PACE: Privacy-Aware Cloud Ecosystems
Lead Research Organisation:
Newcastle University
Department Name: Sch of Computing
Abstract
Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.
Organisations
Publications
Al-Khafajiy M
(2020)
COMITMENT: A Fog Computing Trust Management Approach
in Journal of Parallel and Distributed Computing
Barati M
(2022)
Privacy-Aware Cloud Auditing for GDPR Compliance Verification in Online Healthcare
in IEEE Transactions on Industrial Informatics
Barika M
(2021)
Online Scheduling Technique To Handle Data Velocity Changes in Stream Workflows
in IEEE Transactions on Parallel and Distributed Systems
Barra S
(2018)
Biometrics-as-a-Service: Cloud-Based Technology, Systems, and Applications
in IEEE Cloud Computing
Forkan A
(2022)
CorrDetector: A framework for structural corrosion detection from drone images using ensemble deep learning
in Expert Systems with Applications
Ihtesham M
(2023)
Privacy Preserving and Serverless Homomorphic-Based Searchable Encryption as a Service (SEaaS)
in IEEE Access
Jha D
(2020)
Multiobjective Deployment of Data Analysis Operations in Heterogeneous IoT Infrastructure
in IEEE Transactions on Industrial Informatics
Llanos J
(2023)
Using the blockchain to enable transparent and auditable processing of personal data in cloud- based services: Lessons from the Privacy-Aware Cloud Ecosystems (PACE) project
in Computer Law & Security Review
Luo S
(2021)
Blockchain-Based Task Offloading in Drone-Aided Mobile Edge Computing
in IEEE Network
| Description | Our experiments reveal that existing consensus algorithms (e.g., proof-of-X, practical Byzantine fault tolerance) for DLT platforms, such as Ethereum, have high computation and communication overhead. Hence, we need to investigate a new lightweight consensus approach, which will adopt cryptography mechanisms (such as digital signature, and device trust) to authenticate blocks within the network of trusted IoT/Edge devices, instead of evaluating them using expensive cryptographic puzzles. We will also need to investigate new algorithms for computing dynamic trust value for IoT/Edge devices and Cloud servers, which will take into account various run-time parameters including statistics related to false block authentication. |
| Exploitation Route | The growing decentralisation, digitalisation, and complexity of energy systems (e.g., microgrids) is making central management and operation challenging. Distributed control and management techniques are required to handle these trends. Blockchains or Distributed Ledger Technologies (DLT), primarily designed to manage distributed transactions by removing central control, could help address the challenges surrounding decentralised energy systems (i.e, microgrids). |
| Sectors | Energy,Healthcare,Manufacturing, including Industrial Biotechology |
| Title | IoTSim-OSmosis |
| Description | Osmotic computing paradigm sets out the principles and algorithms for simplifying the deployment of Internet of Things (IoT) applications in integrated edge-cloud environments. Osmotic Computing focuses on strategies and mechanisms to extend the IoT capabilities by defining, designing, and implementing a modern computing model (IoT, edge, cloud, and SD-WAN). IoTSim-Osmosis is a simulation framework that supports the testing and validation of osmotic computing applications. In particular, it enables a unified modelling and simulation of complex IoT applications over heterogeneous edge-cloud SDN-aware environments. IoTSim-Osmosis is capable of capturing the key functions, characteristics, and behaviors of osmotic paradigm. A wide range of osmosis applications can be simulated and evaluated in IoTSim-Osmosis. To handle the complexity and diversity of osmotic applications, IoTSim-Osmosis provides an abstract mechanism called Microelements (MELs), which encapsulates services, resources and data. In particular, any IoT applications can be represented using a graph of MELs as shown in the figure below. |
| Type Of Material | Improvements to research infrastructure |
| Year Produced | 2021 |
| Provided To Others? | Yes |
| Impact | The tool is being used by academic and industrial research communities. It has also motivated further research in this area. https://scholar.google.com/scholar?oi=bibs&hl=en&cites=17488445812004146680 |
| URL | https://rajivranjan.net/iotsim/iotsim-release/ |