Holland, BE, Brain, T & Mohamed Mowjoon, D 2013, 'Running before you can walk: creating blended learning in collaborative spaces', Proceedings of the 24th Annual Conference of the Australasian Association for Engineering Education - AAEE2013, AAEE - Annual Conference of Australasian Association for Engineering Education, Australasian Association for Engineering Education Conference (24th: 2013), Crowne Plaza Hotel, Gold Coast, Queensland, pp. 1-10.
Many universities across Australia are undertaking significant works to upgrade online and face-to-face teaching technologies and campus environments. The University of Technology, Sydney is one of these institutions and, in 2014, the faculty of engineering and IT will occupy a new complex featuring a range of interactive and collaborative learning spaces. There is a growing body of literature evaluating the delivery of courses using online learning environments and collaborative learning spaces (eg Radcliffe et al, 2009, Rasmussen et al 2012). This paper introduces the review of a senior engineering subject delivered in intensive Block mode sessions as a case study for analysing student engagement and experience of interaction using new collaborative learning spaces. Through a post delivery review of the subject this paper assesses and evaluates the learning experience of students in a block mode subject delivered in new collaborative spaces. It analyses findings from two surveys across a range of indicators. Post delivery review of the use of pilot spaces and the quality of student experience of them in combination with new approaches integrated with the online learning environment, can support and inform the transition to wider use of these spaces and innovation in teaching approaches in engineering. This is no small project in a field which has been characterised by an intensive lecture-based model of teaching and learning and so stakeholders need to be `enrolled in its objectives and how they can be aligned with their priorities, and development resourced to ensure success.
Mohamed Mowjoon, D, Agbinya, JI & Chaczko, ZC 2009, 'Replicating Cytokines in Modelling Signal Exchange between Nodes in Wireless Mesh Networks', The International MultiConference of Engineers and Computer Scientists 2009, IAENG International Conference on Communication Systems and Applications, Newswood Limited, International Association of Engineers, Regal Kowloon Hotel, Kowloon, Hong Kong, pp. 315-318.
In recent years wireless mesh network (WMN) technologies and their applications have been actively researched and developed as the promising solution for future wireless mobile networks. Conversely security of WMN is often a secondary reflection in development. In our previous work we proposed Artificial Immune System model to employ in secure routing in WMN. This paper proposes an emerging perception to model danger signal exchange between nodes in WMN by emulating the function of Cytokines in Human Immune System (HIS).
Mohamed Mowjoon, D, Agbinya, JI & Chaczko, ZC 2008, 'Policy-based Danger Management in Artificial Immune System Inspired Secure Routing in Wireless Mesh Networks', Proceedings International MultiConference of Engineers and Computer Scientists, International MultiConference of Engineers and Computer Scientists, International Association of Engineers, Hong Kong, pp. 268-270.
This paper introduces Policy based Management Information Base to manage danger in Artificial Immune System inspired secure routing in Wireless Mesh Networks. WMN management functions are defined and the paper focuses only on the security function. Proposed policy based management and typical operation of the architecture are also reported.
Mohamed Mowjoon, D, Agbinya, JI, Chaczko, ZC & Braun, RM 2008, 'Artificial Immune System Inspired Danger Modelling in Wireless Mesh Networks', Proceedings International Conference on Computer and Communication Engineering (ICCCE08), International Conference on Computer and Communication Engineering, IEEE, Malaysia, pp. 984-988.View/Download from: Publisher's site
In recent years wireless mesh network (WMN) technologies and its applications have been actively researched and developed as the promising solution for future wireless mobile networks. On the other hand security of WMN is often a secondary reflection in development. In our previous work we proposed artificial immune system model to employ in secure routing in WMN. This paper improves and extends the algorithm in our previous work with more achievable danger levels and introduces responsible parameters and model danger in WMN. Moreover this paper proposes the elected network simulator for the experiments.
Mohamed Mowjoon, D, Agbinya, JI, Chaczko, ZC & Chiang, F 2007, 'Self-Organized Classification of Dangers for Secure Wireless Mesh Networks', Australasian Telecommunication Networks and Applications Conference 2007, Australian Telecommunication Networks and Applications Conference, IEEE, Christchurch, New Zealand, pp. 1-6.View/Download from: Publisher's site
This paper introduces danger theory in artificial immune system as a method of responding to danger in wireless mesh networks. It identifies the challenges in deploying Wireless Mesh networks (WMNs) and focus on secure routing as one of the key challenges in deploying WMNs. In order to implement a secure routing system, various Artificial Immune System (AIS) models were analysed. These models have been used in Intrusion Detection System (IDS) and computer security in the literature. In this paper, the authors propose to use Danger models to secure routing in WMNs. The first step in secure routing process is to identify and classify the network dangers and take necessary actions to overcome those dangers. For the classification task, we apply Self-organizing Maps (SOMs) as the classifier to classify the danger levels in WMNs. These identified danger conditions are further deployed as the warning signals for the design of secure routing protocol. The experimental results validate the proposal of applying the Danger Theory (DT) into security area and good performance is also reported by the use of Artificial Neural Network (ANN) classifier.