Energy-Efficient Task Offloading in Edge-Fog IoT Architectures for Wearable Healthcare Applications

I. INTRODUCTION

Continuous physiological monitoring in hospital settings generates a mix of latency-tolerant logging tasks and latency-critical alert tasks, and a one-size-fits-all offloading policy, whether always-on-device or always-edge-hub, fails to jointly optimise for both energy and responsiveness.

II. METHODOLOGY

A mixed-integer programming formulation was developed to assign each processing task from 50 simulated wearable devices to either a ward-level fog node or a facility-level edge hub, minimising a weighted objective of device energy consumption subject to per-task latency deadlines, solved using a genetic algorithm with a population of 80 over 200 generations per scheduling window.

III. RESULTS AND EVALUATION

The genetic-algorithm-based offloading policy reduced average device energy consumption by 38 percent relative to an always-edge-hub baseline, while meeting the 200-millisecond deadline for latency-critical alert tasks in 99.2 percent of simulated cases, compared with 91.4 percent for a static threshold-based offloading rule.

IV. CONCLUSION

Dynamic, optimisation-based task offloading between wearable, fog and edge-hub tiers can substantially extend wearable device battery life without compromising the responsiveness of safety-critical alerts. Future work will validate the approach against real hospital network traces.

V. REFERENCES