Outputs (40)

Adaptive service discovery on service-oriented and spontaneous sensor systems (2012)
Journal Article
Liu, L., Xu, J., Antonopoulos, N., Li, J., & Wu, K. (2012). Adaptive service discovery on service-oriented and spontaneous sensor systems. Adhoc and Sensor Wireless Networks, 14(1-2), 107-132

Natural and man-made disasters can significantly impact both people and environments. Enhanced effect can be achieved through dynamic networking of people, systems and procedures and seamless integration of them to fulfil mission objectives with serv... Read More about Adaptive service discovery on service-oriented and spontaneous sensor systems.

Distributed service integration for disaster monitoring sensor systems (2011)
Journal Article
Liu, L., Antonopoulos, N., Xu, J., Webster, D., & Wu, K. (2011). Distributed service integration for disaster monitoring sensor systems. IET Communications, 5(12), 1777-1784. https://doi.org/10.1049/iet-com.2010.0630

Sensor networks have the potential to revolutionise the capture, processing and communication of critical data for use of disaster rescue and relief. In order to provide a dependable rescue capability through dynamically integrating newly developed a... Read More about Distributed service integration for disaster monitoring sensor systems.

Efficient resource discovery in self-organized unstructured peer-to-peer networks (2008)
Journal Article
Liu, L., Antonopoulos, N., Mackin, S., Xu, J., & Russell, D. (2009). Efficient resource discovery in self-organized unstructured peer-to-peer networks. Concurrency and Computation: Practice and Experience, 21(2), 159-183. https://doi.org/10.1002/cpe.1329

In unstructured peer‐to‐peer (P2P) networks, two autonomous peer nodes can be connected if users in those nodes are interested in each other's data. Owing to the similarity between P2P networks and social networks, where peer nodes can be regarded as... Read More about Efficient resource discovery in self-organized unstructured peer-to-peer networks.

Managing peer-to-peer networks with human tactics in social interactions (2007)
Journal Article
Liu, L., Antonopoulos, N., & Mackin, S. (2008). Managing peer-to-peer networks with human tactics in social interactions. Journal of Supercomputing, 44(3), 217-236. https://doi.org/10.1007/s11227-007-0156-y

Small-world phenomena have been observed in existing peer-to-peer (P2P) networks which has proved useful in the design of P2P file-sharing systems. Most studies of constructing small world behaviours on P2P are based on the concept of clustering peer... Read More about Managing peer-to-peer networks with human tactics in social interactions.

Fault-tolerant peer-to-peer search on small-world networks (2007)
Journal Article
Liu, L., Antonopoulos, N., & Mackin, S. (2007). Fault-tolerant peer-to-peer search on small-world networks. Future Generation Computer Systems, 23(8), 921-931. https://doi.org/10.1016/j.future.2007.03.002

This paper presents a small world architecture for P2P networks (SWAN) for content discovery in multi-group P2P systems. A semi-structured P2P algorithm of SWAN is utilized to create and find long-range shortcuts toward remote peer groups. In SWAN, n... Read More about Fault-tolerant peer-to-peer search on small-world networks.

Clinical and genomics data integration using meta-dimensional approach
Presentation / Conference Contribution
Subhani, M. M., Anjum, A., Koop, A., & Antonopoulos, N. (2016, December). Clinical and genomics data integration using meta-dimensional approach. Presented at UCC '16: 9th International Conference on Utility and Cloud Computing, Shanghai, China

Clinical and genomics datasets contain humongous amount of information which are used in their respective environments independently to produce new science or better explain existing approaches. The interaction of data between these two domains is ve... Read More about Clinical and genomics data integration using meta-dimensional approach.

Traffic monitoring using video analytics in clouds
Presentation / Conference Contribution
Abdullah, T., Anjum, A., Tariq, M. F., Baltaci, Y., & Antonopoulos, N. (2014, December). Traffic monitoring using video analytics in clouds. Presented at 2014 IEEE/ACM 7th International Conference on Utility and Cloud Computing (UCC), London, United Kingdom

Traffic monitoring is a challenging task on crowded roads. Traditional traffic monitoring procedures are manual, expensive, time consuming and involve human operators. They are subjective due to the very involvement of human factor and sometimes prov... Read More about Traffic monitoring using video analytics in clouds.

Spatial frequency based video stream analysis for object classification and recognition in clouds
Presentation / Conference Contribution
Yaseen, M. U., Anjum, A., & Antonopoulos, N. (2016, December). Spatial frequency based video stream analysis for object classification and recognition in clouds. Presented at 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologies, Shanghai, China

The recent rise in multimedia technology has made it easier to perform a number of tasks. One of these tasks is monitoring where cheap cameras are producing large amount of video data. This video data is then processed for object classification to ex... Read More about Spatial frequency based video stream analysis for object classification and recognition in clouds.

Modeling and analysis of a deep learning pipeline for cloud based video analytics
Presentation / Conference Contribution
Yaseen, M. U., Anjum, A., & Antonopoulos, N. (2017, December). Modeling and analysis of a deep learning pipeline for cloud based video analytics. Presented at BDCAT '17: Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, Austin, Texas, USA

Video analytics systems based on deep learning approaches are becoming the basis of many widespread applications including smart cities to aid people and traffic monitoring. These systems necessitate massive amounts of labeled data and training time... Read More about Modeling and analysis of a deep learning pipeline for cloud based video analytics.

An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance
Presentation / Conference Contribution
Miller, A., Panneerselvam, J., Liu, L., & Antonopoulos, N. (2022, December). An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance. Presented at 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC), Vancouver, WA, USA

LADA Diabetes is a complex disease, but often dismissed as a potential individual disease within its own right. A comprehensive understanding of previously unknown aspects of LADA diabetes has the potential to not only ascertain a greater comprehensi... Read More about An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance.