School of Computer Engineering, Kalinga Institute of Industrial Technology Deemed to be University, Bhubaneswar, India.
Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, A.P., India.
PLoS One. 2022 Aug 11;17(8):e0272383. doi: 10.1371/journal.pone.0272383. eCollection 2022.
Collaborative modelling of the Internet of Things (IoT) with Artificial Intelligence (AI) has merged into the Intelligence of Things concept. This recent trend enables sensors to track required parameters and store accumulated data in cloud storage, which can be further utilized by AI based predictive models for automatic decision making. In a smart and sustainable environment, effective waste management is a concern. Poor regulation of waste in surrounding areas leads to rapid spread of contagious disease risks. Traditional waste object management requires more working staff, increases effort, consumes time and is relatively ineffective. In this research, an Intelligence of Things Enabled Smart Waste Management (IoT-SWM) model with predictive capabilities is developed. Here, local sinks (LS) are deployed in specified locations. At every instant, the current status of smart bins in each LS is notified to users to determine the priority level of LS to be emptied. Based on aggregated sensor values for the three smart bins, LS weight and poison gas value, the priority order of emptying LS is computed, and decision is made whether to notify the users with an alert message or not. It also helps in predicting the LS, which is likely to be filled up at a faster rate based on assigned timestamp. This model is implemented in real time with many LS and it was observed that bins, which were close to more crowded sites filled up faster compared to sparse populated areas. Random forest algorithm was used to predict whether an alert notification is to be sent or not. An average mean of 95.8% accuracy was noted while using 60 decision trees in random forest algorithm. The average mean execution latency recorded for training and testing sets is 13.06 sec and 14.39 sec respectively. Observed accuracy rate, precision, recall and f1-score parameters were 95.8%, 96.5%, 98.5% and 97.2% respectively. Model buildup and the validation time computed were 3.26 sec and 4.25 sec respectively. It is also noted that at a threshold value of 0.93 in LS level, the maximum accuracy rate reached was 95.8%. Thus, based on the prediction of random forest approach, a decision to notify the users is taken. Obtained outcome indicates that the waste level can be efficiently determined, and the overflow of dustbins can be easily checked in time.
物联网 (IoT) 与人工智能 (AI) 的协作建模已经融合到物联网智能 (Intelligence of Things) 概念中。这一最新趋势使传感器能够跟踪所需参数，并将累积的数据存储在云存储中，然后由基于人工智能的预测模型进一步利用这些数据进行自动决策。在智能和可持续的环境中，有效的废物管理是一个关注点。周围地区废物管理不善会导致传染病风险迅速传播。传统的废物对象管理需要更多的工作人员，增加工作量，耗费时间，而且效果相对较差。在这项研究中，开发了一种具有预测能力的物联网智能废物管理 (IoT-SWM) 模型。在这里，本地接收器 (LS) 部署在指定位置。在每一个瞬间，每个 LS 中的智能垃圾桶的当前状态都会通知用户，以确定要清空的 LS 的优先级。根据三个智能垃圾桶的传感器值、LS 重量和毒气值的聚合值，计算出 LS 的清空优先级顺序，并决定是否通过警报消息通知用户。它还有助于预测根据分配的时间戳，LS 以更快的速度填满的可能性。该模型在实时环境中实施了许多 LS，并观察到，与人口稀少的地区相比，靠近更拥挤地点的垃圾桶装满的速度更快。随机森林算法用于预测是否发送警报通知。在使用 60 个决策树的随机森林算法中，注意到平均准确率为 95.8%。在训练集和测试集上记录的平均执行延迟分别为 13.06 秒和 14.39 秒。观察到的准确率、精度、召回率和 f1 分数参数分别为 95.8%、96.5%、98.5%和 97.2%。模型构建和验证时间分别为 3.26 秒和 4.25 秒。还注意到，在 LS 级别阈值为 0.93 时，达到的最大准确率为 95.8%。因此，根据随机森林方法的预测，做出通知用户的决定。获得的结果表明，可以有效地确定废物水平，并及时轻松检查垃圾桶的溢出情况。
