Polytechnic School, Catholic University of Murcia (UCAM), 30107 Murcia, Spain.
Sensors (Basel). 2021 Jul 1;21(13):4534. doi: 10.3390/s21134534.
The management and collection of household waste often represents a demanding task for elderly or impaired people. In particular, the increasing generation of plastic waste at home may pose a problem for these groups, as this type of waste accumulates very rapidly and occupies a considerable amount of space. This paper proposes a collaborative infrastructure to monitor household plastic waste. It consists of simple smart bins using a weight scale and a smart application that forecasts the amount of plastic generated for each bin at different time horizons out of the data provided by the smart bins. The application generates optimal routes for the waste-pickers collaborating in the system through a route-planning algorithm. This algorithm takes into account the predicted amount of plastic of each bin and the waste-picker's location and means of transport. This proposal has been evaluated by means of a simulated scenario in Quezon City, Philippines, where severe problems with plastic waste have been identified. A set of 176 experiments have been performed to collect data that allow representing different user behaviors when generating plastic waste. The results show that our proposal enables waste-pickers to collect more than the 80% of the household plastic-waste bins before they are completely full.
家庭垃圾的管理和收集通常对老年人或残障人士来说是一项艰巨的任务。特别是,家庭中越来越多的塑料垃圾可能会给这些群体带来问题,因为这种垃圾积累得非常快,占用了相当大的空间。本文提出了一种用于监测家庭塑料垃圾的协作基础设施。它由使用重量秤的简单智能垃圾桶和一个智能应用程序组成,该应用程序根据智能垃圾桶提供的数据,在不同的时间范围内预测每个垃圾桶产生的塑料量。该应用程序通过路线规划算法为协作系统中的拾荒者生成最佳路线。该算法考虑了每个垃圾桶的预测塑料量以及拾荒者的位置和交通工具。该提案已通过在菲律宾奎松市的模拟场景进行了评估,该场景中发现了严重的塑料垃圾问题。已经进行了一组 176 次实验来收集数据,这些数据可以代表生成塑料垃圾时不同用户的行为。结果表明,我们的提案使拾荒者能够在家庭塑料垃圾桶完全装满之前收集超过 80%的垃圾桶垃圾。