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Networking, Security, and Cloud Research (NSCR)
|Networking, Security, and Cloud Research (NSCR) is a part of the School of Computing, Engineering and Mathematics at the Western Sydney University and is led by Dr. Seyed Shahrestani. The team focuses on security, reliability, performance, and quality of service of large-scale networked systems. The team approach is based on a complementary mix of theoretical, conceptual, applied, and experimental research. Our research spans a broad range of topics including:|
- Digital Ecosystem: Big Data Analytics and Internet of Things $5,000, Western Sydney University
- Wireless/Mobile Community Healthcare, $43,934, Western Sydney University.
- Intelligent Motion Control, $5,000, Western Sydney University
- Equity through online Education, $5,000, Western Sydney University.
- Western Sydney University and Cisco Strategic Alliance, $982,500, Cisco Systems.
Some Indicative Research Projects
Cognitive Radio Networks: Quality of Service Considerations and Enhancements
Student: Nabil Giweli (PhD Candidate)
The rapidly growing number of devices using wireless communication technologies has led to Radio Spectrum (RF) scarcities concerns. The Cognitive Radio (CR) technology offers promising solutions for efficient utilisation of the available RF. As such, the CR is considered as one of the critical technologies for future networks, including 5G and IoT. Primarily, the CR is based on determining the un-occupied spectrums, the spectrum white spaces or holes and opportunistically accessing them when practical. The holes are the frequencies that remain unused by the licensed user, or the Primary User (PU). A device with CR capability, a Secondary User (SU), senses the surrounding spectrum periodically to detect the appearance of the PU in the currently used channel and determine the available spectrum holes. The operation of CR technology requires the execution of mainly four functions.
- Spectrum sensing: sense the surrounding RF to determine spectrum holes and to detect the presence of the relevant PU.
- Spectrum decision: analyse and decide which spectrum hole is the most suitable one, meeting the running application requirements.
- Spectrum sharing: share the available spectrum holes with other SUs as fairly as possible.
- Spectrum mobility: seamlessly switch to another suitable spectrum hole to avoid interference with a detected PU that may wish to start using its licensed frequencies.
One of the primary challenges in broad adoption of CR solutions is how to reduce the QoS degradation caused by the CR functions while achieving higher spectrum utilisation. The primary aim of this study is identifying systematic ways to evaluate and improve the overall QoS levels in CR networks. Notably, for SU devices solely based on sensing approach for assessing the surrounded RF spectrum. The first stage of this study is for classifying spectrum sensing methods and evaluating their effects and impacts on QoS levels. Then a fuzzy logic mechanism is proposed for advanced spectrum sensing strategy. The proposed solution is evaluated for use by various applications running on Wi-Fi networks with CR capability, the so-called White-Fi networks. Consequently, any required revisions on Medium Access Control (MAC) protocol to efficiently handle the proposed sensing strategy to be considered. MATLAB and Riverbed Modeler are used for implementing, simulating and analysing the proposed solutions. The outcomes of this work will help identify the essential factors that degrade the QoS in CR networks and how to mitigate such impact. The proposed sensing strategy and MAC amendments represent the core contribution of this thesis to enhance the QoS in CR networks whereas an efficient RF utilisation is considered. This study results and proposed techniques are expected to benefit any future protocol standardisation efforts for CR solutions based on spectrum sensing.
Deep Learning to Enhance Security of Software-Defined Networks
Student: Ahmed Dawoud (PhD Candidate)
Software defined networks (SDNs) introduce a novel paradigm for data communications. SDN architecture separates the control and forward planes. This architecture presents new characteristics regarding the centralization and network programmability. The network controller is a critical entity as it orchestrates the entire network. It is an attractive target for attackers and intruders. In this work, we revisit intrusion detection techniques to enhance the security of the SDN network controller. Our approach adopts the machine learning algorithms to detect networks anomalies. Recently, there is a breakthrough in the neural network through training multi-layer neural networks. The approach is known as Deep Learning (DL). DL learning has proved to be an immense success in applications such as image processing, speech recognition, and signal processing. In our search, we quantify and adopt unsupervised DL algorithms for anomaly detection in network traffic.
- Big Data Analytics: Secure Access Control
Student: Mohammed Al Zobbi (PhD Candidate)
A glaring need for management of the Big Data and its related areas is the development of a comprehensive access control model. This research investigates the security concerns in the rapidly growing area of the Big Data. Managing the access to a vast size of data with multi-domain users brings about many difficult challenges. Data may be analyzed to reveal patterns and associations. The ubiquitous and intensive nature of access to data for Big Data analytics may result in more privacy violations, for instance, through the increased probability of data re-identification. We are working on a role-based anonymization control for big data that can protect Big Data privacy with high scalability and performance. The framework adopts the k-anonymity based method, in a Bottom-Up Generalization (BUG), to anonymize data records when needed. The framework also provides a granular access control and multiple-level of data anonymization based upon users’ roles. MapReduce frameworks are used for implementation to provide the required efficiency and scalability.
- Indoor Navigation for the Blind and Visually Impaired People Based on the Internet of Things
Student: Payal Mahida (PhD Candidate)
Award: Australian Government Research Training Program (RTP) Scholarship
In the last decade, navigation technology has become popular as it solves many prominent problems of finding locations, the fastest or optimal route to a location, or nearby places. Global Positioning System (GPS) is a suitable and efficient technology for navigating in an outdoor environment. However, the GPS satellite radio signals become weak and distorted when passing through solid walls and obstacles of a building. Despite developing visual maps, individuals face difficulty in finding and navigating to a new place in indoor environments. Following that, avoiding obstacles on the way in real-time for low vision people is an open challenge in such environments. The advent of location-aware enabling technology Internet of Things (IoT) can sense, connect and share data. It has attracted in opening a new set of applications in almost every sector including robotics, games and entertainment, health and safety and commercial products. IoT implies tiny devices embedded in objects capable of storing and retrieving their information from the Internet. With these real, digital and virtual things connected, IoT applications are being introduced to the market every day including comfort and security oriented. IoT-aware applications for disabled people can have a high level of importance as they can improve quality of their life. Our research is based on providing IoT enabled smart environments to Visually Impaired (VI) people. Learning behaviour of changing the environment, analysing user’s profile and providing a line-shore path avoiding real-time obstacles and blockages are few of the benefits to low vision people of our system. Our work aims to simulate and analyse a working prototype of an indoor navigation system which suits the need of VI people and their limitations overcoming their constraints using IoT.
- Internet of Things and Smart Environments: Aging Well and Living Independently
Student: Farhad Ahamed (PhD Candidate)
Award: Australian Government Research Training Program (RTP) Scholarship
The world is facing many issues relevant to an aging population. Australia is no exception to the rapid growth of its proportion of aged people. Technology, and particularly the IoT, can provide many beneficial solutions to address the issues relevant to aging well and living independently. Most seniors prefer to stay in their homes and familiar surroundings, rather than moving to aged-care facilities. Furthermore, the costs associated with such facilities can put substantial pressures on the limited resources of the country. However, the elderly living by themselves may face risks associated with falls, emergencies, or issues relevant to their fitness and health status. Monitoring and communication capabilities of the smart devices and the IoT can partially overcome some of these concerns, allowing for the elders to live longer in their own homes independently. This will alleviate the funding and resource pressures on the society while allowing their relatives and caregivers live with fewer worries and concerns for their well-being and safety.
- eVillage for Rural Areas
Student: Farnaz Farid (PhD Candidate, Completed)
Award: The APA scholarship
Summary: Heterogeneous wireless networks expand the network capacity and coverage by leveraging the network architecture and resources dynamically. However, due to the presence of different communication technologies, the Quality of Service (QoS) evaluation, management, and monitoring of these networks are very challenging tasks. Each communication technology has its characteristics while the applications are utilising them have their QoS requirements. Most current methods are based on analysing the QoS of each application or access network separately. However, these methods do not combine the performance of all the applications and the radio access networks while reporting the QoS of the overall configuration. Therefore, it is hard to get any aggregate performance results using these methods.
To fill this gap, in this thesis, a methodical approach is adopted for the QoS analysis of these types of networks. At first, the approach uses a simple fixed weight-based method and then moves to a more complex dynamic weight-based method and in the end integrates the concepts of fuzzy logic. The proposed methods consider the significance of QoS-related parameters, the available network-based applications, and the available Radio Access Networks (RANs) to characterise the network performance with a set of three integrated QoS metrics. The first metric denotes the performance of each possible application. The second one represents the performance of each active RAN on the network while the third metric characterises the QoS level of the entire network configuration.
To investigate the efficiency of the designed approach, a diverse range of simulation studies utilising different heterogeneous network-based service models are carried out. OPNET is used as the simulator. The simulation results indicate that the approach in this work facilitates better management and monitoring of heterogeneous network configurations and applications utilising them. The simulation studies also show that the unified metrics allow for the choosing of the most appropriate network configuration for an application or service from a catalogue of available configurations. This is done based on the ranking of all network configurations being investigated for their service suitability. Overall, the outcomes from the simulation results analysis demonstrate that the proposed methods can significantly improve the QoS analysis of the heterogeneous networks.
- Privacy of Location Based Services in the Internet of Things
Student: Mahmoud Elkhodr (PhD Candidate, Completed)
Award: The IPRS scholarship and APA award
Summary: The Internet of Things (IoT) refers to a usually massive interconnection of network applications, computers, and many other physical and virtual objects devices or things. Sensors and actuators are of particular interest in IoT. Diverse communication technologies connect the things. However, wireless LANs, mobile systems, Bluetooth, ZigBee and similar evolving technologies play dominant roles for that. This paradigm is a significant shift from a primarily computer-based network model to a fully distributed network of smart objects. This shift poses serious challenges regarding architecture, connectivity, efficiency, and provision of services among many others. However, perhaps, security concerns, and more specifically privacy-related issues top the list of the major challenges. The seamless interconnectivity of things, envisioned in the IoT, highlights the complexity of realizing location privacy in this global infrastructure. To achieve location privacy, objects, specifically those with access to the personal information of users, should not be allowed to communicate in an uncontrolled manner. Therefore, the critical objective of this research is to investigate the extent of privacy-related problems for users of location-based services sharing their information with other users or objects in an IoT environment. It will then move to identify ways to address these issues or to alleviate their impacts.