Search
Results
-
BUTLER: Increasing the Availability of Low-Power Wireless Communication Protocols
(2023)EWSN '22: Proceedings of the 2022 International Conference on Embedded Wireless Systems and NetworksOver the past years, various low-power wireless protocols based on synchronous transmissions (ST) have been developed to meet the high dependability requirements of emerging cyber-physical applications. For example, Wireless Paxos provides consensus, a key mechanism for building fault-tolerant systems through replication. However, Wireless Paxos and other ST-based protocols are themselves not fault-tolerant: They suffer from a single point ...Conference Paper -
Accurate Onboard Predictions for Indoor Energy Harvesting using Random Forests
(2022)2022 11th Mediterranean Conference on Embedded Computing (MECO)Indoor energy harvesting has recently enabled long-term deployments of sustainable IoT sensor nodes. The performance of such systems operating in an energy-neutral manner can be optimized by exploiting energy prediction models. Numerous prediction algorithms have been developed, yet they are primarily intended for outdoor (solar) energy harvesting. Indoor environments are much more challenging to predict since the primary energy is very ...Conference Paper -
Energy-Efficient Bootstrapping in Multi-hop Harvesting-Based Networks
(2023)2023 18th Wireless On-Demand Network Systems and Services Conference (WONS)Short-range multi-hop communication is an energy-efficient way to collect, share, and distribute large amounts of data with Internet of Things (IoT) systems. Nevertheless, the resource demands of wireless communication impose a burden on battery-operated IoT nodes, limiting their lifetime. Energy harvesting can address the energy limitation but introduces significant power variability, which affects reliable operation causing nodes to ...Conference Paper -
Localised Adaptive Spatial-Temporal Graph Neural Network
(2023)KDD '23: Proceedings of the 29th ACM SIGKDD Conference on Knowledge Discovery and Data MiningSpatial-temporal graph models are prevailing for abstracting and modelling spatial and temporal dependencies. In this work, we ask the following question: whether and to what extent can we localise spatial-temporal graph models? We limit our scope to adaptive spatial-temporal graph neural networks (ASTGNNs), the state-of-the-art model architecture. Our approach to localisation involves sparsifying the spatial graph adjacency matrices. To ...Conference Paper -
The Hypervolume Indicator Revisited
(2007)Lecture Notes in Computer Science ~ Evolutionary Multi-Criterion OptimizationConference Paper -
Service adaptions for mixed-criticality systems
(2014)2014 19th Asia and South Pacific Design Automation Conference (ASP-DAC)Conference Paper