A Monte Carlo Based Computation Offloading Algorithm for Feeding Robot IoT System

Ageing is becoming an increasingly major problem in European and Japanese societies. We have so far mainly focused on how to improve the eating experience for both frail elderly and caregivers by introducing and developing the eating aid robot, Bestic, made to get the food from plate to the mouth for frail elderly or person with disabilities. We expand the functionalities of Bestic to create food intake reports automatically so as to decrease the undernutrition among frail elderly and workload of caregivers through collecting data via a vision system connected to the Internet of Things (IoT) system. Since the computation capability of Bestic is very limited, computation offloading, in which resource intensive computational tasks are transferred from Bestic to an external cloud server, is proposed to solve Bestic’s resource limitation. In this paper, we proposed a Monte Carlo algorithm based heuristic computation offloading algorithm, to minimize the total overhead of all the Bestic users after we show that the target optimization problem is NP-hard in a theorem. Numeric results showed that the proposed algorithm is effective in terms of system-wide overhead.