI is unavailable at the source and/or the destination. By classifying the action of the relay as detectable or undetectable, we propose a novel approach that is capable of coping with any malicious attack detected and continuing to work effectively in the presence of unreliable CSI. We demonstrate that the detectable class of attacks can be successfully detected with a high probability. Meanwhile, the undetectable class of attacks does not affect the performance improvements that are achievable by cooperative diversity, even though such an attack may fool the proposed detection approach. We also extend the method to deal with the case in which there is no direct link between the source and the destination. The effectiveness of the proposed approach has been validated by numerical results.