All Outputs (40)

Optimal controller selection and migration in large scale software defined networks for next generation internet of things (2023)
Journal Article
Shahzad, M., Liu, L., Belkout, N., & Antonopoulos, N. (2023). Optimal controller selection and migration in large scale software defined networks for next generation internet of things. SN Applied Sciences, 5(12), Article 309. https://doi.org/10.1007/s42452-023-05535-0

The substantial amount of IoT traffic, coupled with control messages, places a heavy burden on SDN controllers, which compromises their capacity. We investigate how SDN can revolutionize the conventional approach, aiming to overcome the limitations o... Read More about Optimal controller selection and migration in large scale software defined networks for next generation internet of things.

An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance (2022)
Conference Proceeding
Miller, A., Panneerselvam, J., Liu, L., & Antonopoulos, N. (2022). An Ensemble Neural Model for Classification of LADA Diabetes Case, Control and Variable Importance. In 2022 IEEE/ACM 15th International Conference on Utility and Cloud Computing (UCC). https://doi.org/10.1109/ucc56403.2022.00041

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.

A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics (2021)
Journal Article
Ali, H., Tariq, U. U., Hardy, J., Zhai, X., Lu, L., Zheng, Y., …Antonopoulos, N. (2021). A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics. Computer Science Review, 41, https://doi.org/10.1016/j.cosrev.2021.100416

Internet-of-Things (IoT) is an appealing service to revolutionise Smart City (SC) initiatives across the globe. IoT interconnects a plethora of digital devices known as Sensor Nodes (SNs) to the Internet. Due to their high performance and exceptional... Read More about A survey on system level energy optimisation for MPSoCs in IoT and consumer electronics.

A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G (2019)
Journal Article
Lu, L., Liu, L., Panneerselvam, J., Yuan, B., Gu, J., & Antonopoulos, N. (2020). A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G. IEEE Transactions on Cognitive Communications and Networking, 6(2), 486-498. https://doi.org/10.1109/tccn.2019.2954388

The increasing deployments of 5G mobile communication system is expected to bring more processing power and storage supplements to Internet of Things (IoT) and mobile devices. It is foreseeable the billions of devices will be connected and it is extr... Read More about A GRU-Based Prediction Framework for Intelligent Resource Management at Cloud Data Centres in the Age of 5G.

Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters (2019)
Journal Article
Lu, Y., Liu, L., Panneerselvam, J., Zhai, X., Sun, X., & Antonopoulos, N. (2020). Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters. IEEE Transactions on Sustainable Computing, 5(3), 308-318. https://doi.org/10.1109/TSUSC.2019.2905728

Cloud datacenters are turning out to be massive energy consumers and environment polluters, which necessitate the need for promoting sustainable computing approaches for achieving environment-friendly datacentre execution. Direct causes of excess ene... Read More about Latency-Based Analytic Approach to Forecast Cloud Workload Trend for Sustainable Datacenters.

An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence (2019)
Journal Article
Yuan, B., Panneerselvam, J., Liu, L., Antonopoulos, N., & Lu, Y. (2019). An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence. IEEE Transactions on Industrial Informatics, 15(7), 4295-4305. https://doi.org/10.1109/tii.2019.2902877

Real-time data processing applications demand dynamic resource provisioning and efficient service discovery, which is particularly challenging in resource-constraint edge computing environments. Network embedding techniques can potentially aid effect... Read More about An Inductive Content-Augmented Network Embedding Model for Edge Artificial Intelligence.

Cloud-based video analytics using convolutional neural networks (2018)
Journal Article
Yaseen, M. U., Anjum, A., Farid, M., & Antonopoulos, N. (2019). Cloud-based video analytics using convolutional neural networks. Software: Practice and Experience, 49(4), 565-583. https://doi.org/10.1002/spe.2636

Object classification is a vital part of any video analytics system, which could aid in complex applications such as object monitoring and management. Traditional video analytics systems work on shallow networks and are unable to harness the power of... Read More about Cloud-based video analytics using convolutional neural networks.

Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds (2018)
Journal Article
Yaseen, M. U., Anjum, A., Rana, O., & Antonopoulos, N. (2019). Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds. IEEE Transactions on Systems Man and Cybernetics: Systems, 49(1), 253-264. https://doi.org/10.1109/TSMC.2018.2840341

A system to perform video analytics is proposed using a dynamically tuned convolutional network. Videos are fetched from cloud storage, preprocessed, and a model for supporting classification is developed on these video streams using cloud-based infr... Read More about Deep Learning Hyper-Parameter Optimization for Video Analytics in Clouds.

An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. (2018)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity. IEEE Transactions on Emerging Topics in Computing, https://doi.org/10.1109/TETC.2018.2794328

Cloud workloads are increasingly heterogeneous such that a single Cloud job may encompass one to several tasks, and tasks belonging to the same job may behave distinctively during their actual execution. This inherent task heterogeneity imposes incre... Read More about An approach to optimise resource provision with energy-awareness in datacentres by combating task heterogeneity..

An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres (2018)
Journal Article
Panneerselvam, J., Liu, L., Lu, Y., & Antonopoulos, N. (2018). An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres. Future Generation Computer Systems, 83, 239-249. https://doi.org/10.1016/j.future.2017.12.064

Cloud datacentre resources and the arriving jobs are addressed to be exhibiting increased level of heterogeneity. A single Cloud job may encompass one to several number of tasks, such tasks usually exhibit increased level of behavioural heterogeneity... Read More about An investigation into the impacts of task-level behavioural heterogeneity upon energy efficiency in Cloud datacentres.

Modeling and analysis of a deep learning pipeline for cloud based video analytics (2017)
Conference Proceeding
Yaseen, M. U., Anjum, A., & Antonopoulos, N. (2017). Modeling and analysis of a deep learning pipeline for cloud based video analytics. In BDCAT '17 Proceedings of the Fourth IEEE/ACM International Conference on Big Data Computing, Applications and Technologies, (121-130). https://doi.org/10.1145/3148055.3148081

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.

Big data analytics in healthcare: A cloud based framework for generating insights (2017)
Book Chapter
Anjum, A., Aizad, S., Arshad, B., Subhani, M. M., Davies-Tagg, D., Abdullah, T., & Antonopoulos, N. (2017). Big data analytics in healthcare: A cloud based framework for generating insights. In N. Antonopoulos, & L. Gillam (Eds.), Cloud Computing (153-170). Cham: Springer. https://doi.org/10.1007/978-3-319-54645-2_6

With exabytes of data being generated from genome sequencing, a whole new science behind genomics big data has emerged. As technology improves, the cost of sequencing a human genome has gone down considerably increasing the number of genomes being se... Read More about Big data analytics in healthcare: A cloud based framework for generating insights.

InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments (2017)
Journal Article
Panneerselvam, J., Liu, L., & Antonopoulos, N. (2018). InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments. Future Generation Computer Systems, 80, 322-341. https://doi.org/10.1016/j.future.2017.05.022

Cloud Computing has emerged as a low cost anywhere anytime computing paradigm. Given the energy consumption characteristics of the Cloud resources, service providers are under immense pressure to reduce the energy implications of the datacentres. For... Read More about InOt-RePCoN: Forecasting user behavioural trend in large-scale cloud environments.

Efficient service discovery in decentralized online social networks (2017)
Journal Article
Yuan, B., Liu, L., & Antonopoulos, N. (2018). Efficient service discovery in decentralized online social networks. Future Generation Computer Systems, 86, 775-791. https://doi.org/10.1016/j.future.2017.04.022

Online social networks (OSN) have attracted millions of users worldwide over the last decade. There are a series of urgent issues faced by existing OSN such as information overload, single-point of failure and privacy concerns. The booming Internet o... Read More about Efficient service discovery in decentralized online social networks.

Clinical and genomics data integration using meta-dimensional approach (2016)
Conference Proceeding
Subhani, M. M., Anjum, A., Koop, A., & Antonopoulos, N. (2016). Clinical and genomics data integration using meta-dimensional approach. In UCC '16 Proceedings of the 9th International Conference on Utility and Cloud Computinghttps://doi.org/10.1145/2996890.3007896

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.

Spatial frequency based video stream analysis for object classification and recognition in clouds (2016)
Conference Proceeding
Yaseen, M. U., Anjum, A., & Antonopoulos, N. (2016). Spatial frequency based video stream analysis for object classification and recognition in clouds. In Proceedings of the 3rd IEEE/ACM conference on Big Data Computing, Applications and Technologieshttps://doi.org/10.1145/3006299.3006322

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.

Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks (2016)
Journal Article
Panneerselvam, J., Hardy, J., Liu, L., Yuan, B., & Antonopoulos, N. (2016). Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks. IEEE Access, 4, 9129-9144. https://doi.org/10.1109/access.2016.2602321

Given the emergence of mobile cloud computing (MCC), its associated energy implications are witnessed at larger scale. With offloading computationally intensive tasks to the cloud datacentres being the basic concept behind MCC, most of the mobile ter... Read More about Mobilouds: An Energy Efficient MCC Collaborative Framework With Extended Mobile Participation for Next Generation Networks.

An efficient algorithm for partially matched services in internet of services (2016)
Journal Article
Ahmed, M., Liu, L., Hardy, J., Yuan, B., & Antonopoulos, N. (2016). An efficient algorithm for partially matched services in internet of services. Personal and Ubiquitous Computing, 20(3), 283-293. https://doi.org/10.1007/s00779-016-0917-9

Internet of Things (IoT) connects billions of devices in an Internet-like structure. Each device encapsulated as a real-world service which provides functionality and exchanges information with other devices. This large-scale information exchange res... Read More about An efficient algorithm for partially matched services in internet of services.

A socioecological model for advanced service discovery in machine-to-machine communication networks (2016)
Journal Article
Liu, L., Antonopoulos, N., Zheng, M., Zhan, Y., & Ding, Z. (2016). A socioecological model for advanced service discovery in machine-to-machine communication networks. ACM transactions on embedded computing systems, 15(2), Article 38. https://doi.org/10.1145/2811264

The new development of embedded systems has the potential to revolutionize our lives and will have a significant impact on future Internet of Thing (IoT) systems if required services can be automatically discovered and accessed at runtime in Machine-... Read More about A socioecological model for advanced service discovery in machine-to-machine communication networks.

Video stream analysis in clouds: An object detection and classification framework for high performance video analytics (2016)
Journal Article
Tariq, M., Anjum, A., Abdullah, T., Tariq, M. F., Baltaci, Y., & Antonopoulos, N. (2016). Video stream analysis in clouds: An object detection and classification framework for high performance video analytics. IEEE transactions on cloud computing, https://doi.org/10.1109/TCC.2016.2517653

Object detection and classification are the basic tasks in video analytics and become the starting point for other complex applications. Traditional video analytics approaches are manual and time consuming. These are subjective due to the very involv... Read More about Video stream analysis in clouds: An object detection and classification framework for high performance video analytics.