Data for Buffer management for trust computation in resource-constrained IoT networks

To address this, we develop the concept of the memory profile of an agent and that profile’s utility. We formalise the profile eviction problem in a unified profile memory model and show it is NP-complete. To circumvent the inherent complexity, we study the performance of eviction algorithms in a partitioned profile memory model using our utility metric.

Our results show that localised eviction strategies which only consider one specific type of information do not perform well. Thus we propose a novel eviction strategy that globally considers all types of trust information stored and we show that it outperforms local eviction strategies for the majority of memory sizes and agent behaviours. In this paper, we develop a concept of information utility to a trust model and formalise the problem of information eviction, which we prove to be NP-complete.

We then investigate the usefulness of different eviction strategies to maximise the utility of information stored to enable trust-based task offloading.<br><br>Data record consists of a zip archive containing the simulation data.