Poonam Yadav

Jinkun Zhang, Poonam Yadav

Partitioned DNN inference is a promising approach for latency-sensitive intelligent services in edge networks, since it allows different parts of a model to be executed across end devices, edge servers, and the cloud. However, in a multi-hop edge network, partition placement and inference traffic routing are inherently coupled: raw inputs, intermediate features, and final outputs may have very different sizes, while candidate nodes also differ in computation capability. In addition, both communication and computation delays can become congestion-dependent under load. In this paper, we study joint partition placement and routing for fixed-partition DNN inference over heterogeneous multi-hop edge networks. We consider a small number of DNN partitions, each placed at exactly one node without replication, and formulate a congestion-aware mixed discrete--continuous optimization problem that captures both routing and execution costs. To solve it, we develop a practical alternating framework that couples partition placement with congestion-aware forwarding updates. Through numerical evaluation on hierarchical, regular, synthetic irregular, and real backbone-inspired topologies, we show that split flexibility is particularly important in IoT--edge--cloud settings, while congestion-aware refinement becomes increasingly beneficial as the offered load grows. We further illustrate how the preferred operating point depends on the communication--computation tradeoff.

Poonam Yadav, John Darlington

Online Citizen Science platforms are good examples of socio-technical systems where technology-enabled interactions occur between scientists and the general public (volunteers). Citizen Science platforms usually host multiple Citizen Science projects, and allow volunteers to choose the ones to participate in. Recent work in the area has demonstrated a positive feedback loop between participation and learning and creativity in Citizen Science projects, which is one of the motivating factors both for scientists and the volunteers. This emphasises the importance of creating successful Citizen Science platforms, which support this feedback process, and enable enhanced learning and creativity to occur through knowledge sharing and diverse participation. In this paper, we discuss how scientists' and volunteers' motivation and participation influence the design of Citizen Science platforms. We present our summary as guidelines for designing these platforms as user-inspired socio-technical systems. We also present the case-studies on popular Citizen Science platforms, including our own CitizenGrid platform, developed as part of the CCL EU project, as well as Zooniverse, World Community Grid, CrowdCrafting and EpiCollect+ to see how closely these platforms follow our proposed guidelines and how these may be further improved to incorporate the creativity enabled by the collective knowledge sharing.