In recent years, the improvement of cloud computing and mobile computing techniques has led to the availability of a variety of mobile applications (‘apps’) in the app store. For instance, a garbage truck app that can provide the immediate location of a garbage truck, the location of collection points, and forecasted arrival times of garbage trucks would be useful for mobile users. Since the power consumption of apps on mobile devices if of concern to mobile users, an optimised power-saving mechanism for updating messages, which is based on location information, for a proposed garbage truck fleet management system (GTFMS) is proposed and implemented in this paper. The GTFMS is a three- component system that includes the on-board units on garbage trucks, a fleet management system, and a garbage truck app. In this study, an arrival time forecasting method is designed and implemented in the fleet management system, so that the garbage truck app can retrieve the forecasted arrival time via web services. A message updating event is then triggered that reports the location of garbage truck and the forecasted arrival time. In experiments conducted on case studies, the results showed that the mean accuracy of predicted arrival time by the proposed method is about 81.45 per cent. As for power consumption, the cost of traditional mobile apps is 2,880 times that of the mechanism proposed in this study. Consequently, the GTFMS can provide the precise forecasted arrival time of garbage trucks to mobile users, while consuming less power.